Robert A. King

Image coding using vector quantization: a review

A review of vector quantization techniques used for encoding digital images is presented. First, the concept of vector quantization is introduced, then its application to digital images is explained. Spatial, predictive, transform, hybrid, binary, and subband vector quantizers are reviewed. The emphasis is on the usefulness of the vector quantization when it is combined with conventional image coding techniques, or when it is used in different domains. >

Textural features corresponding to textural properties

Five properties of texture, namely, coarseness, contrast, business, complexity, and texture strength, are given conceptual definitions in terms of spatial changes in intensity. These conceptual definitions are then approximated in computational forms. In comparison with human perceptual measurements, the computational measures have shown good correspondences in the rank ordering of ten natural textures. The extent to which the measures approximate visual perception was investigated in the form of texture similarity measurements. These results were also encouraging, although not as good as in the rank ordering of the textures. The differences may be due to the complex mechanism of human usage of multiple cues. Improved classification results were obtained using the above features as compared with two existing texture analysis techniques. The application of the features in agricultural land-use classification is considered. >

On Edge Detection of X-Ray Images Using Fuzzy Sets

The effectiveness of the theory of fuzzy sets in detecting different regional boundaries of X-ray images is demonstrated. The algorithm includes a prior enhancement of the contrast among the regions (having small change in gray levels) using the contrast intensification (INT) operation along with smoothing in the fuzzy property plane before detecting its edges. The property plane is extracted from the spatial domain using S, ¿ and (1 ¿) functions and the fuzzifiers. Final edge detection is achieved using max or min operator. The system performance for different parameter conditions is illustrated by application to an image of a radiograph of the wrist.

The use of self-entropy as a focus measure in digital holography

Abstract An object is numerically retrieved from its hologram when the distance between them is unknown by using a Focus Measure. The self-entropy of the magnitude and phase are both suitable measures, the latter especially in the presence of noise.

Two-dimensional block processors - Structures and implementations

Two-dimensional (2-D) block processing technique for linear filtering of digital images is introduced. New 2-D block structures are derived for 2-D recursive digital filters realized by difference equations and state-space formulations. Several special cases have also been considered and the relevant 2-D block structures are given. The computational costs of different implementation techniques employing high-speed convolution algorithms such as fast Fourier transform, number theoretic transform and polynomial transform have been studied. A comparison among the relative efficiencies of these implementation schemes is made and a suitable method is then proposed using short convolution algorithm which results in a minimized computational time.

Design of recursive and nonrecursive fan filters with complex transformations

This paper develops a technique for designing recursive and nonrecursive two-dimensional digital filters by the application of a complex transformation to a one-dimensional low-pass filter. A set of transformed filters is presented. The appropriate combination of these filters produces zero-phase fan filters. The advantage of such an approach is that the resulting fan filters are inherently stable and no optimization is needed. Several examples are presented for both finite impulse response (FIR) and infinite impulse response (IIR) fan filters and quadrant fan filters. The paper also includes implementation techniques using the properties of finite area arrays which reduce both computational and storage requirements compared with conventional implementations.

Image coding using vector quantization in the transform domain

Two new image-coding systems are proposed. In the first system a one-dimensional Hadamard transform is applied on each row of image data, and a vector-quantization process along the columns of the transformed image data. In the second system a two-dimensional Hadamard transform is carried out on sub-blocks of an image followed by a vector-quantization process on the neighbouring sub-blocks.

Digital Filtering in One and Two Dimensions

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Low-level segmentation of multispectral images via agglomerative clustering of uniform neighbourhoods

price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. M. Ahmadi, M. Azimi-Sadjadi, R. Gorgui-Naguib, R. King, A. Kwabwe Digital Filtering in One and Two Dimensions

Design technique for a class of stable two-dimensional recursive digital filters

Abstract A segmentation approach based on the concept of unsupervised classification of pixels is presented. Mean feature vectors of the classes are obtained from agglomerative-type clustering of the feature values computed over uniform neighbourhoods. Two assumptions are made in this development. The first is that at least one uniform neighbourhood can be found for each of the different categories present in the image. The second is that feature vectors of neighbourhoods representative of a particular category are similar to each other, but different from those of neighbourhoods belonging to other categories. The scheme has been applied to the segmentation of a three-band multispectral image of a terrain with satisfactory results. The method is computationally efficient, and requires minimal memory; hence it can be used in real time.