However , CNNs require learning of many parameters from raw images ; thus , having a sufficient amount of diverse images with class annotations is needed . These annotations are obtained via cumbersome , human labor which is particularly challenging for semantic segmentation since pixel-level annotations are required . In this chapter , we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations , and domain adaptation to transfer the models learned to correctly operate in real scenarios . Fast-growing industrial Oil Palm Plantations ( OPPs ) in the tropical belt of Africa , Southeast Asia and parts of Brazil lead to significant loss of rainforest and contribute to the global warming by the corresponding decrease of carbon dioxide absorption . We propose a novel approach to monitoring of the expansion of OPPs based on an application of state-of-the-art Fully Convolutional Neural Networks ( FCNs ) to solve Semantic Segmentation Problem for Landsat imagery . The proposed approach significantly outperforms per-pixel classification methods based on Random Forest using texture features , NDVI , and all Landsat bands . In these models , the input dimension is twice that of the output and recursive convolutions are applied only two times . Similar dimension reduction occurs in the recurrent convolutional neural networks used for semantic segmentation . As SR methods predict full - sized images , dimension reduction is not allowed . In texture segregation , an example of scene segmentation , we can discern two different processes : texture boundary detection and subsequent surface segregation [ Lamme , V. A. F. , Rodriguez-Rodriguez , V. , & Spekreijse , H. Separate processing dynamics for texture elements , boundaries and surfaces in primary visual cortex of the macaque monkey . Cerebral Cortex , 9 , 406413 , 1999 ] . In the past few decades , neural networks have been extensively adopted in various applications ranging from simple synaptic memory coding to sophisticated pattern recognition problems such as scene analysis and robot vision . Moreover , current studies on neuroscience and physiology have reported that in a typical scene segmentation problem our major senses of perception ( e.g. vision , olfaction , etc . ) are highly chaotic and involved non-linear neural dynamics and oscillations . In this paper , the author proposes an innovative chaotic neural oscillator-namely the Lee-oscillator ( Lee 's Chaotic Neural Oscillator ) to provide a chaotic neural coding and information processing scheme . In the past few decades , neural networks have been extensively adopted in various applications ranging from simple synaptic memory coding to sophisticated pattern recognition problems such as scene analysis . Moreover , current studies on neuroscience and physiology have reported that in a typical scene segmentation problem our major senses of perception ( e.g. , vision , olfaction , etc . ) are highly involved in temporal ( or what we call " transient " ) nonlinear neural dynamics and oscillations . This paper is an extension of the author 's previous work on the dynamic neural model ( EGDLM ) of memory processing and on composite neural oscillators for scene segmentation . Superpixel based methods have recently shown success in scene segmentation and labeling . In scene labeling , a superpixel algorithm is used first to segment the image into visually consistent small regions ; then several feature descriptors are computed and classification is performed for each superpixel . The invention provides a method for detecting a long-distance barrier and belongs to the technical field of robots . The method particularly comprises the following steps : image acquisition , image pre-processing , scene image segmentation , appearance characteristic extraction , topographic pattern judgment , topographic sample database maintenance , topographic pattern statistics modeling , statistics model parameter training and statistics model inference . The invention achieves the effective detection of a multi-mode barrier , improves the accuracy of barrier detection under the condition of unbalanced samples and improves the adaptability of the barrier detection to the changes in online real-time scenes ; the topographic pattern modeling integrates the independent smooth characteristic functions and eliminates the pattern ambiguity caused by characteristic overlapping ; the topographic pattern modeling integrates the correlation smooth characteristic functions and improves the online self-adaptability of the barrier detection results to the changes in real-time illumination ; and the topographic pattern statistics modeling not only integrates the characteristics of the scene areas , but also theoretically integrates the spatial relationship between the scene areas and improves the stability of barrier detection under the condition of mapping deviation . The algorithm can solve the problem of color nonuniformity of the scene that the color feature method can not compensate . After obtaining the candidate area of the scene , the problem of noise is solved by the linear fitting method of Hough transform and least squares method , and the edge result is refined to realize better scene image segmentation . The experimental results show that the method can find out the main colors in the scene accurately and efficiently , and it has good precision and recall rate . 1 Faculity of Information and Technology , College of Statistical 2 International Research Institute MICA , HUST CNRS/UMI-2954 GRENOBLE INP 3 Faculity of Information Technology , Vietnam National University Agriculture hunglv@gso.gov.vn ABSTRACT — Plane detection is an important research in the robotics community . Its results are utilized in many applications such as 3D reconstruction , scene analysis and segmentation , objects localization so on . In context of an assistive system forvisually impaired people , plane detection , or more specilized , table plane detection is a prerequisite step for further procedures such as object localization and recognition . The video data real-time processing method comprises acquiring a current frame image including a specific object in a video taken and/or recorded by an image acquisition device in real time ; or acquiring a current frame image including a specific object in a video played atpresent in real time ; carrying out scene segmentation processing on the current frame image to obtain a foreground image of the current frame image aiming at the specific object ; drawing a three-dimensional scene ; extracting key information of the specific object from the current frame image , and obtaining positional information of the foreground image in the three-dimensional scene according tothe key information ; carrying out fusion processing on the foreground image and the three-dimensional scene according to the positional information to obtain a current frame processed image ; coveringthe original current frame image by the current frame processed image to obtain processed video data ; and displaying the processed video data . According to the invention , a deep learning method is employed , and scene segmentation processing is completed efficiently and accurately . We show the correct operation of the proposed electronic oscillators with simulations and experimental results from a manufactured integrated test circuit . The proposed architecture is intended to perform the scene segmentation stage of an autonomous focal-plane self-contained visual processing system for artificial vision applications . We therefore analysed the decoding process used in some of these approaches and reveal their pros and cons . We evaluate the performance of SegNet on two scene segmentation tasks , CamVid road scene segmentation and SUN RGB - D indoor scene segmentation . Pascal VOC12 has been the benchmark challenge for segmentation over the years . section : Benchmarking We quantify the performance of SegNet on two scene segmentation benchmarks using our Caffe implementation . The first task is road scene segmentation which is of current practical interest for various autonomous driving related problems . a few classes are present in any test image . Scene segmentation is more challenging due to the high variability of indoor scenes and a need to segment a larger number of classes simultaneously . The task of outdoor and indoor scene segmentation are also more practically oriented with current applications such as autonomous driving , robotics and AR . In contrast , image-level labels are much easier to obtain . In this work , we propose a novel method for weakly supervised semantic segmentation with only image-level labels . The method relies on a large scale co-segmentation framework that can produce object masks for a group of images containing objects belonging to the same semantic class . Further , an alternate iterative structure is established to gradually improve segmentation performance , which combines CNN-based inter-image common semantic learning and imaging prior based intra-image modification process . Experiments on Cityscapes dataset demonstrate that the proposed method provides a feasible way to solve weakly supervised semantic segmentation task under complex driving scenes . We address the problem of weakly supervised segmentation ( WSS ) of medical images which is more challenging and has potentially greater applications in the medical imaging community . Training images are labeled only by the classes they contain , and not by the pixel labels . However , constrained Lagrangian optimization has been largely avoided in deep networks , mainly for computational tractability reasons . To the best of our knowledge , the method of Pathak et al. [ 17 ] is the only prior work that addresses constrained deep CNNs in weakly supervised segmentation . It uses the constraints to synthesize fully-labeled training masks ( proposals ) from weak labels , mimicking full supervision and facilitating dual optimization . Moreover , unlike other approaches for weakly-supervised segmentation , the proposed model does n't require further post-processing . The proposed model achieves performance comparable with other approaches that employ saliency masks for the task of weakly-supervised semantic image segmentation on the challenging VOC 2012 dataset . Unsupervised semantic segmentation in the time series domain is a much-studied problem due to its potential to detect unexpected regularities and regimes in poorly understood data . However , the current techniques have several shortcomings , which have limited the adoption of time series semantic segmentation beyond academic settings for three primary reasons . A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation . Despite the remarkable progress , weakly supervised segmentation approaches are still inferior to their fully supervised counterparts . Then , we design a generic classification network equipped with convolutional blocks of different dilated rates . It can produce dense and reliable object localization maps and effectively benefit both weakly- and semi- supervised semantic segmentation . Despite the apparent simplicity , our proposed approach obtains superior performance over state-of-the-arts . Building a graph model use the whole training set and solved by graph cut based algorithm is a common method in weak supervision semantic segmentation task , such as Multi-Image Model ( MIM ) . It has two disadvantages : one is the parameter number of model increased rapidly with the scale growth of training set , which limited applied to large-scale data . In this paper , we deal with a weakly supervised semantic segmentation problem where only training images with image-level labels are available . We propose a weakly supervised semantic segmentation method which is based on CNN-based class-specific saliency maps and fully-connected CRF . In this paper , we propose an approach to simultaneously localize and recognize visual events from raw surveillance videos , employing an end-to-end learning strategy . Our approach formulates the task as weakly-supervised sequential semantic segmentation , in which we utilize a specific convolutional RNN to capture not only the appearance and the motion information but also their temporal evolution patterns . We tested our approach on the VIRAT 2.0 dataset . The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks . In this paper , we present a lightweight network to address this problem , namely LEDNet , which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation . More specifically , the encoder adopts a ResNet as backbone network , where two new operations , channel split and shuffle , are utilized in each residual block to greatly reduce computation cost while maintaining higher segmentation accuracy . For real-time semantic video segmentation , most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions . Some works used a fixed key scheduling policy , while others proposed adaptive key scheduling methods based on heuristic strategies , both of which may lead to suboptimal global performance . Real-time semantic video segmentation is a challenging task due to the strict requirements of inference speed . Recent approaches mainly devote great efforts to reducing the model size for high efficiency . This paper introduces a lightweight convolutional neural network , called FDDWNet , for real-time accurate semantic segmentation . In contrast to recent advances of lightweight networks that prefer to utilize shallow structure , FDDWNet makes an effort to design more deeper network architecture , while maintains faster inference speed and higher segmentation accuracy . The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation . In this paper , we propose an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes . In this paper , we address the problem of real-time image semantic segmentation for indoor scene . As for scene parsing , both accuracy and speed are equally important . Real-time semantic image segmentation on platforms subject to size , weight , and power constraints is a key area of interest for air surveillance and inspection . In this letter , we propose MAVNet : a small , light-weight , deep neural network for real-time semantic segmentation on micro aerial vehicles ( MAVs ) . We propose a novel effective algorithm for real-time semantic segmentation of images that has the best accuracy in its class . Based on a comparative analysis of preliminary segmentation methods , methods for calculating attributes from image segments , as well as various algorithms of machine learning , the most effective methods in terms of their accuracy and performance are identified . We developed a real-time semantic segmentation system to perform dynamic occlusion handling . We designed this system for use in mobile devices with client-server communication for real-time semantic segmentation processing in mobile devices . Additionally , we used a normal monocular camera for practice use . The model ingests raw point clouds and learns a spatial encoding of each point and also an aggregated global point cloud feature . These features are then used for classification and semantic segmentation . PointNet has many desirable properties : it processes the raw points directly without lossy operations like voxelization or projection , and it scales linearly with the number of input points . Automated segmentation of 3D brain tumors can save physicians time and provide an accurate reproducible solution for further tumor analysis and monitoring . Recently , deep learning based segmentation techniques surpassed traditional computer vision methods for dense semantic segmentation . Convolutional neural networks ( CNN ) are able to learn from examples and demonstrate state - of - the - art segmentation accuracy both in 2D natural images and in 3D medical image modalities . [ reference ] ( a ) the accuracy and inference time of different frameworks on Cityscapes dataset . subsubsection : Status of Fast Semantic Segmentation Contrary to the extraordinary development of high - quality semantic segmentation , research along the line to make semantic segmentation run fast while not sacrificing too much quality is left behind . In more recent work , both class segmentation and support relationships are inferred together using a combination of RGB and depth based cues . Another approach focuses on real - time joint reconstruction and semantic segmentation , where Random Forests are used as the classifier . Gupta et al .
However , CNNs require learning of many parameters from raw images ; thus , having a sufficient amount of diverse images with class annotations is needed . These annotations are obtained via cumbersome , human labor which is particularly challenging for semantic segmentation since pixel-level annotations are required . In this chapter , we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations , and domain adaptation to transfer the models learned to correctly operate in real scenarios .
Fast-growing industrial Oil Palm Plantations ( OPPs ) in the tropical belt of Africa , Southeast Asia and parts of Brazil lead to significant loss of rainforest and contribute to the global warming by the corresponding decrease of carbon dioxide absorption . We propose a novel approach to monitoring of the expansion of OPPs based on an application of state-of-the-art Fully Convolutional Neural Networks ( FCNs ) to solve Semantic Segmentation Problem for Landsat imagery . The proposed approach significantly outperforms per-pixel classification methods based on Random Forest using texture features , NDVI , and all Landsat bands .
In these models , the input dimension is twice that of the output and recursive convolutions are applied only two times . Similar dimension reduction occurs in the recurrent convolutional neural networks used for semantic segmentation . As SR methods predict full - sized images , dimension reduction is not allowed .
In texture segregation , an example of scene segmentation , we can discern two different processes : texture boundary detection and subsequent surface segregation [ Lamme , V. A. F. , Rodriguez-Rodriguez , V. , & Spekreijse , H. Separate processing dynamics for texture elements , boundaries and surfaces in primary visual cortex of the macaque monkey . Cerebral Cortex , 9 , 406413 , 1999 ] .
In the past few decades , neural networks have been extensively adopted in various applications ranging from simple synaptic memory coding to sophisticated pattern recognition problems such as scene analysis and robot vision . Moreover , current studies on neuroscience and physiology have reported that in a typical scene segmentation problem our major senses of perception ( e.g. vision , olfaction , etc . ) are highly chaotic and involved non-linear neural dynamics and oscillations . In this paper , the author proposes an innovative chaotic neural oscillator-namely the Lee-oscillator ( Lee 's Chaotic Neural Oscillator ) to provide a chaotic neural coding and information processing scheme .
In the past few decades , neural networks have been extensively adopted in various applications ranging from simple synaptic memory coding to sophisticated pattern recognition problems such as scene analysis . Moreover , current studies on neuroscience and physiology have reported that in a typical scene segmentation problem our major senses of perception ( e.g. , vision , olfaction , etc . ) are highly involved in temporal ( or what we call " transient " ) nonlinear neural dynamics and oscillations . This paper is an extension of the author 's previous work on the dynamic neural model ( EGDLM ) of memory processing and on composite neural oscillators for scene segmentation .
Superpixel based methods have recently shown success in scene segmentation and labeling . In scene labeling , a superpixel algorithm is used first to segment the image into visually consistent small regions ; then several feature descriptors are computed and classification is performed for each superpixel .
The invention provides a method for detecting a long-distance barrier and belongs to the technical field of robots . The method particularly comprises the following steps : image acquisition , image pre-processing , scene image segmentation , appearance characteristic extraction , topographic pattern judgment , topographic sample database maintenance , topographic pattern statistics modeling , statistics model parameter training and statistics model inference . The invention achieves the effective detection of a multi-mode barrier , improves the accuracy of barrier detection under the condition of unbalanced samples and improves the adaptability of the barrier detection to the changes in online real-time scenes ; the topographic pattern modeling integrates the independent smooth characteristic functions and eliminates the pattern ambiguity caused by characteristic overlapping ; the topographic pattern modeling integrates the correlation smooth characteristic functions and improves the online self-adaptability of the barrier detection results to the changes in real-time illumination ; and the topographic pattern statistics modeling not only integrates the characteristics of the scene areas , but also theoretically integrates the spatial relationship between the scene areas and improves the stability of barrier detection under the condition of mapping deviation .
The algorithm can solve the problem of color nonuniformity of the scene that the color feature method can not compensate . After obtaining the candidate area of the scene , the problem of noise is solved by the linear fitting method of Hough transform and least squares method , and the edge result is refined to realize better scene image segmentation . The experimental results show that the method can find out the main colors in the scene accurately and efficiently , and it has good precision and recall rate .
1 Faculity of Information and Technology , College of Statistical 2 International Research Institute MICA , HUST CNRS/UMI-2954 GRENOBLE INP 3 Faculity of Information Technology , Vietnam National University Agriculture hunglv@gso.gov.vn ABSTRACT — Plane detection is an important research in the robotics community . Its results are utilized in many applications such as 3D reconstruction , scene analysis and segmentation , objects localization so on . In context of an assistive system forvisually impaired people , plane detection , or more specilized , table plane detection is a prerequisite step for further procedures such as object localization and recognition .
The video data real-time processing method comprises acquiring a current frame image including a specific object in a video taken and/or recorded by an image acquisition device in real time ; or acquiring a current frame image including a specific object in a video played atpresent in real time ; carrying out scene segmentation processing on the current frame image to obtain a foreground image of the current frame image aiming at the specific object ; drawing a three-dimensional scene ; extracting key information of the specific object from the current frame image , and obtaining positional information of the foreground image in the three-dimensional scene according tothe key information ; carrying out fusion processing on the foreground image and the three-dimensional scene according to the positional information to obtain a current frame processed image ; coveringthe original current frame image by the current frame processed image to obtain processed video data ; and displaying the processed video data . According to the invention , a deep learning method is employed , and scene segmentation processing is completed efficiently and accurately .
We show the correct operation of the proposed electronic oscillators with simulations and experimental results from a manufactured integrated test circuit . The proposed architecture is intended to perform the scene segmentation stage of an autonomous focal-plane self-contained visual processing system for artificial vision applications .
We therefore analysed the decoding process used in some of these approaches and reveal their pros and cons . We evaluate the performance of SegNet on two scene segmentation tasks , CamVid road scene segmentation and SUN RGB - D indoor scene segmentation . Pascal VOC12 has been the benchmark challenge for segmentation over the years .
section : Benchmarking We quantify the performance of SegNet on two scene segmentation benchmarks using our Caffe implementation . The first task is road scene segmentation which is of current practical interest for various autonomous driving related problems .
a few classes are present in any test image . Scene segmentation is more challenging due to the high variability of indoor scenes and a need to segment a larger number of classes simultaneously . The task of outdoor and indoor scene segmentation are also more practically oriented with current applications such as autonomous driving , robotics and AR .
In contrast , image-level labels are much easier to obtain . In this work , we propose a novel method for weakly supervised semantic segmentation with only image-level labels . The method relies on a large scale co-segmentation framework that can produce object masks for a group of images containing objects belonging to the same semantic class .
Further , an alternate iterative structure is established to gradually improve segmentation performance , which combines CNN-based inter-image common semantic learning and imaging prior based intra-image modification process . Experiments on Cityscapes dataset demonstrate that the proposed method provides a feasible way to solve weakly supervised semantic segmentation task under complex driving scenes .
We address the problem of weakly supervised segmentation ( WSS ) of medical images which is more challenging and has potentially greater applications in the medical imaging community . Training images are labeled only by the classes they contain , and not by the pixel labels .
However , constrained Lagrangian optimization has been largely avoided in deep networks , mainly for computational tractability reasons . To the best of our knowledge , the method of Pathak et al. [ 17 ] is the only prior work that addresses constrained deep CNNs in weakly supervised segmentation . It uses the constraints to synthesize fully-labeled training masks ( proposals ) from weak labels , mimicking full supervision and facilitating dual optimization .
Moreover , unlike other approaches for weakly-supervised segmentation , the proposed model does n't require further post-processing . The proposed model achieves performance comparable with other approaches that employ saliency masks for the task of weakly-supervised semantic image segmentation on the challenging VOC 2012 dataset .
Unsupervised semantic segmentation in the time series domain is a much-studied problem due to its potential to detect unexpected regularities and regimes in poorly understood data . However , the current techniques have several shortcomings , which have limited the adoption of time series semantic segmentation beyond academic settings for three primary reasons .
A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation . Despite the remarkable progress , weakly supervised segmentation approaches are still inferior to their fully supervised counterparts .
Then , we design a generic classification network equipped with convolutional blocks of different dilated rates . It can produce dense and reliable object localization maps and effectively benefit both weakly- and semi- supervised semantic segmentation . Despite the apparent simplicity , our proposed approach obtains superior performance over state-of-the-arts .
Building a graph model use the whole training set and solved by graph cut based algorithm is a common method in weak supervision semantic segmentation task , such as Multi-Image Model ( MIM ) . It has two disadvantages : one is the parameter number of model increased rapidly with the scale growth of training set , which limited applied to large-scale data .
In this paper , we deal with a weakly supervised semantic segmentation problem where only training images with image-level labels are available . We propose a weakly supervised semantic segmentation method which is based on CNN-based class-specific saliency maps and fully-connected CRF .
In this paper , we propose an approach to simultaneously localize and recognize visual events from raw surveillance videos , employing an end-to-end learning strategy . Our approach formulates the task as weakly-supervised sequential semantic segmentation , in which we utilize a specific convolutional RNN to capture not only the appearance and the motion information but also their temporal evolution patterns . We tested our approach on the VIRAT 2.0 dataset .
The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks . In this paper , we present a lightweight network to address this problem , namely LEDNet , which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation . More specifically , the encoder adopts a ResNet as backbone network , where two new operations , channel split and shuffle , are utilized in each residual block to greatly reduce computation cost while maintaining higher segmentation accuracy .
For real-time semantic video segmentation , most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions . Some works used a fixed key scheduling policy , while others proposed adaptive key scheduling methods based on heuristic strategies , both of which may lead to suboptimal global performance .
Real-time semantic video segmentation is a challenging task due to the strict requirements of inference speed . Recent approaches mainly devote great efforts to reducing the model size for high efficiency .
This paper introduces a lightweight convolutional neural network , called FDDWNet , for real-time accurate semantic segmentation . In contrast to recent advances of lightweight networks that prefer to utilize shallow structure , FDDWNet makes an effort to design more deeper network architecture , while maintains faster inference speed and higher segmentation accuracy .
The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation . In this paper , we propose an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes .
In this paper , we address the problem of real-time image semantic segmentation for indoor scene . As for scene parsing , both accuracy and speed are equally important .
Real-time semantic image segmentation on platforms subject to size , weight , and power constraints is a key area of interest for air surveillance and inspection . In this letter , we propose MAVNet : a small , light-weight , deep neural network for real-time semantic segmentation on micro aerial vehicles ( MAVs ) .
We propose a novel effective algorithm for real-time semantic segmentation of images that has the best accuracy in its class . Based on a comparative analysis of preliminary segmentation methods , methods for calculating attributes from image segments , as well as various algorithms of machine learning , the most effective methods in terms of their accuracy and performance are identified .
We developed a real-time semantic segmentation system to perform dynamic occlusion handling . We designed this system for use in mobile devices with client-server communication for real-time semantic segmentation processing in mobile devices . Additionally , we used a normal monocular camera for practice use .
The model ingests raw point clouds and learns a spatial encoding of each point and also an aggregated global point cloud feature . These features are then used for classification and semantic segmentation . PointNet has many desirable properties : it processes the raw points directly without lossy operations like voxelization or projection , and it scales linearly with the number of input points .
Automated segmentation of 3D brain tumors can save physicians time and provide an accurate reproducible solution for further tumor analysis and monitoring . Recently , deep learning based segmentation techniques surpassed traditional computer vision methods for dense semantic segmentation . Convolutional neural networks ( CNN ) are able to learn from examples and demonstrate state - of - the - art segmentation accuracy both in 2D natural images and in 3D medical image modalities .
[ reference ] ( a ) the accuracy and inference time of different frameworks on Cityscapes dataset . subsubsection : Status of Fast Semantic Segmentation Contrary to the extraordinary development of high - quality semantic segmentation , research along the line to make semantic segmentation run fast while not sacrificing too much quality is left behind .
In more recent work , both class segmentation and support relationships are inferred together using a combination of RGB and depth based cues . Another approach focuses on real - time joint reconstruction and semantic segmentation , where Random Forests are used as the classifier . Gupta et al .
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA Application of Fully Convolutional Neural Networks to Mapping Industrial Oil Palm Plantations Deeply-Recursive Convolutional Network for Image Super-Resolution Feedforward and Recurrent Processing in Scene Segmentation: Electroencephalography and Functional Magnetic Resonance Imaging Lee-Associator - a chaotic auto-associative network for progressive memory recalling A transient-chaotic autoassociative network (TCAN) based on Lee oscillators Scene labeling using Kernel Codebook Encoding Method for detecting long-distance barrier Color detection and segmentation of the scene based on Gaussian mixture model clustering TABLE PLANE DETECTION USING GEOMETRICAL CONSTRAINTS ON DEPTH IMAGES Video data real-time processing method and device based on virtual world, and computing device Synchronization of nonlinear electronic oscillators for neural computation SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Weakly Supervised Semantic Segmentation Based on Co-segmentation Realizing Pixel-Level Semantic Learning in Complex Driving Scenes based on Only One Annotated Pixel per Class Weakly supervised semantic segmentation of Crohn's disease tissues from abdominal MRI Size-constraint loss for weakly supervised CNN segmentation Amortized Inference and Learning in Latent Conditional Random Fields for Weakly-Supervised Semantic Image Segmentation Matrix Profile VIII: Domain Agnostic Online Semantic Segmentation at Superhuman Performance Levels Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation Weakly supervised semantic segmentation using constrained multi-image model and saliency prior Distinct Class-Specific Saliency Maps for Weakly Supervised Semantic Segmentation Simultaneous Event Localization and Recognition in Surveillance Video Lednet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation Dynamic Face Video Segmentation via Reinforcement Learning TapLab: A Fast Framework for Semantic Video Segmentation Tapping into Compressed-Domain Knowledge FDDWNet: A Lightweight Convolutional Neural Network for Real-Time Semantic Segmentation Real-Time Progressive 3D Semantic Segmentation for Indoor Scenes DSNET: Accelerate Indoor Scene Semantic Segmentation MAVNet: An Effective Semantic Segmentation Micro-Network for MAV-Based Tasks РАЗРАБОТКА АЛГОРИТМА СЕМАНТИЧЕСКОЙ СЕГМЕНТАЦИИ АЭРОФОТОСНИМКОВ РЕАЛЬНОГО ВРЕМЕНИ Development of a Semantic Segmentation System for Dynamic Occlusion Handling in Mixed Reality for Landscape Simulation PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation 3D MRI brain tumor segmentation using autoencoder regularization ICNet for Real-Time Semantic Segmentation on High-Resolution Images SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
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["Computer Science"] ["Environmental Science","Computer Science"] ["Computer Science","Medicine","Psychology"] ["Mathematics","Medicine","Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science"]
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Domain Adaptation in Computer Vision Applications AIST 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) SIU IEEE Transactions on Pattern Analysis and Machine Intelligence ISBI ICDM CVPR ECCV (4) AVSS ICIP ICASSP ICASSP 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition BrainLes@MICCAI ECCV IEEE Transactions on Pattern Analysis and Machine Intelligence
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