3-D motion estimation of rigid objects for video coding applications using an improved iterative version of the E-matrix method

Download

index.pdf

Date

1998-02-01

Author

Alatan, Abdullah Aydın

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

229
views

0
downloads

As an alternative to current two-dimensional (2-D) motion models, a robust three-dimensional (3-D) motion estimation method is proposed to be utilized in object-based video coding applications, Since the popular E-matrix method is well known for its susceptibility to input errors, a performance indicator, which tests the validity of the estimated 3-D motion parameters both explicitly and implicitly, is defined. This indicator is utilized within the RANSAC method to obtain a robust set of 2-D motion correspondences which leads to better 3-D motion parameters for each object, The experimental results support the superiority of the proposed method over direct application of the E-matrix method.

Subject Keywords

3-D motion and structure estimation, RANSAC, Object-based video coding, Object-based motion analysis, E-matrix method

URI

https://hdl.handle.net/11511/42961

Journal

IEEE SIGNAL PROCESSING LETTERS

DOI

https://doi.org/10.1109/97.659545

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

MULTI-RESOLUTION MOTION ESTIMATION FOR MOTION COMPENSATED FRAME INTERPOLATION Guenyel, Bertan; Alatan, Abdullah Aydın (2010-09-29) A multi-resolution motion estimation scheme is proposed for tracking of the true 2D motion in video sequences for motion compensated image interpolation. The proposed algorithm utilizes frames with different resolutions and adaptive block dimensions for efficient representation of motion. Firstly, motion vectors for each block are assigned as a result of predictive search in each pass. Then, the outlier motion vectors are detected and corrected at the end of each pass. Simulation results with respect to dif...
Correlation tracking based on wavelet domain information Ipek, HL; Yilmaz, I; Yardimci, YC; Cetin, AE (2003-08-07) Tracking moving objects in video can be carried out by correlating a template containing object pixels with pixels of the current frame. This approach may produce erroneous results under noise. We determine a set of significant pixels on the object by analyzing the wavelet transform of the template and correlate only these pixels with the current frame to determine the next position of the object. These significant pixels are easily trackable features of the image and increase the performance of the tracker.
TEMPORALLY CONSISTENT LAYER DEPTH ORDERING VIA PIXEL VOTING FOR PSEUDO 3D REPRESENTATION Turetken, Engin; Alatan, Abdullah Aydın (2009-05-06) A new region-based depth ordering algorithm is proposed based on the segmented motion layers with affine motion models. Starting from an initial set of layers that are independently extracted for each frame of an input sequence, relative depth order of every layer is determined following a bottom-to-top approach from local pair-wise relations to a global ordering. Layer sets of consecutive time instants are warped in two opposite directions in time to capture pair-wise occlusion relations of neighboring lay...
Object Segmentation in Multi-view Video via Color, Depth and Motion Cues Cigla, Cevahir; Alatan, Abdullah Aydın (2009-01-01) In the light of dense depth map estimation, motion estimation and object segmentation, the research on multi-view video (MVV) content has becoming increasingly popular due to its wide application areas in the near future. In this work, object segmentation problem is studied by additional cues due to depth and motion fields. Segmentation is achieved by modeling images as graphical models and performing popular Normalized Cuts method with some modifications. In the graphical models, each node is represented b...
3D Object Recognition by Geometric Hashing Eskizara, Omer; Akagündüz, Erdem; Ulusoy, İlkay (2009-01-01) Using transform invariant 3D fatures obtained from a database of 3D range images, geometric hashing is applied for the purpose of 3D object recognition. Mean (H) and Gaussian (K) curvature values within a scale-space of the surface is used Since H and K values are used and a scale-space of the surface is constructed the method is independent of transformation and resolution. The method is tested on the Stuttgart 3D range image database [1].

Citation Formats

A. A. Alatan, “3-D motion estimation of rigid objects for video coding applications using an improved iterative version of the E-matrix method,” IEEE SIGNAL PROCESSING LETTERS, pp. 36–38, 1998, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42961.