The encoding and transmitting of motion vectors are not needed because they can be recovered in the receiver . The paper also presents an image segmentation method suitable for video coding , which can extract the important image regions , such as the moving objects . The above two algorithms are used to implement a video coder with good performance . Abstract Considering the recent progress in the development of practical applications in the field of image processing , it is increasingly important to develop new , efficient and more reliable algorithms to solve an image segmentation problem . To this end , various fusion-based segmentation approaches which use consensus clustering , and which are based on the optimization of a single criterion , have been proposed . This work presents an approach for the usage of bare hands to control an immersive game from an egocentric perspective and built from a proposed case study methodology . We used a DenseNet Convolutional Neural Network ( CNN ) architecture to perform the recognition in real-time , from both indoor and outdoor environments , not requiring any image segmentation process . Our research also generated a vocabulary , considering users ’ preferences , seeking a set of natural and comfortable hand poses and evaluated users ’ satisfaction and performance for an entertainment setup . A color image segmentation algorithm for thermopaint color image segmentation base on labeling region growing is proposed by analyzing the color temperature property of thermopaint and quantizing image according to average image color moment , and this algorithm is to label every region in HSI color space that has vision consistency with human eyes when region segmenting . After image segmenting and feature extracting , according to the color temperature relations and each corresponding disperse point constructed a continuous and smoothing three dimensions space curve , and then begin to temperature recognize through this curve . At the same time , comparing with simulating human eyes recognizing algorithm , using the recognition algorithm according to the color temperature relations table and the color temperature characteristic curve , and contrasting the results of this two methods by experiments . We employ scale-invariant feature transformation for extracting , detecting , and matching 2-D visual features , and their associated depth values are used to perform coarse registration . Then , we use an image-based segmentation technique for detecting regions in the RGB images . Their associated 3-D centroid and the correspondent disparity values are used to refine the initial transformation parameters . The research presented in this report deals with a particular problem of image segmentation analysis , specifically the separation of objects of interest from background . We present a neural network approach to find an optimal-edge detector able to segment a wide variety of images . A specific case – and likely the most intuitive case – of cluster analysis is that which is performed on a two-dimensional Euclidean plane . This has many real-world applications , including image analysis/segmentation and medical imaging . This paper presents a quasi-linear time algorithm for single-linkage hierarchical clustering of points in two-dimensional Euclidean space . By identifying the images one may find multiple applications which are useful to compare its various features and use them to influence anyone as a customer or retailer by avoiding delicacy of the product information . In identifying an image image segmentation has become a pronounced tool . Global and local image segmentation method have recently been used by many researchers including medical , sports , agriculture and many other fields . The surface defect detection of LED bracket is an indispensable step to assure its product qualify . In this paper , an image segmentation algorithm method is proposed for LED bracket detection . Firstly , the basic principle and process of the proposed method is designed and presented , which combines the use of threshold-based segmentation technique and boundary-based segmentation method . Both the size of the picture and the pose of the model are very different , which can be more conducive to the generalization of the application scene . The experiment validates the performance of deep learning in clothing detection and combines image visual segmentation to solve the problem of small target detection . Negative samples are enhanced to improve the adaptability of the system scene . Our approach instead treats every pixel as a CRF node , exploits long - range dependencies , and uses CRF inference to directly optimize a DCNN - driven cost function . We note that mean field had been extensively studied for traditional image segmentation / edge detection tasks , e.g . , geiger1991parallel , geiger1991common , kokkinos2008computational , but recently krahenbuhl2011efficient showed that the inference can be very efficient for fully connected CRF and particularly effective in the context of semantic segmentation . After the first version of our manuscript was made publicly available , it came to our attention that two other groups have independently and concurrently pursued a very similar direction , combining DCNNs and densely connected CRFs bell2014material , zheng2015crfrnn . We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples . The model discovers clusters that accurately match semantic classes , achieving state - of - the - art results in eight unsupervised clustering benchmarks spanning image classification and segmentation . These include STL10 , an unsupervised variant of ImageNet , and CIFAR10 , where we significantly beat the accuracy of our closest competitors by 8 and 9.5 absolute percentage points respectively . Though achieving excellent detection accuracies , these methods may suffer from performance drop when applied to some non - Latin text such as Chinese , as we mentioned earlier . Line - based methods find text regions using some image segmentation algorithms . They also require a sophisticated post - processing step of word partitioning and / or false positive removal . NetWarp utilizes optical flow of adjacent frames to warp internal features across time space in video sequences . We note when a good - accuracy fast image semantic - segmentation framework comes into existence , video segmentation will also be benefited . section : Image Cascade Network Further , the loss should be invariant to permutations of the instance labels within the same semantic category . As a result , most recent successful approaches to instance segmentation have adopted more heuristic approaches that first use an object detector to enumerate candidate instances and then perform pixel - level segmentation of each instance . Alternately one can generate generic proposal segments and then label each one with a semantic detector . Naturally , semantic segmentation - based methods can be taken into consideration to explicitly handle the curve text detection problems . Although pixel - wise segmentation can extract the regions of arbitrary - shaped text instances , it may still fail to separate two text instances when they are relatively close , because their shared adjacent boundaries will probably merge them together as one single text instance ( see Fig . [ reference ] Various methods of instance segmentation have also involved modifying object detection systems to output segments instead of bounding boxes [ reference ][ reference ][ reference ][ reference ] . However , these methods can not produce a segmentation map of the image , as shown in Fig . 1 , without postprocessing as they consider each detection independently . Although our method also uses an object detector , it considers all detections in the image jointly with an initial category - level segmentation , and produces segmentation maps naturally where one pixel can not belong to multiple instances in contrast to the aforementioned approaches . Loss The per - pixel , unnormalized softmax loss is a natural choice for segmenting images of any size into disjoint classes , so we train our nets with it . The softmax operation induces competition between classes and promotes the most confident prediction , but it is not clear that this is necessary or helpful . We thus borrow some concepts to gain more intuitive understanding about our domain adaptation procedure . The “ student ” network follows a curriculum to learn simple knowledge about the target domain before it addresses the hard one of semantically segmenting images . The models inferring the target properties act like “ teachers ” , as they hint what label distributions the final solution ( image annotation ) may have in the target domain at the image and superpixel levels . It encompasses a wide variety of practical applications such as autonomous driving , indoor navigation , virtual/augmented reality etc . This paper gives a review on semantic segmentation from a modern perspective by giving a special attention to deep learning based scene parsing methods . In this review , we take up two central issues of semantic segmentation-accuracy ( labeling quality ) and efficiency ( inference speed ) to comparatively study the performance of existing methods . This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem . We propose to partition the image into three regions for each object to be described : Figure , Border and Ground . The proposed scheme combines the advantages of both worlds -- it can use powerful complex discriminative models as its parts , having at the same time better generalization capabilities . We thoroughly evaluate the proposed method for a simple classification task with artificial data and illustrate its advantages for real-word scenarios on a semantic image segmentation problem . While this framework was shown to lead to high accuracy for image classification , to our best knowledge this is its first application to the segmentation problem . The semantic segmentation process is then guided by a low-level segmentation which enforces local consistency . Finally , to enforce image-level consistency we use global image classifiers : if an image as a whole is unlikely to contain an object class , then the corresponding class is not considered in the segmentation pipeline . Our best results on this collection were achieved by using 45-second long segments with 15-second shifts . Semantic-based segmentation can be divided into three types : similarity-based ( producing segments with high intra-similarity and low inter-similarity ) , lexical-chain-based ( producing segments with frequent lexically connected words ) , and feature-based ( combining various features which signalize a segment break in a machine-learning setting ) . In this work , we mainly focus on feature-based segmentation which allows exploiting various features from all modalities of the data ( including segment length ) in a single trainable model and produces segments which can eventually overlap . The goal of this work is to densely predict a comparatively large set of affordances given only single RGB images . We approach this task by using a convolutional neural network based on the well-known ResNet architecture , which we blend with refinement modules recently proposed in the semantic segmentation literature . A novel cost function , capable of handling incomplete data , is introduced , which is necessary because we make use of segmentations of objects and their parts to generate affordance maps . Label-specific shape priors account for interactions between the geometry and the semantic labels in 3D . In the semantic segmentation step , the labels on the mesh are updated with MRF inference , such that they are compatible with the semantic segmentations in the input images . Also , this step includes prior assumptions about the surface shape of different semantic classes . Extensive experiments are conducted for evaluating the proposed Dual Path Networks . Specifically , we evaluate the proposed architecture on three tasks : image classification , object detection and semantic segmentation , using three standard benchmark datasets : the ImageNet - 1k dataset , Places365 - Standard dataset and the PASCAL VOC datasets . Key properties of the proposed DPNs are studied on the ImageNet - 1k object classification dataset and further verified on the Places365 - Standard scene understanding dataset . Given our volumetric facial representation , the problem of regressing the 3D coordinates of all vertices of a facial scan is reduced to one of 3D binary volume segmentation . We approach this problem using recent CNN architectures from semantic image segmentation and their extensions , as described in the next subsection . subsection : Volumetric Regression Networks Our interest in this problem is partially due to the exciting vision of autonomous driving , where understanding complex inner - city traffic scenes is an essential module and semantic segmentation is one of its key constituents . Machine learning methods for automatic semantic segmentation require massive amounts of high - quality annotated imagery in order to produce effective classifiers that generalize well to novel scenes . However , annotating training imagery for semantic segmentation is a very cumbersome task for humans . paragraph : Experiment setup . Since our ultimate goal is to solve the semantic segmentation problem for real images of urban scenes , we take Cityscapes as the target domain and SYNTHIA as the source domain . The Cityscapes validation set is used as our test set . We achieve state - of - the - art results on instancelevel segmentation of both body parts and humans . Moreover , our results on semantic part segmentation ( which is not - instance aware ) is also competitive with current state - of - theart . All of these results are achieved with a holistic , end - to - end trained model which parses humans at both an instance and category level , and outputs a dynamic number of instances per image , all in a single forward - pass through the network . [ reference ] ( b ) ) . Naturally , semantic segmentation - based methods can be taken into consideration to explicitly handle the curve text detection problems . Although pixel - wise segmentation can extract the regions of arbitrary - shaped text instances , it may still fail to separate two text instances when they are relatively close , because their shared adjacent boundaries will probably merge them together as one single text instance ( see Fig . [ reference ] . The instance embedding provides important details that resolve both inter - and intra - class instance overlap which are not emphasized in the semantic segmentation loss . section : Conclusion and Future Work Our first baseline is consistent with current trends in the research community . It combines a GAN with a semantic segmentation objective . The generator is trained to synthesize an image that fools the discriminator [ reference ] . Finally , we perform another round of adaptation between the adapted source data and the target data in feature - space , using one of the intermediate layers of the task model . Additionally , we do not use the semantic loss for the segmentation experiments as it would require loading generators , discriminators , and an additional semantic segmenter into memory all at once for two images . We did not have the required memory for this at the time of submission , but leave it to future work to deploy model parallelism or experiment with larger GPU memory . There have been many researches being done about pedestrian detection , as pedestrians are exposed to lot of danger on the street according to the fatality statistics . To increase the well-being of the pedestrians there has been many breakthroughs in computer vision researches and its application in actual vehicles . To solve the problem of heavy time-consuming in real-time pedestrian detection , a pedestrian detection algorithm based on Particle Swarm Optimization ( PSO ) for multimodal function is developed . This method treats the pedestrian detection problem as the multimodal function in a solution space , and uses PSO to solve the multimodal function . Pedestrians ' feature is described by Histogram of Sparse Code ( HSC ) and classified by linear SVM . One solution is to simplify this working flow as a single-stage detector . However , current single-stage detectors ( e.g. SSD ) have not presented competitive accuracy on common pedestrian detection benchmarks . This paper is towards a successful pedestrian detector enjoying the speed of SSD while maintaining the accuracy of Faster R-CNN . Nevertheless , a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets . For the boosting-based detectors which are popular in pedestrian detection literature , a possible cause for this drop is that in their boosting training process , low-resolution samples , which are usually more difficult to be detected due to the missing details , are still treated equally importantly as high-resolution samples , resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages . To address this problem , we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm , which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process , and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets . We design an Enhanced-Motion-HOG classification scheme to recognize pedestrians at the road intersection . To verify the feasibility and efficiency of our system , we implement a prototype system and compared the accuracy of the pedestrian detection scheme with the original HOG method and the motion detection method . Experimental results demonstrate that our scheme outperforms these schemes in terms of detection accuracy and processing time . The approaches of gray-scale thresholding segmentation method are described . Then the experimental system is established by using the infrared CCD device for pedestrian image detection . The image segmentation results generated by the algorithm in the experiment demonstrate that the Otsu thresholding segmentation method has achieved a kind of algorithm on automatic detection and segmentation of infrared image information in regions of interest of image . An abandoned object is detected in one or more image ( s ) by : determining whether one or more detected objects in a foreground of the images having a potential unclaimed object or having ; Applying a trained pedestrian detection means to the possible stray object to determine whether the possible stray object having at least a portion of a pedestrian ; and dividing the possible stray object as abandoned object in dependence on whether the possible stray object is not at least a portion of a pedestrian . The trained pedestrian detection means , is trained using positive training patterns , which consist of at least parts of human bodies in one or more attitude ( s ) , and / or negative training patterns , which consist of at least portions of abandoned objects . Our experiments demonstrate that the proposed approach can outperform Viola 's well-known pedestrian detector in both detection accuracy and generalization ability . In addition , even though our approach is presented in pedestrian detection scenario , it has been extended to human activity recognition application and remarkable performance has been achieved . [ ] introduce a novel bounding box regression loss to detect pedestrians in the crowd scenes . Although numerous pedestrian detection methods are presented in literature , how to robustly detect each individual pedestrian in crowded scenarios is still one of the most critical issues for pedestrian detectors . section : Occlusion - aware R - CNN section : Conclusions In this paper , we present a new occlusion - aware R - CNN method to improve the pedestrian detection accuracy in crowded scenes . Specifically , we design a new aggregation loss to reduce the false detections of the adjacent overlapping pedestrians , by simultaneously enforcing the proposals to be close to the associated objects , and locate compactly . Meanwhile , to effectively handle partial occlusion , we propose a new part occlusion - aware RoI pooling unit to replace the RoI pooling layer in the Fast R - CNN module of the detector , which integrates the prior structure information of human body with visibility prediction into the network to handle occlusion . Our method is trained in an end - to - end fashion and achieves the state - of - the - art accuracy on three pedestrian detection datasets , i.e. , CityPersons , ETH , and INRIA , and performs on - pair with the state - of - the - arts on Caltech . In the future , we plan to improve the method in two aspects . But we have to increase its parameter setting from 3 scales and 3 aspect ratios in to 10 scales and 7 aspect ratios in order to make it work on KITTI . Finally , our RPN performs on par with Faster R - CNN on car , and outperforms it on pedestrian and cyclist using the same number of proposals per image . Our new architecture can better handle scale variation using image pyramid . Secondly , a scale - aware weighting mechanism is proposed to lift the contribution of the sub - network specialized for the current input scales and boost the final detection performance in a wide input scale range . Thirdly , extensive experiments on several challenging pedestrian datasets demonstrate that SAF R - CNN delivers new state - of - the - art performance on three out of four challenging pedestrian benchmarks . section : Related Work It can be seen that SAF R - CNN significantly outperforms all four baselines . It verifies the superiority of using our scale - aware weighting technique in SAF R - CNN to detect the pedestrian instances with various sizes . We also compare the testing time of SAF R - CNN with other baselines . The high - level feature from the fine - tuned CNN has shown a discriminative ability and it is more compact than the activations in the intermediate layers . So in our model , the pedestrian descriptor in the identification model are directly supervised by the verification loss . As shown in Fig . The system allows scanning of the foot soles on a daily basis which may lead to early recognition of foot problems . The goal of the present study is to determine whether pixel classification is a useful intermediate step towards automatically assessing the images of the foot soles for signs of diabetic foot disease . If successful , this approach will further relief health care professionals in assessing the foot and enable the placement of more devices in the future . We propose in this work a method for segmentation and automatic recognition of white blood cells WBC ( nucleus and cytoplasm ) by selecting the relevant pixels for the pixels-based classification task . Different pixel selection algorithms are applied as a prior step to the pixel-based classification task where a comparative study of five pixel selection methods from three type group : condensation , edition and hybrid algorithms are performed . The proposed method comprises four phases : Convolutional neural network ( CNN ) is well-known for its powerful capability on image classification . In hyperspectral images ( HSIs ) , fixed-size spatial window is generally used as the input of CNN for pixel-wise classification . However , single fixed-size spatial architecture hinders the excellent performance of CNN due to the neglect of various land-cover distributions in HSIs . The proposed model also consists of an ensemble module that combines outputs from models with different kernel sizes , such as , 3 × 3 , 5 × 5 and 7 × 7 to combine multi-scale features for efficient detections . The model was tested on the ICDAR 2003 , SVT , ICDAR 2015 and the Total-Text dataset where it proved to be superior to other state of the art encoder-decoder architectures for pixel level classification . This paper proposes a novel spectral-spatial classification method inspired by the support vector machine ( SVM ) and superpixel segmentation . Core ideas of the proposed method are twofold : 1 ) the HSI is first classified by the pixel-wise classifier ( i.e. SVM ) ; 2 ) a fast superpixel segmentation-based spatial processing is , for the first time , introduced in this study to refine the homogeneity and consistency of the classification maps . Experiments are conducted on two benchmark HSIs ( i.e. the Indian Pines data and the Washington , D.C. Mall data ) with different spectral and spatial resolutions . We present a novel method for automated segmentation of axons in extremely noisy videos obtained via two-photon microscopy in awake mice . We formulate segmentation as a pixel-wise classification problem in which a pixel is classified into " axon " or " non-axon " based on its feature vector . In order to deal with high levels of noise , the features of our classifier are derived from spatio-temporal Independent Component Analysis ( stICA ) which effectively isolates noise from signal components while leveraging temporal coherence from the video . We modelled this feature as a new source derived from the principal image . The obtained results show the effectiveness of our fusion strategy and the interesting impact of introducing the spatial autocorrelation in the pixel-wise classification process . In the first step , we perform a polarimetric feature extraction procedure . The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixel-wise supervised classification . Based on the common coherency and covariance matrix , we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification . Over the last years , the most successful semantic segmentation approaches have been based on convolutional neural networks ( CNNs ) . Early approaches constrained their output to a bottom - up segmentation followed by a CNN based region classification [ reference ] . Rather than classifying entire regions in the first place , the approach by Farabet et al . performs pixel - wise classification using CNN features originating from multiple scales , followed by aggregation of these noisy pixel predictions over superpixel regions [ reference ] . The introduction of so - called fully convolutional networks ( FCNs ) for semantic image segmentation by Long et al . and references therein illustrate how context can be used to help in different tasks . As for semantic segmentation , per pixel classification , is often ambiguous in the presence of only local information . However , the task becomes much simpler if contextual information , from the whole image , is available . We also investigate several architectures which take into account both the local shape of tumors as well as their context . One problem with many machine learning methods is that they perform pixel classification without taking into account the local dependencies of labels ( i.e. segmentation labels are conditionally independent given the input image ) . To account for this , one can employ structured output methods such as conditional random fields ( CRFs ) , for which inference can be computationally expensive . Long etal . [ ] formulates semantic segmentation as a per - pixel classification problem . Following the line of FCN - based semantic segmentation . Importantly , the network h φ of the segmentation task is composed of two 1×1 convolutional layers , one with 256 and the other with 63 2 channels ( Figure 2 ) . This allows every pixel classifier to utilise information contained in the entire RoW and thus to have a complete view of its corresponding candidate window in x , which is critical to disambiguate between instances that look like the target ( e.g. last row of Figure 5 ) , also known as distractors [ reference ] . With the aim of producing a more accurate object mask , we follow the strategy of [ reference ] , which merges low and high resolution features using multiple refinement modules made of upsampling layers and skip connections . Deep Convolutional Neural Networks ( DCNNs ) have recently shown state of the art performance in high level vision tasks , such as image classification and object detection . This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel - level classification ( also called ” semantic image segmentation ” ) . We show that responses at the final layer of DCNNs are not sufficiently localized for accurate object segmentation . A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos , which in our opinion should partially resort to deep-rooted annotation bias . Motivated by this , we propose a novel method integrated with somatic topological line localization ( TLL ) and temporal feature aggregation for detecting multi-scale pedestrians , which works particularly well with small-scale pedestrians that are relatively far from the camera . Moreover , a post-processing scheme based on Markov Random Field ( MRF ) is introduced to eliminate ambiguities in occlusion cases . Redundant wavelet transform ( RWT ) overcomes the deficiency of shift variant of discrete wavelet transform , and it has better performance in motion estimation when compared to discrete wavelet transform . Addressing the problem of the detection of multi-pedestrian with different speed , we present an algorithm of pedestrian detection based on motion estimation using RWT , combining histogram of oriented gradients ( HOG ) and support vector machine ( SVM ) . Firstly , three intensities of movement ( IoM ) are estimated using RWT and the corresponding areas are segmented . A critical issue in pedestrian detection is to detect small - scale objects that will introduce feeble contrast and motion blur in images and videos , which in our opinion should partially resort to deep - rooted annotation bias . Motivated by this , we propose a novel method integrated with somatic topological line localization ( TLL ) and temporal feature aggregation for detecting multi - scale pedestrians , which works particularly well with small - scale pedestrians that are relatively far from the camera . Moreover , a post - processing scheme based on Markov Random Field ( MRF ) is introduced to eliminate ambiguities in occlusion cases . Meanwhile , the deep - rooted bounding - box based annotation bias is challenged by our work , which is thought - provoking to rethink how to provide classifiers with discriminative information . We devise a unified FCN based network to locate the topological somatic line for detecting multi - scale pedestrian instances while introduce a post - processing scheme based on MRF to eliminate ambiguities in occlusion cases . A temporal feature aggregation scheme is integrated to propagate temporal cues across frames and further improves the detection performance . Caltech Results : We present our main result on the Caltech Pedestrian Dataset [ 10 ] , see Fig . 5 ( b ) , generated using the official evaluation code available online3 . The Caltech dataset has become the standard for evaluating pedestrian detectors and the latest methods based on deep learning ( JointDeep ) [ 25 ] , multi - resolution models ( MT - DPM )
The encoding and transmitting of motion vectors are not needed because they can be recovered in the receiver . The paper also presents an image segmentation method suitable for video coding , which can extract the important image regions , such as the moving objects . The above two algorithms are used to implement a video coder with good performance .
Abstract Considering the recent progress in the development of practical applications in the field of image processing , it is increasingly important to develop new , efficient and more reliable algorithms to solve an image segmentation problem . To this end , various fusion-based segmentation approaches which use consensus clustering , and which are based on the optimization of a single criterion , have been proposed .
This work presents an approach for the usage of bare hands to control an immersive game from an egocentric perspective and built from a proposed case study methodology . We used a DenseNet Convolutional Neural Network ( CNN ) architecture to perform the recognition in real-time , from both indoor and outdoor environments , not requiring any image segmentation process . Our research also generated a vocabulary , considering users ’ preferences , seeking a set of natural and comfortable hand poses and evaluated users ’ satisfaction and performance for an entertainment setup .
A color image segmentation algorithm for thermopaint color image segmentation base on labeling region growing is proposed by analyzing the color temperature property of thermopaint and quantizing image according to average image color moment , and this algorithm is to label every region in HSI color space that has vision consistency with human eyes when region segmenting . After image segmenting and feature extracting , according to the color temperature relations and each corresponding disperse point constructed a continuous and smoothing three dimensions space curve , and then begin to temperature recognize through this curve . At the same time , comparing with simulating human eyes recognizing algorithm , using the recognition algorithm according to the color temperature relations table and the color temperature characteristic curve , and contrasting the results of this two methods by experiments .
We employ scale-invariant feature transformation for extracting , detecting , and matching 2-D visual features , and their associated depth values are used to perform coarse registration . Then , we use an image-based segmentation technique for detecting regions in the RGB images . Their associated 3-D centroid and the correspondent disparity values are used to refine the initial transformation parameters .
The research presented in this report deals with a particular problem of image segmentation analysis , specifically the separation of objects of interest from background . We present a neural network approach to find an optimal-edge detector able to segment a wide variety of images .
A specific case – and likely the most intuitive case – of cluster analysis is that which is performed on a two-dimensional Euclidean plane . This has many real-world applications , including image analysis/segmentation and medical imaging . This paper presents a quasi-linear time algorithm for single-linkage hierarchical clustering of points in two-dimensional Euclidean space .
By identifying the images one may find multiple applications which are useful to compare its various features and use them to influence anyone as a customer or retailer by avoiding delicacy of the product information . In identifying an image image segmentation has become a pronounced tool . Global and local image segmentation method have recently been used by many researchers including medical , sports , agriculture and many other fields .
The surface defect detection of LED bracket is an indispensable step to assure its product qualify . In this paper , an image segmentation algorithm method is proposed for LED bracket detection . Firstly , the basic principle and process of the proposed method is designed and presented , which combines the use of threshold-based segmentation technique and boundary-based segmentation method .
Both the size of the picture and the pose of the model are very different , which can be more conducive to the generalization of the application scene . The experiment validates the performance of deep learning in clothing detection and combines image visual segmentation to solve the problem of small target detection . Negative samples are enhanced to improve the adaptability of the system scene .
Our approach instead treats every pixel as a CRF node , exploits long - range dependencies , and uses CRF inference to directly optimize a DCNN - driven cost function . We note that mean field had been extensively studied for traditional image segmentation / edge detection tasks , e.g . , geiger1991parallel , geiger1991common , kokkinos2008computational , but recently krahenbuhl2011efficient showed that the inference can be very efficient for fully connected CRF and particularly effective in the context of semantic segmentation . After the first version of our manuscript was made publicly available , it came to our attention that two other groups have independently and concurrently pursued a very similar direction , combining DCNNs and densely connected CRFs bell2014material , zheng2015crfrnn .
We present a novel clustering objective that learns a neural network classifier from scratch , given only unlabelled data samples . The model discovers clusters that accurately match semantic classes , achieving state - of - the - art results in eight unsupervised clustering benchmarks spanning image classification and segmentation . These include STL10 , an unsupervised variant of ImageNet , and CIFAR10 , where we significantly beat the accuracy of our closest competitors by 8 and 9.5 absolute percentage points respectively .
Though achieving excellent detection accuracies , these methods may suffer from performance drop when applied to some non - Latin text such as Chinese , as we mentioned earlier . Line - based methods find text regions using some image segmentation algorithms . They also require a sophisticated post - processing step of word partitioning and / or false positive removal .
NetWarp utilizes optical flow of adjacent frames to warp internal features across time space in video sequences . We note when a good - accuracy fast image semantic - segmentation framework comes into existence , video segmentation will also be benefited . section : Image Cascade Network
Further , the loss should be invariant to permutations of the instance labels within the same semantic category . As a result , most recent successful approaches to instance segmentation have adopted more heuristic approaches that first use an object detector to enumerate candidate instances and then perform pixel - level segmentation of each instance . Alternately one can generate generic proposal segments and then label each one with a semantic detector .
Naturally , semantic segmentation - based methods can be taken into consideration to explicitly handle the curve text detection problems . Although pixel - wise segmentation can extract the regions of arbitrary - shaped text instances , it may still fail to separate two text instances when they are relatively close , because their shared adjacent boundaries will probably merge them together as one single text instance ( see Fig . [ reference ]
Various methods of instance segmentation have also involved modifying object detection systems to output segments instead of bounding boxes [ reference ][ reference ][ reference ][ reference ] . However , these methods can not produce a segmentation map of the image , as shown in Fig . 1 , without postprocessing as they consider each detection independently . Although our method also uses an object detector , it considers all detections in the image jointly with an initial category - level segmentation , and produces segmentation maps naturally where one pixel can not belong to multiple instances in contrast to the aforementioned approaches .
Loss The per - pixel , unnormalized softmax loss is a natural choice for segmenting images of any size into disjoint classes , so we train our nets with it . The softmax operation induces competition between classes and promotes the most confident prediction , but it is not clear that this is necessary or helpful .
We thus borrow some concepts to gain more intuitive understanding about our domain adaptation procedure . The “ student ” network follows a curriculum to learn simple knowledge about the target domain before it addresses the hard one of semantically segmenting images . The models inferring the target properties act like “ teachers ” , as they hint what label distributions the final solution ( image annotation ) may have in the target domain at the image and superpixel levels .
It encompasses a wide variety of practical applications such as autonomous driving , indoor navigation , virtual/augmented reality etc . This paper gives a review on semantic segmentation from a modern perspective by giving a special attention to deep learning based scene parsing methods . In this review , we take up two central issues of semantic segmentation-accuracy ( labeling quality ) and efficiency ( inference speed ) to comparatively study the performance of existing methods .
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem . We propose to partition the image into three regions for each object to be described : Figure , Border and Ground .
The proposed scheme combines the advantages of both worlds -- it can use powerful complex discriminative models as its parts , having at the same time better generalization capabilities . We thoroughly evaluate the proposed method for a simple classification task with artificial data and illustrate its advantages for real-word scenarios on a semantic image segmentation problem .
While this framework was shown to lead to high accuracy for image classification , to our best knowledge this is its first application to the segmentation problem . The semantic segmentation process is then guided by a low-level segmentation which enforces local consistency . Finally , to enforce image-level consistency we use global image classifiers : if an image as a whole is unlikely to contain an object class , then the corresponding class is not considered in the segmentation pipeline .
Our best results on this collection were achieved by using 45-second long segments with 15-second shifts . Semantic-based segmentation can be divided into three types : similarity-based ( producing segments with high intra-similarity and low inter-similarity ) , lexical-chain-based ( producing segments with frequent lexically connected words ) , and feature-based ( combining various features which signalize a segment break in a machine-learning setting ) . In this work , we mainly focus on feature-based segmentation which allows exploiting various features from all modalities of the data ( including segment length ) in a single trainable model and produces segments which can eventually overlap .
The goal of this work is to densely predict a comparatively large set of affordances given only single RGB images . We approach this task by using a convolutional neural network based on the well-known ResNet architecture , which we blend with refinement modules recently proposed in the semantic segmentation literature . A novel cost function , capable of handling incomplete data , is introduced , which is necessary because we make use of segmentations of objects and their parts to generate affordance maps .
Label-specific shape priors account for interactions between the geometry and the semantic labels in 3D . In the semantic segmentation step , the labels on the mesh are updated with MRF inference , such that they are compatible with the semantic segmentations in the input images . Also , this step includes prior assumptions about the surface shape of different semantic classes .
Extensive experiments are conducted for evaluating the proposed Dual Path Networks . Specifically , we evaluate the proposed architecture on three tasks : image classification , object detection and semantic segmentation , using three standard benchmark datasets : the ImageNet - 1k dataset , Places365 - Standard dataset and the PASCAL VOC datasets . Key properties of the proposed DPNs are studied on the ImageNet - 1k object classification dataset and further verified on the Places365 - Standard scene understanding dataset .
Given our volumetric facial representation , the problem of regressing the 3D coordinates of all vertices of a facial scan is reduced to one of 3D binary volume segmentation . We approach this problem using recent CNN architectures from semantic image segmentation and their extensions , as described in the next subsection . subsection : Volumetric Regression Networks
Our interest in this problem is partially due to the exciting vision of autonomous driving , where understanding complex inner - city traffic scenes is an essential module and semantic segmentation is one of its key constituents . Machine learning methods for automatic semantic segmentation require massive amounts of high - quality annotated imagery in order to produce effective classifiers that generalize well to novel scenes . However , annotating training imagery for semantic segmentation is a very cumbersome task for humans .
paragraph : Experiment setup . Since our ultimate goal is to solve the semantic segmentation problem for real images of urban scenes , we take Cityscapes as the target domain and SYNTHIA as the source domain . The Cityscapes validation set is used as our test set .
We achieve state - of - the - art results on instancelevel segmentation of both body parts and humans . Moreover , our results on semantic part segmentation ( which is not - instance aware ) is also competitive with current state - of - theart . All of these results are achieved with a holistic , end - to - end trained model which parses humans at both an instance and category level , and outputs a dynamic number of instances per image , all in a single forward - pass through the network .
[ reference ] ( b ) ) . Naturally , semantic segmentation - based methods can be taken into consideration to explicitly handle the curve text detection problems . Although pixel - wise segmentation can extract the regions of arbitrary - shaped text instances , it may still fail to separate two text instances when they are relatively close , because their shared adjacent boundaries will probably merge them together as one single text instance ( see Fig .
[ reference ] . The instance embedding provides important details that resolve both inter - and intra - class instance overlap which are not emphasized in the semantic segmentation loss . section : Conclusion and Future Work
Our first baseline is consistent with current trends in the research community . It combines a GAN with a semantic segmentation objective . The generator is trained to synthesize an image that fools the discriminator [ reference ] .
Finally , we perform another round of adaptation between the adapted source data and the target data in feature - space , using one of the intermediate layers of the task model . Additionally , we do not use the semantic loss for the segmentation experiments as it would require loading generators , discriminators , and an additional semantic segmenter into memory all at once for two images . We did not have the required memory for this at the time of submission , but leave it to future work to deploy model parallelism or experiment with larger GPU memory .
There have been many researches being done about pedestrian detection , as pedestrians are exposed to lot of danger on the street according to the fatality statistics . To increase the well-being of the pedestrians there has been many breakthroughs in computer vision researches and its application in actual vehicles .
To solve the problem of heavy time-consuming in real-time pedestrian detection , a pedestrian detection algorithm based on Particle Swarm Optimization ( PSO ) for multimodal function is developed . This method treats the pedestrian detection problem as the multimodal function in a solution space , and uses PSO to solve the multimodal function . Pedestrians ' feature is described by Histogram of Sparse Code ( HSC ) and classified by linear SVM .
One solution is to simplify this working flow as a single-stage detector . However , current single-stage detectors ( e.g. SSD ) have not presented competitive accuracy on common pedestrian detection benchmarks . This paper is towards a successful pedestrian detector enjoying the speed of SSD while maintaining the accuracy of Faster R-CNN .
Nevertheless , a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets . For the boosting-based detectors which are popular in pedestrian detection literature , a possible cause for this drop is that in their boosting training process , low-resolution samples , which are usually more difficult to be detected due to the missing details , are still treated equally importantly as high-resolution samples , resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages . To address this problem , we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm , which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process , and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets .
We design an Enhanced-Motion-HOG classification scheme to recognize pedestrians at the road intersection . To verify the feasibility and efficiency of our system , we implement a prototype system and compared the accuracy of the pedestrian detection scheme with the original HOG method and the motion detection method . Experimental results demonstrate that our scheme outperforms these schemes in terms of detection accuracy and processing time .
The approaches of gray-scale thresholding segmentation method are described . Then the experimental system is established by using the infrared CCD device for pedestrian image detection . The image segmentation results generated by the algorithm in the experiment demonstrate that the Otsu thresholding segmentation method has achieved a kind of algorithm on automatic detection and segmentation of infrared image information in regions of interest of image .
An abandoned object is detected in one or more image ( s ) by : determining whether one or more detected objects in a foreground of the images having a potential unclaimed object or having ; Applying a trained pedestrian detection means to the possible stray object to determine whether the possible stray object having at least a portion of a pedestrian ; and dividing the possible stray object as abandoned object in dependence on whether the possible stray object is not at least a portion of a pedestrian . The trained pedestrian detection means , is trained using positive training patterns , which consist of at least parts of human bodies in one or more attitude ( s ) , and / or negative training patterns , which consist of at least portions of abandoned objects .
Our experiments demonstrate that the proposed approach can outperform Viola 's well-known pedestrian detector in both detection accuracy and generalization ability . In addition , even though our approach is presented in pedestrian detection scenario , it has been extended to human activity recognition application and remarkable performance has been achieved .
[ ] introduce a novel bounding box regression loss to detect pedestrians in the crowd scenes . Although numerous pedestrian detection methods are presented in literature , how to robustly detect each individual pedestrian in crowded scenarios is still one of the most critical issues for pedestrian detectors . section : Occlusion - aware R - CNN
section : Conclusions In this paper , we present a new occlusion - aware R - CNN method to improve the pedestrian detection accuracy in crowded scenes . Specifically , we design a new aggregation loss to reduce the false detections of the adjacent overlapping pedestrians , by simultaneously enforcing the proposals to be close to the associated objects , and locate compactly . Meanwhile , to effectively handle partial occlusion , we propose a new part occlusion - aware RoI pooling unit to replace the RoI pooling layer in the Fast R - CNN module of the detector , which integrates the prior structure information of human body with visibility prediction into the network to handle occlusion . Our method is trained in an end - to - end fashion and achieves the state - of - the - art accuracy on three pedestrian detection datasets , i.e. , CityPersons , ETH , and INRIA , and performs on - pair with the state - of - the - arts on Caltech . In the future , we plan to improve the method in two aspects .
But we have to increase its parameter setting from 3 scales and 3 aspect ratios in to 10 scales and 7 aspect ratios in order to make it work on KITTI . Finally , our RPN performs on par with Faster R - CNN on car , and outperforms it on pedestrian and cyclist using the same number of proposals per image . Our new architecture can better handle scale variation using image pyramid .
Secondly , a scale - aware weighting mechanism is proposed to lift the contribution of the sub - network specialized for the current input scales and boost the final detection performance in a wide input scale range . Thirdly , extensive experiments on several challenging pedestrian datasets demonstrate that SAF R - CNN delivers new state - of - the - art performance on three out of four challenging pedestrian benchmarks . section : Related Work
It can be seen that SAF R - CNN significantly outperforms all four baselines . It verifies the superiority of using our scale - aware weighting technique in SAF R - CNN to detect the pedestrian instances with various sizes . We also compare the testing time of SAF R - CNN with other baselines .
The high - level feature from the fine - tuned CNN has shown a discriminative ability and it is more compact than the activations in the intermediate layers . So in our model , the pedestrian descriptor in the identification model are directly supervised by the verification loss . As shown in Fig .
The system allows scanning of the foot soles on a daily basis which may lead to early recognition of foot problems . The goal of the present study is to determine whether pixel classification is a useful intermediate step towards automatically assessing the images of the foot soles for signs of diabetic foot disease . If successful , this approach will further relief health care professionals in assessing the foot and enable the placement of more devices in the future .
We propose in this work a method for segmentation and automatic recognition of white blood cells WBC ( nucleus and cytoplasm ) by selecting the relevant pixels for the pixels-based classification task . Different pixel selection algorithms are applied as a prior step to the pixel-based classification task where a comparative study of five pixel selection methods from three type group : condensation , edition and hybrid algorithms are performed . The proposed method comprises four phases :
Convolutional neural network ( CNN ) is well-known for its powerful capability on image classification . In hyperspectral images ( HSIs ) , fixed-size spatial window is generally used as the input of CNN for pixel-wise classification . However , single fixed-size spatial architecture hinders the excellent performance of CNN due to the neglect of various land-cover distributions in HSIs .
The proposed model also consists of an ensemble module that combines outputs from models with different kernel sizes , such as , 3 × 3 , 5 × 5 and 7 × 7 to combine multi-scale features for efficient detections . The model was tested on the ICDAR 2003 , SVT , ICDAR 2015 and the Total-Text dataset where it proved to be superior to other state of the art encoder-decoder architectures for pixel level classification .
This paper proposes a novel spectral-spatial classification method inspired by the support vector machine ( SVM ) and superpixel segmentation . Core ideas of the proposed method are twofold : 1 ) the HSI is first classified by the pixel-wise classifier ( i.e. SVM ) ; 2 ) a fast superpixel segmentation-based spatial processing is , for the first time , introduced in this study to refine the homogeneity and consistency of the classification maps . Experiments are conducted on two benchmark HSIs ( i.e. the Indian Pines data and the Washington , D.C. Mall data ) with different spectral and spatial resolutions .
We present a novel method for automated segmentation of axons in extremely noisy videos obtained via two-photon microscopy in awake mice . We formulate segmentation as a pixel-wise classification problem in which a pixel is classified into " axon " or " non-axon " based on its feature vector . In order to deal with high levels of noise , the features of our classifier are derived from spatio-temporal Independent Component Analysis ( stICA ) which effectively isolates noise from signal components while leveraging temporal coherence from the video .
We modelled this feature as a new source derived from the principal image . The obtained results show the effectiveness of our fusion strategy and the interesting impact of introducing the spatial autocorrelation in the pixel-wise classification process .
In the first step , we perform a polarimetric feature extraction procedure . The resulting feature vectors are then ingested into a trained neural network classifier to arrive at a pixel-wise supervised classification . Based on the common coherency and covariance matrix , we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification .
Over the last years , the most successful semantic segmentation approaches have been based on convolutional neural networks ( CNNs ) . Early approaches constrained their output to a bottom - up segmentation followed by a CNN based region classification [ reference ] . Rather than classifying entire regions in the first place , the approach by Farabet et al . performs pixel - wise classification using CNN features originating from multiple scales , followed by aggregation of these noisy pixel predictions over superpixel regions [ reference ] . The introduction of so - called fully convolutional networks ( FCNs ) for semantic image segmentation by Long et al .
and references therein illustrate how context can be used to help in different tasks . As for semantic segmentation , per pixel classification , is often ambiguous in the presence of only local information . However , the task becomes much simpler if contextual information , from the whole image , is available .
We also investigate several architectures which take into account both the local shape of tumors as well as their context . One problem with many machine learning methods is that they perform pixel classification without taking into account the local dependencies of labels ( i.e. segmentation labels are conditionally independent given the input image ) . To account for this , one can employ structured output methods such as conditional random fields ( CRFs ) , for which inference can be computationally expensive .
Long etal . [ ] formulates semantic segmentation as a per - pixel classification problem . Following the line of FCN - based semantic segmentation .
Importantly , the network h φ of the segmentation task is composed of two 1×1 convolutional layers , one with 256 and the other with 63 2 channels ( Figure 2 ) . This allows every pixel classifier to utilise information contained in the entire RoW and thus to have a complete view of its corresponding candidate window in x , which is critical to disambiguate between instances that look like the target ( e.g. last row of Figure 5 ) , also known as distractors [ reference ] . With the aim of producing a more accurate object mask , we follow the strategy of [ reference ] , which merges low and high resolution features using multiple refinement modules made of upsampling layers and skip connections .
Deep Convolutional Neural Networks ( DCNNs ) have recently shown state of the art performance in high level vision tasks , such as image classification and object detection . This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel - level classification ( also called ” semantic image segmentation ” ) . We show that responses at the final layer of DCNNs are not sufficiently localized for accurate object segmentation .
A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos , which in our opinion should partially resort to deep-rooted annotation bias . Motivated by this , we propose a novel method integrated with somatic topological line localization ( TLL ) and temporal feature aggregation for detecting multi-scale pedestrians , which works particularly well with small-scale pedestrians that are relatively far from the camera . Moreover , a post-processing scheme based on Markov Random Field ( MRF ) is introduced to eliminate ambiguities in occlusion cases .
Redundant wavelet transform ( RWT ) overcomes the deficiency of shift variant of discrete wavelet transform , and it has better performance in motion estimation when compared to discrete wavelet transform . Addressing the problem of the detection of multi-pedestrian with different speed , we present an algorithm of pedestrian detection based on motion estimation using RWT , combining histogram of oriented gradients ( HOG ) and support vector machine ( SVM ) . Firstly , three intensities of movement ( IoM ) are estimated using RWT and the corresponding areas are segmented .
A critical issue in pedestrian detection is to detect small - scale objects that will introduce feeble contrast and motion blur in images and videos , which in our opinion should partially resort to deep - rooted annotation bias . Motivated by this , we propose a novel method integrated with somatic topological line localization ( TLL ) and temporal feature aggregation for detecting multi - scale pedestrians , which works particularly well with small - scale pedestrians that are relatively far from the camera . Moreover , a post - processing scheme based on Markov Random Field ( MRF ) is introduced to eliminate ambiguities in occlusion cases .
Meanwhile , the deep - rooted bounding - box based annotation bias is challenged by our work , which is thought - provoking to rethink how to provide classifiers with discriminative information . We devise a unified FCN based network to locate the topological somatic line for detecting multi - scale pedestrian instances while introduce a post - processing scheme based on MRF to eliminate ambiguities in occlusion cases . A temporal feature aggregation scheme is integrated to propagate temporal cues across frames and further improves the detection performance .
Caltech Results : We present our main result on the Caltech Pedestrian Dataset [ 10 ] , see Fig . 5 ( b ) , generated using the official evaluation code available online3 . The Caltech dataset has become the standard for evaluating pedestrian detectors and the latest methods based on deep learning ( JointDeep ) [ 25 ] , multi - resolution models ( MT - DPM )
MOTION ESTIMATION USING THE NEIGHBORING DECODED IMAGE EFA-BMFM: A multi-criteria framework for the fusion of colour image segmentation FPVRGame: Deep Learning for Hand Pose Recognition in Real-Time Using Low-End HMD Research on Thermopaint Color Image Segmentation and Its Application in Temperature Recognition Mapping Indoor Spaces by Adaptive Coarse-to-Fine Registration of RGB-D Data Outlier accommodation using genetic algorithms in neural networks-based image segmentation Fast Deterministic Single-Linkage 2D-Spatial Cluster Analysis Evaluation of some recent image segmentation method's An image segmentation algorithm for LED bracket's detection A Deep Learning Method for Suit Detection in Images Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Invariant Information Clustering for Unsupervised Image Classification and Segmentation Detecting Oriented Text in Natural Images by Linking Segments ICNet for Real-Time Semantic Segmentation on High-Resolution Images Recurrent Pixel Embedding for Instance Grouping Shape Robust Text Detection With Progressive Scale Expansion Network Holistic, Instance-Level Human Parsing Fully Convolutional Networks for Semantic Segmentation Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes A Review on Semantic Segmentation from a Modern Perspective Improving spatial codification in semantic segmentation Implicit Modeling - A Generalization of Discriminative and Generative Approaches An Efficient Approach to Semantic Segmentation Segmentation strategies for passage retrieval in audio-visual documents Learning to Segment Affordances Semantically Informed Multiview Surface Refinement Dual Path Networks Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes Holistic, Instance-Level Human Parsing Shape Robust Text Detection With Progressive Scale Expansion Network Recurrent Pixel Embedding for Instance Grouping Photographic Image Synthesis with Cascaded Refinement Networks CyCADA: Cycle-Consistent Adversarial Domain Adaptation Pedestrian Detection by Neuromorphic Visual Information Processing A pedestrian detection method based on PSO and multimodal function Learning Efficient Single-Stage Pedestrian Detectors by Asymptotic Localization Fitting Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features † A Human Recognition System for Pedestrian Crosswalk Research on CCD Infrared Image Threshold Segmentation Improved detection of stray objects using of detecting pedestrians Contour-motion feature (CMF): A space-time approach for robust pedestrian detection Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection Scale-Aware Fast R-CNN for Pedestrian Detection A Discriminatively Learned CNN Embedding for Person Reidentification Pixel classification for automated diabetic foot diagnosis Application of pixel selection in pixel-based classification for automatic white blood cell segmentation Divide-and-Conquer Dual-Architecture Convolutional Neural Network for Classification of Hyperspectral Images Multi scale mirror connection based encoder decoder network for text localization Spectral-spatial hyperspectral image classification via SVM and superpixel segmentation Automated Axon Segmentation from Highly Noisy Microscopic Videos Fusion of spatial autocorrelation and spectral data for remote sensing image classification Evaluation of polarimetric features for sea ice characterization at X, C and L-band SAR Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes ParseNet: Looking Wider to See Better Brain tumor segmentation with Deep Neural Networks ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Fast Online Object Tracking and Segmentation: A Unifying Approach Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs Small-Scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation Pedestrian detection based on redundant wavelet transform Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation Local Decorrelation For Improved Pedestrian Detection
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