Existing approaches attempt to classify the scene based on 2D images or 2.5D range images . Here , we study scene recognition from 3D point cloud ( or voxel ) data , and show that it greatly outperforms methods based on 2D birds-eye views . Moreover , we advocate multi-task learning as a way of improving scene recognition , building on the fact that the scene type is highly correlated with the objects in the scene , and therefore with its semantic segmentation into different object classes . Probabilistic Graphical Models ( PGMs ) in general , and Undirected Graphical Models ( UGMs ) in particular , become suitable frameworks to capture and conveniently model the uncertainty inherent in a variety of problems . When applied to real world applications , such as scene object recognition , they turn into a reliable and widespread resorted tool . The effectiveness of UGMs is tight to the particularities of the problem to be solved and , especially , to the chosen learning strategy . Results regarding success rate , computational burden , and scalability are analyzed , including the benefits of using parallelization techniques for gaining in efficiency . Cutting-edge strategies for learning UGMs are applied to scene object recognition . Two state-of-the-art datasets , NYUv2 and Cornell-RGBD , are used as testbed . In this paper , scene recognition problem , which is a frequently-studied field of computer vision , is tackled . Proposed algorithm utilizes bag of words ( BoW ) method along with considering sub-segments in the image during classification . We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance . The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem . However , when the DBM is used in scene recognition , it is difficult to handle large images due to its computational complexity . In this paper , we present a deep learning method based on Convolutional Neural Networks ( CNN ) and DBM for scene image recognition . First , in order to categorize large images , the CNN is utilized to preprocess images for dimensional reduction . Moreover , a maxout objective function is adopted to adaptively choose the optimal partition pattern to characterize the image layout . The experimental results on the scene recognition benchmarks demonstrate the effectiveness of the proposed RS-Pooling architecture . The participants were instructed to make a binary choice indicating whether they wished to remember or forget the image ; both stimulus and response times were stored for analysis . The participants then performed a scene recognition test to confirm whether or not they remembered the images . The results revealed that the participants intentionally memorising a visual scene demonstrate significantly greater brain complexity levels in the prefrontal and frontal lobe than when purposefully forgetting a scene ; p < 0.05 ( two-tailed ) . After completing a way finding task in a university campus , we tested participant 's memory for the test route using sketch map , mirror-image discrimination , and scene recognition tests . In addition , we tracked participant 's eye movements during the scene recognition test . Results were consistent with the hypothesis that active travelers had enhanced memory for the built envi- ronment . Images taken by airborne and laboratory sensors have been used for many purposes including autonomous guidance , robot vision , medical diagnosis , automated inspection and automated measurements . Scene recognition operations are relatively costly in computing time due to the large amount of data needed to be analyzed . To obtain real-time operations , image processing and scene recognition can be done with several microprocessors operating in parallel . The structure of the object model ( OM ) is described . This structure is a component that stores different representations of the object and puts at user ’s disposal an interface whose operations are used in the scene recognition process . Research on SAR interferometry is mainly focused on the two main application areas , namely the generation of accurate digital elevation models and change detection , and also on the crucial processing steps like interferogram filtering , phase unwrapping , co-registration etc . This study rather aims to make use of interferometric SAR ( InSAR ) images , once they are generated , for scene recognition purposes . In literature , limited research is available on the use of InSAR images for object recognition and scene classification . First , the scale-invariant feature transform(SIFT)local feature descriptors were employed to construct the feature matrix of the scene image data set , whose dimension was reduced by using principal component analysis(PCA).Second , the feature matrix was clustered through using affinity propagation(AP)method to obtain the reasonable capacity of visual dictionary . And the reasonable capacity of visual dictionary was employed to train support vector machine(SVM)and scene type recognition . Finally , the proposed method was compared with the K-means which was the classic method for the acquisition of visual dictionary capacity . Our work , on the other hand , using the distillation strategy for learning the real style convolutional filters for semantic segmentation . Our work is also inspired by the previous works on scene understanding , in which they also exploited the intrinsic spatial layout prior of scene images for different tasks in various ways . We share a similar philosophy with those work , but aim to solve the distribution alignment between two domains for the urban scene semantic segmentation , which results in a totally different methodology . We initialize it using weights from Model A , and tune it with the Places 365 data for 45k iterations . This is reasonable since there are only object categories in this dataset , while Places 365 is for scene classification tasks . We then compare our method with previous ones on the test set in Table [ reference ] . Previous work has demonstrated that supervised training of CNNs on large image datasets results in very powerful learned features zeiler2014visualizing . Additionally , supervised CNNs trained on scene classification learn object detectors Oquab14 . We demonstrate that an unsupervised DCGAN trained on a large image dataset can also learn a hierarchy of features that are interesting . The feature representation learned by these networks achieves state - of - the - art performance not only on the classification task for which the network was trained , but also on various other visual recognition tasks , for example : classification on Caltech - 101 [ 2 , 3 ] , Caltech - 256 [ 2 ] and the CaltechUCSD birds dataset [ 3 ] ; scene recognition on the SUN - 397 database [ 3 ] ; detection on the PASCAL VOC dataset [ 4 ] . This capability to generalize to new datasets makes supervised CNN training an attractive approach for generic visual feature learning . section : Introduction Computing correspondences between related images is a central issue in many computer vision problems , ranging from scene recognition to optical flow estimation forsyth2011computer , Szeliski2010 . The goal of a matching algorithm is to discover shared visual content between two images , and to establish as many as possible precise point - wise correspondences , called matches . Scene text detection is challenging because scene texts have different sizes , width - height aspect ratios , font styles , lighting , perspective distortion , orientation , etc . As the orientation information is useful for scene text recognition and other tasks , scene text detection is different from common object detection tasks that the text orientation should be also be predicted in addition to the axis - aligned bounding box information . While most previous text detection methods are designed for detecting horizontal or near - horizontal texts [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] , some methods try to address the arbitrary - oriented text detection problem He is also the Vice - director of the National Center of Anti - Counterfeiting Technology , HUST . His research interests include object recognition , shape analysis , scene text recognition and intelligent systems . He serves as an associate editor for Pattern Recognition , Pattern Recognition Letters , Neurocomputing and Frontiers of Computer Science . The active perception makes an important step towards the integration of the sensing in the planning phase of the object manipulation . Closing the perception-planning loop iteratively , this helps to reduce the uncertainty in the sensing and planning offering better scene parsing and object manipulation applications . In this paper we show the active perception concept using an industrial like scene with different pipes which are sensed with a depth camera . While there has been a lot of recent work on object recognition and image understanding , the focus has been on carefully establishing mathematical models for images , scenes , and objects . In this paper , we propose a novel , nonparametric approach for object recognition and scene parsing using a new technology we name label transfer . For an input image , our system first retrieves its nearest neighbors from a large database containing fully annotated images . In this paper we present a framework for semantic scene parsing and object recognition based on dense depth maps . Five view-independent 3D features that vary with object class are extracted from dense depth maps at a superpixel level for training a classifier using randomized decision forest technique . Recently , there have been increasing interests in semantic scene parsing , which are mainly based on prior models . However , as prior models only consider the common merits within objects , they ignore the inner color coherence lied in the single object and suffer from detail loss . Furthermore , to fully exploit the limited annotations , distance metric learning is conducted across different training images instead of simply adopting an image-dependent manner . We conduct extensive experiments on two challenging scene parsing benchmarks of PASCAL-Context and ADE 20 K to validate the effectiveness of our PDML , and competitive mIoU scores are achieved . Ablation studies demonstrate the effectiveness of the proposed modules . We achieve state-of-the-art performance on three scene parsing benchmarks Cityscapes , ADE20 K and LIP . In this paper , we design a novel type of neural network that directly consumes point clouds , which well respects the permutation invariance of points in the input . Our network , named PointNet , provides a unified architecture for applications ranging from object classification , part segmentation , to scene semantic parsing . Though simple , PointNet is highly efficient and effective . A CRF is a probabilistic graphical model that encodes the contextual relationships between the data elements in a scene . In this thesis , we study the potential of contextual information at different scales ( ranges ) in scene parsing problems . First , we propose a model that utilizes the local context of the scene via a pairwise CRF . Scene Parsing is a crucial step to enable autonomous systems to understand and interact with their surroundings . Supervised deep learning methods have made great progress in solving scene parsing problems , however , come at the cost of laborious manual pixel-level annotation . Synthetic data as well as weak supervision have been investigated to alleviate this effort . The second , Proximal Relationship Ambiguity , arises when multiple subject-predicate-object triplets appear in close proximity with the same predicate , and the model struggles to infer the correct subject-object pairings ( e.g. mis-pairing musicians and their instruments ) . We propose a set of contrastive loss formulations that specifically target these types of errors within the scene graph parsing problem , collectively termed the Graphical Contrastive Losses . These losses explicitly force the model to disambiguate related and unrelated instances through margin constraints specific to each type of confusion . The discovery of key and distinctive parts is critical for scene parsing and understanding . However , it is a challenging problem due to the weakly supervised condition , i.e. , no annotation for parts is available . A limitation of this approach is that many models lack an explicit representation of semantic and physical relationships such as the spatial , temporal , semantic and fluent context of objects and events in a scene . In this work , we relate the problem of capturing computational context to the scene parsing literature . We then propose a method of representing and learning contextual relationships from data using stochastic grammars implemented as And-Or Graphs with applications to the Naval Tactical C2 Domain . This topic is of broad interest for potential applications of automatic driving , robot sensing , to name a few . Difficulty of scene parsing is closely related to scene and label variety . The pioneer scene parsing task is to classify 33 scenes for 2 , 688 images on LMO dataset . Our pyramid scene parsing network ( PSPNet ) illustrated in Fig . [ reference ] is then described to improve performance for open - vocabulary object and stuff identification in complex scene parsing . subsection : Important Observations So a large amount of labels and vast distributions of scenes come into existence . Inspecting the prediction results of the FCN baseline provided in , we summarize several common issues for complex - scene parsing . paragraph : Mismatched Relationship It is followed by a convolution layer to generate the final prediction map in ( d ) . To explain our structure , PSPNet provides an effective global contextual prior for pixel - level scene parsing . The pyramid pooling module can collect levels of information , more representative than global pooling . Metrics We report metrics from common semantic segmentation and scene parsing evaluations that are variations on pixel accuracy and region intersection over union ( IU ) : pixel accuracy : Based on PSOLA Algorithm.- A Novel Blind Equalizer Based on Dual-Mode MCMA and DD Algorithm.- Robust Secret Key Based Authentication Scheme Using Smart Cards.- A Dynamically Configurable Multimedia Middleware.- Adaptive VoIP Smoothing of Pareto Traffic Based on Optimal E-Model Quality.- Indoor Scene Reconstruction Using a Projection-Based Registration Technique of Multi-view Depth Images.- Image-Based Relighting in Dynamic Scenes.- Stippling Technique Based on Color Analysis.- Photometry Data Coding for Three-Dimensional Mesh Models Using Connectivity and Geometry Information.- Adaptation of MPEG-4 BIFS Scenes into MPEG-4 LASeR Scenes in MPEG-21 DIA Framework.- Performance Evaluation of H.264 Mapping Strategies over IEEE 802.11e WLAN for Robust Video Streaming.- Reducing Spatial Resolution for MPEG-2 to H.264/AVC Transcoding.- Low-Bitrate Video Quality Enhancement by Frame Rate Up-Conversion and Adaptive Frame Encoding.- Face Recognition Using Neighborhood Preserving Projections.- However , how to assess the quality of such synthesized 3D scenes is still a challenging problem . To address this , we present a novel approach through so-called Human-Centric Metrics ( HCMs ) for quantitatively evaluating the layout quality of certain objects , and thus to facilitate indoor scene synthesis . Our HCMs consider both the human-object factors to assess the functional accessibility of indoor objects , and the human-environment factors to assess the subjective comfort in the scene . Intelligent systems need complex and detailed models of their environment to achieve more sophisticated tasks , such as assistance to the user . Vision sensors provide rich information and are broadly used to obtain these models , for example , indoor scene modeling from monocular images has been widely studied . A common initial step in those settings is the estimation of the \(3\)D layout of the scene . In addition , a novel attention mask is introduced to guide the appearance adaptation to the embedded foreground objects which helps preserve image details and resolution and also provide better reference for synthesis in geometry domain . Extensive experiments on scene text image synthesis , automated portrait editing and indoor rendering tasks show that the proposed AC-GAN achieves superior synthesis performance qualitatively and quantitatively . We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design . We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor scene design , and further develop a weaklysupervised algorithm for extracting the knowledge graph representation from a small dataset using both structure and parameter learning . The proposed framework is flexible , transferable , and readable . Our algorithm significantly outperforms existing methods and achieves around 90 % precision and recall , getting to the range of production-ready performance . The vector representation allows 3D model popup for better indoor scene visualization , direct model manipulation for architectural remodeling , and further computational applications such as data analysis . Our system is efficient : we have converted hundred thousand production-level floorplan images into the vector representation and generated 3D popup models . The paper also proposes a new room segmentation algorithm and an offset-map reconstruction algorithm that are used in the framework and can enforce architectural shape priors far beyond existing state-of-the-art . The structured scene representation enables a variety of novel applications , ranging from indoor scene visualization , automated floorplan generation , Inverse-CAD , and more . We have tested our framework and algorithms on six synthetic and five real datasets with qualitative and quantitative evaluations . In this paper , we tackle problems of indoor dynamic reconstructed scene created using multiple static Kinect sensors ; toward this goal , we propose a dynamic object detection algorithm based on multi-layer growing neural gas for reconstructed scenes and creating a dynamic hierarchical structured space ; in fact the proposed technique creates a multi-layer structure for representing a point cloud as points , fragments , objects/groups , and environment . Moreover the proposed algorithm uses statistical outlier removal technique and a down-sampling algorithm based on growing neural gas in order to remove edge and shadow noises being very common in reconstructed scene created using multiple static Kinect sensors . Moreover , we adopt the recently developed Deep Convolutional Neural Network ( CNN ) descriptor as a high dimensional local feature and implement image classification with the proposed SCFVC . Our experimental evaluations demonstrate that our method not only significantly outperforms the traditional GMM based Fisher vector encoding but also achieves the state-of-the-art performance in generic object recognition , indoor scene , and fine-grained image classification problems . We coin this new form of affordance as intangible affordance . By fusing functionality and affordance into indoor scene generation , we propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and numerous photorealistic 2D images thereof , with associated ground truth information , for the purposes of training , benchmarking , and diagnosing learning-based computer vision and robotics algorithms . We present four case studies on integrating forces and functionality in object manipulations in the field of robotics , showcasing the significance and benefits of explicit modeling of the functionality in task executions . The classic stragedy to construct BSP trees is to use the triangular face as the partitioning plane , so that the trees have higher complexity and need longer preprocessing time . So , this stragedy is fit to indoor scenes ' rendering or collision detection . Aiming at the rendering of large-scale outdoor scenes comprised of small models , this paper presents an approach to construct object-level BSP trees and use them for fast rendering . We also provide a Caffe implementation of SegNet and a web demo at . Deep Convolutional Neural Networks , Semantic Pixel - Wise Segmentation , Indoor Scenes , Road Scenes , Encoder , Decoder , Pooling , Upsampling . section : Introduction Structural information encoded in disparity can be treated as supplementary information of RGB images , which is expected to boost performance . Therefore , in this work we propose to fuse disparity information in street scene understanding task . And we design four methods to incorporate disparity information into semantic segmentation framework . A Photorealistic Synthetic Dataset for Street Scene Parsing . We introduce Synscapes -- a synthetic dataset for street scene parsing created using photorealistic rendering techniques , and show state-of-the-art results for training and validation as well as new types of analysis . We study the behavior of networks trained on real data when performing inference on synthetic data : a key factor in determining the equivalence of simulation environments . The proposed method utilizes the spatial layout of the image by building local pixel classifiers that are location constrained , i.e. , trained with pixels from a local neighborhood region only . Our proposed local learning works well in different challenging image parsing problems , such as pedestrian parsing , street-view scene parsing and object segmentation , and outperforms existing results that rely on one unified pixel classifier . To better understand the behavior of our local classifier , we perform bias-variance analysis , and demonstrate that the proposed local classifier essentially performs spatial smoothness over the target estimator that uses appearance information and location , which explains why the local classifier is more discriminative but can still handle mis-alignment .
Existing approaches attempt to classify the scene based on 2D images or 2.5D range images . Here , we study scene recognition from 3D point cloud ( or voxel ) data , and show that it greatly outperforms methods based on 2D birds-eye views . Moreover , we advocate multi-task learning as a way of improving scene recognition , building on the fact that the scene type is highly correlated with the objects in the scene , and therefore with its semantic segmentation into different object classes .
Probabilistic Graphical Models ( PGMs ) in general , and Undirected Graphical Models ( UGMs ) in particular , become suitable frameworks to capture and conveniently model the uncertainty inherent in a variety of problems . When applied to real world applications , such as scene object recognition , they turn into a reliable and widespread resorted tool . The effectiveness of UGMs is tight to the particularities of the problem to be solved and , especially , to the chosen learning strategy .
Results regarding success rate , computational burden , and scalability are analyzed , including the benefits of using parallelization techniques for gaining in efficiency . Cutting-edge strategies for learning UGMs are applied to scene object recognition . Two state-of-the-art datasets , NYUv2 and Cornell-RGBD , are used as testbed .
In this paper , scene recognition problem , which is a frequently-studied field of computer vision , is tackled . Proposed algorithm utilizes bag of words ( BoW ) method along with considering sub-segments in the image during classification .
We implement a weighted voting scheme based on genetic algorithms for the combination of different classifiers in order to improve the recognition performance . The results have proved the validity of our approach and how the proper combination of independent classifier models makes it possible to find a better and more efficient solution for the scene recognition problem .
However , when the DBM is used in scene recognition , it is difficult to handle large images due to its computational complexity . In this paper , we present a deep learning method based on Convolutional Neural Networks ( CNN ) and DBM for scene image recognition . First , in order to categorize large images , the CNN is utilized to preprocess images for dimensional reduction .
Moreover , a maxout objective function is adopted to adaptively choose the optimal partition pattern to characterize the image layout . The experimental results on the scene recognition benchmarks demonstrate the effectiveness of the proposed RS-Pooling architecture .
The participants were instructed to make a binary choice indicating whether they wished to remember or forget the image ; both stimulus and response times were stored for analysis . The participants then performed a scene recognition test to confirm whether or not they remembered the images . The results revealed that the participants intentionally memorising a visual scene demonstrate significantly greater brain complexity levels in the prefrontal and frontal lobe than when purposefully forgetting a scene ; p < 0.05 ( two-tailed ) .
After completing a way finding task in a university campus , we tested participant 's memory for the test route using sketch map , mirror-image discrimination , and scene recognition tests . In addition , we tracked participant 's eye movements during the scene recognition test . Results were consistent with the hypothesis that active travelers had enhanced memory for the built envi- ronment .
Images taken by airborne and laboratory sensors have been used for many purposes including autonomous guidance , robot vision , medical diagnosis , automated inspection and automated measurements . Scene recognition operations are relatively costly in computing time due to the large amount of data needed to be analyzed . To obtain real-time operations , image processing and scene recognition can be done with several microprocessors operating in parallel .
The structure of the object model ( OM ) is described . This structure is a component that stores different representations of the object and puts at user ’s disposal an interface whose operations are used in the scene recognition process .
Research on SAR interferometry is mainly focused on the two main application areas , namely the generation of accurate digital elevation models and change detection , and also on the crucial processing steps like interferogram filtering , phase unwrapping , co-registration etc . This study rather aims to make use of interferometric SAR ( InSAR ) images , once they are generated , for scene recognition purposes . In literature , limited research is available on the use of InSAR images for object recognition and scene classification .
First , the scale-invariant feature transform(SIFT)local feature descriptors were employed to construct the feature matrix of the scene image data set , whose dimension was reduced by using principal component analysis(PCA).Second , the feature matrix was clustered through using affinity propagation(AP)method to obtain the reasonable capacity of visual dictionary . And the reasonable capacity of visual dictionary was employed to train support vector machine(SVM)and scene type recognition . Finally , the proposed method was compared with the K-means which was the classic method for the acquisition of visual dictionary capacity .
Our work , on the other hand , using the distillation strategy for learning the real style convolutional filters for semantic segmentation . Our work is also inspired by the previous works on scene understanding , in which they also exploited the intrinsic spatial layout prior of scene images for different tasks in various ways . We share a similar philosophy with those work , but aim to solve the distribution alignment between two domains for the urban scene semantic segmentation , which results in a totally different methodology .
We initialize it using weights from Model A , and tune it with the Places 365 data for 45k iterations . This is reasonable since there are only object categories in this dataset , while Places 365 is for scene classification tasks . We then compare our method with previous ones on the test set in Table [ reference ] .
Previous work has demonstrated that supervised training of CNNs on large image datasets results in very powerful learned features zeiler2014visualizing . Additionally , supervised CNNs trained on scene classification learn object detectors Oquab14 . We demonstrate that an unsupervised DCGAN trained on a large image dataset can also learn a hierarchy of features that are interesting .
The feature representation learned by these networks achieves state - of - the - art performance not only on the classification task for which the network was trained , but also on various other visual recognition tasks , for example : classification on Caltech - 101 [ 2 , 3 ] , Caltech - 256 [ 2 ] and the CaltechUCSD birds dataset [ 3 ] ; scene recognition on the SUN - 397 database [ 3 ] ; detection on the PASCAL VOC dataset [ 4 ] . This capability to generalize to new datasets makes supervised CNN training an attractive approach for generic visual feature learning .
section : Introduction Computing correspondences between related images is a central issue in many computer vision problems , ranging from scene recognition to optical flow estimation forsyth2011computer , Szeliski2010 . The goal of a matching algorithm is to discover shared visual content between two images , and to establish as many as possible precise point - wise correspondences , called matches .
Scene text detection is challenging because scene texts have different sizes , width - height aspect ratios , font styles , lighting , perspective distortion , orientation , etc . As the orientation information is useful for scene text recognition and other tasks , scene text detection is different from common object detection tasks that the text orientation should be also be predicted in addition to the axis - aligned bounding box information . While most previous text detection methods are designed for detecting horizontal or near - horizontal texts [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] , some methods try to address the arbitrary - oriented text detection problem
He is also the Vice - director of the National Center of Anti - Counterfeiting Technology , HUST . His research interests include object recognition , shape analysis , scene text recognition and intelligent systems . He serves as an associate editor for Pattern Recognition , Pattern Recognition Letters , Neurocomputing and Frontiers of Computer Science .
The active perception makes an important step towards the integration of the sensing in the planning phase of the object manipulation . Closing the perception-planning loop iteratively , this helps to reduce the uncertainty in the sensing and planning offering better scene parsing and object manipulation applications . In this paper we show the active perception concept using an industrial like scene with different pipes which are sensed with a depth camera .
While there has been a lot of recent work on object recognition and image understanding , the focus has been on carefully establishing mathematical models for images , scenes , and objects . In this paper , we propose a novel , nonparametric approach for object recognition and scene parsing using a new technology we name label transfer . For an input image , our system first retrieves its nearest neighbors from a large database containing fully annotated images .
In this paper we present a framework for semantic scene parsing and object recognition based on dense depth maps . Five view-independent 3D features that vary with object class are extracted from dense depth maps at a superpixel level for training a classifier using randomized decision forest technique .
Recently , there have been increasing interests in semantic scene parsing , which are mainly based on prior models . However , as prior models only consider the common merits within objects , they ignore the inner color coherence lied in the single object and suffer from detail loss .
Furthermore , to fully exploit the limited annotations , distance metric learning is conducted across different training images instead of simply adopting an image-dependent manner . We conduct extensive experiments on two challenging scene parsing benchmarks of PASCAL-Context and ADE 20 K to validate the effectiveness of our PDML , and competitive mIoU scores are achieved .
Ablation studies demonstrate the effectiveness of the proposed modules . We achieve state-of-the-art performance on three scene parsing benchmarks Cityscapes , ADE20 K and LIP .
In this paper , we design a novel type of neural network that directly consumes point clouds , which well respects the permutation invariance of points in the input . Our network , named PointNet , provides a unified architecture for applications ranging from object classification , part segmentation , to scene semantic parsing . Though simple , PointNet is highly efficient and effective .
A CRF is a probabilistic graphical model that encodes the contextual relationships between the data elements in a scene . In this thesis , we study the potential of contextual information at different scales ( ranges ) in scene parsing problems . First , we propose a model that utilizes the local context of the scene via a pairwise CRF .
Scene Parsing is a crucial step to enable autonomous systems to understand and interact with their surroundings . Supervised deep learning methods have made great progress in solving scene parsing problems , however , come at the cost of laborious manual pixel-level annotation . Synthetic data as well as weak supervision have been investigated to alleviate this effort .
The second , Proximal Relationship Ambiguity , arises when multiple subject-predicate-object triplets appear in close proximity with the same predicate , and the model struggles to infer the correct subject-object pairings ( e.g. mis-pairing musicians and their instruments ) . We propose a set of contrastive loss formulations that specifically target these types of errors within the scene graph parsing problem , collectively termed the Graphical Contrastive Losses . These losses explicitly force the model to disambiguate related and unrelated instances through margin constraints specific to each type of confusion .
The discovery of key and distinctive parts is critical for scene parsing and understanding . However , it is a challenging problem due to the weakly supervised condition , i.e. , no annotation for parts is available .
A limitation of this approach is that many models lack an explicit representation of semantic and physical relationships such as the spatial , temporal , semantic and fluent context of objects and events in a scene . In this work , we relate the problem of capturing computational context to the scene parsing literature . We then propose a method of representing and learning contextual relationships from data using stochastic grammars implemented as And-Or Graphs with applications to the Naval Tactical C2 Domain .
This topic is of broad interest for potential applications of automatic driving , robot sensing , to name a few . Difficulty of scene parsing is closely related to scene and label variety . The pioneer scene parsing task is to classify 33 scenes for 2 , 688 images on LMO dataset .
Our pyramid scene parsing network ( PSPNet ) illustrated in Fig . [ reference ] is then described to improve performance for open - vocabulary object and stuff identification in complex scene parsing . subsection : Important Observations
So a large amount of labels and vast distributions of scenes come into existence . Inspecting the prediction results of the FCN baseline provided in , we summarize several common issues for complex - scene parsing . paragraph : Mismatched Relationship
It is followed by a convolution layer to generate the final prediction map in ( d ) . To explain our structure , PSPNet provides an effective global contextual prior for pixel - level scene parsing . The pyramid pooling module can collect levels of information , more representative than global pooling .
Metrics We report metrics from common semantic segmentation and scene parsing evaluations that are variations on pixel accuracy and region intersection over union ( IU ) : pixel accuracy :
Based on PSOLA Algorithm.- A Novel Blind Equalizer Based on Dual-Mode MCMA and DD Algorithm.- Robust Secret Key Based Authentication Scheme Using Smart Cards.- A Dynamically Configurable Multimedia Middleware.- Adaptive VoIP Smoothing of Pareto Traffic Based on Optimal E-Model Quality.- Indoor Scene Reconstruction Using a Projection-Based Registration Technique of Multi-view Depth Images.- Image-Based Relighting in Dynamic Scenes.- Stippling Technique Based on Color Analysis.- Photometry Data Coding for Three-Dimensional Mesh Models Using Connectivity and Geometry Information.- Adaptation of MPEG-4 BIFS Scenes into MPEG-4 LASeR Scenes in MPEG-21 DIA Framework.- Performance Evaluation of H.264 Mapping Strategies over IEEE 802.11e WLAN for Robust Video Streaming.- Reducing Spatial Resolution for MPEG-2 to H.264/AVC Transcoding.- Low-Bitrate Video Quality Enhancement by Frame Rate Up-Conversion and Adaptive Frame Encoding.- Face Recognition Using Neighborhood Preserving Projections.-
However , how to assess the quality of such synthesized 3D scenes is still a challenging problem . To address this , we present a novel approach through so-called Human-Centric Metrics ( HCMs ) for quantitatively evaluating the layout quality of certain objects , and thus to facilitate indoor scene synthesis . Our HCMs consider both the human-object factors to assess the functional accessibility of indoor objects , and the human-environment factors to assess the subjective comfort in the scene .
Intelligent systems need complex and detailed models of their environment to achieve more sophisticated tasks , such as assistance to the user . Vision sensors provide rich information and are broadly used to obtain these models , for example , indoor scene modeling from monocular images has been widely studied . A common initial step in those settings is the estimation of the \(3\)D layout of the scene .
In addition , a novel attention mask is introduced to guide the appearance adaptation to the embedded foreground objects which helps preserve image details and resolution and also provide better reference for synthesis in geometry domain . Extensive experiments on scene text image synthesis , automated portrait editing and indoor rendering tasks show that the proposed AC-GAN achieves superior synthesis performance qualitatively and quantitatively .
We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design . We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor scene design , and further develop a weaklysupervised algorithm for extracting the knowledge graph representation from a small dataset using both structure and parameter learning . The proposed framework is flexible , transferable , and readable .
Our algorithm significantly outperforms existing methods and achieves around 90 % precision and recall , getting to the range of production-ready performance . The vector representation allows 3D model popup for better indoor scene visualization , direct model manipulation for architectural remodeling , and further computational applications such as data analysis . Our system is efficient : we have converted hundred thousand production-level floorplan images into the vector representation and generated 3D popup models .
The paper also proposes a new room segmentation algorithm and an offset-map reconstruction algorithm that are used in the framework and can enforce architectural shape priors far beyond existing state-of-the-art . The structured scene representation enables a variety of novel applications , ranging from indoor scene visualization , automated floorplan generation , Inverse-CAD , and more . We have tested our framework and algorithms on six synthetic and five real datasets with qualitative and quantitative evaluations .
In this paper , we tackle problems of indoor dynamic reconstructed scene created using multiple static Kinect sensors ; toward this goal , we propose a dynamic object detection algorithm based on multi-layer growing neural gas for reconstructed scenes and creating a dynamic hierarchical structured space ; in fact the proposed technique creates a multi-layer structure for representing a point cloud as points , fragments , objects/groups , and environment . Moreover the proposed algorithm uses statistical outlier removal technique and a down-sampling algorithm based on growing neural gas in order to remove edge and shadow noises being very common in reconstructed scene created using multiple static Kinect sensors .
Moreover , we adopt the recently developed Deep Convolutional Neural Network ( CNN ) descriptor as a high dimensional local feature and implement image classification with the proposed SCFVC . Our experimental evaluations demonstrate that our method not only significantly outperforms the traditional GMM based Fisher vector encoding but also achieves the state-of-the-art performance in generic object recognition , indoor scene , and fine-grained image classification problems .
We coin this new form of affordance as intangible affordance . By fusing functionality and affordance into indoor scene generation , we propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and numerous photorealistic 2D images thereof , with associated ground truth information , for the purposes of training , benchmarking , and diagnosing learning-based computer vision and robotics algorithms . We present four case studies on integrating forces and functionality in object manipulations in the field of robotics , showcasing the significance and benefits of explicit modeling of the functionality in task executions .
The classic stragedy to construct BSP trees is to use the triangular face as the partitioning plane , so that the trees have higher complexity and need longer preprocessing time . So , this stragedy is fit to indoor scenes ' rendering or collision detection . Aiming at the rendering of large-scale outdoor scenes comprised of small models , this paper presents an approach to construct object-level BSP trees and use them for fast rendering .
We also provide a Caffe implementation of SegNet and a web demo at . Deep Convolutional Neural Networks , Semantic Pixel - Wise Segmentation , Indoor Scenes , Road Scenes , Encoder , Decoder , Pooling , Upsampling . section : Introduction
Structural information encoded in disparity can be treated as supplementary information of RGB images , which is expected to boost performance . Therefore , in this work we propose to fuse disparity information in street scene understanding task . And we design four methods to incorporate disparity information into semantic segmentation framework .
A Photorealistic Synthetic Dataset for Street Scene Parsing . We introduce Synscapes -- a synthetic dataset for street scene parsing created using photorealistic rendering techniques , and show state-of-the-art results for training and validation as well as new types of analysis . We study the behavior of networks trained on real data when performing inference on synthetic data : a key factor in determining the equivalence of simulation environments .
The proposed method utilizes the spatial layout of the image by building local pixel classifiers that are location constrained , i.e. , trained with pixels from a local neighborhood region only . Our proposed local learning works well in different challenging image parsing problems , such as pedestrian parsing , street-view scene parsing and object segmentation , and outperforms existing results that rely on one unified pixel classifier . To better understand the behavior of our local classifier , we perform bias-variance analysis , and demonstrate that the proposed local classifier essentially performs spatial smoothness over the target estimator that uses appearance information and location , which explains why the local classifier is more discriminative but can still handle mis-alignment .
Indoor Scene Recognition in 3D A survey on learning approaches for Undirected Graphical Models. Application to scene object recognition Scene recognition with majority voting among sub-section levels Indoor Scene Recognition based on Weighted Voting Schemes Natural scene recognition based on Convolutional Neural Networks and Deep Boltzmannn Machines Randomized spatial pooling in deep convolutional networks for scene recognition Multiscale Entropy as a New Feature for EEG and fNIRS Analysis Way-Finding Improves Visual Memory for Built Environments Architectures For Parallel Image Processing Computational model for recognition of scene objects with an active sensor On the Use of TanDEM-X Bistatic InSAR Images for Scene Recognition Research on reasonable capacity of visual dictionary obtaining ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Wider or Deeper: Revisiting the ResNet Model for Visual Recognition Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Discriminative Unsupervised Feature Learning with Convolutional Neural Networks DeepMatching: Hierarchical Deformable Dense Matching R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection PCL: Proposal Cluster Learning for Weakly Supervised Object Detection Active perception for object manipulation Nonparametric Scene Parsing via Label Transfer Semantic Segmentation of Urban Scenes Using Dense Depth Maps Scene Parsing Based on A Two-Level Conditional Random Field Weakly Supervised Scene Parsing with Point-based Distance Metric Learning CaseNet: Content-Adaptive Scale Interaction Networks for Scene Parsing PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation On the Role of Context at Different Scales in Scene Parsing What Synthesis Is Missing: Depth Adaptation Integrated With Weak Supervision for Indoor Scene Parsing Graphical Contrastive Losses for Scene Graph Parsing Learning a Representative and Discriminative Part Model with Deep Convolutional Features for Scene Recognition Inferring Context Through Scene Understanding Pyramid Scene Parsing Network Fully Convolutional Networks for Semantic Segmentation Advances in Multimedia Information Processing - PCM 2005, 6th Pacific-Rim Conference on Multimedia, Jeju Island, Korea, November 13-16, 2005, Proceedings, Part I Human-centric metrics for indoor scene assessment and synthesis 3D Layout Propagation to Improve Object Recognition in Egocentric Videos Adaptive Composition GAN towards Realistic Image Synthesis Knowledge graph construction with structure and parameter learning for indoor scene design Raster-to-Vector: Revisiting Floorplan Transformation Structured Indoor Modeling 3D Object Detection for Reconstructed Scene Using Multi-layer Growing Neural Gas Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors Visual Commonsense Reasoning: Functionality, Physics, Causality, and Utility Rendering Large-Scale Outdoor Scenes with Object-Level BSP Trees SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Semantic Segmentation of Street Scenes Using Disparity Information Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing Learning location constrained pixel classifiers for image parsing
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2020 2017 2016 2019 2015 2015 2020 2014 1985 2007 2017 2014 2018 2019 2016 2014 2016 2017 2020 2016 2011 2010 2013 2018 2019 2017 2017 2019 2019 2014 2017 2017 2017 2005 2020 2014 2019 2018 2017 2015 2015 2014 2018 2010 2017 2019 2018 2017
SIU ECMR ICIP ET4S@GIScience 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Pattern Recognit. ICLR NIPS International Journal of Computer Vision ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence ICCP ECCV (4) CVPR ACCV (1) AAAI Spring Symposia 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence Advances in Multimedia Information Processing - PCM 2005 ECCV Workshops (3) ICCV ICCV INNS-CIIS IEEE Transactions on Pattern Analysis and Machine Intelligence ICIG (1)
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