The goal is to develop a method that can exploit all possible views provided by a 3D model a single 3D model represents infinitely many 2D views of the same object . I have developed a method that uses the 3D geometry of an object for pose estimation . The method can then also learn additional real-world statistics , such as which poses appear more frequently , which area is more likely to contain an object , and which parts are commonly occluded and discriminative . We present an efficient method for geolocalization in urban environments starting from a coarse estimate of the location provided by a GPS and using a simple untextured 2.5D model of the surrounding buildings . Our key contribution is a novel efficient and robust method to optimize the pose : We train a Deep Network to predict the best direction to improve a pose estimate , given a semantic segmentation of the input image and a rendering of the buildings from this estimate . We then iteratively apply this CNN until converging to a good pose . By making use of a simulated industrial robot system performing various pick and place tasks , we show the effect of model building when making use of visual knowledge in terms of visually extracted pose hypotheses as well as action knowledge in terms of pose hypotheses verified by action execution . We show that the use of action knowledge significantly improves the pose estimation process . The proposed Visual SLAM method includes newly designed feature extraction , matching , localization and mapping modules , which jointly use object features and point features to estimate camera 6-DOF poses and do richer map construction . A group of organized raster points is used to represent objects during feature matching and pose estimation process in the proposed Visual SLAM pipeline . Owing to the object feature fusion in the co-visibility graph it could conduct scale aware bundle adjustments to reduce accumulated error . The main contribution of this work stands in deriving statistical information from the Image Processing step , by associating a covariance matrix to the heatmaps returned by the CNN for each detected feature . This information is included in the CEPPnP to improve the accuracy of the pose estimation step during filter initialization . The derived measurement covariance matrix is used in a tightly-coupled EKF to allow an enhanced representation of the measurements error from the feature detection step . Traditional implementations of image-based servoing have relied on partial pose estimation , negating much of the advantage gained from using this method . This paper will present an alternative implementation of image-based servoing for approaching objects on the ground plane , which disposes of the pose estimation step and fully relies only on image features . To evaluate the suitability of both visual servoing methods to F-180 , the task of docking with the ball is used as a basis of the investigation . In this paper , we present an object detection and pose estimation algorithm that runs on low-cost and limited-processing hardware . The algorithm relies on two main steps : in the detection phase , it employs a Convolutional Neural Network ( CNN ) – namely MobileNet SSD – to recognize and track objects of interest in the scene ; while in the pose estimation phase , the algorithm uses stereo correspondences to 3D reconstruct the spatial coordinates of multiple ORB features in the bounding box of the recognized object . The final position of the object in space is computed using a weighted average of the same spatial coordinates of the stereo-corresponded key points , where the weights are exponentially proportional to the level of ORB stereo matching . The first one is CNN-based image retrieval phase , CNN features extracted by pre-trained deep convolutional neural networks ( DCNNs ) from images are utilized to compare the similarity , and the output of this part are the matched images of the target image . The second one is pose estimation phase that computes accurate localization result . Owing to the robust CNN feature extractor , our scheme is applicable to complex indoor environments and easily transplanted to outdoor environments . We propose a novel framework which integrates human hand detection and pose estimation into one single pipeline . Unlike most of previous works which only focus on the pose estimation part subject to some strong assumptions or relying on a weak detector to detect human hands , we employ a deep learning architecture to complete both aforementioned tasks . By letting three different neural networks share the convolutional layers , this deeply learning architecture can efficiently and accurately detect human hands and compute their hand pose configuration . In this work , we address the problem of visual navigation for multi-modal stereo setups . More precisely , we focus on finding correspondences between visible and long-wave infrared ( thermal ) images for pose estimation purposes . We present a new visual odometry method for air vehicles using mutual information and phase congruency . Only complicated low-level vision modules like depth from stereo or depth from shading can recover some of the surface geometry of the scene . Recent advances in fast range imaging have however paved the way towards 3D computer vision , since range data of the scene can now be obtained with sufficient accuracy and speed for object recognition and pose estimation purposes . This article proposes the coded-light range-imaging method together with superquadric segmentation to approach this task . This paper presents an original approach for autonomous navigation based on RGB-D data and known 3D markers , where the basic concept is to detect and recognize the markers and then to use them for a straightforward pose estimation solution . The developed algorithms can allow a quadrotor to autonomously fly in ( cooperative ) GPS denied environments and/or when there is no natural or artificial illumination of the scene , by following a predetermined path consisting of successive targets having a well defined shape and/or color . Comparison is provided against both classical deformable models and state-of-the-art techniques in the complex problem domain of segmenting aortic root structure from computerized tomography scans . The proposed method shows improvement in both pose parameter estimation and segmentation performance . To tackle these challenges , existing multi - person pose estimation approaches usually perform joint detection and partition separately , mainly following two different strategies . The top - down strategy first detects persons and then performs pose estimation for each single person individually . The bottom - up strategy , in contrast , generates all joint candidates at first , and then tries to partition them to corresponding person instances . Modeling objects via their axes of symmetry , and in particular , using skeletons , has a long history in computer vision . Skeletonization algorithms provide a concise and effective representation of deformable objects , while supporting many applications , including object recognition and retrieval , pose estimation , hand gesture recognition , shape matching , scene text detection , and road detection in aerial scenes . Early algorithms for computing skeletons directly from images yield a gradient intensity map , driven by geometric constraints between skeletal pixels and edge fragments . First , if sufficient keypoints fail to be detected , then pose recovery is impossible . Second , the accuracy of the pose estimate depends upon the quality of the 3D head model . Generic head models can introduce errors for any given participant , and the process of deforming the head model to adapt to each participant requires significant amounts of data and can be computationally expensive . 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 . As shown in Figure 7 and Table 3 , our method outperforms all existing methods on the three 3D hand pose estimation datasets in standard evaluation metrics . This shows the superiority of voxel - to - voxel prediction , which is firstly used in 3D hand pose estimation . [ reference ] ) consists of a shared backbone for feature extraction , detection subnets for keypoint and person detection / segmentation , and a final network which carries out the pose estimation , i.e. assigning detected keypoints to person instances . Our major contribution lies in the pose estimation step where the network implements a novel assignment method . This network receives keypoint and person detections , and produces a pose for each detected person by assigning keypoints to person boxes using a learned function . Early methods ( prior to deep learning ) used hand - crafted HOG features to detect body parts and probabilistic graphical models to represent the pose structure ( tree - based ; non - tree based ) . Deep neural networks based models have quickly dominated the pose estimation problem after the initial work by Toshev et al . who used the AlexNet architecture to directly regress spatial joint coordinates . Tompson et al . learned pose structure by combining deep features along with graphical models . The detection of keypoints is very important for several computer applications . There are several ways for keypoint detection and pattern recognition . Earliest work proposes a trainable filter called COSFIRE filter which is a combination of bank of log gabor filters . The more computationally intensive stage of keypoint detection is implemented on the FPGA . In this paper , we present novel , efficient , scalable architectures for the keypoint detection step . The architectures are based on parallelizable and pipelined computational flow . Then , we present very SIFT-specific attacks . The attacks are performed either during the keypoint detection step or during the keypoint description step . We also present a security-oriented Picture in Picture attack deluding CBIRS using post filtering geometric verifications . In image processing , the detection of keypoint plays a significant role in foundation work of many image applications . Most widely distributed features in images are corners . Inspired by their robustness and generality , we propose a novel CNN based method to accurately localize documents in real-time . We model localization problem as a key point detection problem . The four corners of the documents are jointly predicted by a Deep Convolutional Neural Network . The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor . TPBRIEF directly enables keypoint detecting and feature describing on original distorted images , whereas other approaches correct the distortion through image resampling , which introduces artifacts and adds time cost . With ALPGMS , omnidirectional images are divided into circular arches named adapted log polar grids . And the SIFT(scale invariant feature transform)[1 ] algorithm with high robustness and distinctive feature description[2,3 ] becomes the most effective one of all the methods . However , the keypoint detection operation in standard SIFT algorithm needs to be performed in every layer of DOG ( difference of Gaussian ) pyramid , so the workload of a large number of the obtained keypoints limits the application in real-time system [ 4,5 ] . Considering the aforementioned limitation , recent research focuses on improving the performance of SIFT , such as SURF[6 ] and PCA-SIFT[7 ] . CornerNet - Squeeze is suitable for real - time detection , improving both the efficiency and accuracy of the popular real - time detector YOLOv3 ( 34.4 % AP at 34ms for CornerNet - Squeeze compared to 33.0 % AP at 39ms for YOLOv3 on COCO ) . Together these contributions for the first time reveal the potential of keypoint - based detection to be useful for applications requiring processing efficiency . ⌊⌋ section : Introduction Keypoint - based object detection is a class of methods that generate object bounding boxes by detecting and grouping their keypoints . CornerNet , the state - of - the - art among them , detects and groups the top - left and bottom - right corners of bounding boxes ; it uses a stacked hourglass network to predict the heatmaps of the corners and then uses associate embeddings to group them . Our design and results show that this is possible for the first time , particularly in the context of object detection . Contributions Our contributions are three - fold : ( 1 ) We propose CornerNet - Saccade and CornerNet - Squeeze , two novel approaches to improving the efficiency of keypoint - based object detection ; ( 2 ) On COCO , we improve the efficiency of state - of - the - art keypoint based detection by 6 fold and the AP from 42.2 % to 43.2 % , ( 3 ) On COCO , we improve both the accuracy and efficiency of state - of - the art real - time object detection ( to 34.4 % at 30ms from 33.0 % at 39ms of YOLOv3 ) . Finally , RoIAlign shows a gain of 1.5 mask AP and 0.5 box AP when used with FPN , which has finer multi - level strides . For keypoint detection that requires finer alignment , RoIAlign shows large gains even with FPN ( Table 6 ) . Mask Branch : Implementation Details : We make minor modifications to the segmentation system when adapting it for keypoints . For each of the K keypoints of an instance , the training target is a one - hot m × m binary mask where only a single pixel is labeled as foreground . The first three winning teams for the instance segmentation task were all reportedly based on an extension of the Mask R - CNN framework . section : Keypoint Detection We report enhanced results of keypoint detection in Table 9 . a novel bottom - up multi - person pose estimation architecture that combines a multi - task model with a novel assignment method . MultiPoseNet can jointly handle person detection , keypoint detection , person segmentation and pose estimation problems . The novel assignment method is implemented by the Pose Residual Network ( PRN ) which receives keypoint and person detections , and produces accurate poses by assigning keypoints to person instances . Our model ( Fig . [ reference ] ) consists of a shared backbone for feature extraction , detection subnets for keypoint and person detection / segmentation , and a final network which carries out the pose estimation , i.e. assigning detected keypoints to person instances . Our major contribution lies in the pose estimation step where the network implements a novel assignment method . Gkioxari et al . proposed k - poselets to jointly detect people and keypoints . Most of the recent approaches use Convolutional Neural Networks ( CNNs ) to detect body parts and relationships between them in an end - to - end manner , then use assignment algorithms to form individual skeletons . document : Fine - Grained Head Pose Estimation Without Keypoints Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation , modeling attention , fitting 3D models to video and performing face alignment . The mean of each Gaussian is taken to be the location of the keypoint in the nearest neighbor in the training set found using cosine similarity on pool 5 features , and we use a fixed standard deviation of 22 pixels . Let be the output score of our local detector for keypoint , and let be the prior score . We combine these to yield a final score , where is a tradeoff parameter . The perception of reachability ( i.e. , whether an object is within reach ) relies on body representations and action simulation . Similarly , egocentric distance estimation ( i.e. , the perception of the distance an object is from the self ) is thought to be partly derived from embodied action simulation . Although motor simulation is important for both , it is unclear whether the cognitive processes underlying these behaviors rely on the same motor processes . We show that since interactions between individuals often induce a well-ordered series of back-and-forth responses , it is possible to learn a temporal model of the interlinked poses even though one party is largely out of view . We demonstrate our idea on a variety of domains with dyadic interaction and show the substantial impact on egocentric body pose estimation , which improves the state of the art . The direct comparison between the different spatial representations revealed a bilateral fronto-parietal network , mainly right sided , that was more involved in the egocentric categorical representations . A right fronto-parietal circuitry was specialized for egocentric coordinate representations . A bilateral occipital network was more involved in the allocentric categorical representations . We present a simple model that can accurately capture human performance by considering relative ( allocentric ) spatial information as an independent localization estimate which degrades with distance and is optimally integrated with egocentric spatial information . Critically , allocentric information was encoded without cost to egocentric estimation , demonstrating independent storage of the two sources of information . Finally , when egocentric and allocentric estimates were put in conflict , the model successfully predicted the resulting localization errors . The purpose of this study was to examine the effect of navigation mode ( passive versus active ) on the virtual/real transfer of spatial learning , according to viewpoint displacement ( ground : 1 m 75 versus aerial : 4 m ) and as a function of the recall tasks used . We hypothesize that active navigation during learning can enhance performances when route strategy is favored by egocentric match between learning ( ground-level viewpoint ) and recall ( egocentric frame-based tasks ) . Sixty-four subjects ( 32 men and 32 women ) participated in the experiment . The vestibular signal plays a significant role in sensing changes in head orientation during rotations and in determining the magnitude of the rotations , but has only minor contributions in updating the internal representation of object positions with respect to the body after body rotations . The small contribution of the vestibular signal in egocentric object localization was evidenced in experiments in which the subjects reported the remembered position of eccentric earth-fixed targets after passive body rotations . The experiment reported here tested whether motor systems , such as the oculomotor system , make use of vestibular signals to generate accurate goal-directed motor responses toward a target whose position needs to be updated with respect to the body during and after whole-body rotations . 10 video sequences are shot for each object under different illuminations and backgrounds . We use this dataset and a SIFT-based recognition system to analyze and quantitatively characterize the main challenges in egocentric object recognition , such as motion blur and hand occlusion , along with its unique constraints , such as hand color , location prior and temporal consistency . SIFT-based recognition has an average recognition rate of 12 % , and reaches 20 % through enforcing temporal consistency . Beyond the obvious applications , including general online conversations and gaming , we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity . One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously . This functionality enables the instructor to effectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner . Satisfactory mapping is fundamental to human intentional action with mobile maps in the sense that its solution presumed by and underlies almost all map-related tasks : finding a location , recognizing and identifying a location , investigating a location , exploring an area , understanding a spatial relationship between locations , finding a route between locations etc . Furthermore , it underlies egocentric spatial orientation and updating , “ the mechanisms involved in locating positions in space relative to oneself after a given spatial transformation ” , and can thus be regarded as cognitively primary to tasks in location-based services . Previous research Previous research has been very technologyor , say , maporiented . The authors describe the different steps of 3-D conformal external irradiation . They mention as well two procedures of isocentric repositioning which are mandatory for the matching of anatomical data : the first one is acquired before the simulation and the second one just before the radiotherapy session . Moreover , humans also share skeleton similarities with some animals ( especially four-footed mammals ) . Therefore , the easily available human pose dataset , which is of a much larger scale than our labeled animal dataset , provides important prior knowledge to boost up the performance on animal pose estimation . Experiments show that our proposed method leverages these pieces of prior knowledge well and achieves convincing results on animal pose estimation . While a number of algorithms for automated analysis of open field experiments has been presented , most of these do not utilize deep learning methods . Therefore , we compare the performance of different deep learning approaches to perform animal localization in open field studies . As our key methodological contribution , we present a novel softargmax-based loss function that can be applied to fully convolutional networks such as the U-Net to allow direct landmark regression from fully convolutional architectures . However , the large size of equipment and wires severely limit its practical applications . NEW METHODS This study presents a wearable system with bidirectional brain-computer interface based on Wi-Fi technology , which can be used for olfactory electrophysiological recording and animal motion control . RESULTS On the " brain-to-computer " side , the results show that the wireless system can record high-quality olfactory electrophysiological signals for over a month . COMPARISON WITH EXISTING METHODS Most existing wireless BCI systems are designed for either recording or stimulating while our system is a bidirectional BCI featured with both functions . Taking advantage of our years of experience in olfactory decoding , we developed the first wireless system for olfactory electrophysiological recording and animal motion control . It provides high-quality recording and efficient motion control for a long time . Special attention is paid to the limitation of the method taking into account the possibility of its utilizing in laboratory conditions . The proposed algorithm of video analysis has three looped stages : animal location tracking , detecting specific points in the region of interest , and estimation of temperature in these points . The presented method for measuring temperature of the biological object has the following advantage : it minimizes influence on the object of the interest , and thus allows clearer understanding of animal reaction to medication or treatment . It involves several applications such as determination of specific parameters ( attitude , acceleration , and position ) via a new generation of Mechatronic systems . The aim of this paper concerns the animal motion estimation problem using low-cost sensors fusion . A quaternion-based nonlinear observer for the tracking of rigid body attitude ( orientation ) and heading using measurements provided from low cost inertial and magnetic sensors is presented . Many approaches have been developed to judge the inspection effectiveness such as comparing the inspection results to organizationpsilas historical data , establishing an organizational baseline for defect density , or estimating the number of remaining defects . Towards that end , capture-recapture techniques , originally proposed by biologists for animal population estimation , could be used to estimate the number of remaining defects in a software artifact . This paper reviews the different concepts for defect prediction techniques , provides an elaboration for basic concepts of capture-recapture models with their estimators , its applicability to software defect prediction , state of the art of research for this technique in software inspection and an overall evaluation for capture-recapture technique with its challenges . This talk explains some of the concepts and practical issues associated with model‐based tracking . It uses simulations and real datasets to investigate and demonstrate the effects of homogeneous sound speed profile assumptions on animal position estimates . [ Work supported by ONR . ] The main system components are : A Seifert Isovolt Titan 225kV X‐ray tube with focal spot sizes of 0.4 and 3 mm , which is mounted on a custom made “ gantry ” and has a special collimating assembly that allows field sizes down to 0.5 mm diameter at the 34 cm isocenter . A second main component is a computer‐controlled robotic arm ( Adept Viper S650 ) for animal positioning . The six degrees of freedom ( 6DOF ) robot can be programmed to move with an accuracy ±0.020 mm in XYZ directions . Deep neural networks are highly effective tools for human and animal pose estimation . However , robustness to out-of-domain data remains a challenge .
The goal is to develop a method that can exploit all possible views provided by a 3D model a single 3D model represents infinitely many 2D views of the same object . I have developed a method that uses the 3D geometry of an object for pose estimation . The method can then also learn additional real-world statistics , such as which poses appear more frequently , which area is more likely to contain an object , and which parts are commonly occluded and discriminative .
We present an efficient method for geolocalization in urban environments starting from a coarse estimate of the location provided by a GPS and using a simple untextured 2.5D model of the surrounding buildings . Our key contribution is a novel efficient and robust method to optimize the pose : We train a Deep Network to predict the best direction to improve a pose estimate , given a semantic segmentation of the input image and a rendering of the buildings from this estimate . We then iteratively apply this CNN until converging to a good pose .
By making use of a simulated industrial robot system performing various pick and place tasks , we show the effect of model building when making use of visual knowledge in terms of visually extracted pose hypotheses as well as action knowledge in terms of pose hypotheses verified by action execution . We show that the use of action knowledge significantly improves the pose estimation process .
The proposed Visual SLAM method includes newly designed feature extraction , matching , localization and mapping modules , which jointly use object features and point features to estimate camera 6-DOF poses and do richer map construction . A group of organized raster points is used to represent objects during feature matching and pose estimation process in the proposed Visual SLAM pipeline . Owing to the object feature fusion in the co-visibility graph it could conduct scale aware bundle adjustments to reduce accumulated error .
The main contribution of this work stands in deriving statistical information from the Image Processing step , by associating a covariance matrix to the heatmaps returned by the CNN for each detected feature . This information is included in the CEPPnP to improve the accuracy of the pose estimation step during filter initialization . The derived measurement covariance matrix is used in a tightly-coupled EKF to allow an enhanced representation of the measurements error from the feature detection step .
Traditional implementations of image-based servoing have relied on partial pose estimation , negating much of the advantage gained from using this method . This paper will present an alternative implementation of image-based servoing for approaching objects on the ground plane , which disposes of the pose estimation step and fully relies only on image features . To evaluate the suitability of both visual servoing methods to F-180 , the task of docking with the ball is used as a basis of the investigation .
In this paper , we present an object detection and pose estimation algorithm that runs on low-cost and limited-processing hardware . The algorithm relies on two main steps : in the detection phase , it employs a Convolutional Neural Network ( CNN ) – namely MobileNet SSD – to recognize and track objects of interest in the scene ; while in the pose estimation phase , the algorithm uses stereo correspondences to 3D reconstruct the spatial coordinates of multiple ORB features in the bounding box of the recognized object . The final position of the object in space is computed using a weighted average of the same spatial coordinates of the stereo-corresponded key points , where the weights are exponentially proportional to the level of ORB stereo matching .
The first one is CNN-based image retrieval phase , CNN features extracted by pre-trained deep convolutional neural networks ( DCNNs ) from images are utilized to compare the similarity , and the output of this part are the matched images of the target image . The second one is pose estimation phase that computes accurate localization result . Owing to the robust CNN feature extractor , our scheme is applicable to complex indoor environments and easily transplanted to outdoor environments .
We propose a novel framework which integrates human hand detection and pose estimation into one single pipeline . Unlike most of previous works which only focus on the pose estimation part subject to some strong assumptions or relying on a weak detector to detect human hands , we employ a deep learning architecture to complete both aforementioned tasks . By letting three different neural networks share the convolutional layers , this deeply learning architecture can efficiently and accurately detect human hands and compute their hand pose configuration .
In this work , we address the problem of visual navigation for multi-modal stereo setups . More precisely , we focus on finding correspondences between visible and long-wave infrared ( thermal ) images for pose estimation purposes . We present a new visual odometry method for air vehicles using mutual information and phase congruency .
Only complicated low-level vision modules like depth from stereo or depth from shading can recover some of the surface geometry of the scene . Recent advances in fast range imaging have however paved the way towards 3D computer vision , since range data of the scene can now be obtained with sufficient accuracy and speed for object recognition and pose estimation purposes . This article proposes the coded-light range-imaging method together with superquadric segmentation to approach this task .
This paper presents an original approach for autonomous navigation based on RGB-D data and known 3D markers , where the basic concept is to detect and recognize the markers and then to use them for a straightforward pose estimation solution . The developed algorithms can allow a quadrotor to autonomously fly in ( cooperative ) GPS denied environments and/or when there is no natural or artificial illumination of the scene , by following a predetermined path consisting of successive targets having a well defined shape and/or color .
Comparison is provided against both classical deformable models and state-of-the-art techniques in the complex problem domain of segmenting aortic root structure from computerized tomography scans . The proposed method shows improvement in both pose parameter estimation and segmentation performance .
To tackle these challenges , existing multi - person pose estimation approaches usually perform joint detection and partition separately , mainly following two different strategies . The top - down strategy first detects persons and then performs pose estimation for each single person individually . The bottom - up strategy , in contrast , generates all joint candidates at first , and then tries to partition them to corresponding person instances .
Modeling objects via their axes of symmetry , and in particular , using skeletons , has a long history in computer vision . Skeletonization algorithms provide a concise and effective representation of deformable objects , while supporting many applications , including object recognition and retrieval , pose estimation , hand gesture recognition , shape matching , scene text detection , and road detection in aerial scenes . Early algorithms for computing skeletons directly from images yield a gradient intensity map , driven by geometric constraints between skeletal pixels and edge fragments .
First , if sufficient keypoints fail to be detected , then pose recovery is impossible . Second , the accuracy of the pose estimate depends upon the quality of the 3D head model . Generic head models can introduce errors for any given participant , and the process of deforming the head model to adapt to each participant requires significant amounts of data and can be computationally expensive .
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 .
As shown in Figure 7 and Table 3 , our method outperforms all existing methods on the three 3D hand pose estimation datasets in standard evaluation metrics . This shows the superiority of voxel - to - voxel prediction , which is firstly used in 3D hand pose estimation .
[ reference ] ) consists of a shared backbone for feature extraction , detection subnets for keypoint and person detection / segmentation , and a final network which carries out the pose estimation , i.e. assigning detected keypoints to person instances . Our major contribution lies in the pose estimation step where the network implements a novel assignment method . This network receives keypoint and person detections , and produces a pose for each detected person by assigning keypoints to person boxes using a learned function .
Early methods ( prior to deep learning ) used hand - crafted HOG features to detect body parts and probabilistic graphical models to represent the pose structure ( tree - based ; non - tree based ) . Deep neural networks based models have quickly dominated the pose estimation problem after the initial work by Toshev et al . who used the AlexNet architecture to directly regress spatial joint coordinates . Tompson et al . learned pose structure by combining deep features along with graphical models .
The detection of keypoints is very important for several computer applications . There are several ways for keypoint detection and pattern recognition . Earliest work proposes a trainable filter called COSFIRE filter which is a combination of bank of log gabor filters .
The more computationally intensive stage of keypoint detection is implemented on the FPGA . In this paper , we present novel , efficient , scalable architectures for the keypoint detection step . The architectures are based on parallelizable and pipelined computational flow .
Then , we present very SIFT-specific attacks . The attacks are performed either during the keypoint detection step or during the keypoint description step . We also present a security-oriented Picture in Picture attack deluding CBIRS using post filtering geometric verifications .
In image processing , the detection of keypoint plays a significant role in foundation work of many image applications . Most widely distributed features in images are corners .
Inspired by their robustness and generality , we propose a novel CNN based method to accurately localize documents in real-time . We model localization problem as a key point detection problem . The four corners of the documents are jointly predicted by a Deep Convolutional Neural Network .
The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor . TPBRIEF directly enables keypoint detecting and feature describing on original distorted images , whereas other approaches correct the distortion through image resampling , which introduces artifacts and adds time cost . With ALPGMS , omnidirectional images are divided into circular arches named adapted log polar grids .
And the SIFT(scale invariant feature transform)[1 ] algorithm with high robustness and distinctive feature description[2,3 ] becomes the most effective one of all the methods . However , the keypoint detection operation in standard SIFT algorithm needs to be performed in every layer of DOG ( difference of Gaussian ) pyramid , so the workload of a large number of the obtained keypoints limits the application in real-time system [ 4,5 ] . Considering the aforementioned limitation , recent research focuses on improving the performance of SIFT , such as SURF[6 ] and PCA-SIFT[7 ] .
CornerNet - Squeeze is suitable for real - time detection , improving both the efficiency and accuracy of the popular real - time detector YOLOv3 ( 34.4 % AP at 34ms for CornerNet - Squeeze compared to 33.0 % AP at 39ms for YOLOv3 on COCO ) . Together these contributions for the first time reveal the potential of keypoint - based detection to be useful for applications requiring processing efficiency . ⌊⌋ section : Introduction Keypoint - based object detection is a class of methods that generate object bounding boxes by detecting and grouping their keypoints . CornerNet , the state - of - the - art among them , detects and groups the top - left and bottom - right corners of bounding boxes ; it uses a stacked hourglass network to predict the heatmaps of the corners and then uses associate embeddings to group them .
Our design and results show that this is possible for the first time , particularly in the context of object detection . Contributions Our contributions are three - fold : ( 1 ) We propose CornerNet - Saccade and CornerNet - Squeeze , two novel approaches to improving the efficiency of keypoint - based object detection ; ( 2 ) On COCO , we improve the efficiency of state - of - the - art keypoint based detection by 6 fold and the AP from 42.2 % to 43.2 % , ( 3 ) On COCO , we improve both the accuracy and efficiency of state - of - the art real - time object detection ( to 34.4 % at 30ms from 33.0 % at 39ms of YOLOv3 ) .
Finally , RoIAlign shows a gain of 1.5 mask AP and 0.5 box AP when used with FPN , which has finer multi - level strides . For keypoint detection that requires finer alignment , RoIAlign shows large gains even with FPN ( Table 6 ) . Mask Branch :
Implementation Details : We make minor modifications to the segmentation system when adapting it for keypoints . For each of the K keypoints of an instance , the training target is a one - hot m × m binary mask where only a single pixel is labeled as foreground .
The first three winning teams for the instance segmentation task were all reportedly based on an extension of the Mask R - CNN framework . section : Keypoint Detection We report enhanced results of keypoint detection in Table 9 .
a novel bottom - up multi - person pose estimation architecture that combines a multi - task model with a novel assignment method . MultiPoseNet can jointly handle person detection , keypoint detection , person segmentation and pose estimation problems . The novel assignment method is implemented by the Pose Residual Network ( PRN ) which receives keypoint and person detections , and produces accurate poses by assigning keypoints to person instances .
Our model ( Fig . [ reference ] ) consists of a shared backbone for feature extraction , detection subnets for keypoint and person detection / segmentation , and a final network which carries out the pose estimation , i.e. assigning detected keypoints to person instances . Our major contribution lies in the pose estimation step where the network implements a novel assignment method .
Gkioxari et al . proposed k - poselets to jointly detect people and keypoints . Most of the recent approaches use Convolutional Neural Networks ( CNNs ) to detect body parts and relationships between them in an end - to - end manner , then use assignment algorithms to form individual skeletons .
document : Fine - Grained Head Pose Estimation Without Keypoints Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation , modeling attention , fitting 3D models to video and performing face alignment .
The mean of each Gaussian is taken to be the location of the keypoint in the nearest neighbor in the training set found using cosine similarity on pool 5 features , and we use a fixed standard deviation of 22 pixels . Let be the output score of our local detector for keypoint , and let be the prior score . We combine these to yield a final score , where is a tradeoff parameter .
The perception of reachability ( i.e. , whether an object is within reach ) relies on body representations and action simulation . Similarly , egocentric distance estimation ( i.e. , the perception of the distance an object is from the self ) is thought to be partly derived from embodied action simulation . Although motor simulation is important for both , it is unclear whether the cognitive processes underlying these behaviors rely on the same motor processes .
We show that since interactions between individuals often induce a well-ordered series of back-and-forth responses , it is possible to learn a temporal model of the interlinked poses even though one party is largely out of view . We demonstrate our idea on a variety of domains with dyadic interaction and show the substantial impact on egocentric body pose estimation , which improves the state of the art .
The direct comparison between the different spatial representations revealed a bilateral fronto-parietal network , mainly right sided , that was more involved in the egocentric categorical representations . A right fronto-parietal circuitry was specialized for egocentric coordinate representations . A bilateral occipital network was more involved in the allocentric categorical representations .
We present a simple model that can accurately capture human performance by considering relative ( allocentric ) spatial information as an independent localization estimate which degrades with distance and is optimally integrated with egocentric spatial information . Critically , allocentric information was encoded without cost to egocentric estimation , demonstrating independent storage of the two sources of information . Finally , when egocentric and allocentric estimates were put in conflict , the model successfully predicted the resulting localization errors .
The purpose of this study was to examine the effect of navigation mode ( passive versus active ) on the virtual/real transfer of spatial learning , according to viewpoint displacement ( ground : 1 m 75 versus aerial : 4 m ) and as a function of the recall tasks used . We hypothesize that active navigation during learning can enhance performances when route strategy is favored by egocentric match between learning ( ground-level viewpoint ) and recall ( egocentric frame-based tasks ) . Sixty-four subjects ( 32 men and 32 women ) participated in the experiment .
The vestibular signal plays a significant role in sensing changes in head orientation during rotations and in determining the magnitude of the rotations , but has only minor contributions in updating the internal representation of object positions with respect to the body after body rotations . The small contribution of the vestibular signal in egocentric object localization was evidenced in experiments in which the subjects reported the remembered position of eccentric earth-fixed targets after passive body rotations . The experiment reported here tested whether motor systems , such as the oculomotor system , make use of vestibular signals to generate accurate goal-directed motor responses toward a target whose position needs to be updated with respect to the body during and after whole-body rotations .
10 video sequences are shot for each object under different illuminations and backgrounds . We use this dataset and a SIFT-based recognition system to analyze and quantitatively characterize the main challenges in egocentric object recognition , such as motion blur and hand occlusion , along with its unique constraints , such as hand color , location prior and temporal consistency . SIFT-based recognition has an average recognition rate of 12 % , and reaches 20 % through enforcing temporal consistency .
Beyond the obvious applications , including general online conversations and gaming , we hypothesize that our proposed system would be particularly beneficial to education by offering rich visual contents and interactivity . One distinct feature is the integration of egocentric pose recognition that allows participants to use their gestures to demonstrate and manipulate virtual objects simultaneously . This functionality enables the instructor to effectively and efficiently explain and illustrate complex concepts or sophisticated problems in an intuitive manner .
Satisfactory mapping is fundamental to human intentional action with mobile maps in the sense that its solution presumed by and underlies almost all map-related tasks : finding a location , recognizing and identifying a location , investigating a location , exploring an area , understanding a spatial relationship between locations , finding a route between locations etc . Furthermore , it underlies egocentric spatial orientation and updating , “ the mechanisms involved in locating positions in space relative to oneself after a given spatial transformation ” , and can thus be regarded as cognitively primary to tasks in location-based services . Previous research Previous research has been very technologyor , say , maporiented .
The authors describe the different steps of 3-D conformal external irradiation . They mention as well two procedures of isocentric repositioning which are mandatory for the matching of anatomical data : the first one is acquired before the simulation and the second one just before the radiotherapy session .
Moreover , humans also share skeleton similarities with some animals ( especially four-footed mammals ) . Therefore , the easily available human pose dataset , which is of a much larger scale than our labeled animal dataset , provides important prior knowledge to boost up the performance on animal pose estimation . Experiments show that our proposed method leverages these pieces of prior knowledge well and achieves convincing results on animal pose estimation .
While a number of algorithms for automated analysis of open field experiments has been presented , most of these do not utilize deep learning methods . Therefore , we compare the performance of different deep learning approaches to perform animal localization in open field studies . As our key methodological contribution , we present a novel softargmax-based loss function that can be applied to fully convolutional networks such as the U-Net to allow direct landmark regression from fully convolutional architectures .
However , the large size of equipment and wires severely limit its practical applications . NEW METHODS This study presents a wearable system with bidirectional brain-computer interface based on Wi-Fi technology , which can be used for olfactory electrophysiological recording and animal motion control . RESULTS On the " brain-to-computer " side , the results show that the wireless system can record high-quality olfactory electrophysiological signals for over a month .
COMPARISON WITH EXISTING METHODS Most existing wireless BCI systems are designed for either recording or stimulating while our system is a bidirectional BCI featured with both functions . Taking advantage of our years of experience in olfactory decoding , we developed the first wireless system for olfactory electrophysiological recording and animal motion control . It provides high-quality recording and efficient motion control for a long time .
Special attention is paid to the limitation of the method taking into account the possibility of its utilizing in laboratory conditions . The proposed algorithm of video analysis has three looped stages : animal location tracking , detecting specific points in the region of interest , and estimation of temperature in these points . The presented method for measuring temperature of the biological object has the following advantage : it minimizes influence on the object of the interest , and thus allows clearer understanding of animal reaction to medication or treatment .
It involves several applications such as determination of specific parameters ( attitude , acceleration , and position ) via a new generation of Mechatronic systems . The aim of this paper concerns the animal motion estimation problem using low-cost sensors fusion . A quaternion-based nonlinear observer for the tracking of rigid body attitude ( orientation ) and heading using measurements provided from low cost inertial and magnetic sensors is presented .
Many approaches have been developed to judge the inspection effectiveness such as comparing the inspection results to organizationpsilas historical data , establishing an organizational baseline for defect density , or estimating the number of remaining defects . Towards that end , capture-recapture techniques , originally proposed by biologists for animal population estimation , could be used to estimate the number of remaining defects in a software artifact . This paper reviews the different concepts for defect prediction techniques , provides an elaboration for basic concepts of capture-recapture models with their estimators , its applicability to software defect prediction , state of the art of research for this technique in software inspection and an overall evaluation for capture-recapture technique with its challenges .
This talk explains some of the concepts and practical issues associated with model‐based tracking . It uses simulations and real datasets to investigate and demonstrate the effects of homogeneous sound speed profile assumptions on animal position estimates . [ Work supported by ONR . ]
The main system components are : A Seifert Isovolt Titan 225kV X‐ray tube with focal spot sizes of 0.4 and 3 mm , which is mounted on a custom made “ gantry ” and has a special collimating assembly that allows field sizes down to 0.5 mm diameter at the 34 cm isocenter . A second main component is a computer‐controlled robotic arm ( Adept Viper S650 ) for animal positioning . The six degrees of freedom ( 6DOF ) robot can be programmed to move with an accuracy ±0.020 mm in XYZ directions .
Deep neural networks are highly effective tools for human and animal pose estimation . However , robustness to out-of-domain data remains a challenge .
Toward visual understanding of everyday object Learning to Align Semantic Segmentation and 2.5D Maps for Geolocalization Using Online Modelled Spatial Constraints for Pose Estimation in an Industrial Setting A Novel Texture-Less Object Oriented Visual SLAM System CNN-Based Pose Estimation System for Close-Proximity Operations Around Uncooperative Spacecraft On-Board Vision Using Visual-Servoing for RoboCup F-180 League Mobile Robots Object Detection and Pose Estimation Using CNN in Embedded Hardware for Assistive Technology Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free Deep learning for integrated hand detection and pose estimation Multi-spectral visual odometry for unmanned air vehicles Volumetric segmentation of range images for printed circuit board inspection RGB-D camera-based quadrotor navigation in GPS-denied and low light environments using known 3D markers Graph convolutional neural network for multi-scale feature learning Generative Partition Networks for Multi-Person Pose Estimation DeepFlux for Skeletons in the Wild Fine-Grained Head Pose Estimation Without Keypoints BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network Combination filter for keypoint detection and pattern recognition in traffic visual surveillance Embedded hardware architectures for scale and rotation invariant feature detection Security analysis of image copy detection systems based on SIFT descriptors A novel method of corner detector for SAR images based on Bilateral Filter Real-Time Document Localization in Natural Images by Recursive Application of a CNN Robust and efficient method for matching features in omnidirectional images Capturing and tracking of building area based on structure saliency in airborne remote sensing video CornerNet-Lite: Efficient Keypoint Based Object Detection Mask R-CNN MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network Fine-Grained Head Pose Estimation Without Keypoints Do Convnets Learn Correspondence? Evidence for the embodiment of space perception: concurrent hand but not arm action moderates reachability and egocentric distance perception You2Me: Inferring Body Pose in Egocentric Video via First and Second Person Interactions Neural correlates of egocentric and allocentric frames of reference combined with metric and non-metric spatial relations Independent working memory resources for egocentric and allocentric spatial information Virtual/Real Transfer in a Large-Scale Environment: Impact of Active Navigation as a Function of the Viewpoint Displacement Effect and Recall Tasks Visual object localization through vestibular and neck inputs. 2: Updating off-mid-sagittal-plane target positions. Egocentric recognition of handled objects: Benchmark and analysis An Immersive Telepresence System Using RGB-D Sensors and Head Mounted Display Understanding users’ strategies with mobile maps [3-D conformal radiotherapy procedure and registration of pre- and intra-radiotherapeutic data]. Cross-Domain Adaptation for Animal Pose Estimation Robust Open Field Rodent Tracking Using a Fully Convolutional Network and a Softargmax Distance Loss A wearable system for olfactory electrophysiological recording and animal motion control Automated long-term contactless temperature monitoring in animals via a thermographic camera A rigid body attitude estimation for Bio-logging application: A quaternion-based nonlinear filter approach Capture-recapture techniques in software verification Using model‐based tracking methods to reduce position uncertainty. SU‐GG‐J‐170: Small Animal Conformal Radiation Therapy Device Pretraining boosts out-of-domain robustness for pose estimation
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CVPR RoboCup SSCI ICPR SMC ArXiv 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2017 IEEE International Conference on Computer Vision (ICCV) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ECCV IGARSS ICDAR ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence ECCV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) NIPS CVPR Workshops ISM Bildverarbeitung f&#252;r die Medizin EMBC IROS
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