Many automotive safety applications in modern cars make use of cameras and object detection to analyze the surrounding environment . Pedestrians , animals and other vehicles can be detected and safety actions can be taken before dangerous situations arise . This thesis makes significant contributions to the object detection problem in computer vision . The object detection problem is , given a digital image of a scene , to detect the relevant object in the image . The quality of the object detection greatly influenced the training process object detection . Viola-Jones method is often used in object detection process where this method requires a classifier file obtained from the training process . This research is used to determine the trend of the number of the training process on the quality of bottle caps object detection fanta , sprite , Frestea , coca-cola and soda . The paper presents test procedures designed for application-specific integrated circuit ( ASIC ) CMOS VLSI chip that implements a synchronized oscillator neural network with a matrix size of 32 × 32 for object detecting in binary images . Networks of synchronized oscillators are recently developed tool for image segmentation and analysis . The images are separated into 1000 different classes organized by the WordNet hierarchy . In the challenge ” object detection with additional training data ” GoogleNet has achieved about 44 % precision . These results have demonstrated the potential which lies in this kind of architecture . 3D object detection in RGB-D image has received considerable attention recently . But potential searching space in testing image is large , and extracting handcraft feature for every candidate bounding box is computational expensive . Three-dimensional ( 3D ) object detection is an important research in 3D computer vision with significant applications in many fields , such as automatic driving , robotics , and human – computer interaction . However , the low precision is an urgent problem in the field of 3D object detection . We develop new representations and algorithms for three-dimensional ( 3D ) object detection and spatial layout prediction in cluttered indoor scenes . We first propose a clouds of oriented gradient ( COG ) descriptor that links the 2D appearance and 3D pose of object categories , and thus accurately models how perspective projection affects perceived image gradients . In autonomous driving community , numerous benchmarks have been established to assist the tasks of 3D/2D object detection , stereo vision , semantic/instance segmentation . However , the more meaningful dynamic evolution of the surrounding objects of ego-vehicle is rarely exploited , and lacks a large-scale dataset platform . ( 2 ) adopt pretrained R-CNNs [ 2 ] to extract raw feature from both RGB and Depth domains . Experiments on RMRC dataset demonstrate that the bimodal based deep feature learning framework helps 3D scene object detection . A basic set of tasks has been implemented . The demonstrator incorporates augmented reality techniques with a focus on 3D stereo computer vision and graphics . Index Terms – cognitive robotics , information representation , unified message format , augmented reality , 3D stereo vision Note that we intentionally do not scale the point cloud , because the bounding sphere size of a partial point cloud can be greatly affected by viewpoints and the real size of the point cloud helps the box size estimation . In our experiments , we find that coordinate transformations such as the one above and the previous frustum rotation are critical for 3D detection result as shown in Tab . [ reference ] . Because SUN - RGBD does not have a direct mapping between the 2D and 3D object annotations , for each 3D object annotation , we project the 8 corners of the 3D box to the image plane and use the minimum enclosing 2D bounding box as training data for the 2D object detector and our models . We report 3D detection performance of our models on the same 10 object categories as in . Because these 10 object categories contain relatively large objects , we also show detection results on the 19 categories from to show our model We show that using object segmentation performs better for this task compared to a standard sliding window detector , in particular in presence of partial occlusion . We then apply a CNN to predict the 3D pose of the detected objects . While the predicted 3D pose can be represented directly by a translation and a rotation , we achieve better accuracy by using a representation similar to the one used in [ reference ] for object parts : We predict the 2D projections of the corners of the object 's bounding box , and compute the 3D pose from these 2D - 3D correspondences with a PnP algorithm . Convolutional neural networks ( CNNs ) have demonstrated a strong ability to extract semantics from images during object detection ; however , the extracted semantics are typically have strong scale priors for a specific circumstance . In this paper , we investigate the influence of head scale and contextual information , and then propose a scale-invariant method for head detection . Our method can dynamically detect heads depending on the complexity of the image . Prototypes were developed and tested in real life conditions by involving patients completing rehabilitation exercises with physiotherapists . The user experience of the demonstration will include prototypes for different applications : knee joint and 3D printed palm prosthetic dynamics monitoring , posture and head position detection in real-time . Finally , we use the nearest neighbor method to estimate the head pose . The experiment results show : accurate head detecting helps to estimate the head pose . This method can be used for complex conditions of accurate head pose estimation . Crowd image analysis has various application areas such as surveillance , crowd management and augmented reality . Existing techniques can detect multiple faces in a single crowd image , but small head/face size and additional non facial regions in the head bounding box makes the head detection ( HD ) challenging . Additionally , in existing head pose estimations ( HPE ) of multiple heads in an image , individual cropped head image is passed through a network one by one , instead of estimating poses of multiple heads at the same time . The invention discloses a passenger flow counting detection method based on depth images . The method includes the steps that ( 1 ) a motion sensing camera is arranged above a door of a bus and is used for collecting depth image videos of passengers getting on and off the bus ; ( 2 ) the obtained depth images are pre-processed ; ( 3 ) denoising is carried out on the depth images pre-processed in the second step , then laying processing is carried out many times to obtain multiple binary images , and the binary images are combined to obtain the frame of binary images ; ( 4 ) head detection judgment is carried out ; ( 5 ) heat trajectory tracking is carried out ; ( 6 ) whether the passengers get on the bus or get off the bus is judged ; ( 7 ) the passengers getting on and off the bus are calculated and passenger flow counting is achieved . Detection analysis is carried out by means of the depth images , the defect that counting accuracy of a traditional passenger flow counting method is poor when the passengers are crowded and light rays change is overcome , the crowdedness of the passenger flow volume can be well learnt about , the aim of accurately counting the passenger flow volume is achieved , and real-time , clear and accurate passenger traffic statistical data are provided for an intelligent public transit system . Moreover , we avoid relying on the presence of body parts other than heads or on background subtraction , which have limited effectiveness under heavy clutter . We cast the head detection problem as a stereo MRF-based optimization of a dense pedestrian height map , and we introduce a constraint which aligns the height gradient according to the vertical vanishing point direction . We validate the method in an outdoor setting with varying pedestrian density levels . Head location is represented with 3-D location and area features calculated by using Viola-Jones face detector . Pointing'04 database is used as training dataset for head movement estimation , the recorded real video is used for head location detection . Both processes employ recorded real video frames as test dataset . Head pose is an important indicator of a person 's attention , gestures , and communicative behavior with applications in human computer interaction , multimedia and vision systems . In this paper , we present a novel head pose estimation system by performing head region detection using the Kinect [ 2 ] , followed by face detection , feature tracking , and finally head pose estimation using an active camera . Ten feature points on the face are defined and tracked by an Active Appearance Model ( AAM ) . Implementation details . For head detection , we use a version of our model that contains the two head parts ( neck and head top ) . This makes our TD / BU model related to the hierarchical model defined in that also uses easier - to - detect parts to guide the rest of the inference process . A recent fatal accident of an autonomous vehicle opens a debate about the use of infrared technology in the sensor suite for autonomous driving to increase visibility for robust object detection . Thermal imaging has an advantage over lidar , radar , and camera because it can detect the heat difference emitted by objects in the infrared spectrum . This way , these algorithms are able to recognize small differences between the objects . In addition , they allow a robust detection of objects , even under complicated imaging conditions . The FLCCA are even with non-artificial images up to 12 times faster than pixel based cross correlation algorithms . In addition , a metric localization method using artificial landmarks is fused to complement the deficiency of topology map and guide to action behavior . The recognition algorithm uses a modified local Zernike moments and a probabilistic voting method for the robust detection of objects in cluttered indoor environments . An artificial landmark is designed to have a three-dimensional multi-colored structure and the projection distortion of the structure encodes the distance and viewing direction of the robot . Additionally , high resolution digital imaging devices and increased computational power became available . By leveraging these methodical and technical advancements current methods obtain encouraging results in well defined environments for robust object class detection , tracking and pixel-wise semantic scene labeling and give rise to renewed hope for further progress in scene understanding for real environments . This thesis improves state-of-the-art scene understanding with monocular cameras and aims for applications on mobile platforms such as service robots or driver assistance for automotive safety . Additionally , high resolution digital imaging devices and increased computational power became available . By leveraging these methodical and technical advancements current methods obtain encouraging results in well defined environments for robust object class detection , tracking and pixel-wise semantic scene labeling and give rise to renewed hope for further progress in scene understanding for real environments . This book improves state-of-the-art scene understanding with monocular cameras and aims for applications on mobile platforms such as service robots or driver assistance for automotive safety . The output is an object-level labeled segmentation of the input subscene . Experiments demonstrate that the unified contextual analysis framework achieves robust object detection and recognition over cluttered subscenes . Graphical abstractDisplay Omitted HighlightsTo address the specific challenges in object analysis at subscene level , this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts.our method combines the detection of individual objects and object groups within the same framework , enabling contextual analysis without knowing objects . Although , these algorithms perform exceptionally well under normal lighting conditions , however their detection and recognition accuracy abruptly degrades under non-uniform illuminations , such as strong sunlight and bad lighting conditions . In this paper , we apply our own developed Multi-Scale Retinex ( MSR ) algorithm as a pre-processing module to boost the accuracy of the AdaBoost algorithm , which is considered to be state-of-the-art algorithm for robust object detection and recognition . Simulation results show that the MSR can be reliably and effectively used under non-uniform illuminations to boost the accuracy of the AdaBoost . As a result , it is possible to reduce the RRC calculation process to about 1/4 in comparison with normal RRC . By combining these techniques , we can achieve robust real-time object detection . We have validated it using the designed database . The obtained results are quite effective , achieving real-time , robust abandoned object detection in an industrial plant scenario . The backbone of our perception strategy is based on two main contributions presented in this work . First , we introduce a fast convexity test between neighboring patches , which is a part of a two-level segmentation leading to a robust detection of object candidates . Second , we formulate a numerically stable form of superquadric fitting , which allows for an extension of the feasible region of the corresponding optimization problem . However , current saliency models can not meet the needs for diversified scenes due to their limited generalization capability . To tackle this problem , in this paper , we propose a hybrid saliency model , which can fuse heterogeneous visual cues for robust salient object detection . A new contour cue is first introduced to provide discriminative saliency information for scene description . The controlled rectifier neuron function was incorporated in neuromorphic neural network , for introducing the efficiency of segmentation and saliency map generation used in noisy image processing of X-ray CT data and dark road video data . The neuromorphic neural network is proposed to the brain imaging analytic , based on the visual cortex-inspired deep neural network developed for 3 dimensional tooth segmentation and robust visual object detection . Experiment results illustrated the effectiveness and feasibility of our proposed method with flexible requirements of clinical diagnostic decision data , from segmentation to overall survival analysis . With such strategies , sensor parameters values can be selected and can be purposefully changed in order to effectively perform the task at hand . The focus here is on vision sensor planning for the task of robustly detecting object features . For this task , camera and illumination parameters such as position , orientation , and optical settings are determined so that object features are , for example , visible , in focus , within the sensor field of view , magnified as required , and imaged with sufficient contrast . By adding local part structural supports , the detection performance can be significantly boosted . Therefore , it is essential to combine both local and global descriptions for a robust detection . subsection : Coupling structure
Many automotive safety applications in modern cars make use of cameras and object detection to analyze the surrounding environment . Pedestrians , animals and other vehicles can be detected and safety actions can be taken before dangerous situations arise .
This thesis makes significant contributions to the object detection problem in computer vision . The object detection problem is , given a digital image of a scene , to detect the relevant object in the image .
The quality of the object detection greatly influenced the training process object detection . Viola-Jones method is often used in object detection process where this method requires a classifier file obtained from the training process . This research is used to determine the trend of the number of the training process on the quality of bottle caps object detection fanta , sprite , Frestea , coca-cola and soda .
The paper presents test procedures designed for application-specific integrated circuit ( ASIC ) CMOS VLSI chip that implements a synchronized oscillator neural network with a matrix size of 32 × 32 for object detecting in binary images . Networks of synchronized oscillators are recently developed tool for image segmentation and analysis .
The images are separated into 1000 different classes organized by the WordNet hierarchy . In the challenge ” object detection with additional training data ” GoogleNet has achieved about 44 % precision . These results have demonstrated the potential which lies in this kind of architecture .
3D object detection in RGB-D image has received considerable attention recently . But potential searching space in testing image is large , and extracting handcraft feature for every candidate bounding box is computational expensive .
Three-dimensional ( 3D ) object detection is an important research in 3D computer vision with significant applications in many fields , such as automatic driving , robotics , and human – computer interaction . However , the low precision is an urgent problem in the field of 3D object detection .
We develop new representations and algorithms for three-dimensional ( 3D ) object detection and spatial layout prediction in cluttered indoor scenes . We first propose a clouds of oriented gradient ( COG ) descriptor that links the 2D appearance and 3D pose of object categories , and thus accurately models how perspective projection affects perceived image gradients .
In autonomous driving community , numerous benchmarks have been established to assist the tasks of 3D/2D object detection , stereo vision , semantic/instance segmentation . However , the more meaningful dynamic evolution of the surrounding objects of ego-vehicle is rarely exploited , and lacks a large-scale dataset platform .
( 2 ) adopt pretrained R-CNNs [ 2 ] to extract raw feature from both RGB and Depth domains . Experiments on RMRC dataset demonstrate that the bimodal based deep feature learning framework helps 3D scene object detection .
A basic set of tasks has been implemented . The demonstrator incorporates augmented reality techniques with a focus on 3D stereo computer vision and graphics . Index Terms – cognitive robotics , information representation , unified message format , augmented reality , 3D stereo vision
Note that we intentionally do not scale the point cloud , because the bounding sphere size of a partial point cloud can be greatly affected by viewpoints and the real size of the point cloud helps the box size estimation . In our experiments , we find that coordinate transformations such as the one above and the previous frustum rotation are critical for 3D detection result as shown in Tab . [ reference ] .
Because SUN - RGBD does not have a direct mapping between the 2D and 3D object annotations , for each 3D object annotation , we project the 8 corners of the 3D box to the image plane and use the minimum enclosing 2D bounding box as training data for the 2D object detector and our models . We report 3D detection performance of our models on the same 10 object categories as in . Because these 10 object categories contain relatively large objects , we also show detection results on the 19 categories from to show our model
We show that using object segmentation performs better for this task compared to a standard sliding window detector , in particular in presence of partial occlusion . We then apply a CNN to predict the 3D pose of the detected objects . While the predicted 3D pose can be represented directly by a translation and a rotation , we achieve better accuracy by using a representation similar to the one used in [ reference ] for object parts : We predict the 2D projections of the corners of the object 's bounding box , and compute the 3D pose from these 2D - 3D correspondences with a PnP algorithm .
Convolutional neural networks ( CNNs ) have demonstrated a strong ability to extract semantics from images during object detection ; however , the extracted semantics are typically have strong scale priors for a specific circumstance . In this paper , we investigate the influence of head scale and contextual information , and then propose a scale-invariant method for head detection . Our method can dynamically detect heads depending on the complexity of the image .
Prototypes were developed and tested in real life conditions by involving patients completing rehabilitation exercises with physiotherapists . The user experience of the demonstration will include prototypes for different applications : knee joint and 3D printed palm prosthetic dynamics monitoring , posture and head position detection in real-time .
Finally , we use the nearest neighbor method to estimate the head pose . The experiment results show : accurate head detecting helps to estimate the head pose . This method can be used for complex conditions of accurate head pose estimation .
Crowd image analysis has various application areas such as surveillance , crowd management and augmented reality . Existing techniques can detect multiple faces in a single crowd image , but small head/face size and additional non facial regions in the head bounding box makes the head detection ( HD ) challenging . Additionally , in existing head pose estimations ( HPE ) of multiple heads in an image , individual cropped head image is passed through a network one by one , instead of estimating poses of multiple heads at the same time .
The invention discloses a passenger flow counting detection method based on depth images . The method includes the steps that ( 1 ) a motion sensing camera is arranged above a door of a bus and is used for collecting depth image videos of passengers getting on and off the bus ; ( 2 ) the obtained depth images are pre-processed ; ( 3 ) denoising is carried out on the depth images pre-processed in the second step , then laying processing is carried out many times to obtain multiple binary images , and the binary images are combined to obtain the frame of binary images ; ( 4 ) head detection judgment is carried out ; ( 5 ) heat trajectory tracking is carried out ; ( 6 ) whether the passengers get on the bus or get off the bus is judged ; ( 7 ) the passengers getting on and off the bus are calculated and passenger flow counting is achieved . Detection analysis is carried out by means of the depth images , the defect that counting accuracy of a traditional passenger flow counting method is poor when the passengers are crowded and light rays change is overcome , the crowdedness of the passenger flow volume can be well learnt about , the aim of accurately counting the passenger flow volume is achieved , and real-time , clear and accurate passenger traffic statistical data are provided for an intelligent public transit system .
Moreover , we avoid relying on the presence of body parts other than heads or on background subtraction , which have limited effectiveness under heavy clutter . We cast the head detection problem as a stereo MRF-based optimization of a dense pedestrian height map , and we introduce a constraint which aligns the height gradient according to the vertical vanishing point direction . We validate the method in an outdoor setting with varying pedestrian density levels .
Head location is represented with 3-D location and area features calculated by using Viola-Jones face detector . Pointing'04 database is used as training dataset for head movement estimation , the recorded real video is used for head location detection . Both processes employ recorded real video frames as test dataset .
Head pose is an important indicator of a person 's attention , gestures , and communicative behavior with applications in human computer interaction , multimedia and vision systems . In this paper , we present a novel head pose estimation system by performing head region detection using the Kinect [ 2 ] , followed by face detection , feature tracking , and finally head pose estimation using an active camera . Ten feature points on the face are defined and tracked by an Active Appearance Model ( AAM ) .
Implementation details . For head detection , we use a version of our model that contains the two head parts ( neck and head top ) . This makes our TD / BU model related to the hierarchical model defined in that also uses easier - to - detect parts to guide the rest of the inference process .
A recent fatal accident of an autonomous vehicle opens a debate about the use of infrared technology in the sensor suite for autonomous driving to increase visibility for robust object detection . Thermal imaging has an advantage over lidar , radar , and camera because it can detect the heat difference emitted by objects in the infrared spectrum .
This way , these algorithms are able to recognize small differences between the objects . In addition , they allow a robust detection of objects , even under complicated imaging conditions . The FLCCA are even with non-artificial images up to 12 times faster than pixel based cross correlation algorithms .
In addition , a metric localization method using artificial landmarks is fused to complement the deficiency of topology map and guide to action behavior . The recognition algorithm uses a modified local Zernike moments and a probabilistic voting method for the robust detection of objects in cluttered indoor environments . An artificial landmark is designed to have a three-dimensional multi-colored structure and the projection distortion of the structure encodes the distance and viewing direction of the robot .
Additionally , high resolution digital imaging devices and increased computational power became available . By leveraging these methodical and technical advancements current methods obtain encouraging results in well defined environments for robust object class detection , tracking and pixel-wise semantic scene labeling and give rise to renewed hope for further progress in scene understanding for real environments . This thesis improves state-of-the-art scene understanding with monocular cameras and aims for applications on mobile platforms such as service robots or driver assistance for automotive safety .
Additionally , high resolution digital imaging devices and increased computational power became available . By leveraging these methodical and technical advancements current methods obtain encouraging results in well defined environments for robust object class detection , tracking and pixel-wise semantic scene labeling and give rise to renewed hope for further progress in scene understanding for real environments . This book improves state-of-the-art scene understanding with monocular cameras and aims for applications on mobile platforms such as service robots or driver assistance for automotive safety .
The output is an object-level labeled segmentation of the input subscene . Experiments demonstrate that the unified contextual analysis framework achieves robust object detection and recognition over cluttered subscenes . Graphical abstractDisplay Omitted HighlightsTo address the specific challenges in object analysis at subscene level , this work proposes a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts.our method combines the detection of individual objects and object groups within the same framework , enabling contextual analysis without knowing objects .
Although , these algorithms perform exceptionally well under normal lighting conditions , however their detection and recognition accuracy abruptly degrades under non-uniform illuminations , such as strong sunlight and bad lighting conditions . In this paper , we apply our own developed Multi-Scale Retinex ( MSR ) algorithm as a pre-processing module to boost the accuracy of the AdaBoost algorithm , which is considered to be state-of-the-art algorithm for robust object detection and recognition . Simulation results show that the MSR can be reliably and effectively used under non-uniform illuminations to boost the accuracy of the AdaBoost .
As a result , it is possible to reduce the RRC calculation process to about 1/4 in comparison with normal RRC . By combining these techniques , we can achieve robust real-time object detection .
We have validated it using the designed database . The obtained results are quite effective , achieving real-time , robust abandoned object detection in an industrial plant scenario .
The backbone of our perception strategy is based on two main contributions presented in this work . First , we introduce a fast convexity test between neighboring patches , which is a part of a two-level segmentation leading to a robust detection of object candidates . Second , we formulate a numerically stable form of superquadric fitting , which allows for an extension of the feasible region of the corresponding optimization problem .
However , current saliency models can not meet the needs for diversified scenes due to their limited generalization capability . To tackle this problem , in this paper , we propose a hybrid saliency model , which can fuse heterogeneous visual cues for robust salient object detection . A new contour cue is first introduced to provide discriminative saliency information for scene description .
The controlled rectifier neuron function was incorporated in neuromorphic neural network , for introducing the efficiency of segmentation and saliency map generation used in noisy image processing of X-ray CT data and dark road video data . The neuromorphic neural network is proposed to the brain imaging analytic , based on the visual cortex-inspired deep neural network developed for 3 dimensional tooth segmentation and robust visual object detection . Experiment results illustrated the effectiveness and feasibility of our proposed method with flexible requirements of clinical diagnostic decision data , from segmentation to overall survival analysis .
With such strategies , sensor parameters values can be selected and can be purposefully changed in order to effectively perform the task at hand . The focus here is on vision sensor planning for the task of robustly detecting object features . For this task , camera and illumination parameters such as position , orientation , and optical settings are determined so that object features are , for example , visible , in focus , within the sensor field of view , magnified as required , and imaged with sufficient contrast .
By adding local part structural supports , the detection performance can be significantly boosted . Therefore , it is essential to combine both local and global descriptions for a robust detection . subsection : Coupling structure
Semi-Supervised Learning for Object Detection Global Optimizing Flows for Active Contours PENGARUH JUMLAH FILE TRAINING TERHADAP AKURASI PENDETEKSIAN OBYEK PADA METODE VIOLA JONES Test Procedures for Synchronized Oscillators Network CMOS VLSI Chip DeXpression: Deep Convolutional Neural Network for Expression Recognition Faster 3D Object Detection in RGB-D Image Using 3D Selective Search and Object Pruning 3D-GIoU: 3D Generalized Intersection over Union for Object Detection in Point Cloud Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving Towards 3D object detection with bimodal deep Boltzmann machines over RGBD imagery An internet protocol for the brain: toward a unified message format as an information representation in cognitive robotics Frustum PointNets for 3D Object Detection from RGB-D Data PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth Scale Mapping and Dynamic Re-Detecting in Dense Head Detection Demo: Wearable Sensor System for Human Biomechanics Monitoring Head Detection by the Adaboost Algorithm for Head Pose Estimation WNet: Joint Multiple Head Detection and Head Pose Estimation from a Spectator Crowd Image Passenger flow counting detection method based on depth images Geometry-Based Multiple Camera Head Detection in Dense Crowds Integration of motion and localization features for head movement classification 3D Head Pose Estimation Based on Scene Flow and Generic Head Model ArtTrack: Articulated Multi-Person Tracking in the Wild Thermal Object Detection using Domain Adaptation through Style Consistency Merkmalslistenbasierte Kreuzkorrelationsmethoden für die medizinische Bildverarbeitung Robust self-localization of mobile robots using artificial and natural landmarks Monocular visual scene understanding from mobile platforms Visual Scene Understanding from Mobile Platforms Data-driven contextual modeling for 3D scene understanding Boosting the Accuracy of AdaBoost for Object Detection and Recognition Robust object detection and adaptive background estimation based on radial reach correlation Anomaly detection with a moving camera using multiscale video analysis Recognition and Localization of Sacks for Autonomous Container Unloading by Fitting Superquadrics in Noisy, Partial Views from a Low-cost RGBD Sensor New Contour Cue-Based Hybrid Sparse Learning for Salient Object Detection. Neuromorphic Neural Network for Multimodal Brain Image Segmentation and Overall Survival Analysis A survey of sensor planning in computer vision CoupleNet: Coupling Global Structure with Local Parts for Object Detection
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ArXiv CVPR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) ICIP EWSN ACCV Workshops SIU ICME 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CIRA FIT FCV BrainLes@MICCAI (2) 2017 IEEE International Conference on Computer Vision (ICCV)
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