Haralambos Sahinoglou

Bio: Haralambos Sahinoglou is an academic researcher from Aristotle University of Thessaloniki. The author has contributed to research in topics: Motion estimation. The author has an hindex of 1, co-authored 2 publications receiving 84 citations.

Evaluation of multiresolution block matching techniques for motion and disparity estimation

Abstract: Multiresolution block matching methods for both monocular and stereoscopic image sequence coding are evaluated. These methods are seen to drastically reduce the amount of processing needed for block correspondence without seriously affecting the quality of the reconstructed images. The evaluation criteria are the prediction error and the speed of the algorithm for motion, disparity, and fused motion and disparity estimation, in comparison with the full search (exhaustive) method. A new method is also proposed based in multiresolution techniques, for efficient coding of the disparity or the displacement vector field.

Multiple-candidate hierachical block matching with inherent smoothness properties

Abstract: In this paper, we present a multiple candidate hierarchical block matching (MCHBM) estimation approach of the apparent motion vector field (MVF) in image sequences. In contrast to the standard hierarchical block matching (HBM) approach, which considers only the best solution at each level (single candidate case), MCHBM considers the H best candidate solution vectors and the associated matching error at that level. Then, the H selected candidate solution vectors are projected to the next higher resolution level in order to serve as initial estimates for the search process at that level, which improves all H estimates. Thus, at the highest resolution level, the final vector is selected by taking into account the global suitability of the vector and not just the local error. The resulting MVF approximates the true motion by avoiding local minima which lead to solutions that differ from the true MVF. The multiple candidate approach is considered in combination with the overlapped multi-grid and multi-resolution HBM estimation methods. The final algorithm has very good smoothness properties regardless of the application of any additional magnitude and/or smoothness constraints. Experimental results on video-conference image sequences demonstrate the improved performance of the proposed methods.

Optical flow modeling and computation

Abstract: We propose a survey of optical flow estimation focusing on recent developments.We adopt a classification approach organizing methods in a comprehensive framework.The paper is conceived as a tutorial introducing and explaining the main concepts. Optical flow estimation is one of the oldest and still most active research domains in computer vision. In 35years, many methodological concepts have been introduced and have progressively improved performances, while opening the way to new challenges. In the last decade, the growing interest in evaluation benchmarks has stimulated a great amount of work. In this paper, we propose a survey of optical flow estimation classifying the main principles elaborated during this evolution, with a particular concern given to recent developments. It is conceived as a tutorial organizing in a comprehensive framework current approaches and practices. We give insights on the motivations, interests and limitations of modeling and optimization techniques, and we highlight similarities between methods to allow for a clear understanding of their behavior.

Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results

Abstract: Block matching motion estimation is the heart of video coding systems. During the last two decades, hundreds of fast algorithms and VLSI architectures have been proposed. In this paper, we try to provide an extensive exploration of motion estimation with our new developments. The main concepts of fast algorithms can be classified into six categories: reduction in search positions, simplification of matching criterion, bitwidth reduction, predictive search, hierarchical search, and fast full search. Comparisons of various algorithms in terms of video quality and computational complexity are given as useful guidelines for software applications. As for hardware implementations, full search architectures derived from systolic mapping are first introduced. The systolic arrays can be divided into inter-type and intra-type with 1-D, 2-D, and tree structures. Hexagonal plots are presented for system designers to clearly evaluate the architectures in six aspects including gate count, required frequency, hard-ware utilization, memory bandwidth, memory bitwidth, and latency. Next, architectures supporting fast algorithms are also reviewed. Finally, we propose our algorithmic and architectural co-development. The main idea is quick checking of the entire search range with simplified matching criterion to globally eliminate impossible candidates, followed by finer selection among potential best matched candidates. The operations of the two stages are mapped to the same hardware for resource sharing. Simulation results show that our design is ten times more area-speed efficient than full search architectures while the video quality is competitively the same.

Object-based coding of stereo image sequences using joint 3-D motion/disparity compensation

Abstract: An object-based coding scheme is proposed for the coding of a stereoscopic image sequence using motion and disparity information. A hierarchical block-based motion estimation approach is used for initialization, while disparity estimation is performed using a pixel-based hierarchical dynamic programming algorithm. A split-and-merge segmentation procedure based on three-dimensional (3-D) motion modeling is then used to determine regions with similar motion parameters. The segmentation part of the algorithm is interleaved with the estimation part in order to optimize the coding performance of the procedure. Furthermore, a technique is examined for propagating the segmentation information with time. A 3-D motion-compensated prediction technique is used for both intensity and depth image sequence coding. Error images and depth maps are encoded using discrete cosine transform (DCT) and Huffman methods. Alternately, an efficient wireframe depth modeling technique may be used to convey depth information to the receiver. Motion and wireframe model parameters are then quantized and transmitted to the decoder along with the segmentation information. As a straightforward application, the use of the depth map information for the generation of intermediate views at the receiver is also discussed. The performance of the proposed compression methods is evaluated experimentally and is compared to other stereoscopic image sequence coding schemes.

Disparity and occlusion estimation in multiocular systems and their coding for the communication of multiview image sequences

Abstract: An efficient disparity estimation and occlusion detection algorithm for multiocular systems is presented. A dynamic programming algorithm, using a multiview matching cost as well as pure geometrical constraints, is used to estimate disparity and to identify the occluded areas in the extreme left and right views. A significant advantage of the proposed approach is that the exact number of views in which each point appears (is not occluded) can be determined. The disparity and occlusion information obtained may then be used to create virtual images from intermediate viewpoints. Furthermore, techniques are developed for the coding of occlusion and disparity information, which is needed at the receiver for the reproduction of a multiview sequence using the two encoded extreme views. Experimental results illustrate the performance of the proposed techniques.

Video object segmentation using Bayes-based temporal tracking and trajectory-based region merging

Abstract: A novel unsupervised video object segmentation algorithm is presented, aiming to segment a video sequence to objects: spatiotemporal regions representing a meaningful part of the sequence. The proposed algorithm consists of three stages: initial segmentation of the first frame using color, motion, and position information, based on a variant of the K-means-with-connectivity-constraint algorithm; a temporal tracking algorithm, using a Bayes classifier and rule-based processing to reassign changed pixels to existing regions and to efficiently handle the introduction of new regions; and a trajectory-based region merging procedure that employs the long-term trajectory of regions, rather than the motion at the frame level, so as to group them to objects with different motion. As shown by experimental evaluation, this scheme can efficiently segment video sequences with fast moving or newly appearing objects. A comparison with other methods shows segmentation results corresponding more accurately to the real objects appearing on the image sequence.