Mohammad Farhang Daemi

Global description of edge patterns using moments

Abstract Shape recognition has traditionally been accomplished using well bounded segmented regions or their closed contours. In this way, the internal edge details of the objects are ignored. There are cases such as in character recognition and in object recognition when patterns are not closed contours. In this paper, such patterns are defined as edge patterns and Edge Standard Moments (ESM) are developed which are invariant to location, scale and rotation. Results of patterns matching with character recognition and polyhedral object recognition are presented.

Classification of coal images by a multi-scale segmentation techniques

This paper describes development of an automated and efficient technique for classifying different major maceral groups within polished coal blocks. Coal utilisation processes can be significantly affected by the distribution of macerals in the feed coal. Classical manual maceral analysis requires a highly skilled operator and the time to perform an analysis can depend on the complexity of the sample and on the work load of the operator. Also if different operators are employed lower levels of reproducibility may result. The aim of segmentation is to partition the images into different types of macerals. A multi-scale approach to segmentation is defined in which the result of each process at a given resolution is used to adjust the other process at the next resolution. This approach combines a suitable statistical model for distribution of pixel values within each macerals group and a transition distribution from coarse to fine scale, based on a son-father relationship, which is defined between the nodes in adjacent levels. This transition function is based on the idea that neighboring pixels are similar to one another. This is mainly true due to the high resolution of these images understudy, which means that the pixel size is significantly smaller than the size of most of the different regions of interest.

Image structural information assessment

Many image information assessment techniques have been proposed in the past. Most of them are based on the image histogram. When the histogram of an image is derived, the structural component of the image is generally ignored. The information assessed using the histogram is thus independent of the image spatial attributes. In this investigation an original computational technique for the assessment of the structural information content of an image is described. The developed technique produces as output an information measure which is proportional to the image geometrical attributes such as the size, the shape complexity, etc. The technique is normalised and tested on a series of artificial images. The results obtained are analysed to show how the normalised information is related to the image geometrical attributes. The results obtained are also confirmed when English character images are considered. Finally, conclusions will be drawn in terms of the suitability of the technique for applications in computer imaging problems.

Weighted Hough transform

A new method is proposed here termed weighted Hough transform (WHT). The advantage of the WHT is that it can be applied to the differential image directly without the need for thresholding. In the WHT the contribution of each pixel to the parameter domain is weighted according to its value. It is well known that the performance of the conventional Hough transform is dependent on the threshold value used. The new method is therefore a generalizing of the Hough transform to overcome this problems.

An Adaptive Estimation and Segmentation Technique for Determination of Major Maceral Groups in Coal

This paper describes the development of an automated image based system for the classification of macerals in polished coal blocks. Coal petrology, and especially the estimation of the maceral content of a coal, has traditionally been considered to be a highly skilled and time consuming operation. However the recent upsurge in interest in this subject, driven by environmental legislation related to the utilisation of coal, has necessitated the development of a reliable automated system for maceral analysis. Manual maceral analysis is time consuming and its accuracy is largely dependent upon the skill of the operator. The major drawbacks to manual maceral analysis are related to time and operator fatigue, which can develop after the analysis of only one or two polished blocks. The reproducibility of the results from manual maceral analysis is also dependent upon the experience of the operator.

Pattern recognition based on information theory principles

One of the main problems faced in the development of pattern recognition algorithms is assessment of their performance. This paper describes the development of a novel technique for the assessment of information content of 2-D patterns encountered in practical pattern recognition problems. The technique is demonstrated by its application to multi-font typed character recognition. In this work we first developed an information model applicable to any pattern, and its elaboration to measure recognition performance, and second we used this model to derive parameters such as the resolution required to distinguish between the patterns. This has resulted in a powerful method for assessing the performance of any pattern recognition system.

New automatic threshold selection algorithm for edge detection

In this paper, a novel approach is proposed for selecting the thresholds of edge strength maps from its local histogram. This threshold selection technique is based on finding the threshold for small blocks of the edge map. For each block the threshold is chosen using an iterative procedure. The effect of the choice of the size of the block is discussed. In this paper, the edge strength map is quantized to reduce the computation of the iterative threshold selection algorithm as well as the memory requirement. It is shown that the quantization of the edge map improves the performance of the local iterative threshold selection algorithm. Typical examples of the tests carried out are presented.

Image compression using a novel edge-based coding algorithm

In this paper, we present a novel edge-based coding algorithm for image compression. The proposed coding scheme is the predictive version of the original algorithm, which we presented earlier in literature. In the original version, an image is block coded according to the level of visual activity of individual blocks, following a novel edge-oriented classification stage. Each block is then represented by a set of parameters associated with the pattern appearing inside the block. The use of these parameters at the receiver reduces the cost of reconstruction significantly. In the present study, we extend and improve the performance of the existing technique by exploiting the expected spatial redundancy across the neighboring blocks. Satisfactory coded images at competitive bit rate with other block-based coding techniques have been obtained.

Information-based shape description with scale, translation and rotation invariance

This paper proposes a technique based on information theory principles for shape description of 2-D images. By its essence the technique is invariant to rotation and translation. We first describe how the translational information content of an image may be defined, identified, assessed and analysed. The effect of the size is then assessed and quantified which permits us to propose a modified definition of translational information content invariant to scale. In order to demonstrate the feasibility of the developed techniques, a series of tests were carried out on rectangular and triangular shapes. The results illustrate the power of the translational information assessment techniques in analysing and describing 2-D images. The use of these techniques in shape description is also introduced.

Rotational information in shape description

Using information theory, this investigation propose a novel technique for shape description which is invariant to translation, rotation and in most cases also to scale. This new numeric shape descriptor is based on the measure of rotational information content of an image. In this paper we first review some popular metric shape description features. These features are then used to analyze the feasibility of using the rotational information for shape description, and by means of a comparative study we show how the rotational information is related to well known metric shape descriptors such as area, circularity and elongation. Finally, the results obtained are discussed and analyzed, and conclusions drawn in terms of the suitability of the technique for shape description in image recognition problems.