The Experts below are selected from a list of 204 Experts worldwide ranked by ideXlab platform

Gene Cheung - One of the best experts on this subject based on the ideXlab platform.

  • Arbitrarily Shaped Motion Prediction for Depth Video Compression using Arithmetic Edge Coding
    2016
    Co-Authors: Ismael Daribo Member, Dinei Florencio, Senior Member, Gene Cheung
    Abstract:

    Abstract—Depth image compression is important for com-pact representation of 3D visual data in “texture-plus-depth” format, where texture and depth maps from one or more viewpoints are encoded and transmitted. A decoder can then synthesize a freely chosen virtual view via depth-image-based rendering (DIBR) using nearby coded texture and depth maps as reference. Further, depth information can be used in other image processing applications beyond view synthesis, such as Object identification, segmentation, etc. In this paper, we leverage on the observation that “neighboring pixels of similar depth have similar motion ” to efficiently encode depth video. Specifically, we divide a depth block containing two zones of distinct values (e.g., foreground and background) into two arbitrarily shaped regions (sub-blocks) along the dividing boundary before performing sep-arate motion prediction (MP). While such arbitrarily shaped sub-block MP can lead to very small prediction residuals (resulting in few bits required for residual coding), it incurs an overhead to transmit the dividing boundaries for sub-block identification at decoder. To minimize this overhead, we first devise a scheme called arithmetic edge coding (AEC) to efficiently code boundaries that divide blocks into sub-blocks. Specifically, we propose to incorporate the boundary geometrical correlation in an adaptive arithmetic coder in the form of a statistical model. Then, we propose two optimiza-tion procedures to further improve the edge coding perfor-mance of AEC for a given depth image. The first procedure operates within a code block, and allows lossy compression of the detected block boundary to lower the cost of AEC, with an option to augment boundary depth pixel values matching the new boundary, given the augmented pixels do not adversely affect synthesized view distortion. The second procedure operates across code blocks, and systematically identifies blocks along an Object Contour that should be coded using sub-block MP via a rate-distortion optimized trellis. Experimental results show an average overall bitrate reduction of up to 33 % over classical H.264/AVC. I

  • arbitrarily shaped motion prediction for depth video compression using arithmetic edge coding
    IEEE Transactions on Image Processing, 2014
    Co-Authors: Ismael Daribo, Dinei Florencio, Gene Cheung
    Abstract:

    Depth image compression is important for compact representation of 3D visual data in texture-plus-depth format, where texture and depth maps from one or more viewpoints are encoded and transmitted. A decoder can then synthesize a freely chosen virtual view via depth-image-based rendering using nearby coded texture and depth maps as reference. Further, depth information can be used in other image processing applications beyond view synthesis, such as Object identification, segmentation, and so on. In this paper, we leverage on the observation that neighboring pixels of similar depth have similar motion to efficiently encode depth video. Specifically, we divide a depth block containing two zones of distinct values (e.g., foreground and background) into two arbitrarily shaped regions (sub-blocks) along the dividing boundary before performing separate motion prediction (MP). While such arbitrarily shaped sub-block MP can lead to very small prediction residuals (resulting in few bits required for residual coding), it incurs an overhead to transmit the dividing boundaries for sub-block identification at decoder. To minimize this overhead, we first devise a scheme called arithmetic edge coding (AEC) to efficiently code boundaries that divide blocks into sub-blocks. Specifically, we propose to incorporate the boundary geometrical correlation in an adaptive arithmetic coder in the form of a statistical model. Then, we propose two optimization procedures to further improve the edge coding performance of AEC for a given depth image. The first procedure operates within a code block, and allows lossy compression of the detected block boundary to lower the cost of AEC, with an option to augment boundary depth pixel values matching the new boundary, given the augmented pixels do not adversely affect synthesized view distortion. The second procedure operates across code blocks, and systematically identifies blocks along an Object Contour that should be coded using sub-block MP via a rate-distortion optimized trellis. Experimental results show an average overall bitrate reduction of up to 33% over classical H.264/AVC.

Bulent Sankur - One of the best experts on this subject based on the ideXlab platform.

  • video Object tracking with feedback of performance measures
    IEEE Transactions on Circuits and Systems for Video Technology, 2003
    Co-Authors: Cigdem Eroglu Erdem, A M Tekalp, Bulent Sankur
    Abstract:

    Presents a scalable Object tracking framework, which is capable of tracking the Contour of nonrigid Objects in the presence of occlusion. The framework consists of open-loop boundary prediction and closed-loop boundary correction parts. The open-loop prediction block adaptively divides the Object Contour into subContours, and estimates the mapping parameters for each subsegment. The closed-loop boundary correction block employs a suitably weighted combination of low-level features such as color edge, color segmentation, motion models, and motion segmentation for each subContour. Performance evaluation measures are used in a feedback loop to evaluate the goodness of the segmentation/tracking in order to adjust the weights assigned to each of these low-level features for each subContour at each frame. The framework is scalable because it can be adapted to track a coarse estimate of the boundary of selected Objects in real-time, as well as pixel-accurate boundary tracking in off-line mode. The proposed method does not depend on any single motion or shape model, and does not need training. Experimental results demonstrate that the algorithm is able to track the Object boundaries under significant occlusion and background clutter.

Horst Bischof - One of the best experts on this subject based on the ideXlab platform.

  • robust planar target tracking and pose estimation from a single concavity
    International Symposium on Mixed and Augmented Reality, 2011
    Co-Authors: Michael Donoser, Peter Kontschieder, Horst Bischof
    Abstract:

    In this paper we introduce a novel real-time method to track weakly textured planar Objects and to simultaneously estimate their 3D pose. The basic idea is to adapt the classic tracking-by-detection approach, which seeks for the Object to be tracked independently in each frame, for tracking non-textured Objects. In order to robustly estimate the 3D pose of such Objects in each frame, we have to tackle three demanding problems. First, we need to find a stable representation of the Object which is discriminable against the background and highly repetitive. Second, we have to robustly relocate this representation in every frame, also during considerable viewpoint changes. Finally, we have to estimate the pose from a single, closed Object Contour. Of course, all demands shall be accommodated at low computational costs and in real-time. To attack the above mentioned problems, we propose to exploit the properties of Maximally Stable Extremal Regions (MSERs) for detecting the required Contours in an efficient manner and to apply random ferns as efficient and robust classifier for tracking. To estimate the 3D pose, we construct a perspectively invariant frame on the closed Contour which is intrinsically provided by the extracted MSER. In our experiments we obtain robust tracking results with accurate poses on various challenging image sequences at a single requirement: One MSER used for tracking has to have at least one concavity that sufficiently deviates from its convex hull.

Phengann Heng - One of the best experts on this subject based on the ideXlab platform.

  • two stage Object tracking method based on kernel and active Contour
    IEEE Transactions on Circuits and Systems for Video Technology, 2010
    Co-Authors: Qiang Chen, Phengann Heng
    Abstract:

    This letter presents a two-stage Object tracking method by combining a region-based method and a Contour-based method. First, a kernel-based method is adopted to locate the Object region. Then the diffusion snake is used to evolve the Object Contour in order to improve the tracking precision. In the first Object localization stage, the initial target position is predicted and evaluated by the Kalman filter and the Bhattacharyya coefficient, respectively. In the Contour evolution stage, the active Contour is evolved on the basis of an Object feature image generated with the color information in the initial Object region. In the process of the evolution, similarities of the target region are compared to ensure that the Object Contour evolves in the right way. The comparison between our method and the kernel-based method demonstrates that our method can effectively cope with the severe deformation of Object Contour, so the tracking precision of our method is higher.

Behn Carsten - One of the best experts on this subject based on the ideXlab platform.

  • Effects of multi-point contacts during Object Contour scanning using a biologically-inspired tactile sensor
    'MDPI AG', 2020
    Co-Authors: Merker Lukas, Fischer Calderon, Sebastian J., Scharff Moritz, Alencastre Miranda, Jorge Hernan, Behn Carsten
    Abstract:

    Vibrissae are an important tactile sense organ of many mammals, in particular rodents like rats and mice. For instance, these animals use them in order to detect different Object features, e.g., Object-distances and -shapes. In engineering, vibrissae have long been established as a natural paragon for developing tactile sensors. So far, having Object shape scanning and reconstruction in mind, almost all mechanical vibrissa models are restricted to contact scenarios with a single discrete contact force. Here, we deal with the effect of multi-point contacts in a specific scanning scenario, where an artificial vibrissa is swept along partly concave Object Contours. The vibrissa is modeled as a cylindrical, one-sided clamped Euler-Bernoulli bending rod undergoing large deflections. The elasticae and the support reactions during scanning are theoretically calculated and measured in experiments, using a spring steel wire, attached to a force/torque-sensor. The experiments validate the simulation results and show that the assumption of a quasi-static scanning displacement is a satisfying approach. Beyond single- and two-point contacts, a distinction is made between tip and tangential contacts. It is shown that, in theory, these contact phases can be identified solely based on the support reactions, what is new in literature. In this way, multipoint contacts are reliably detected and filtered in order to discard incorrectly reconstructed contact points

  • Object shape recognition and reconstruction using pivoted tactile sensors
    'Hindawi Limited', 2018
    Co-Authors: Merker Lukas, Will Christoph, Steigenberger Joachim, Behn Carsten
    Abstract:

    Many mammals use some special tactile hairs, the so-called mystacial macrovibrissae, to acquire information about their environment. In doing so, rats and mice, e.g., are able to detect Object distances, shapes, and surface textures. Inspired by the biological paradigm, we present a mechanical model for Object Contour scanning and shape reconstruction, considering a single vibrissa as a cylindrically shaped Euler-Bernoulli-bending rod, which is pivoted by a bearing. In doing so, we adapt our model for a rotational scanning movement, which is in contrast to many previous modeling approaches. Describing a single rotational quasi-static sweep of the vibrissa along a strict convex Contour function using nonlinear Euler-Bernoulli theory, we end up in a boundary-value problem with some unknown parameters. In a first step, we use shooting methods in an algorithm to repeatedly solve this boundary-value problem (changing the vibrissa base angle) and generate the support reactions during a sweep along an Object Contour. Afterwards, we use these support reactions to reconstruct the Object Contour solving an initial-value problem. Finally, we extend the scanning process adding a second sweep of the vibrissa in opposite direction in order to enlarge the reconstructable area of the profile

  • Object Contour sensing using artificial rotatable vibrissae
    2017
    Co-Authors: Merker Lukas, Will Christoph, Steigenberger Joachim, Behn Carsten
    Abstract:

    Recent research topics in bionics focus on the analysis and synthesis of mammal’s perception of their environment by means of their vibrissae. Using these complex tactile sense organs, rats and mice, for example, are capable of detecting the distance to an Object, its Contour and its surface texture. In this paper, we focus on developing and investigating a biologically inspired mechanical model for Object scanning and Contour reconstruction. A vibrissa – used for the transmission of a stimulus – is frequently modeled as a cylindrically shaped Euler-Bernoulli-bending rod, which is one-sided clamped and swept along an Object translationally. Due to the biological paradigm, the scanning process within the present paper is adapted for a rotational movement of the vibrissa. Firstly, we consider a single quasi-static sweep of the vibrissa along a strictly convex profile using nonlinear Euler-Bernoulli theory. The investigation leads to a general boundary-value problem with some unknown parameters, which have to be determined in using shooting methods. Then, it is possible to calculate the support reactions of the system. These support reactions together with the boundary conditions to the support, which all form quantities an animal solely relies on in nature, are used for the reconstruction of the Object Contour. Afterwards, the scanning process is extended by rotating the vibrissa in opposite direction in order to enlarge the reconstructable area of the profile