Top-performing instance segmentation methods such as Mask R-CNN rely on ROI operations ( typically ROIPool or ROIAlign ) to obtain the final instance masks . In contrast , we propose to solve instance segmentation from a new perspective . Instead of using instance-wise ROIs as inputs to a network of fixed weights , we employ dynamic instance-aware networks , conditioned on instances . Instance segmentation is one of the actively studied research topics in computer vision in which many objects of interest should be separated individually . While many feed-forward networks produce high-quality segmentation on different types of images , their results often suffer from topological errors ( merging or splitting ) for segmentation of many objects , requiring post-processing . We evaluate our method on the challenging KITTI object detection benchmark [ 2 ] both on the official metric of 3D orientation estimation and also on the accuracy of the obtained 3D bounding boxes . Although conceptually simple , our method outperforms more complex and computationally expensive approaches that leverage semantic segmentation , instance level segmentation and flat ground priors [ 4 ] and sub-category detection [23][24 ] . Our discrete-continuous loss also produces state of the art results for 3D viewpoint estimation on the Pascal 3D+ dataset[26 ] . In each CRF unit we designed terms to capture the short range and long range dependencies from various neighbors . The accurate instance-level segmentation that our network produce is reflected by the considerable improvements obtained over previous work at high APr thresholds . We demonstrate the effectiveness of our model with extensive experiments on challenging dataset subset of PASCAL VOC 2012 . Despite their advantages , these methods are limited to detecting a pre-defined , fixed number of lanes , e.g. ego-lanes , and can not cope with lane changes . In this paper , we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end . To parametrize the segmented lane instances before fitting the lane , we further propose to apply a learned perspective transformation , conditioned on the image , in contrast to a fixed ” bird’s-eye view ” transformation . We propose an end-to-end trainable network that can simultaneously detect and recognize text of arbitrary shape , making substantial progress on the open problem of reading scene text of irregular shape . We formulate arbitrary shape text detection as an instance segmentation problem ; an attention model is then used to decode the textual content of each irregularly shaped text region without rectification . To extract useful irregularly shaped text instance features from image scale features , we propose a simple yet effective RoI masking step . Automatic instance segmentation is a problem that occurs in many biomedical applications . State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods . To resolve the ambiguity in the identification task , we encode teeth spatial relationships as an additional feature input in the identification task , which helps to remarkably improve the identification accuracy . Our evaluation , comparison and comprehensive ablation studies demonstrate that our method produces accurate instance segmentation and identification results automatically and outperforms the state-of-the-art approaches . To the best of our knowledge , our method is the first to use neural networks to achieve automatic tooth segmentation and identification from CBCT images . We prove the equivalence of existing clustering methods to some of those combinations , and introduce new algorithms for combinations which have not been studied . An extensive comparison is performed to evaluate properties of the clustering algorithms in the context of instance segmentation in images , including robustness to noise and efficiency . We show how one of the new algorithms proposed in our framework outperforms all previously known agglomerative methods for signed graphs , both on the competitive CREMI 2016 EM segmentation benchmark and on the CityScapes dataset . In recent years , instance segmentation research has been considered as an extension of object detection and semantic segmentation , which can provide pixel-level annotations on detected objects . Several approaches for instance segmentation exploit object detection network to generate bounding box and segment each bounding box with segmentation network . likewise adapt deep classification nets to semantic segmentation , but do so in hybrid proposal - classifier models . These approaches fine - tune an R - CNN system by sampling bounding boxes and / or region proposals for detection , semantic segmentation , and instance segmentation . Neither method is learned end - to - end . The Res2Net based method tends to segment all parts of objects regardless of object size . subsection : Instance Segmentation Instance Segmentation is the combination of object detection and semantic segmentation . section : Experiments We now describe experiments in training our framework to deal a variety of pixel - labeling problems , including boundary detection , object proposal detection , semantic segmentation and instance - level semantic segmentation . subsection : Tasks , Datasets and Implementation Fig . [ reference ] shows the embedding visualization , as well as predicted semantic segmentation and instance - level segmentation . From the visualization , we can see the instance - level semantic segmentation outputs complete object instances even though semantic segmentation results are noisy , such as the bike in the first image in Fig . By zooming in one plate , we can observe a “ colorful Mobius ring ” , indicating the embedding features for the boundary also capture boundary orientation and the signed distance to the boundary . section : Additional Results on Instance - Level Semantic Segmentation We show more instance - level semantic segmentation results on PASCAL VOC 2012 dataset based on our model in Figure [ reference ] , [ reference ] and [ reference ] . Firstly , we will investigate whether the scale expansion algorithm can be trained along with the network in an end - to - end manner . Secondly , the progressive scale expansion algorithm can be introduced to the general instance - level segmentation tasks , especially in those bencnmarks with many crowded object instances . We are cleaning our codes and will release them soon . Our main contributions are summarized as follows : We present an end - end efficient and trainable solution for multi - oriented text detection from an instance aware segmentation perspective , excluding any redundant pipelines . During feature extraction , feature maps are composed in a fused fashion to adaptively satisfy the finer representation of text instance . In section [ reference ] we discuss our proposed method in detail . In section [ reference ] we set up experiments on two instance segmentation benchmarks and show that we get a performance that is competitive with the state - of - the - art . section : Related Work The data are randomly organized into 2 splits , consisting of 452 training and 296 testing images with publicly available annotations . For fair comparison , we report the F orward ( F ) and B ackward ( B ) scores over the episode 4 , 5 and 6 for instance segmentation evaluation . Refer to for more details . [ reference ] . We also generalize FPNs to instance segmentation proposals in Sec . 6 . Our obtained networks have performed consistently and considerably better when trained with larger batch sizes ( or training crop sizes ) , leading to a new high - score on the challenging Mapillary Vistas dataset in a single - scale , single - model inference setting . In future works , we will investigate the consequences of our approach for problems like object detection , instance - specific segmentation and learning in 3D. Derivations for gradient computation are provided in the Appendix . Acknowledgements . The fact that our network is trained with part - level annotations may also help it identify small features of humans better , leading to more precise segmentations and thus improvements at high AP r thresholds . Our AP r at each IoU threshold for human instance segmentation is higher than that for part instance segmentation ( Tab . 1 ) . This is because parts are smaller than entire humans , and thus more difficult to localise accurately . The methodology was evaluated using both expert ’s manual annotations and estimations of a recently presented Intravascular Ultrasound ( IVUS ) reconstruction method . As far as the manual annotation validation process is concerned , the mean value of the comparison metrics for the 3D segmentation were 0.749 and 1.746 for the Dice coefficient and Hausdorff distance , respectively . On the other hand , the correlation coefficients for the degree of stenosis 1 , the degree of stenosis 2 , the plaque burden , the minimal lumen area and the minimal lumen diameter , when comparing the derived from the proposed methodology 3D models with the IVUS reconstructed models , were 0.79 , 0.77 , 0.75 , 0.85 , 0.81 , respectively . It consists of a contracting encoder part to analyze the input volume and a successive decoder part to generate integrated 3D segmentation results . While 3D U-Net can be trained for any 3D segmentation task , its direct application to airway segmentation is challenging due to differently sized airway branches . In this work , we combine 3D deep learning with image-based tracking in order to automatically extract the airways . Segmentation of a 3-dimensional ( 3D ) polygonal mesh is a method of breaking the mesh down into \meaningful " connected subsets of vertices called regions . The method used here is based on the watershed segmentation scheme which appears prominently in the image segmentation literature and was later applied to the 3D segmentation problem . The watershed algorithm is based on a scalar value ( curvature ) assigned to every mesh vertex which purports to capture some essence of the local mesh shape . This paper presents Markov surfaces , a probabilistic algorithm for user-assisted segmentation of elongated structures in 3D images . The 3D segmentation problem is formulated as a path-finding problem , where path probabilities are described by Markov chains . Users define points , curves , or regions on 2D image slices , and the algorithm connects these user-defined features in a way that respects the underlying elongated structure in data . First , different from other methods which derive the shape prior in an offline phase , the shape prior of the objects is estimated directly by extracting medial surfaces from the data set . Second , the 3D image segmentation problem is posed as Maximum A Posteriori ( MAP ) estimation of Markov Random Field ( MRF ) . First , the MAPMRF formulation minimizes the Gibbs energy function , and then we use graph cuts to obtain the optimal solution to the energy function . The main task is to learn shape information about individual object instances in order to accurately separate them , including connected and incompletely scanned objects . We solve the 3D instance-labeling problem with a multi-task learning strategy . The first goal is to learn an abstract feature embedding , which groups voxels with the same instance label close to each other while separating clusters with different instance labels from each other . Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders . We propose a solution to this problem , which should be useful for a future 3D segmentation challenge . ( c ) . paragraph : 3D Instance Segmentation PointNet . The network takes a point cloud in frustum and predicts a probability score for each point that indicates how likely the point belongs to the object of interest . Human segmentation in photo images is a challenging and important problem that finds numerous applications ranging from album making and photo classification to image retrieval . Previous works on human segmentation usually demand a time-consuming training phase for complex shape-matching processes . Attributed to pose variation ( frontal , profile , et . ) , the color and texture difference of clothes , the presence of noise , low contrast , uneven illumination and complex background . There are enormous difficultly in human image segmentation . In this paper , we propose an automatic human image segmentation method based on the face detection and biased normalized cuts . Human instance segmentation is a core problem for human-centric scene understanding and segmenting human instances poses a unique challenge to vision systems due to large intra-class variations in both appearance and shape , and complicated occlusion patterns . In this paper , we propose a new pose-aware human instance segmentation method . In this paper , we present a brand new pose-based instance segmentation framework for humans which separates instances based on human pose , rather than proposal region detection . We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem , and can moreover better handle occlusion . Furthermore , there are few public datasets containing many heavily occluded humans along with comprehensive annotations , which makes this a challenging problem seldom noticed by researchers . Therefore , in this paper we introduce a new benchmark " Occluded Human ( OCHuman ) " , which focuses on occluded humans with comprehensive annotations including bounding-box , human pose and instance masks . This dataset contains 8110 detailed annotated human instances within 4731 images . With an average 0.67 MaxIoU for each person , OCHuman is the most complex and challenging dataset related to human instance segmentation . Through this dataset , we want to emphasize occlusion as a challenging problem for researchers to study . In this work , we propose a method for instance based human segmentation in images and videos , extending the recent detector-based conditional random field model of Ladicky et.al . Instance based human segmentation involves pixel level labeling of an image , partitioning it into distinct human instances and background . This chapter introduces a novel Adaptive Temporal Encoding Network ( ATEN ) that alternatively performs temporal encoding among key frames and flow-guided feature propagation from other consecutive frames between two key frames . Specifically , ATEN first incorporates a Parsin-RCNN to produce the instance-level parsing result for each key frame , which integrates global human parsing and instance-level human segmentation into a unified model . To balance accuracy and efficiency , flow-guided feature propagation is used to directly parse consecutive frames according to their identified temporal consistency with key frames . In an effort to advance this research agenda , we present a comparative analysis of four different data segmentation techniques . In particular , we propose affect- and pose-based data segmentation , as alternatives to human-based segmentation , and fixed-window segmentation . In a study of ten dyads working on an open-ended engineering design task , we find that affect- and pose-based segmentation are more effective , than traditional approaches , for drawing correlations between learning-relevant constructs , and multimodal behaviors . Along with this CNN architecture , image enhancement and refinement techniques are exploited to improve the segmentation results . Our proposed algorithm produces improved segmented regions that meet the human level segmentation results . In addition , we evaluate our approach using different metrics to show the quantitative outperformance . , we operate at a more detailed part level , enabling us to extract more comprehensive information of the scene . Furthermore , with our part - level instance segmentation of humans , we can easily recover human - level instance segmentation ( by taking the union of all parts assigned to a particular instance as shown in Fig . 1d ) , and we show significant improvement over previous state - of - the - art in human instance - segmentation when doing so . Our approach is based on a deep Convolutional Neural Network ( CNN ) , which consists of an initial category - level part segmentation module . Note that although [ reference ] also performed instance - level segmentation on the same dataset , their evaluation was only done using human instance labels , which is similar to our following experiment on human instance segmentation . section : Results on Human Instance Segmentation We can trivially obtain instance - level segmentations of humans ( Fig 1d ) , as mentioned in Sec . Our future work is to train the object detector end - to - end as well . Moreover , the improvement that we obtained in instance segmentation of humans as a result of first segmenting parts motivates us to explore weakly - supervised methods which do not require explicit object part annotations . In our main paper , we reported our AP r results averaged over all classes . The need to address the output space structure is more apparent when considering problems where the set of output labels is n’t fixed . Our motivating example is object instance segmentation , where the model generates a collection of segments corresponding to object instances . This problem ca n’t be treated as k - way classification since the number of objects is n’t known in advance . FCN [ reference ] sums partial scores for each category over multiple scales to compute semantic segmentations . Hypercolumns [ reference ] uses a similar method for object instance segmentation . Several other approaches ( HyperNet [ reference ] , ParseNet [ reference ] , and ION [ reference ] ) concatenate features of multiple layers before computing predictions , which is equivalent to summing transformed features . Also , there is the more challenging task of simultaneous annotation of multiple people . In addition , there is work like that of Oliveira et al . that performs human part segmentation based on fully convolutional networks . Our work focuses solely on the task of keypoint localization of a single person ’s pose from an RGB image . We also develop an unsupervised speaker segmentation algorithm using a novel scheme based on quasi-GMM and LSP correlation analysis . Without a priori knowledge , this algorithm can support the open-set speaker , online speaker modeling and real time segmentation . Experimental results indicate that the proposed algorithms can produce very satisfactory results . Abstract In this chapter , we propose a novel approach called the HARIS-Heuristic approach for real-time segmentation to analyze a brain 's magnetic resonance image ( MRI ) for tumor identification . MRIs are pre-processed using an adaptive contourlet transform to remove noise and a structural augmentation technique to remove the skull region . This paper introduces a novel contour-based approach named deep snake for real-time instance segmentation . Unlike some recent methods that directly regress the coordinates of the object boundary points from an image , deep snake uses a neural network to iteratively deform an initial contour to match the object boundary , which implements the classic idea of snake algorithms with a learning-based approach . However , due to the heterogeneous material , size , shape and arrangement of the bricks , it is highly challenging to develop a widely adoptable solution for the problem via conventional geometric and radiometry based approaches . In this paper , we propose a new technique which combines the strength of deep learning for brick seed localization , and the Watershed algorithm for accurate instance segmentation . More specifically , we adopt a U-Net-based delineation algorithm for robust marker generation in the Watershed process , which provides as output the accurate contours of the individual bricks , and also separates them from the mortar regions . Dijkstra algorithm can be adopted to distinguish different parts of boundaries in medical image segmentation problem , which can be a reference for further segmentation operation . However , classical Dijkstra algorithm can hardly adapt to real time image segmentation problem owing to its exponentially O(n 2 ) computing complexity , especially for the increasing number of nodes . In this paper , we designed and implemented a parallel shortest path algorithm accelerated by GPU for medical image segmentation problem . document : ICNet for Real - Time Semantic Segmentation on High - Resolution Images We focus on the challenging task of real - time semantic segmentation in this paper . It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel - wise label inference . We expect that each sub - task is simpler than the original instance segmentation task , and is more easily addressed by convolutional networks . Driven by this decomposition , we propose Multi - task Network Cascades ( MNCs ) for accurate and fast instance - aware semantic segmentation . Our network cascades have three stages , each of which addresses one sub - task . section : Conclusion We have presented Multi - task Network Cascades for fast and accurate instance segmentation . We believe that the idea of exploiting network cascades in a multi - task learning framework is general . We directly estimate disparity instead of depth and later convert it to depth using the provided camera intrinsics . As a loss function , we use cross - entropy with a softmax for semantic segmentation , and MSE for depth and instance segmentation . We visualize the architecture in Figure 8 . It encompasses not only identifying objects in a scene , but also their relationships within the given context . With this goal , a recent line of works tackles 3D semantic segmentation and scene layout prediction . In our work we focus on scene graphs , a data structure that organizes the entities of a scene in a graph , where objects are nodes and their relationships modeled as edges . Dear editor , For semantic segmentation tasks , convolutional neural network ( CNN ) based methods have been prevalent for both 2D image semantic segmentation and 3D semantic segmentation . Though traditional methods often use local features to segment a target ( For example , in [ 1 ] both 2D local features and 3D local features are used to boost recognition ability ) , CNN based methods [ 2,3 ] exhibit much better performance than traditional methods [ 4 ] . Instance segmentation in 3D images is a fundamental task in biomedical image analysis . While deep learning models often work well for 2D instance segmentation , 3D instance segmentation still faces critical challenges , such as insufficient training data due to various annotation difficulties in 3D biomedical images . Common 3D annotation methods ( e.g. , full voxel annotation ) incur high workloads and costs for labeling enough instances for training deep learning 3D instance segmentation models . This paper introduces a novel approach for 3D semantic instance segmentation on point clouds . A 3D convolutional neural network called submanifold sparse convolutional network is used to generate semantic predictions and instance embeddings simultaneously . This semantic instance completion of a 3D scene opens up many new possibilities in enabling meaningful interactions with a scene , for instance for virtual or robotic agents . Rather than considering 3D semantic instance segmentation and scan completion separately , we propose 3D-SIC , a new end-to-end 3D convolutional neural network which jointly learns to detect object instances and predict their complete geometry -- achieving significantly better performance than treating these tasks independently . 3D-SIC leverages joint color-geometry feature learning and a fully-convolutional 3D network to effectively infer semantic instance completion for 3D scans at scale . We introduce Similarity Group Proposal Network ( SGPN ) , a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds . SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal , from which we can directly extract instance segmentation results . This paper describes a 3D semantic scene segmentation with convolutional neural networks for unordered point clouds of autonomous robots . Euclidean coordinates and RGB color spaces are used as well as multi-scaling layers . 3D semantic segmentation of point cloud aims at assigning semantic labels to each point by utilizing and respecting the 3D representation of the data . Detailed 3D semantic segmentation of urban areas can assist policymakers , insurance companies , governmental agencies for applications such as urban growth assessment , disaster management , and traffic supervision . Semantic understanding of 3D environments is critical for both the unmanned system and the human involved virtual/augmented reality ( VR/AR ) immersive experience . Spatially-sparse convolution , taking advantage of the intrinsic sparsity of 3D point cloud data , makes high resolution 3D convolutional neural networks tractable with state-of-the-art results on 3D semantic segmentation problems . However , the exhaustive computations limits the practical usage of semantic 3D perception for VR/AR applications in portable devices . In this paper , we propose a novel joint instance and semantic segmentation approach , which is called JSNet , in order to address the instance and semantic segmentation of 3D point clouds simultaneously . Firstly , we build an effective backbone network to extract robust features from the raw point clouds . More details of the 2D detector training are provided in the supplementary . subsection : 3D Instance Segmentation Given a 2D image region ( and its corresponding 3D frustum ) , several methods might be used to obtain 3D location of the object : One straightforward solution is to directly regress 3D object locations ( e.g. , by 3D bounding box ) from a depth map using 2D CNNs . Given the training set of 2D images and constructed volumes , we learn this mapping using a CNN . Our CNN architecture for 3D segmentation is based on the “ hourglass network ” of an extension of the fully convolutional network of using skip connections and residual learning . Our volumetric architecture consists of two hourglass modules which are stacked together without intermediate supervision . Despite their popularity , most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes . In this work we propose an approach to 3D image segmentation based on a volumetric , fully convolutional , neural network . Our CNN is trained end - to - end on MRI volumes depicting prostate , and learns to predict segmentation for the whole volume at once . The standard approach to image instance segmentation is to perform the object detection first , and then segment the object from the detection bounding-box . More recently , deep learning methods like Mask R-CNN perform them jointly . The goal of this new task is simultaneous detection , segmentation and tracking of instances in videos . In words , it is the first time that the image instance segmentation problem is extended to the video domain . To facilitate research on this new task , we propose a large-scale benchmark called YouTube-VIS , which consists of 2,883 high-resolution YouTube videos , a 40-category label set and 131k high-quality instance masks . One-shot image segmentation aims to undertake the segmentation task of a novel class with only one training image available . The difficulty lies in that image segmentation has structured data representations , which yields a many-to-many message passing problem .
Top-performing instance segmentation methods such as Mask R-CNN rely on ROI operations ( typically ROIPool or ROIAlign ) to obtain the final instance masks . In contrast , we propose to solve instance segmentation from a new perspective . Instead of using instance-wise ROIs as inputs to a network of fixed weights , we employ dynamic instance-aware networks , conditioned on instances .
Instance segmentation is one of the actively studied research topics in computer vision in which many objects of interest should be separated individually . While many feed-forward networks produce high-quality segmentation on different types of images , their results often suffer from topological errors ( merging or splitting ) for segmentation of many objects , requiring post-processing .
We evaluate our method on the challenging KITTI object detection benchmark [ 2 ] both on the official metric of 3D orientation estimation and also on the accuracy of the obtained 3D bounding boxes . Although conceptually simple , our method outperforms more complex and computationally expensive approaches that leverage semantic segmentation , instance level segmentation and flat ground priors [ 4 ] and sub-category detection [23][24 ] . Our discrete-continuous loss also produces state of the art results for 3D viewpoint estimation on the Pascal 3D+ dataset[26 ] .
In each CRF unit we designed terms to capture the short range and long range dependencies from various neighbors . The accurate instance-level segmentation that our network produce is reflected by the considerable improvements obtained over previous work at high APr thresholds . We demonstrate the effectiveness of our model with extensive experiments on challenging dataset subset of PASCAL VOC 2012 .
Despite their advantages , these methods are limited to detecting a pre-defined , fixed number of lanes , e.g. ego-lanes , and can not cope with lane changes . In this paper , we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end . To parametrize the segmented lane instances before fitting the lane , we further propose to apply a learned perspective transformation , conditioned on the image , in contrast to a fixed ” bird’s-eye view ” transformation .
We propose an end-to-end trainable network that can simultaneously detect and recognize text of arbitrary shape , making substantial progress on the open problem of reading scene text of irregular shape . We formulate arbitrary shape text detection as an instance segmentation problem ; an attention model is then used to decode the textual content of each irregularly shaped text region without rectification . To extract useful irregularly shaped text instance features from image scale features , we propose a simple yet effective RoI masking step .
Automatic instance segmentation is a problem that occurs in many biomedical applications . State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods .
To resolve the ambiguity in the identification task , we encode teeth spatial relationships as an additional feature input in the identification task , which helps to remarkably improve the identification accuracy . Our evaluation , comparison and comprehensive ablation studies demonstrate that our method produces accurate instance segmentation and identification results automatically and outperforms the state-of-the-art approaches . To the best of our knowledge , our method is the first to use neural networks to achieve automatic tooth segmentation and identification from CBCT images .
We prove the equivalence of existing clustering methods to some of those combinations , and introduce new algorithms for combinations which have not been studied . An extensive comparison is performed to evaluate properties of the clustering algorithms in the context of instance segmentation in images , including robustness to noise and efficiency . We show how one of the new algorithms proposed in our framework outperforms all previously known agglomerative methods for signed graphs , both on the competitive CREMI 2016 EM segmentation benchmark and on the CityScapes dataset .
In recent years , instance segmentation research has been considered as an extension of object detection and semantic segmentation , which can provide pixel-level annotations on detected objects . Several approaches for instance segmentation exploit object detection network to generate bounding box and segment each bounding box with segmentation network .
likewise adapt deep classification nets to semantic segmentation , but do so in hybrid proposal - classifier models . These approaches fine - tune an R - CNN system by sampling bounding boxes and / or region proposals for detection , semantic segmentation , and instance segmentation . Neither method is learned end - to - end .
The Res2Net based method tends to segment all parts of objects regardless of object size . subsection : Instance Segmentation Instance Segmentation is the combination of object detection and semantic segmentation .
section : Experiments We now describe experiments in training our framework to deal a variety of pixel - labeling problems , including boundary detection , object proposal detection , semantic segmentation and instance - level semantic segmentation . subsection : Tasks , Datasets and Implementation
Fig . [ reference ] shows the embedding visualization , as well as predicted semantic segmentation and instance - level segmentation . From the visualization , we can see the instance - level semantic segmentation outputs complete object instances even though semantic segmentation results are noisy , such as the bike in the first image in Fig .
By zooming in one plate , we can observe a “ colorful Mobius ring ” , indicating the embedding features for the boundary also capture boundary orientation and the signed distance to the boundary . section : Additional Results on Instance - Level Semantic Segmentation We show more instance - level semantic segmentation results on PASCAL VOC 2012 dataset based on our model in Figure [ reference ] , [ reference ] and [ reference ] .
Firstly , we will investigate whether the scale expansion algorithm can be trained along with the network in an end - to - end manner . Secondly , the progressive scale expansion algorithm can be introduced to the general instance - level segmentation tasks , especially in those bencnmarks with many crowded object instances . We are cleaning our codes and will release them soon .
Our main contributions are summarized as follows : We present an end - end efficient and trainable solution for multi - oriented text detection from an instance aware segmentation perspective , excluding any redundant pipelines . During feature extraction , feature maps are composed in a fused fashion to adaptively satisfy the finer representation of text instance .
In section [ reference ] we discuss our proposed method in detail . In section [ reference ] we set up experiments on two instance segmentation benchmarks and show that we get a performance that is competitive with the state - of - the - art . section : Related Work
The data are randomly organized into 2 splits , consisting of 452 training and 296 testing images with publicly available annotations . For fair comparison , we report the F orward ( F ) and B ackward ( B ) scores over the episode 4 , 5 and 6 for instance segmentation evaluation . Refer to for more details .
[ reference ] . We also generalize FPNs to instance segmentation proposals in Sec . 6 .
Our obtained networks have performed consistently and considerably better when trained with larger batch sizes ( or training crop sizes ) , leading to a new high - score on the challenging Mapillary Vistas dataset in a single - scale , single - model inference setting . In future works , we will investigate the consequences of our approach for problems like object detection , instance - specific segmentation and learning in 3D. Derivations for gradient computation are provided in the Appendix . Acknowledgements .
The fact that our network is trained with part - level annotations may also help it identify small features of humans better , leading to more precise segmentations and thus improvements at high AP r thresholds . Our AP r at each IoU threshold for human instance segmentation is higher than that for part instance segmentation ( Tab . 1 ) . This is because parts are smaller than entire humans , and thus more difficult to localise accurately .
The methodology was evaluated using both expert ’s manual annotations and estimations of a recently presented Intravascular Ultrasound ( IVUS ) reconstruction method . As far as the manual annotation validation process is concerned , the mean value of the comparison metrics for the 3D segmentation were 0.749 and 1.746 for the Dice coefficient and Hausdorff distance , respectively . On the other hand , the correlation coefficients for the degree of stenosis 1 , the degree of stenosis 2 , the plaque burden , the minimal lumen area and the minimal lumen diameter , when comparing the derived from the proposed methodology 3D models with the IVUS reconstructed models , were 0.79 , 0.77 , 0.75 , 0.85 , 0.81 , respectively .
It consists of a contracting encoder part to analyze the input volume and a successive decoder part to generate integrated 3D segmentation results . While 3D U-Net can be trained for any 3D segmentation task , its direct application to airway segmentation is challenging due to differently sized airway branches . In this work , we combine 3D deep learning with image-based tracking in order to automatically extract the airways .
Segmentation of a 3-dimensional ( 3D ) polygonal mesh is a method of breaking the mesh down into \meaningful " connected subsets of vertices called regions . The method used here is based on the watershed segmentation scheme which appears prominently in the image segmentation literature and was later applied to the 3D segmentation problem . The watershed algorithm is based on a scalar value ( curvature ) assigned to every mesh vertex which purports to capture some essence of the local mesh shape .
This paper presents Markov surfaces , a probabilistic algorithm for user-assisted segmentation of elongated structures in 3D images . The 3D segmentation problem is formulated as a path-finding problem , where path probabilities are described by Markov chains . Users define points , curves , or regions on 2D image slices , and the algorithm connects these user-defined features in a way that respects the underlying elongated structure in data .
First , different from other methods which derive the shape prior in an offline phase , the shape prior of the objects is estimated directly by extracting medial surfaces from the data set . Second , the 3D image segmentation problem is posed as Maximum A Posteriori ( MAP ) estimation of Markov Random Field ( MRF ) . First , the MAPMRF formulation minimizes the Gibbs energy function , and then we use graph cuts to obtain the optimal solution to the energy function .
The main task is to learn shape information about individual object instances in order to accurately separate them , including connected and incompletely scanned objects . We solve the 3D instance-labeling problem with a multi-task learning strategy . The first goal is to learn an abstract feature embedding , which groups voxels with the same instance label close to each other while separating clusters with different instance labels from each other .
Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders . We propose a solution to this problem , which should be useful for a future 3D segmentation challenge .
( c ) . paragraph : 3D Instance Segmentation PointNet . The network takes a point cloud in frustum and predicts a probability score for each point that indicates how likely the point belongs to the object of interest .
Human segmentation in photo images is a challenging and important problem that finds numerous applications ranging from album making and photo classification to image retrieval . Previous works on human segmentation usually demand a time-consuming training phase for complex shape-matching processes .
Attributed to pose variation ( frontal , profile , et . ) , the color and texture difference of clothes , the presence of noise , low contrast , uneven illumination and complex background . There are enormous difficultly in human image segmentation . In this paper , we propose an automatic human image segmentation method based on the face detection and biased normalized cuts .
Human instance segmentation is a core problem for human-centric scene understanding and segmenting human instances poses a unique challenge to vision systems due to large intra-class variations in both appearance and shape , and complicated occlusion patterns . In this paper , we propose a new pose-aware human instance segmentation method .
In this paper , we present a brand new pose-based instance segmentation framework for humans which separates instances based on human pose , rather than proposal region detection . We demonstrate that our pose-based framework can achieve better accuracy than the state-of-art detection-based approach on the human instance segmentation problem , and can moreover better handle occlusion . Furthermore , there are few public datasets containing many heavily occluded humans along with comprehensive annotations , which makes this a challenging problem seldom noticed by researchers . Therefore , in this paper we introduce a new benchmark " Occluded Human ( OCHuman ) " , which focuses on occluded humans with comprehensive annotations including bounding-box , human pose and instance masks . This dataset contains 8110 detailed annotated human instances within 4731 images . With an average 0.67 MaxIoU for each person , OCHuman is the most complex and challenging dataset related to human instance segmentation . Through this dataset , we want to emphasize occlusion as a challenging problem for researchers to study .
In this work , we propose a method for instance based human segmentation in images and videos , extending the recent detector-based conditional random field model of Ladicky et.al . Instance based human segmentation involves pixel level labeling of an image , partitioning it into distinct human instances and background .
This chapter introduces a novel Adaptive Temporal Encoding Network ( ATEN ) that alternatively performs temporal encoding among key frames and flow-guided feature propagation from other consecutive frames between two key frames . Specifically , ATEN first incorporates a Parsin-RCNN to produce the instance-level parsing result for each key frame , which integrates global human parsing and instance-level human segmentation into a unified model . To balance accuracy and efficiency , flow-guided feature propagation is used to directly parse consecutive frames according to their identified temporal consistency with key frames .
In an effort to advance this research agenda , we present a comparative analysis of four different data segmentation techniques . In particular , we propose affect- and pose-based data segmentation , as alternatives to human-based segmentation , and fixed-window segmentation . In a study of ten dyads working on an open-ended engineering design task , we find that affect- and pose-based segmentation are more effective , than traditional approaches , for drawing correlations between learning-relevant constructs , and multimodal behaviors .
Along with this CNN architecture , image enhancement and refinement techniques are exploited to improve the segmentation results . Our proposed algorithm produces improved segmented regions that meet the human level segmentation results . In addition , we evaluate our approach using different metrics to show the quantitative outperformance .
, we operate at a more detailed part level , enabling us to extract more comprehensive information of the scene . Furthermore , with our part - level instance segmentation of humans , we can easily recover human - level instance segmentation ( by taking the union of all parts assigned to a particular instance as shown in Fig . 1d ) , and we show significant improvement over previous state - of - the - art in human instance - segmentation when doing so . Our approach is based on a deep Convolutional Neural Network ( CNN ) , which consists of an initial category - level part segmentation module .
Note that although [ reference ] also performed instance - level segmentation on the same dataset , their evaluation was only done using human instance labels , which is similar to our following experiment on human instance segmentation . section : Results on Human Instance Segmentation We can trivially obtain instance - level segmentations of humans ( Fig 1d ) , as mentioned in Sec .
Our future work is to train the object detector end - to - end as well . Moreover , the improvement that we obtained in instance segmentation of humans as a result of first segmenting parts motivates us to explore weakly - supervised methods which do not require explicit object part annotations . In our main paper , we reported our AP r results averaged over all classes .
The need to address the output space structure is more apparent when considering problems where the set of output labels is n’t fixed . Our motivating example is object instance segmentation , where the model generates a collection of segments corresponding to object instances . This problem ca n’t be treated as k - way classification since the number of objects is n’t known in advance .
FCN [ reference ] sums partial scores for each category over multiple scales to compute semantic segmentations . Hypercolumns [ reference ] uses a similar method for object instance segmentation . Several other approaches ( HyperNet [ reference ] , ParseNet [ reference ] , and ION [ reference ] ) concatenate features of multiple layers before computing predictions , which is equivalent to summing transformed features .
Also , there is the more challenging task of simultaneous annotation of multiple people . In addition , there is work like that of Oliveira et al . that performs human part segmentation based on fully convolutional networks . Our work focuses solely on the task of keypoint localization of a single person ’s pose from an RGB image .
We also develop an unsupervised speaker segmentation algorithm using a novel scheme based on quasi-GMM and LSP correlation analysis . Without a priori knowledge , this algorithm can support the open-set speaker , online speaker modeling and real time segmentation . Experimental results indicate that the proposed algorithms can produce very satisfactory results .
Abstract In this chapter , we propose a novel approach called the HARIS-Heuristic approach for real-time segmentation to analyze a brain 's magnetic resonance image ( MRI ) for tumor identification . MRIs are pre-processed using an adaptive contourlet transform to remove noise and a structural augmentation technique to remove the skull region .
This paper introduces a novel contour-based approach named deep snake for real-time instance segmentation . Unlike some recent methods that directly regress the coordinates of the object boundary points from an image , deep snake uses a neural network to iteratively deform an initial contour to match the object boundary , which implements the classic idea of snake algorithms with a learning-based approach .
However , due to the heterogeneous material , size , shape and arrangement of the bricks , it is highly challenging to develop a widely adoptable solution for the problem via conventional geometric and radiometry based approaches . In this paper , we propose a new technique which combines the strength of deep learning for brick seed localization , and the Watershed algorithm for accurate instance segmentation . More specifically , we adopt a U-Net-based delineation algorithm for robust marker generation in the Watershed process , which provides as output the accurate contours of the individual bricks , and also separates them from the mortar regions .
Dijkstra algorithm can be adopted to distinguish different parts of boundaries in medical image segmentation problem , which can be a reference for further segmentation operation . However , classical Dijkstra algorithm can hardly adapt to real time image segmentation problem owing to its exponentially O(n 2 ) computing complexity , especially for the increasing number of nodes . In this paper , we designed and implemented a parallel shortest path algorithm accelerated by GPU for medical image segmentation problem .
document : ICNet for Real - Time Semantic Segmentation on High - Resolution Images We focus on the challenging task of real - time semantic segmentation in this paper . It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel - wise label inference .
We expect that each sub - task is simpler than the original instance segmentation task , and is more easily addressed by convolutional networks . Driven by this decomposition , we propose Multi - task Network Cascades ( MNCs ) for accurate and fast instance - aware semantic segmentation . Our network cascades have three stages , each of which addresses one sub - task .
section : Conclusion We have presented Multi - task Network Cascades for fast and accurate instance segmentation . We believe that the idea of exploiting network cascades in a multi - task learning framework is general .
We directly estimate disparity instead of depth and later convert it to depth using the provided camera intrinsics . As a loss function , we use cross - entropy with a softmax for semantic segmentation , and MSE for depth and instance segmentation . We visualize the architecture in Figure 8 .
It encompasses not only identifying objects in a scene , but also their relationships within the given context . With this goal , a recent line of works tackles 3D semantic segmentation and scene layout prediction . In our work we focus on scene graphs , a data structure that organizes the entities of a scene in a graph , where objects are nodes and their relationships modeled as edges .
Dear editor , For semantic segmentation tasks , convolutional neural network ( CNN ) based methods have been prevalent for both 2D image semantic segmentation and 3D semantic segmentation . Though traditional methods often use local features to segment a target ( For example , in [ 1 ] both 2D local features and 3D local features are used to boost recognition ability ) , CNN based methods [ 2,3 ] exhibit much better performance than traditional methods [ 4 ] .
Instance segmentation in 3D images is a fundamental task in biomedical image analysis . While deep learning models often work well for 2D instance segmentation , 3D instance segmentation still faces critical challenges , such as insufficient training data due to various annotation difficulties in 3D biomedical images . Common 3D annotation methods ( e.g. , full voxel annotation ) incur high workloads and costs for labeling enough instances for training deep learning 3D instance segmentation models .
This paper introduces a novel approach for 3D semantic instance segmentation on point clouds . A 3D convolutional neural network called submanifold sparse convolutional network is used to generate semantic predictions and instance embeddings simultaneously .
This semantic instance completion of a 3D scene opens up many new possibilities in enabling meaningful interactions with a scene , for instance for virtual or robotic agents . Rather than considering 3D semantic instance segmentation and scan completion separately , we propose 3D-SIC , a new end-to-end 3D convolutional neural network which jointly learns to detect object instances and predict their complete geometry -- achieving significantly better performance than treating these tasks independently . 3D-SIC leverages joint color-geometry feature learning and a fully-convolutional 3D network to effectively infer semantic instance completion for 3D scans at scale .
We introduce Similarity Group Proposal Network ( SGPN ) , a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds . SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal , from which we can directly extract instance segmentation results .
This paper describes a 3D semantic scene segmentation with convolutional neural networks for unordered point clouds of autonomous robots . Euclidean coordinates and RGB color spaces are used as well as multi-scaling layers .
3D semantic segmentation of point cloud aims at assigning semantic labels to each point by utilizing and respecting the 3D representation of the data . Detailed 3D semantic segmentation of urban areas can assist policymakers , insurance companies , governmental agencies for applications such as urban growth assessment , disaster management , and traffic supervision .
Semantic understanding of 3D environments is critical for both the unmanned system and the human involved virtual/augmented reality ( VR/AR ) immersive experience . Spatially-sparse convolution , taking advantage of the intrinsic sparsity of 3D point cloud data , makes high resolution 3D convolutional neural networks tractable with state-of-the-art results on 3D semantic segmentation problems . However , the exhaustive computations limits the practical usage of semantic 3D perception for VR/AR applications in portable devices .
In this paper , we propose a novel joint instance and semantic segmentation approach , which is called JSNet , in order to address the instance and semantic segmentation of 3D point clouds simultaneously . Firstly , we build an effective backbone network to extract robust features from the raw point clouds .
More details of the 2D detector training are provided in the supplementary . subsection : 3D Instance Segmentation Given a 2D image region ( and its corresponding 3D frustum ) , several methods might be used to obtain 3D location of the object : One straightforward solution is to directly regress 3D object locations ( e.g. , by 3D bounding box ) from a depth map using 2D CNNs .
Given the training set of 2D images and constructed volumes , we learn this mapping using a CNN . Our CNN architecture for 3D segmentation is based on the “ hourglass network ” of an extension of the fully convolutional network of using skip connections and residual learning . Our volumetric architecture consists of two hourglass modules which are stacked together without intermediate supervision .
Despite their popularity , most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes . In this work we propose an approach to 3D image segmentation based on a volumetric , fully convolutional , neural network . Our CNN is trained end - to - end on MRI volumes depicting prostate , and learns to predict segmentation for the whole volume at once .
The standard approach to image instance segmentation is to perform the object detection first , and then segment the object from the detection bounding-box . More recently , deep learning methods like Mask R-CNN perform them jointly .
The goal of this new task is simultaneous detection , segmentation and tracking of instances in videos . In words , it is the first time that the image instance segmentation problem is extended to the video domain . To facilitate research on this new task , we propose a large-scale benchmark called YouTube-VIS , which consists of 2,883 high-resolution YouTube videos , a 40-category label set and 131k high-quality instance masks .
One-shot image segmentation aims to undertake the segmentation task of a novel class with only one training image available . The difficulty lies in that image segmentation has structured data representations , which yields a many-to-many message passing problem .
Conditional Convolutions for Instance Segmentation Reinforced Coloring for End-to-End Instance Segmentation 3D Bounding Box Estimation Using Deep Learning and Geometry Semi-supervised hierarchical semantic object parsing Towards End-to-End Lane Detection: an Instance Segmentation Approach Towards Unconstrained End-to-End Text Spotting Instance Segmentation of Biomedical Images with an Object-Aware Embedding Learned with Local Constraints ToothNet: Automatic Tooth Instance Segmentation and Identification From Cone Beam CT Images A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation Real-Time Multi-class Instance Segmentation with One-Time Deep Embedding Clustering Fully Convolutional Networks for Semantic Segmentation Res2Net: A New Multi-scale Backbone Architecture Recurrent Pixel Embedding for Instance Grouping Shape Robust Text Detection With Progressive Scale Expansion Network Fused Text Segmentation Networks for Multi-oriented Scene Text Detection Semantic Instance Segmentation with a Discriminative Loss Function Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing Feature Pyramid Networks for Object Detection In-place Activated BatchNorm for Memory-Optimized Training of DNNs Holistic, Instance-Level Human Parsing 3D reconstruction of coronary arteries and atherosclerotic plaques based on computed tomography angiography images Tracking and Segmentation of the Airways in Chest CT Using a Fully Convolutional Network Improved Curvature Estimation for Watershed Segmentation of 3-Dimensional Meshes Markov surfaces: A probabilistic framework for user-assisted three-dimensional image segmentation Electron Microscopy Image Segmentation with Graph Cuts Utilizing Estimated Symmetric Three-Dimensional Shape Prior 3D Instance Segmentation via Multi-Task Metric Learning Crowdsourcing the creation of image segmentation algorithms for connectomics Frustum PointNets for 3D Object Detection from RGB-D Data Segmenting Human From Photo Images Based on a Coarse-to-Fine Scheme The Human Image Segmentation Algorithm Based on Face Detection and Biased Normalized Cuts PoSeg: Pose-Aware Refinement Network for Human Instance Segmentation Pose2Seg: Detection Free Human Instance Segmentation Human Instance Segmentation from Video using Detector-based Conditional Random Fields Video Instance-Level Human Parsing Exploring Behavior Representation for Learning Analytics SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation Holistic, Instance-Level Human Parsing Recurrent Pixel Embedding for Instance Grouping Feature Pyramid Networks for Object Detection Stacked Hourglass Networks for Human Pose Estimation Content analysis for audio classification and segmentation A systematic approach for identification of tumor regions in the human brain through HARIS algorithm Deep Snake for Real-Time Instance Segmentation CNN-Based Watershed Marker Extraction for Brick Segmentation in Masonry Walls MRI Image Segmentation Based on a GPU Shortest Path Algorithm ICNet for Real-Time Semantic Segmentation on High-Resolution Images Instance-Aware Semantic Segmentation via Multi-task Network Cascades Multi-Task Learning as Multi-Objective Optimization Learning 3D Semantic Scene Graphs From 3D Indoor Reconstructions Multi-view based neural network for semantic segmentation on 3D scenes Deep Learning Based Instance Segmentation in 3D Biomedical Images Using Weak Annotation 3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation 3D-SIC: 3D Semantic Instance Completion for RGB-D Scans SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation Semantic Scene Segmentation of Unordered Point Clouds on Autonomous Robots A Point-Wise LiDAR and Image Multimodal Fusion Network (PMNet) for Aerial Point Cloud 3D Semantic Segmentation Live Semantic 3D Perception for Immersive Augmented Reality JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds Frustum PointNets for 3D Object Detection from RGB-D Data Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation Pose2Seg: Detection Free Human Instance Segmentation Video Instance Segmentation Pyramid Graph Networks With Connection Attentions for Region-Based One-Shot Semantic Segmentation
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CVPR ACPR (1) IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE transactions on pattern analysis and machine intelligence 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2018 24th International Conference on Pattern Recognition (ICPR) ArXiv ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition BMVC MICCAI (2) ISVC (2) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition CCCV (1) BMVC ICMI BMVC 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ICIAR (1) ECCV 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NeurIPS MICCAI (4) ECMR AAAI 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) 2016 Fourth International Conference on 3D Vision (3DV) ICCV
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