Contextual modeling is a critical issue in scene understanding . Object detection accuracy can be improved by exploiting tendencies that are common among object configurations . Interactive motion segmentation is an important task for scene understanding and analysis . Despite recent progress state-of-theart approaches still have difficulties in adapting to the diversity of spatially varying motion fields . An overview . The major task of the scene understanding process is to find the scene which best explains the observed data . In general the observed data , the same radar signals , can be generated by different scenes . It consists of a glasses-type wearable device with a front-view camera , eye-view camera , mounted display , earphone , and computing unit for signal processing . The scene-understanding process on the input image from the front-view camera can be computationally accelerated with the support of the eye-view camera that monitors the eye position of the user . For efficient information processing , the eye view camera catches the user 's visual intention and attention in a given situation . Abstract . Point cloud segmentation is a crucial step in scene understanding and interpretation . The goal is to decompose the initial data into sets of workable clusters with similar properties . Texture recognition plays an important role in many computer vision tasks including segmentation , scene understanding and interpretation , medical imaging and object recognition . In some situations , the correct identification of particular textures is more important compared to others , for example recognition of enemy uniforms for automatic defense systems , or isolation of textures related to tumors in medical images . We integrate an incremental conceptual clustering system ( INC ) with a com-puter vision system , developed in our Artificial Intelligence and Computer Vision laboratories . The project consists of : ( 1 ) developing spatial knowledge models and cognitive functions within a spatial learning system , and ( 2 ) evaluating the plausibility , effectiveness , and constraints of the system in both the general-type spatial reasoning tasks in cognitive systems and the following scene analysis/understanding tasks : sensor fusion , object recognition , model instantiation , image/scene description , and reasoning about a scene . Scene image understanding has drawn much attention for its intriguing applications in the past years . In this paper , we propose a unified probabilistic graphical model called Topic-based Coherent Region Annotation ( TCRA ) for weakly-supervised scene region annotation . The automatic detection , recognition , and segmentation of object classes in remote sensed images is of crucial importance for scene interpretation and understanding . However , it is a difficult task because of the high variability of satellite data . High-resolution remote sensing scene classification ( HR-RSSC ) plays an increasingly important role since it aims to enhance the scene semantic understanding . Recently , convolutional neural networks ( CNNs ) proved their effectiveness in learning powerful feature representations for various visual recognition tasks . Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images . Different from pure object recognition tasks , the relation triplets of subject-predicate-object lie on an extreme diversity space , such asperson-behind-person andcar-behind-building , while suffering from the problem of combinatorial explosion . The proposed contributions obtain superior results against state-of-art algorithms among all public datasets , especially multiple cases in a global scale . We conclude that the spatial and context information of each candidate axis inside an image can be used as a local or global symmetry measure for further image analysis and scene understanding purposes . This strongly contrasts with other VQA datasets of real images , where a correct answer is often obvious without looking at the image , by relying on systematic regularities of frequent questions and answers . Performance improvements reported on such datasets are difficult to interpret as actual progress in scene understanding and reasoning as they might similarly be taken to represent a better modeling of the language prior of the dataset . This hampers , or at best obscures , progress toward the greater goal of general VQA . This comparison reveals the memory versus accuracy trade - off involved in achieving good segmentation performance . SegNet was primarily motivated by scene understanding applications . Hence , it is designed to be efficient both in terms of memory and computational time during inference . section : Introduction Semantic segmentation has a wide array of applications ranging from scene understanding , inferring support - relationships among objects to autonomous driving . Early methods that relied on low - level vision cues have fast been superseded by popular machine learning algorithms . Su et al . defined six groups of attributes to build intermediate - level features for image classification . Li et al . introduced the concept of an ‘ object bank ’ which enables objects to be used as attributes for scene representation . subsection : Image Captioning section : C.3 Scene understanding For scene understanding experiments , we use the Cityscapes dataset [ reference ] . Third , the data rates for RGBD are higher than for RGB , increasing storage and data transfer times . As a consequence , for a wide range of applications in domains such as pedestrian tracking and safety monitoring in autonomous driving , computer graphics , driver alertness monitoring , and social scene understanding from video , there remains a need for an RGB - based 3D head pose estimation solution which is fast and reliable . The key contributions of our work are the following : Proposing a method to predict head pose Euler angles directly from image intensities using a multi loss network which has a loss for each angle and each loss has two components : a pose bin classification and a regression component . At the crux of our perception unification framework is an effort to attain efficient understanding by proposing a deep architecture built on residual factorized convolution and pyramidical representation . A comprehensive set of experiments demonstrates the accurate scene parsing results with promise of real-time inference speed . Crucially , real-world performance over state-of-art approaches qualifies the proposed framework for assistance when deployed to two wearable navigation systems , including a pair of commercial smart glasses and a prototype of customized device . However , they are quite sensitive to initial settings . To address these limitations , in this paper we propose a novel Deep Multiphase Level Set ( DMLS ) method for semantic scene parsing , which efficiently incorporates multiphase level sets into deep neural networks . The proposed method consists of three modules , i.e. , recurrent FCNs , adaptive multiphase level set , and deeply supervised learning . Experiments that mask foreground and background investigate the role of context and shape . Results on the object and scene labeling of PASCAL - Context reinforce merging object segmentation and scene parsing as unified pixelwise prediction . In the next section , we review related work on deep classification nets , FCNs , recent approaches to semantic segmentation using convnets , and extensions to FCNs . The other choice is to operate on different scale image input . Farabet et al . use this way to learn multi - scale feature for scene parsing . For single image SR , the lower solution image always has much more sharp detail information than the interpolation result . In this paper , we exploit the capability of global context information by different - region - based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network ( PSPNet ) . Our global prior representation is effective to produce good quality results on the scene parsing task , while PSPNet provides a superior framework for pixel - level prediction . The proposed approach achieves state - of - the - art performance on various datasets . Our approach is evaluated on both semantic segmentation and geometric labeling tasks , including the Stanford Background , Sift Flow , Geometric Context , and NYUv2 RGB-D data set . The competitive results demonstrate that our stacked learning to search method provides an effective alternative paradigm for scene labeling . Here , a range of hand gestures are used to highlight and select 3D points within the structure and in this instance used to insert 3D graphical objects into the scene . Structure recovery , gesture recognition , scene annotation and augmentation are achieved in parallel and at video-rate . We introduce in this work a novel stochastic inference process , for scene annotation and object class segmentation , based on finite state machines ( FSMs ) . The design principle of our framework is generative and based on building , for a given scene , finite state machines that encode annotation lattices , and inference consists in finding and scoring the best configurations in these lattices . Consider the constraint graph shown in Figure 2 , for the prob lem of label ing the cube . The scene labeling problem Waltz . , 1975 provides an appl icat ion domain here , but those unfami l ia r w i t h it can regard the cube as an abstract constraint graph The graph in Figure 2b is s imply a redrawing of the constraint graph in Figure 2a . Observe tha t the variables F and C are not jo ined by a const ra in t , nor are B and D , not A and E. There are no edges between variables at the same level in Figure 2b . A remarkable fact is that our approach also outperforms recent CNN-based methods . HighlightsWe propose a novel flexible segmentation graph ( FSG ) representation to capture multi-scale visual context for scene labeling problem . In the scenario of FSG , we establish a contextual fusion model to formulate multi-scale context . The proposed image annotation model is evaluated separately on the first and on the second tier using a dataset of outdoor images . The results outperform the published results obtained on the same image collection , both on the object-level and on scene-level annotation . Different subsets of features composed of dominant colours , image moments , and GIST descriptors , as well as different classification methods ( RAKEL , ML-kNN and Naive Bayes ) , were tested in the first tier . Auto-context learns an integrated low-level and context model , and is very general and easy to implement . Under nearly the identical parameter setting in the training , we apply the algorithm on three challenging vision applications : object segmentation , human body configuration , and scene region labeling . It typically takes about 30 ~ 70 seconds to run the algorithm in testing . Massive traffic scene data for algorithm research and model training is the fundamental for self-driving car technology development . In the procedure of scene image labeling , the most accurate method is manual annotation , but with the increasing of the amount of image data , artificial annotation method becomes infeasible due to its disadvantages of vast cost , inefficiency and subjective deviation . In order to reduce the time and cost of annotation processing while ensuring the accuracy , this paper proposed a semi-automatic annotation procedure . Scene classification from images is a challenging problem in computer vision due to its significant variability of scale , illumination , and view . Recently , Latent Dirichlet Allocation ( LDA ) model has grown popular in computer vision field , especially in scene labeling and classification . However , the effectiveness of the LDA model for the scene classification has not yet been addressed thoroughly . Scene labeling research has mostly focused on outdoor scenes , leaving the harder case of indoor scenes poorly understood . Microsoft Kinect dramatically changed the landscape , showing great potentials for RGB-D perception ( color+depth ) . The category and appearance features are utilized as the prior constraints to guide the direction of object label propagation . In this hierarchical model , the scene-level labeling and the object-level labeling have a mutual relationship , which regions and objects are optimized interactively . The experimental results on two datasets show the well performance of our method . The design of the semantic labeling branch is inspired by the fully convolutional network in for semantic labeling . It is also used in our previous work for scene labeling . subsection : 3D Translation Estimation In this paper , we propose a panoramic mesh modeling method from multiple range data for indoor scene reconstruction . The input to the proposed method is several sets of point clouds obtained from different viewpoints . In this paper , we present a new method for line segments matching for indoor reconstruction . Instead of matching individual segments via a descriptor like most methods do , we match segment chains that have a distinctive topology using a dynamic programing formulation . We propose a novel approach to automatic and flexible indoor scene synthesis using an energy-based method . We regard indoor scene synthesis as a multiple-object optimization problem with furniture location and orientation according to the user 's intention , as a constraint on the energy of the optimization problem . Based on the relationship of objects , the embedded aesthetic criterion , the design criterion for proper placement and human movement in a scene , we design five energy functions , the overlap constraint , pairwise constraint , wall constraint , aisle constraint , angle constraint and penalty item , are proposed . We present a fast framework for indoor scene synthesis , given a room geometry and a list of objects with learnt priors . Unlike existing data-driven solutions , which often extract priors by co-occurrence analysis and statistical model fitting , our method measures the strengths of spatial relations by tests for complete spatial randomness ( CSR ) , and extracts complex priors based on samples with the ability to accurately represent discrete layout patterns . Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model . Recently , there has been an increasing amount of research related to indoor environment reconstruction . Therefore , this paper reviews the state-of-the-art techniques for the three-dimensional ( 3D ) reconstruction of indoor environments . Third , there is no research to construct indoor spatial standard data from triangle mesh data . For resolving these problems , we propose the a method for generating triangular mesh data for indoor geometric representation based in the observations mentioned above . First this method removes unnecessary objects and reduces the number of surfaces from the original fine-grained triangular mesh data using the properties of indoor space . To evaluate the performance of our proposed algorithm , the proposed approach is applied to RGB-D SLAM , which is tested in different indoor environments . The experimental results show that applying our post-rectification algorithm to indoor scenario reconstruction can result in more accurate and more detailed 3-D reconstruction of objects than other state-of-the-art methods , highlighting the robustness and efficiency of our proposed algorithm . This paper presents a vision-based framework for indoor robot navigation which exploits semantic segmentation and deep learning towards accurate and efficient indoor scene mapping and collision-free navigation for hardware constrained robotics . Firstly , a scheme for accurate and efficient path extraction using deep convolutional neural networks ( DCNNs ) and transfer learning for semantic pixel-wise segmentation is put forward . The first task is road scene segmentation which is of current practical interest for various autonomous driving related problems . The second task is indoor scene segmentation which is of immediate interest to several augmented reality ( AR ) applications . The input RGB images for both tasks were . It is clear from our experiments and other independent benchmarks that outdoor scene images captured from a moving car are easier to segment and deep architectures perform robustly . We hope our experiments will encourage researchers to engage their attention towards the more challenging indoor scene segmentation task . An important choice we had to make when benchmarking different deep architectures of varying parameterization was the manner in which to train them .
Contextual modeling is a critical issue in scene understanding . Object detection accuracy can be improved by exploiting tendencies that are common among object configurations .
Interactive motion segmentation is an important task for scene understanding and analysis . Despite recent progress state-of-theart approaches still have difficulties in adapting to the diversity of spatially varying motion fields .
An overview . The major task of the scene understanding process is to find the scene which best explains the observed data . In general the observed data , the same radar signals , can be generated by different scenes .
It consists of a glasses-type wearable device with a front-view camera , eye-view camera , mounted display , earphone , and computing unit for signal processing . The scene-understanding process on the input image from the front-view camera can be computationally accelerated with the support of the eye-view camera that monitors the eye position of the user . For efficient information processing , the eye view camera catches the user 's visual intention and attention in a given situation .
Abstract . Point cloud segmentation is a crucial step in scene understanding and interpretation . The goal is to decompose the initial data into sets of workable clusters with similar properties .
Texture recognition plays an important role in many computer vision tasks including segmentation , scene understanding and interpretation , medical imaging and object recognition . In some situations , the correct identification of particular textures is more important compared to others , for example recognition of enemy uniforms for automatic defense systems , or isolation of textures related to tumors in medical images .
We integrate an incremental conceptual clustering system ( INC ) with a com-puter vision system , developed in our Artificial Intelligence and Computer Vision laboratories . The project consists of : ( 1 ) developing spatial knowledge models and cognitive functions within a spatial learning system , and ( 2 ) evaluating the plausibility , effectiveness , and constraints of the system in both the general-type spatial reasoning tasks in cognitive systems and the following scene analysis/understanding tasks : sensor fusion , object recognition , model instantiation , image/scene description , and reasoning about a scene .
Scene image understanding has drawn much attention for its intriguing applications in the past years . In this paper , we propose a unified probabilistic graphical model called Topic-based Coherent Region Annotation ( TCRA ) for weakly-supervised scene region annotation .
The automatic detection , recognition , and segmentation of object classes in remote sensed images is of crucial importance for scene interpretation and understanding . However , it is a difficult task because of the high variability of satellite data .
High-resolution remote sensing scene classification ( HR-RSSC ) plays an increasingly important role since it aims to enhance the scene semantic understanding . Recently , convolutional neural networks ( CNNs ) proved their effectiveness in learning powerful feature representations for various visual recognition tasks .
Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images . Different from pure object recognition tasks , the relation triplets of subject-predicate-object lie on an extreme diversity space , such asperson-behind-person andcar-behind-building , while suffering from the problem of combinatorial explosion .
The proposed contributions obtain superior results against state-of-art algorithms among all public datasets , especially multiple cases in a global scale . We conclude that the spatial and context information of each candidate axis inside an image can be used as a local or global symmetry measure for further image analysis and scene understanding purposes .
This strongly contrasts with other VQA datasets of real images , where a correct answer is often obvious without looking at the image , by relying on systematic regularities of frequent questions and answers . Performance improvements reported on such datasets are difficult to interpret as actual progress in scene understanding and reasoning as they might similarly be taken to represent a better modeling of the language prior of the dataset . This hampers , or at best obscures , progress toward the greater goal of general VQA .
This comparison reveals the memory versus accuracy trade - off involved in achieving good segmentation performance . SegNet was primarily motivated by scene understanding applications . Hence , it is designed to be efficient both in terms of memory and computational time during inference .
section : Introduction Semantic segmentation has a wide array of applications ranging from scene understanding , inferring support - relationships among objects to autonomous driving . Early methods that relied on low - level vision cues have fast been superseded by popular machine learning algorithms .
Su et al . defined six groups of attributes to build intermediate - level features for image classification . Li et al . introduced the concept of an ‘ object bank ’ which enables objects to be used as attributes for scene representation . subsection : Image Captioning
section : C.3 Scene understanding For scene understanding experiments , we use the Cityscapes dataset [ reference ] .
Third , the data rates for RGBD are higher than for RGB , increasing storage and data transfer times . As a consequence , for a wide range of applications in domains such as pedestrian tracking and safety monitoring in autonomous driving , computer graphics , driver alertness monitoring , and social scene understanding from video , there remains a need for an RGB - based 3D head pose estimation solution which is fast and reliable . The key contributions of our work are the following : Proposing a method to predict head pose Euler angles directly from image intensities using a multi loss network which has a loss for each angle and each loss has two components : a pose bin classification and a regression component .
At the crux of our perception unification framework is an effort to attain efficient understanding by proposing a deep architecture built on residual factorized convolution and pyramidical representation . A comprehensive set of experiments demonstrates the accurate scene parsing results with promise of real-time inference speed . Crucially , real-world performance over state-of-art approaches qualifies the proposed framework for assistance when deployed to two wearable navigation systems , including a pair of commercial smart glasses and a prototype of customized device .
However , they are quite sensitive to initial settings . To address these limitations , in this paper we propose a novel Deep Multiphase Level Set ( DMLS ) method for semantic scene parsing , which efficiently incorporates multiphase level sets into deep neural networks . The proposed method consists of three modules , i.e. , recurrent FCNs , adaptive multiphase level set , and deeply supervised learning .
Experiments that mask foreground and background investigate the role of context and shape . Results on the object and scene labeling of PASCAL - Context reinforce merging object segmentation and scene parsing as unified pixelwise prediction . In the next section , we review related work on deep classification nets , FCNs , recent approaches to semantic segmentation using convnets , and extensions to FCNs .
The other choice is to operate on different scale image input . Farabet et al . use this way to learn multi - scale feature for scene parsing . For single image SR , the lower solution image always has much more sharp detail information than the interpolation result .
In this paper , we exploit the capability of global context information by different - region - based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network ( PSPNet ) . Our global prior representation is effective to produce good quality results on the scene parsing task , while PSPNet provides a superior framework for pixel - level prediction . The proposed approach achieves state - of - the - art performance on various datasets .
Our approach is evaluated on both semantic segmentation and geometric labeling tasks , including the Stanford Background , Sift Flow , Geometric Context , and NYUv2 RGB-D data set . The competitive results demonstrate that our stacked learning to search method provides an effective alternative paradigm for scene labeling .
Here , a range of hand gestures are used to highlight and select 3D points within the structure and in this instance used to insert 3D graphical objects into the scene . Structure recovery , gesture recognition , scene annotation and augmentation are achieved in parallel and at video-rate .
We introduce in this work a novel stochastic inference process , for scene annotation and object class segmentation , based on finite state machines ( FSMs ) . The design principle of our framework is generative and based on building , for a given scene , finite state machines that encode annotation lattices , and inference consists in finding and scoring the best configurations in these lattices .
Consider the constraint graph shown in Figure 2 , for the prob lem of label ing the cube . The scene labeling problem Waltz . , 1975 provides an appl icat ion domain here , but those unfami l ia r w i t h it can regard the cube as an abstract constraint graph The graph in Figure 2b is s imply a redrawing of the constraint graph in Figure 2a . Observe tha t the variables F and C are not jo ined by a const ra in t , nor are B and D , not A and E. There are no edges between variables at the same level in Figure 2b .
A remarkable fact is that our approach also outperforms recent CNN-based methods . HighlightsWe propose a novel flexible segmentation graph ( FSG ) representation to capture multi-scale visual context for scene labeling problem . In the scenario of FSG , we establish a contextual fusion model to formulate multi-scale context .
The proposed image annotation model is evaluated separately on the first and on the second tier using a dataset of outdoor images . The results outperform the published results obtained on the same image collection , both on the object-level and on scene-level annotation . Different subsets of features composed of dominant colours , image moments , and GIST descriptors , as well as different classification methods ( RAKEL , ML-kNN and Naive Bayes ) , were tested in the first tier .
Auto-context learns an integrated low-level and context model , and is very general and easy to implement . Under nearly the identical parameter setting in the training , we apply the algorithm on three challenging vision applications : object segmentation , human body configuration , and scene region labeling . It typically takes about 30 ~ 70 seconds to run the algorithm in testing .
Massive traffic scene data for algorithm research and model training is the fundamental for self-driving car technology development . In the procedure of scene image labeling , the most accurate method is manual annotation , but with the increasing of the amount of image data , artificial annotation method becomes infeasible due to its disadvantages of vast cost , inefficiency and subjective deviation . In order to reduce the time and cost of annotation processing while ensuring the accuracy , this paper proposed a semi-automatic annotation procedure .
Scene classification from images is a challenging problem in computer vision due to its significant variability of scale , illumination , and view . Recently , Latent Dirichlet Allocation ( LDA ) model has grown popular in computer vision field , especially in scene labeling and classification . However , the effectiveness of the LDA model for the scene classification has not yet been addressed thoroughly .
Scene labeling research has mostly focused on outdoor scenes , leaving the harder case of indoor scenes poorly understood . Microsoft Kinect dramatically changed the landscape , showing great potentials for RGB-D perception ( color+depth ) .
The category and appearance features are utilized as the prior constraints to guide the direction of object label propagation . In this hierarchical model , the scene-level labeling and the object-level labeling have a mutual relationship , which regions and objects are optimized interactively . The experimental results on two datasets show the well performance of our method .
The design of the semantic labeling branch is inspired by the fully convolutional network in for semantic labeling . It is also used in our previous work for scene labeling . subsection : 3D Translation Estimation
In this paper , we propose a panoramic mesh modeling method from multiple range data for indoor scene reconstruction . The input to the proposed method is several sets of point clouds obtained from different viewpoints .
In this paper , we present a new method for line segments matching for indoor reconstruction . Instead of matching individual segments via a descriptor like most methods do , we match segment chains that have a distinctive topology using a dynamic programing formulation .
We propose a novel approach to automatic and flexible indoor scene synthesis using an energy-based method . We regard indoor scene synthesis as a multiple-object optimization problem with furniture location and orientation according to the user 's intention , as a constraint on the energy of the optimization problem . Based on the relationship of objects , the embedded aesthetic criterion , the design criterion for proper placement and human movement in a scene , we design five energy functions , the overlap constraint , pairwise constraint , wall constraint , aisle constraint , angle constraint and penalty item , are proposed .
We present a fast framework for indoor scene synthesis , given a room geometry and a list of objects with learnt priors . Unlike existing data-driven solutions , which often extract priors by co-occurrence analysis and statistical model fitting , our method measures the strengths of spatial relations by tests for complete spatial randomness ( CSR ) , and extracts complex priors based on samples with the ability to accurately represent discrete layout patterns .
Indoor environment model reconstruction has emerged as a significant and challenging task in terms of the provision of a semantically rich and geometrically accurate indoor model . Recently , there has been an increasing amount of research related to indoor environment reconstruction . Therefore , this paper reviews the state-of-the-art techniques for the three-dimensional ( 3D ) reconstruction of indoor environments .
Third , there is no research to construct indoor spatial standard data from triangle mesh data . For resolving these problems , we propose the a method for generating triangular mesh data for indoor geometric representation based in the observations mentioned above . First this method removes unnecessary objects and reduces the number of surfaces from the original fine-grained triangular mesh data using the properties of indoor space .
To evaluate the performance of our proposed algorithm , the proposed approach is applied to RGB-D SLAM , which is tested in different indoor environments . The experimental results show that applying our post-rectification algorithm to indoor scenario reconstruction can result in more accurate and more detailed 3-D reconstruction of objects than other state-of-the-art methods , highlighting the robustness and efficiency of our proposed algorithm .
This paper presents a vision-based framework for indoor robot navigation which exploits semantic segmentation and deep learning towards accurate and efficient indoor scene mapping and collision-free navigation for hardware constrained robotics . Firstly , a scheme for accurate and efficient path extraction using deep convolutional neural networks ( DCNNs ) and transfer learning for semantic pixel-wise segmentation is put forward .
The first task is road scene segmentation which is of current practical interest for various autonomous driving related problems . The second task is indoor scene segmentation which is of immediate interest to several augmented reality ( AR ) applications . The input RGB images for both tasks were .
It is clear from our experiments and other independent benchmarks that outdoor scene images captured from a moving car are easier to segment and deep architectures perform robustly . We hope our experiments will encourage researchers to engage their attention towards the more challenging indoor scene segmentation task . An important choice we had to make when benchmarking different deep architectures of varying parameterization was the manner in which to train them .
Abnormal Object Detection by Canonical Scene-Based Contextual Model Interactive Motion Segmentation Scene understanding from SAR images. An overview Human visual augmentation using wearable glasses with multiple cameras and information fusion of human eye tracking and scene understanding Segmentation of Large Unstructured Point Clouds Using Octree-Based Region Growing And Conditional Random Fields Cost-sensitive texture classification Spatial Reasoning: Learning from Observations Weakly-supervised region annotation for understanding scene images Simultaneous hierarchical segmentation and vectorization of satellite images through combined non-uniform data sampling and anisotropic triangulation Remote Sensing Scene Classification Using Convolutional Features and Deep Forest Classifier Context-Dependent Diffusion Network for Visual Relationship Detection Reflection Symmetry Detection in Images : Application to Photography Analysis. (Détection de symétrie de réflexion dans les images : application à l'analyse de photographie) Graph-Structured Representations for Visual Question Answering SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Image Captioning and Visual Question Answering Based on Attributes and External Knowledge Multi-Task Learning as Multi-Objective Optimization Fine-Grained Head Pose Estimation Without Keypoints Semantic perception of curbs beyond traversability for real-world navigation assistance systems Deep Multiphase Level Set for Scene Parsing Fully Convolutional Networks for Semantic Segmentation Single image super-resolution with dilated convolution based multi-scale information learning inception module Pyramid Scene Parsing Network Stacked Learning to Search for Scene Labeling Interaction between hand and wearable camera in 2d and 3d environments Structured Scene Decoding with Finite State Machines Taking Advantage of Stable Sets of Variables in Constraint Satisfaction Problems Multi-scale context for scene labeling via flexible segmentation graph Two-tier image annotation model based on a multi-label classifier and fuzzy-knowledge representation scheme Auto-context and its application to high-level vision tasks A Semi-Automatic Annotation Technology for Traffic Scene Image Labeling Based on Deep Learning Preprocessing Evaluating effectiveness of Latent Dirichlet Allocation model for scene classification RGB-(D) scene labeling: Features and algorithms Hierarchical semantic segmentation of image scene with object labeling PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Panoramic Mesh Model Generation from Multiple Range Data for Indoor Scene Reconstruction Indoor Calibration Using Segment Chains Flexible Indoor Scene Synthesis via a Multi-object Particle Swarm Intelligence Optimization Algorithm and User Intentions Fast 3D Indoor Scene Synthesis with Discrete and Exact Layout Pattern Extraction A Review of Techniques for 3D Reconstruction of Indoor Environments INDOOR SPATIAL DATA CONSTRUCTION FROM TRIANGLE MESH A Smart Post-Rectification Algorithm Based on an ANN Considering Reflectivity and Distance for Indoor Scenario Reconstruction Semantic Scene Segmentation for Indoor Robot Navigation via Deep Learning SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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ECCV (3) DAGM-Symposium HRI IEEE Congress on Evolutionary Computation ACM Multimedia 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE Transactions on Pattern Analysis and Machine Intelligence NeurIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ICVES IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 IEEE International Conference on Image Processing (ICIP) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) BMVC ICIP IJCAI CVPR CSE/EUC (1) WOCC CVPR Robotics: Science and Systems PCM (2) DAGM-Symposium CW IEEE Transactions on Pattern Analysis and Machine Intelligence
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