A recursive partition tree approximator is proposed to do classification . This approach combined with our previous work on hand segmentation forms a new framework which addresses the three key aspects of hand sign interpretation : hand shape , location , and movement . The framework has been tested to recognize 28 different hand signs . Then , in accordance with three stages of hand gesture recognition , we suggest a new method for static hand gesture recognition . Firstly , we apply the improved RCE neural network to hand image segmentation . Secondly , we use Freeman chain code to extract the distance from hand edge to the palm-center as feature vectors . Use of hand gesture can be used to provide natural interaction . Hand image segmentation is the most important step in every hand gesture recognition system . All subsequent steps heavily rely on the quality of the segmentation . Unlike existing methods , our study requires no special device or mark , and users are free to move their hands . Via dense optical flow and a skin filter , we perform complete hand region segmentation in a complex environment . We find the maximum value of the local centroid distance outside the centroid circles and identify fingertips . In this paper , a real time finger counter is proposed for counting the number of fingers ( 1 - 5 ) shown in a hand gesture . This system involves five phases namely input video capturing , preprocessing , hand region segmentation , feature extraction and recognition . In the first phase , the gesture video is captured using a low cost USB camera in real time . Hand gesture recognition is one of the most popular Human Computer Interface . The first step in most vision-based gesture recognition system is the hand detection and segmentation . Since hands are involved in a variety of daily tasks , the detection work suffers from both extreme illumination changes and the intrinsic variability of hand appearance . The system consists of two algorithmic stages , comprising an efficient preprocessing phase that generates candidate hand-region proposals , followed by their deep-learning based classification . Specifically , the first stage exploits own earlier work on hand detection and segmentation in videos that also contain the signer ’s face , allowing face detection to drive skin-tone based hand segmentation , with motion further utilized to localize hands , extending it with a peak detection module that yields proposal regions likely to contain the signs of interest . These regions are then classified by a variant of a convolutional neural network that extends traditional convolutions to quadratic operations on the inputs , being , to our knowledge , the first application of such architecture to this task . A fuzzy region growing based on fuzzy connecting is presented to obtain the final segmentation result . The comparison between the result of our method and the hand-labeled segmentation of a radiology expert shows that this scheme is efficient . The experimental results ( based on 4 patients studied ) show an average probability of correct detection equal to 96 % and an average probability of false detection equal to 5 % . A fuzzy region growing based on fuzzy connecting is presented to obtain the final segmentation result . The comparison between the result of our method and the hand-labeled segmentation of a radiology expert shows that this scheme is efficient . The experimental results ( based on 4 patients studied ) show an average probability of correct detection equal to 96 % and an average probability of false detection equal to 5 % . This algorithm is based on three main steps : hand segmentation , hand tracking and gesture recognition from hand features . For the hand segmentation step we use the colour cue due to the characteristic colour values of human skin , its invariant properties and its computational simplicity . To prevent errors from hand segmentation we add a second step , hand tracking . The proposed method was applied on several medical images obtained from MRI-guided prostate biopsies . The robustness of this idea was showed by comparison with hand-labeled obtained segmentation results . For many applications in this domain , it is necessary to accurately segment not only hands of the camera wearer but also the hands of others with whom he is interacting . Here , we take an in-depth look at the hand segmentation problem . In the quest for robust hand segmentation methods , we evaluated the performance of the state of the art semantic segmentation methods , off the shelf and fine-tuned , on existing datasets . This paper presents a vision based system that provides a feasible solution to Arabic Sign Language ( ArSL ) recognition of static gestures of alphabets . The proposed method does n't require that signers wear gloves or any other marker devices to simplify the process of hand segmenting . Haar-like algorithm is used to track the hand in the consecutive frames , and then bounded images become the region of interest after employing special preprocessing techniques : skin detection and size normalization . paragraph : Representation learning : One of the underlying debates is that should we spend more time collecting data for individual tasks such as detection and segmentation . Our findings show there is still a lot to be gained from representation learning . From Eq . 2 we can see that the mask prediction is a function of both the image to segment x and the target object in z. In this way , z can be used as a reference to guide the segmentation process , such that objects of any arbitrary class can be tracked . This clearly means that , given a different reference image z , the network will produce a different segmentation mask for x. Loss function . paragraph : Dice similarity coefficient ( DSC ) Let and be , respectively , the reference and automatic segmentations of a given tissue class and for a given subject . The DSC can be defined as : DSC values are within a range , 1 indicating perfect overlap and 0 corresponding to a total mismatch . This is unlike a large number of segmentation methods in the literature , which often propose a joint model of the segmentation labels , effectively modeling the direct dependencies between spatially close labels . One approach is to define a conditional random field ( CRF ) over the labels and perform mean - field message passing inference to produce a complete segmentation . In this case , the final label at a given position is effectively influenced by the models beliefs about what the label is in the vicinity of that position . We propose an image cascade network ( ICNet ) that incorporates multi - resolution branches under proper label guidance to address this challenge . We provide in - depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high - quality segmentation . Our system yields real - time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes , CamVid and COCO - Stuff . ICNet gets much faster inference speed than other methods on this high resolution , reaching the real - time speed of 27.8 fps , 5.7 times faster than the second one and 5.1 times faster compared to the basic model . Apart from high efficiency , it also accomplishes high quality segmentation . Visual results are provided in the supplementary material . Various methods of instance segmentation have also involved modifying object detection systems to output segments instead of bounding boxes [ reference ][ reference ][ reference ][ reference ] . However , these methods can not produce a segmentation map of the image , as shown in Fig . 1 , without postprocessing as they consider each detection independently . Although our method also uses an object detector , it considers all detections in the image jointly with an initial category - level segmentation , and produces segmentation maps naturally where one pixel can not belong to multiple instances in contrast to the aforementioned approaches . Recently deep convolutional networks ( ConvNet ) with sophisticated design have been employed to address it , but the improvement over traditional random forest based methods is not so apparent . To exploit the good practice and promote the performance for hand pose estimation , we propose a tree-structured Region Ensemble Network ( REN ) for directly 3D coordinate regression . It first partitions the last convolution outputs of ConvNet into several grid regions . This thesis also demonstrates the e ectiveness of the improved PSO in hand posture recovery in case of tracking loss . The last contribution of this thesis is the formulation of the hand posture estimation as a bi-objective problem for the rst time in the literature . The objectives identi ed and used are to minimise the error ( maximise accuracy ) and minimise the number of points in the point cloud , thus reducing the computational cost . One of the shortcomings of the existing model-based 3D hand tracking methods is the fact that they consider a fixed hand model , i.e. one with fixed shape parameters . In this work we propose an online model-based method that tackles jointly the hand pose tracking and the hand shape estimation problems . The hand pose is estimated using a hierarchical particle filter . An Integrated Systems Approach . We propose a new approach to the hand pose estimation problem using only volume information . We describe a thermal and color image-based approach to generate silhouettes of the hand with which voxel images are produced using shape-from-silhouette . The use of CNN opens new opportunities for improvements in this research by providing the capability of learning from many samples . This work pretends to advance a step further on the hand pose estimation problem . With this aim , the hand pose estimation using CNN by modifying the output data with a new representation of the hand pose is proposed . Furthermore , different parts of the hand display very similar visual appearance ; it is difficult to tell fingers apart in video . These properties of hands put hard requirements on visual features used for hand pose estimation and tracking . In this paper , we evaluate three different state-of-the-art visual shape descriptors , which are commonly used for hand and human body pose estimation . Hand pose estimation is an important task in areas such as human computer interaction ( HCI ) , sign language recognition and robotics . Due to the high variability in hand appearance and many degrees of freedom ( DoFs ) of the hand , hand pose estimation and tracking is very challenging , and different sources of data and methods are used to solve this problem . In the paper , we propose a method for model-based full DoF hand pose estimation from a single RGB-D image . In this paper , we propose a novel framework for hand localization and pose estimation from a single depth image . For hand localization , unlike most existing methods that using heuristic strategies , e.g. color segmentation , we propose Hierarchical Hand location Networks ( HHLN ) to estimate the hand location from coarse to fine in depth images , which is robust to the complex environment and efficient . Estimating the 3D pose of a hand interacting with an object is a challenging task , harder than hand-only pose estimation as the object can cause heavy occlusion on the hand . We present a two stage discriminative approach using convolutional neural networks ( CNN ) . 3D hand pose estimation from a single RGB image is important but challenging due to the lack of sufficiently large hand pose datasets with accurate 3D hand keypoint annotations for training . In this work , we present an effective method for generating realistic hand poses , and show that existing algorithms for hand pose estimation can be greatly improved by augmenting training data with the generated hand poses , which come naturally with ground-truth annotations . 3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications . The state-of-the-art methods directly regress 3D hand meshes from 2D depth images via 2D convolutional neural networks , which leads to artefacts in the estimations due to perspective distortions in the images . In the extensive experiments , the proposed approach improves over the state of the art by47.8 % on the SynHand5 M dataset . Moreover , our augmentation policy for voxelized depth maps further enhances the accuracy of 3D hand pose estimation on real data . Our method produces visually more reasonable and realistic hand shapes on NYU and BigHand2.2 M datasets compared to the existing approaches . However , these research efforts have been hampered by the computational complexity caused by inverse kinematics and 3D reconstruction . In this paper , our objective focuses on 3D hand posture estimation based on a single 2D image . We introduce the human hand model with 27 degrees of freedom ( DOFs ) and analyze some of its constraints to reduce the 27 to 12 DOFs without any significant degradation of performance . We propose a new dataset for 3D hand+object pose estimation from color images , together with a method for efficiently annotating this dataset , and a 3D pose prediction method based on this dataset . The current lack of training data makes the 3D hand+object pose estimation very challenging . Low-cost consumer depth cameras have enabled reasonable 3D hand pose trackingfrom single depth images . Such 3D hand pose tracking can be an integralpart of many computer vision applications such as ... As a benefit , significant performance gains over the existing methods can be obtained , as demonstrated on two popular public datasets with diverse camera viewpoints and background . Moreover , integrating SPS-Net with a simple 3D hand pose estimator , we achieved the leading result on the 3D Hand Pose Tracking Task of the Hand2017 Challenge - the only world-wide open challenge of its kind . Experiments on synthetic data demonstrate the competitiveness of our approach compared with existing multivariate regression models . In addition , empirically our approach has been validated with very promising results on two exemplar real-world applications : The first concerns the prediction of \textit{Big-Five } personality based on user behaviors at social network sites ( SNSs ) , while the second is 3D human hand pose estimation from depth images . The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives . 3D human hand pose estimation ( HPE ) is an essential methodology for smart human computer interfaces . Especially , 3D hand pose estimation without attached or hand-held sensors provides a more natural and convenient way . 3D hand pose inference from monocular RGB data is a challenging problem . CNN-based approaches have shown great promise in tackling this problem . Abstract 3D Hand pose estimation has received an increasing amount of attention , especially since consumer depth cameras came onto the market in 2010 . Although substantial progress has occurred recently , no overview has kept up with the latest developments . We hope that the proposed system to become a milestone of 3D hand and human pose estimation problems from a single depth map . Now , we assume that the term " 3D pose estimation " refers to the localization of the hand or human body keypoints in 3D space . Our contributions can be summarized as follows . document : Exploiting temporal information for 3D human pose estimation In this work , we address the problem of 3D human pose estimation from a sequence of 2D human poses . document : Towards Viewpoint Invariant 3D Human Pose Estimation We propose a viewpoint invariant model for 3D human pose estimation from a single depth image . To achieve this , our discriminative model embeds local regions into a learned viewpoint invariant feature space . Reaching the required level of accuracy was only possible by proper modelling of the laser scanner . The system has been evaluated by hand measuring 680 marked points . To make the integration of the robot system into the overall system as smooth as possible the robot uses information from the existing CAD model of the environment in combination with a SICK LMS 291 laser scanner to localize the robot . Here we evaluated such decision-making in a rapid movement foraging task . On a given trial , participants were presented with 15 targets of varying size and value and were instructed to harvest as much reward as possible by either moving a handle to the targets ( hand task ) or by briefly fixating them ( eye task ) . The short trial duration enabled participants to harvest about half the targets , ensuring that total reward was due to choice behavior . The maximal accuracy achieves 90 % . The results show that the PSE is a sensitive parameter for EEG of imaginary hand movements . The method is simple and quick and it provides a promising method for on-line BCI system . The maximal accuracy achieves 90 % . The results show that the PSE is a sensitive parameter for EEG of imaginary hand movements . The method is simple and quick and it provides a promising method for on-line BCI system . Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security , hand signing , and smart-home and gaming . These applications capture human motions in real-time from video sensors , the data patterns are nonstationary and ever changing . It will also lead to the development of more sophisticated surgical procedures , previously considered difficult to perform . Tele-robotic systems , steady-hand robot and Micron active hand-held instrument are currently existing techniques for tremor cancellation . We propose the design of an active glove , capable of sensing the surgeon ’s hand tremors and compensate them by generating counter forces on the surgeon ’s fingers . The SVM classification of our features achieved an average accuracy of 79,14 % . We concluded that HOS study could be an accurate tool in the recognition of hand movement . An electric hand which can hold the object is attached to the tip of the robot arm . In the position follow-up control , the position of one robot arm is automatically moved close to the other moving robot arm , and then smooth hand delivery is realized in combination with manual operation . In the experiment , we make a comparison between the case in which the position follow-up control is carried out and the case in which the control is not performed . Toward this end , we apply the psychological pattern called DISC behavioral style classification model , which is commonly used in psychology and sociology , to the e-commerce context . A virtual second-hand goods transaction application for the Android platform was used for the experiment , and the performance of the recommendation system was compared to that of other methods based on the results obtained through actual distribution . RF wireless power transfer and energy harvesting have gained a lot of interest nowadays due to its importance as a convenient feature in hand-held and Internet of Things devices . One bottleneck in both these applications is on converting the received RF energy to a regulated voltage level that a mixed signal load can use . The central mechanism relies on the addition of global and local attributes , and skin colour information to the network which allow us to automatically model and track 2D gestures . Application to hand gesture video tracking is presented . This paper focuses on the research of humanoid dexterous hand with tendon-driven . The structure of the dexterous hand is designed and its kinematics , dynamics and the relationship between the finger joints and the fingertip force are deduced . V2V - PoseNet : Voxel - to - Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map section : Abstract We hope that the proposed system to become a milestone of 3D hand and human pose estimation problems from a single depth map . Now , we assume that the term " 3D pose estimation " refers to the localization of the hand or human body keypoints in 3D space . Our contributions can be summarized as follows . Oberweger et al . [ reference ] improved their preceding work [ reference ] by utilizing recent network architecture , data augmentation , and better initial hand localization . Hybrid methods are proposed to combine the generative and discriminative approach . This approach allows us to obtain good performance on the T - LESS dataset . Finally , we show that we can add an optional last step to refine the pose estimates by using the " feedback loop " proposed in [ reference ] for hand detection in depth images : We train a network to improve the prediction of the 2D projections by comparing the input image and a rendering of the object for the initial pose estimate . This allows us to improve even more our results on the LINEMOD and Occlusion datasets . “ Second - hand TIDDA 1.6 T Mannual gear has gotten some problems , please everybody help me to solve it ? ” . This question has 5 topics : Car , Second - hand Car , Motor Dom , Autocar Conversation and Autocar Service . EXAM only misclassified the last topic . Despite its potential , computer vision has yet to assume a significant role in today ’s self - driving cars . Classic computer vision techniques simply have not provided the robustness required for production grade automotives ; these techniques require intensive hand engineering , road modeling , and special case handling . Considering the seemingly infinite number of specific driving situations , environments , and unexpected obstacles , the task of scaling classic computer vision to robust , human - level performance would prove monumental and is likely to be unrealistic . The new training methods and other new concepts can also lead to significant improvements of shallow neural networks with just a few hidden layers . Convolutional neural networks ( CNNs ) were initially proposed by LeCun for the recognition of hand - written digits . A CNN consists of two layers : a convolutional layer , followed by a subsampling layer . We present a novel approach for 2D hand keypoint localization from regular color input . The proposed approach relies on an appropriately designed Convolutional Neural Network ( CNN ) that computes a set of heatmaps , one per hand keypoint of interest . We describe our design for a CPM that combines the advantages of deep convolutional architectures with the implicit spatial modeling afforded by the pose machine framework . section : Keypoint Localization Using Local Image Evidence The first stage of a convolutional pose machine predicts part beliefs from only local image evidence . In addition , there is work like that of Oliveira et al . that performs human part segmentation based on fully convolutional networks . Our work focuses solely on the task of keypoint localization of a single person ’s pose from an RGB image . Our hourglass module before stacking is closely connected to fully convolutional networks and other designs that process spatial information at multiple scales for dense prediction .
A recursive partition tree approximator is proposed to do classification . This approach combined with our previous work on hand segmentation forms a new framework which addresses the three key aspects of hand sign interpretation : hand shape , location , and movement . The framework has been tested to recognize 28 different hand signs .
Then , in accordance with three stages of hand gesture recognition , we suggest a new method for static hand gesture recognition . Firstly , we apply the improved RCE neural network to hand image segmentation . Secondly , we use Freeman chain code to extract the distance from hand edge to the palm-center as feature vectors .
Use of hand gesture can be used to provide natural interaction . Hand image segmentation is the most important step in every hand gesture recognition system . All subsequent steps heavily rely on the quality of the segmentation .
Unlike existing methods , our study requires no special device or mark , and users are free to move their hands . Via dense optical flow and a skin filter , we perform complete hand region segmentation in a complex environment . We find the maximum value of the local centroid distance outside the centroid circles and identify fingertips .
In this paper , a real time finger counter is proposed for counting the number of fingers ( 1 - 5 ) shown in a hand gesture . This system involves five phases namely input video capturing , preprocessing , hand region segmentation , feature extraction and recognition . In the first phase , the gesture video is captured using a low cost USB camera in real time .
Hand gesture recognition is one of the most popular Human Computer Interface . The first step in most vision-based gesture recognition system is the hand detection and segmentation . Since hands are involved in a variety of daily tasks , the detection work suffers from both extreme illumination changes and the intrinsic variability of hand appearance .
The system consists of two algorithmic stages , comprising an efficient preprocessing phase that generates candidate hand-region proposals , followed by their deep-learning based classification . Specifically , the first stage exploits own earlier work on hand detection and segmentation in videos that also contain the signer ’s face , allowing face detection to drive skin-tone based hand segmentation , with motion further utilized to localize hands , extending it with a peak detection module that yields proposal regions likely to contain the signs of interest . These regions are then classified by a variant of a convolutional neural network that extends traditional convolutions to quadratic operations on the inputs , being , to our knowledge , the first application of such architecture to this task .
A fuzzy region growing based on fuzzy connecting is presented to obtain the final segmentation result . The comparison between the result of our method and the hand-labeled segmentation of a radiology expert shows that this scheme is efficient . The experimental results ( based on 4 patients studied ) show an average probability of correct detection equal to 96 % and an average probability of false detection equal to 5 % .
A fuzzy region growing based on fuzzy connecting is presented to obtain the final segmentation result . The comparison between the result of our method and the hand-labeled segmentation of a radiology expert shows that this scheme is efficient . The experimental results ( based on 4 patients studied ) show an average probability of correct detection equal to 96 % and an average probability of false detection equal to 5 % .
This algorithm is based on three main steps : hand segmentation , hand tracking and gesture recognition from hand features . For the hand segmentation step we use the colour cue due to the characteristic colour values of human skin , its invariant properties and its computational simplicity . To prevent errors from hand segmentation we add a second step , hand tracking .
The proposed method was applied on several medical images obtained from MRI-guided prostate biopsies . The robustness of this idea was showed by comparison with hand-labeled obtained segmentation results .
For many applications in this domain , it is necessary to accurately segment not only hands of the camera wearer but also the hands of others with whom he is interacting . Here , we take an in-depth look at the hand segmentation problem . In the quest for robust hand segmentation methods , we evaluated the performance of the state of the art semantic segmentation methods , off the shelf and fine-tuned , on existing datasets .
This paper presents a vision based system that provides a feasible solution to Arabic Sign Language ( ArSL ) recognition of static gestures of alphabets . The proposed method does n't require that signers wear gloves or any other marker devices to simplify the process of hand segmenting . Haar-like algorithm is used to track the hand in the consecutive frames , and then bounded images become the region of interest after employing special preprocessing techniques : skin detection and size normalization .
paragraph : Representation learning : One of the underlying debates is that should we spend more time collecting data for individual tasks such as detection and segmentation . Our findings show there is still a lot to be gained from representation learning .
From Eq . 2 we can see that the mask prediction is a function of both the image to segment x and the target object in z. In this way , z can be used as a reference to guide the segmentation process , such that objects of any arbitrary class can be tracked . This clearly means that , given a different reference image z , the network will produce a different segmentation mask for x. Loss function .
paragraph : Dice similarity coefficient ( DSC ) Let and be , respectively , the reference and automatic segmentations of a given tissue class and for a given subject . The DSC can be defined as : DSC values are within a range , 1 indicating perfect overlap and 0 corresponding to a total mismatch .
This is unlike a large number of segmentation methods in the literature , which often propose a joint model of the segmentation labels , effectively modeling the direct dependencies between spatially close labels . One approach is to define a conditional random field ( CRF ) over the labels and perform mean - field message passing inference to produce a complete segmentation . In this case , the final label at a given position is effectively influenced by the models beliefs about what the label is in the vicinity of that position .
We propose an image cascade network ( ICNet ) that incorporates multi - resolution branches under proper label guidance to address this challenge . We provide in - depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high - quality segmentation . Our system yields real - time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes , CamVid and COCO - Stuff .
ICNet gets much faster inference speed than other methods on this high resolution , reaching the real - time speed of 27.8 fps , 5.7 times faster than the second one and 5.1 times faster compared to the basic model . Apart from high efficiency , it also accomplishes high quality segmentation . Visual results are provided in the supplementary material .
Various methods of instance segmentation have also involved modifying object detection systems to output segments instead of bounding boxes [ reference ][ reference ][ reference ][ reference ] . However , these methods can not produce a segmentation map of the image , as shown in Fig . 1 , without postprocessing as they consider each detection independently . Although our method also uses an object detector , it considers all detections in the image jointly with an initial category - level segmentation , and produces segmentation maps naturally where one pixel can not belong to multiple instances in contrast to the aforementioned approaches .
Recently deep convolutional networks ( ConvNet ) with sophisticated design have been employed to address it , but the improvement over traditional random forest based methods is not so apparent . To exploit the good practice and promote the performance for hand pose estimation , we propose a tree-structured Region Ensemble Network ( REN ) for directly 3D coordinate regression . It first partitions the last convolution outputs of ConvNet into several grid regions .
This thesis also demonstrates the e ectiveness of the improved PSO in hand posture recovery in case of tracking loss . The last contribution of this thesis is the formulation of the hand posture estimation as a bi-objective problem for the rst time in the literature . The objectives identi ed and used are to minimise the error ( maximise accuracy ) and minimise the number of points in the point cloud , thus reducing the computational cost .
One of the shortcomings of the existing model-based 3D hand tracking methods is the fact that they consider a fixed hand model , i.e. one with fixed shape parameters . In this work we propose an online model-based method that tackles jointly the hand pose tracking and the hand shape estimation problems . The hand pose is estimated using a hierarchical particle filter .
An Integrated Systems Approach . We propose a new approach to the hand pose estimation problem using only volume information . We describe a thermal and color image-based approach to generate silhouettes of the hand with which voxel images are produced using shape-from-silhouette .
The use of CNN opens new opportunities for improvements in this research by providing the capability of learning from many samples . This work pretends to advance a step further on the hand pose estimation problem . With this aim , the hand pose estimation using CNN by modifying the output data with a new representation of the hand pose is proposed .
Furthermore , different parts of the hand display very similar visual appearance ; it is difficult to tell fingers apart in video . These properties of hands put hard requirements on visual features used for hand pose estimation and tracking . In this paper , we evaluate three different state-of-the-art visual shape descriptors , which are commonly used for hand and human body pose estimation .
Hand pose estimation is an important task in areas such as human computer interaction ( HCI ) , sign language recognition and robotics . Due to the high variability in hand appearance and many degrees of freedom ( DoFs ) of the hand , hand pose estimation and tracking is very challenging , and different sources of data and methods are used to solve this problem . In the paper , we propose a method for model-based full DoF hand pose estimation from a single RGB-D image .
In this paper , we propose a novel framework for hand localization and pose estimation from a single depth image . For hand localization , unlike most existing methods that using heuristic strategies , e.g. color segmentation , we propose Hierarchical Hand location Networks ( HHLN ) to estimate the hand location from coarse to fine in depth images , which is robust to the complex environment and efficient .
Estimating the 3D pose of a hand interacting with an object is a challenging task , harder than hand-only pose estimation as the object can cause heavy occlusion on the hand . We present a two stage discriminative approach using convolutional neural networks ( CNN ) .
3D hand pose estimation from a single RGB image is important but challenging due to the lack of sufficiently large hand pose datasets with accurate 3D hand keypoint annotations for training . In this work , we present an effective method for generating realistic hand poses , and show that existing algorithms for hand pose estimation can be greatly improved by augmenting training data with the generated hand poses , which come naturally with ground-truth annotations .
3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications . The state-of-the-art methods directly regress 3D hand meshes from 2D depth images via 2D convolutional neural networks , which leads to artefacts in the estimations due to perspective distortions in the images .
In the extensive experiments , the proposed approach improves over the state of the art by47.8 % on the SynHand5 M dataset . Moreover , our augmentation policy for voxelized depth maps further enhances the accuracy of 3D hand pose estimation on real data . Our method produces visually more reasonable and realistic hand shapes on NYU and BigHand2.2 M datasets compared to the existing approaches .
However , these research efforts have been hampered by the computational complexity caused by inverse kinematics and 3D reconstruction . In this paper , our objective focuses on 3D hand posture estimation based on a single 2D image . We introduce the human hand model with 27 degrees of freedom ( DOFs ) and analyze some of its constraints to reduce the 27 to 12 DOFs without any significant degradation of performance .
We propose a new dataset for 3D hand+object pose estimation from color images , together with a method for efficiently annotating this dataset , and a 3D pose prediction method based on this dataset . The current lack of training data makes the 3D hand+object pose estimation very challenging .
Low-cost consumer depth cameras have enabled reasonable 3D hand pose trackingfrom single depth images . Such 3D hand pose tracking can be an integralpart of many computer vision applications such as ...
As a benefit , significant performance gains over the existing methods can be obtained , as demonstrated on two popular public datasets with diverse camera viewpoints and background . Moreover , integrating SPS-Net with a simple 3D hand pose estimator , we achieved the leading result on the 3D Hand Pose Tracking Task of the Hand2017 Challenge - the only world-wide open challenge of its kind .
Experiments on synthetic data demonstrate the competitiveness of our approach compared with existing multivariate regression models . In addition , empirically our approach has been validated with very promising results on two exemplar real-world applications : The first concerns the prediction of \textit{Big-Five } personality based on user behaviors at social network sites ( SNSs ) , while the second is 3D human hand pose estimation from depth images . The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives .
3D human hand pose estimation ( HPE ) is an essential methodology for smart human computer interfaces . Especially , 3D hand pose estimation without attached or hand-held sensors provides a more natural and convenient way .
3D hand pose inference from monocular RGB data is a challenging problem . CNN-based approaches have shown great promise in tackling this problem .
Abstract 3D Hand pose estimation has received an increasing amount of attention , especially since consumer depth cameras came onto the market in 2010 . Although substantial progress has occurred recently , no overview has kept up with the latest developments .
We hope that the proposed system to become a milestone of 3D hand and human pose estimation problems from a single depth map . Now , we assume that the term " 3D pose estimation " refers to the localization of the hand or human body keypoints in 3D space . Our contributions can be summarized as follows .
document : Exploiting temporal information for 3D human pose estimation In this work , we address the problem of 3D human pose estimation from a sequence of 2D human poses .
document : Towards Viewpoint Invariant 3D Human Pose Estimation We propose a viewpoint invariant model for 3D human pose estimation from a single depth image . To achieve this , our discriminative model embeds local regions into a learned viewpoint invariant feature space .
Reaching the required level of accuracy was only possible by proper modelling of the laser scanner . The system has been evaluated by hand measuring 680 marked points . To make the integration of the robot system into the overall system as smooth as possible the robot uses information from the existing CAD model of the environment in combination with a SICK LMS 291 laser scanner to localize the robot .
Here we evaluated such decision-making in a rapid movement foraging task . On a given trial , participants were presented with 15 targets of varying size and value and were instructed to harvest as much reward as possible by either moving a handle to the targets ( hand task ) or by briefly fixating them ( eye task ) . The short trial duration enabled participants to harvest about half the targets , ensuring that total reward was due to choice behavior .
The maximal accuracy achieves 90 % . The results show that the PSE is a sensitive parameter for EEG of imaginary hand movements . The method is simple and quick and it provides a promising method for on-line BCI system .
The maximal accuracy achieves 90 % . The results show that the PSE is a sensitive parameter for EEG of imaginary hand movements . The method is simple and quick and it provides a promising method for on-line BCI system .
Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security , hand signing , and smart-home and gaming . These applications capture human motions in real-time from video sensors , the data patterns are nonstationary and ever changing .
It will also lead to the development of more sophisticated surgical procedures , previously considered difficult to perform . Tele-robotic systems , steady-hand robot and Micron active hand-held instrument are currently existing techniques for tremor cancellation . We propose the design of an active glove , capable of sensing the surgeon ’s hand tremors and compensate them by generating counter forces on the surgeon ’s fingers .
The SVM classification of our features achieved an average accuracy of 79,14 % . We concluded that HOS study could be an accurate tool in the recognition of hand movement .
An electric hand which can hold the object is attached to the tip of the robot arm . In the position follow-up control , the position of one robot arm is automatically moved close to the other moving robot arm , and then smooth hand delivery is realized in combination with manual operation . In the experiment , we make a comparison between the case in which the position follow-up control is carried out and the case in which the control is not performed .
Toward this end , we apply the psychological pattern called DISC behavioral style classification model , which is commonly used in psychology and sociology , to the e-commerce context . A virtual second-hand goods transaction application for the Android platform was used for the experiment , and the performance of the recommendation system was compared to that of other methods based on the results obtained through actual distribution .
RF wireless power transfer and energy harvesting have gained a lot of interest nowadays due to its importance as a convenient feature in hand-held and Internet of Things devices . One bottleneck in both these applications is on converting the received RF energy to a regulated voltage level that a mixed signal load can use .
The central mechanism relies on the addition of global and local attributes , and skin colour information to the network which allow us to automatically model and track 2D gestures . Application to hand gesture video tracking is presented .
This paper focuses on the research of humanoid dexterous hand with tendon-driven . The structure of the dexterous hand is designed and its kinematics , dynamics and the relationship between the finger joints and the fingertip force are deduced .
V2V - PoseNet : Voxel - to - Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map section : Abstract
We hope that the proposed system to become a milestone of 3D hand and human pose estimation problems from a single depth map . Now , we assume that the term " 3D pose estimation " refers to the localization of the hand or human body keypoints in 3D space . Our contributions can be summarized as follows .
Oberweger et al . [ reference ] improved their preceding work [ reference ] by utilizing recent network architecture , data augmentation , and better initial hand localization . Hybrid methods are proposed to combine the generative and discriminative approach .
This approach allows us to obtain good performance on the T - LESS dataset . Finally , we show that we can add an optional last step to refine the pose estimates by using the " feedback loop " proposed in [ reference ] for hand detection in depth images : We train a network to improve the prediction of the 2D projections by comparing the input image and a rendering of the object for the initial pose estimate . This allows us to improve even more our results on the LINEMOD and Occlusion datasets .
“ Second - hand TIDDA 1.6 T Mannual gear has gotten some problems , please everybody help me to solve it ? ” . This question has 5 topics : Car , Second - hand Car , Motor Dom , Autocar Conversation and Autocar Service . EXAM only misclassified the last topic .
Despite its potential , computer vision has yet to assume a significant role in today ’s self - driving cars . Classic computer vision techniques simply have not provided the robustness required for production grade automotives ; these techniques require intensive hand engineering , road modeling , and special case handling . Considering the seemingly infinite number of specific driving situations , environments , and unexpected obstacles , the task of scaling classic computer vision to robust , human - level performance would prove monumental and is likely to be unrealistic .
The new training methods and other new concepts can also lead to significant improvements of shallow neural networks with just a few hidden layers . Convolutional neural networks ( CNNs ) were initially proposed by LeCun for the recognition of hand - written digits . A CNN consists of two layers : a convolutional layer , followed by a subsampling layer .
We present a novel approach for 2D hand keypoint localization from regular color input . The proposed approach relies on an appropriately designed Convolutional Neural Network ( CNN ) that computes a set of heatmaps , one per hand keypoint of interest .
We describe our design for a CPM that combines the advantages of deep convolutional architectures with the implicit spatial modeling afforded by the pose machine framework . section : Keypoint Localization Using Local Image Evidence The first stage of a convolutional pose machine predicts part beliefs from only local image evidence .
In addition , there is work like that of Oliveira et al . that performs human part segmentation based on fully convolutional networks . Our work focuses solely on the task of keypoint localization of a single person ’s pose from an RGB image . Our hourglass module before stacking is closely connected to fully convolutional networks and other designs that process spatial information at multiple scales for dense prediction .
Appearance-Based Hand Sign Recognition from Intensity Image Sequences Improved RCE neural network and its application in human-robot interaction based on hand gesture recognition Hand Gesture Segmentation Robust Fingertip Detection in a Complex Environment Vision Based Real Time Finger Counter for Hand Gesture Recognition 2D Hand Detection Using Multi-Feature Skin Model Supervised Cascaded CNN Fingerspelled Alphabet Sign Recognition in Upper-Body Videos Fuzzy Information Fusion Scheme Used to Segment Brain Tumor from MR Images Fuzzy Information Fusion Scheme Used to Segment Brain Tumor from Magnetic Resonance Images Hand Tracking and Gesture Recognition for Human-Computer Interaction A novel ant colonies approach to medical image segmentation Analysis of Hand Segmentation in the Wild Real-Time Arabic Sign Language (ArSL) Recognition Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Fast Online Object Tracking and Segmentation: A Unifying Approach Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation Brain tumor segmentation with Deep Neural Networks ICNet for Real-Time Semantic Segmentation on High-Resolution Images Holistic, Instance-Level Human Parsing Towards Good Practices for Deep 3D Hand Pose Estimation Evolutionary Hand Posture Estimation for Image-based Gesture Detection Systems Model-based 3D Hand Tracking with on-line Shape Adaptation Multimodal Voxelization and Kinematically Constrained Gaussian Mixture Models for Full Hand Pose Estimation: An Integrated Systems Approach Hand pose estimation from depth data with Convolutional Neural Networks Inferring hand pose: A comparative study of visual shape features Hand Pose Estimation from a Single RGB-D Image A Novel Framework of Hand Localization and Hand Pose Estimation 3D Hand-Object Pose Estimation from Depth with Convolutional Neural Networks TAGAN: Tonality Aligned Generative Adversarial Networks for Realistic Hand Pose Synthesis HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation From a Single Depth Map Model-based 3D hand posture estimation from a single 2D image HO-3D: A Multi-User, Multi-Object Dataset for Joint 3D Hand-Object Pose Estimation 3D Hand Pose Tracking from Depth Images using Deep Reinforcement Learning A Soft Proposal Segmentation Network (SPS-Net) for Hand Segmentation on Depth Videos Multivariate Regression with Grossly Corrupted Observations: A Robust Approach and its Applications Real Time 3D Pose Estimation of Both Human Hands via RGB-Depth Camera and Deep Convolutional Neural Networks Monocular RGB Hand Pose Inference from Unsupervised Refinable Nets A survey on 3D hand pose estimation: Cameras, methods, and datasets V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map Exploiting Temporal Information for 3D Human Pose Estimation Towards Viewpoint Invariant 3D Human Pose Estimation A mobile robot system for automatic floor marking Rapid target foraging with reach or gaze: The hand looks further ahead than the eye Power spectral entropy analysis of EEG signal based-on BCI Feature Extraction of EEG Signals Using Power Spectral Entropy Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm Sensing and Cancellation of Tremors in Surgeon's Hands during Microsurgery Classification of left and right hand movement from EEG signals by HOS features Position Follow-up Control for Hand Delivery of Object Between Moving Robot Arms of Remote Robot Systems with Force Feedback Personalized mobile e-commerce system using DISC psychological model Reverse Power Delivery Network for Wireless Power Transfer Hand gesture modelling and tracking using a Self-Organising Network A Tendon-Driven Robotic Dexterous Hand Design for Grasping V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth Explicit Interaction Model towards Text Classification An Empirical Evaluation of Deep Learning on Highway Driving Deep Learning For Smile Recognition Accurate Hand Keypoint Localization on Mobile Devices Convolutional Pose Machines Stacked Hourglass Networks for Human Pose Estimation
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