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Volume 146
Issue 3
IEE Proceedings - Vision, Image and Signal Processing
Volume 146, Issue 3, June 1999
Volumes & issues:
Volume 146, Issue 3
June 1999
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- Author(s): S.K. Mitra and U. Heute
- Source: IEE Proceedings - Vision, Image and Signal Processing, Volume 146, Issue 3, p. 109 –123
- DOI: 10.1049/ip-vis:19990442
- Type: Article
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p.
109
–123
(15)
The authors present a tutorial paper in which they review the concept of structural sub-band decomposition of sequences, a generalisation of polyphase decomposition. The idea of using a very simple analysis-synthesis structure allows for computational efficiency. It is applicable both for signals and for systems, which may thus be designed and realised advantageously. The use of such decomposition is shown, in terms of theory and applications, for transform-based spectral analysis, FIR filter design and implementation, and adaptive filtering. Advantages are demonstrated for cases of narrow-band signals and systems. - Author(s): S. J. Roberts
- Source: IEE Proceedings - Vision, Image and Signal Processing, Volume 146, Issue 3, p. 124 –129
- DOI: 10.1049/ip-vis:19990428
- Type: Article
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p.
124
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(6)
Extreme value theory is a branch of statistics that concerns the distribution of data of unusually low or high value, i.e. in the tails of some distribution. These extremal points are important in many applications as they represent the outlying regions of normal events against which we may wish to define abnormal events. In the context of density modelling, novelty detection or radial-basis function systems, points that lie outside of the range of expected extreme values may be flagged as outliers. There has been interest in the area of novelty detection, but decisions as to whether a point is an outlier or not tend to be made on the basis of exceeding some (heuristic) threshold. It is shown that a more principled approach may be taken using extreme value statistics. - Author(s): C. Alberola-López ; J. R. Casar-Corredera ; G. de Miguel-Vela
- Source: IEE Proceedings - Vision, Image and Signal Processing, Volume 146, Issue 3, p. 130 –136
- DOI: 10.1049/ip-vis:19990236
- Type: Article
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p.
130
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(7)
The authors introduce a new non-Gaussian CFAR detector, specifically a CFAR detector for gamma-distributed textured backgrounds. The design and analysis of the detector is carried out in several steps: basic design, analysis in only noise conditions, bias removal and analysis in the presence of correlation. The authors also discuss the possibility of applying a data prewhitening technique to control the false alarm rate in correlated textured patterns. In terms of detection, they analyse the performance of their detector for a certain model of target. They compare its performance to that of the ideal detector, and quantify in which conditions the former behaves closely enough to the latter. - Author(s): E. Izquierdo and M. Ghanbari
- Source: IEE Proceedings - Vision, Image and Signal Processing, Volume 146, Issue 3, p. 137 –143
- DOI: 10.1049/ip-vis:19990197
- Type: Article
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p.
137
–143
(7)
Gaussian filter kernels can be used to smooth textures for image segmentation. In so-called anisotropic diffusion techniques, the smoothing process is adapted according to the edge direction to preserve the edges. However, the segment borders obtained with this approach do not necessarily coincide with physical object contours, especially in the case of textured objects. A novel segmentation technique involving weighted Gaussian filtering is introduced. The extraction of true object masks is performed by smoothing edges due to texture and preserving true object borders. In this process, additional features such as disparity or motion are taken into account. The method presented has been successfully applied in the context of object segmentation to natural scenes and object-based disparity estimation for stereoscopic applications. - Author(s): T. Meier and K. N. Ngan
- Source: IEE Proceedings - Vision, Image and Signal Processing, Volume 146, Issue 3, p. 144 –150
- DOI: 10.1049/ip-vis:19990335
- Type: Article
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p.
144
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(7)
To enable content-based functionalities in video coding, a decomposition of the scene into physical objects is required. Such objects are normally not characterised by homogeneous colour, intensity, or optical flow. Therefore, conventional techniques based on these low-level features cannot perform the desired segmentation. The authors address segmentation and tracking of moving objects and presents a new video object plane (VOP) segmentation algorithm that extracts semantically meaningful objects. A morphological motion filter detects physical objects by identifying areas that are moving differently from the background. A new filter criterion is introduced that measures the deviation of the estimated local motion from the synthesised global motion. A two-dimensional binary model is derived for the object of interest and tracked throughout the sequence by a Hausdorff object tracker. To accommodate for rotations and changes in shape, the model is updated every frame by a two-stage method that accounts for rigid and non-rigid moving parts of the object. The binary model then guides the actual VOP extraction, whereby a novel boundary post-processor ensures high boundary accuracy. Experimental results demonstrate the performance of the proposed algorithm. - Author(s): D. Gimeno Gost and L. Torres
- Source: IEE Proceedings - Vision, Image and Signal Processing, Volume 146, Issue 3, p. 151 –158
- DOI: 10.1049/ip-vis:19990332
- Type: Article
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p.
151
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Vector quantisation (VQ) has been extensively used as an effective image coding technique. One of the most important steps in the whole process is the design of the codebook. The codebook is generally designed using the LBG algorithm which uses a large training set of empirical data that is statistically representative of the images to be encoded. The LBG algorithm, although quite effective for practical applications, is computationally very expensive and the resulting codebook has to be recalculated each time the type of image to be encoded changes. Stochastic vector quantisation (SVQ) provides an alternative way for the generation of the codebook. In SVQ, a model for the image is computed first, and then the codewords are generated according to this model and not according to some specific training sequence. The SVQ approach presents good coding performance for moderate compression ratios and different type of images. On the other hand, in the context of synthetic and natural hybrid coding (SNHC), there is always need for techniques which may provide very high compression and high quality for homogeneous textures. A new stochastic vector quantisation approach using linear prediction which is able to provide very high compression ratios with graceful degradation for homogeneous textures is presented. Owing to the specific construction of the method, there is no block effect in the synthetised image. Results, implementation details, generation of the bit stream and comparisons with the verification model of MPEG-4 are presented which prove the validity of the approach. The technique has been proposed as a still image coding technique in the SNHC standardisation group of MPEG. - Author(s): M. Craizer ; E. A. B. da Silva ; E. G. Ramas
- Source: IEE Proceedings - Vision, Image and Signal Processing, Volume 146, Issue 3, p. 159 –164
- DOI: 10.1049/ip-vis:19990022
- Type: Article
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p.
159
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(6)
Embedded wavelet coders have become very popular in image compression applications, owing to their simplicity and high coding efficiency. Most of them incorporate some form of successive approximation scalar quantisation. Recently developed algorithms for successive approximation vector quantisation have been shown to be capable of outperforming successive approximation scalar quantisation ones. In the paper, some algorithms for successive approximation vector quantisation we analysed. Results that were previously known only on an experimental basis are derived analytically. An improved algorithm is also developed and is proved to be convergent. These algorithms are applied to the coding of wavelet coefficients of images. Experimental results show that the improved algorithm is more stable in a rate×distortion sense, while maintaining coding performances compatible with the state-of-the-art. - Author(s): S. S. O. Choy ; Y. -H. Chan ; W. -C. Siu
- Source: IEE Proceedings - Vision, Image and Signal Processing, Volume 146, Issue 3, p. 165 –171
- DOI: 10.1049/ip-vis:19990161
- Type: Article
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p.
165
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The authors study the application of image restoration technology in improving the coding performance of a vector quantisation (VQ) image compression codec. Restoration of VQ-compressed images is rarely addressed in the literature, and direct applications of existing restoration techniques are generally inadequate to deal with this problem. A restoration algorithm is proposed, specific to VQ-compressed images, that makes good use of the codebook to derive useful a priori information for restoration. The proposed restoration algorithm is shown to be capable of improving the quality of a VQ-compressed image to a much greater extent, compared with other existing restoration approaches. As no extra information, other than the codebook, is required to carry out the restoration with the proposed algorithm, no transmission overhead is necessary and, hence, it can be fully compatible with any VQ codec when used to improve coding performance.
Structural sub-band decomposition of sequences and its applications in signal processing
Novelty detection using extreme value statistics
Object CFAR detection in gamma-distributed textured-background images
Nonlinear Gaussian filtering approach for object segmentation
Segmentation and tracking of moving objects for content-based video coding
Efficient coding of homogeneous textures using stochastic vector quantisation and linear prediction
Convergent algorithms for successive approximation vector quantisation with applications to wavelet image compression
Regularized restoration of vector quantisation compressed images
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