Abstract book (pdf) - ICPR 2010

09:00-11:10, Paper WeAT8.47
Robust Fourier-Based Image Alignment with Gradient Complex Image
Su, Hong-Ren, National Tsing Hua Univ.
Lai, Shang-Hong, National Tsing Hua Univ.
Tsai, Ya-Hui, Industrial Tech. Res. Inst.
The paper proposes a robust image alignment framework based on Fourier transform of a gradient complex image. The
proposed Fourier-based algorithm can handle translation, rotation, and scaling, and it is robust against noise and non-uniform
illumination. The proposed alignment algorithm is further extended to work under occlusion by partitioning the template
and performing the Fourier-based alignment for all partitioned sub-templates in a voting framework. Our experiments
show superior alignment results by using the proposed robust Fourier-based alignment over the previous related methods.
09:00-11:10, Paper WeAT8.48
Rate Control of H.264 Encoded Sequences by Dropping Frames in the Compressed Domain
Kapotas, Spyridon, Hellenic Open Univ.
Skodras, Athanassios N., Hellenic Open Univ.
A new technique for controlling the bitrate of H.264 encoded sequences is presented. Bitrate control is achieved by dropping
frames directly in the compressed domain. The dropped frames are carefully selected so as to either eliminate or cause
non perceptible drift errors in the decoder. The technique suits well H.264 encoded sequences such as movies and tv news,
which are transmitted over wireless networks.
09:00-11:10, Paper WeAT8.49
Statistical Analysis of Kalman Filters by Conversion to Gauss Helmert Models with Applications to Process Noise Estimation
Petersen, Arne, Christian-Albrechts-Univ. of Kiel
Koch, Reinhard, Univ. of Kiel
This paper introduces a reformulation of the extended Kalman Filter using the Gauss-Helmert model for least squares estimation.
By proving the equivalence of both estimators it is shown how the methods of statistical analysis in least squares
estimation can be applied to the prediction and update process in Kalman Filtering. Especially the efficient computation
of the reliability (or redundancy) matrix allows the implementation of self supervising systems. As an application an unparameterized
method for estimating the variances of the filters process noise is presented.
09:00-11:10, Paper WeAT8.50
Color Adjacency Modeling for Improved Image and Video Segmentation
Price, Brian, Brigham Young Univ.
Morse, Bryan, Brigham Young Univ.
Cohen, Scott, Adobe Systems
Color models are often used for representing object appearance for foreground segmentation applications. The relationships
between colors can be just as useful for object selection. In this paper, we present a method of modeling color adjacency
relationships. By using color adjacency models, the importance of an edge in a given application can be determined and
scaled accordingly. We apply our model to foreground segmentation of similar images and video. We show that given one
previously-segmented image, we can greatly reduce the error when automatically segmenting other images by using our
color adjacency model to weight the likelihood that an edge is part of the desired object boundary.
09:00-11:10, Paper WeAT8.51
Paired Transform Slice Theorem of 2-D Image Reconstruction from Projections
Dursun, Serkan, Univ. of Texas at San Antonio
Du, Nan, Univ. of Texas at San Antonio
Grigoryan, Artyom M., Univ. of Texas at San Antonio
This paper discusses the paired transform-based method of reconstruction of 2-D images from their projections. The complete
set of basic functions of the 2-D discrete paired transform are defined by specific directions, i.e. the transform is di-
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