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Orthogonal Fourier–Mellin (OFM) moments have better feature representation capabilities, and are more robust to image noise than the conventional Zernike moments and pseudo-Zernike moments. However, OFM moments have not been extensively used as feature descriptors since they do not possess scale invariance. This paper discusses the drawbacks of the existing methods of extracting OFM moments, and proposes an improved OFM moments. A part of the theory, which proves the improved OFM moments possesses invariance of rotation and scale, is given. The performance of the improved OFM moments is experimentally examined using trademark images, and the invariance of the improved OFM moments is shown to have been greatly improved over the current methods.

This paper introduces a family of rectangularity measures. The measures depend on two parameters which enable their flexibility, i.e. the possibility to adapt with respect to a concrete application. Several rectangularity measures exist in the literature, and they are designed to evaluate numerically how much the shape considered differs from a perfect rectangle. None of these measures distinguishes rectangles whose edge ratios differ, i.e. they assume that all rectangles (including squares) have the same shape. Such property can be a disadvantage in applications. In this paper, we consider differently elongated rectangles to have different shapes, and propose a family of new rectangularity measures which assigns different values to rectangles whose edge ratios differ. The new rectangularity measures are invariant with respect to translation, rotation and scaling transformations. They range over the interval ]0, 1] and attain the value 1 only for perfect rectangles with a desired edge ratio.

In this paper, we propose a simple, but efficient method to recognize two-dimensional shapes without regard to their translation, rotation, and scaling factors. In our scheme, we use all of the boundary points to calculate the first principal component, which is the first shape feature. Next, by dividing the boundary points into groups by projecting them onto the first principal component, each shape is partitioned into several blocks. These blocks are processed separately to produce the remaining shape features. In shape matching, we compare two shapes by calculating the difference between the two sets of features to see whether the two shapes are similar or not.

The amount of storage used to represent a shape in our method is fixed, unlike most other shape recognition schemes. The time complexity of our shape matching algorithm is also O(n), where n is the number of blocks. Therefore, the matching algorithm takes little computation time, and is independent of translation, rotation, and scaling of shapes.

This chapter reviews and discusses various aspects of texture analysis. The concentration is on the various methods of extracting textural features from images. The geometric, random field, fractal, and signal processing models of texture are presented. The major classes of texture processing problems such as segmentation, classification, and shape from texture are discussed. The possible application areas of texture such as automated inspection, document processing, and remote sensing are summarized. A bibliography is provided at the end for further reading.

This paper addresses the problem of pose invariant generic object recognition by proposing a novel framework which fuses information obtained from 3D appearance and 2D shape cues. The 3D appearance model of the object is captured using linear subspace analysis techniques (2D PCA and ICA) and is used to reduce the search space to a few rank-ordered candidates. For matching based on 2D shape features, we propose the use of distance transform based correlation. We have explored various measures for fusion of 3D appearance and 2D shape cues. Fusion of two linear subspace classifiers, 2D PCA and ICA, gives the best set of rank ordered samples, and then a combined decision of distance transform based shape matching and subspace classifiers selects the object class and pose for recognition. Experiments were conducted on COIL-100 database which contains a wide variety of objects from different classes with complex appearance and shape characteristics. The proposed technique is shown to outperform the other existing techniques in literature. Experimentation shows that the proposed method is more robust to noise.

In this paper, we present an image retrieval system based on the content. The content of images include both low level features such as colors, textures, and high level features such as spatial constraints and shapes of relevant regions. Based on object technology, the image features and behaviors are modeled and stored in a database. Images can be retrieved by examples (show me images similar to this image) or by selecting properties from pickers such as a sketched shape, a color histogram, a spatial constraint interface, a list of key words and a combination of these. The integration of high and low level features in the object-oriented database is an important property of our work.

As jumping is an effective method of moving over rough terrain, there is much interest in building robots that can jump. Deformation of a soft robot's body is an effective method to induce jumping. Our aim was to develop a jumping robot by deformation of a circular shell made of spring steel to result in the highest jump. Higher jumping requires enlargement of the contact area between the robot body and the floor. We developed a jumping mechanism that utilized a dish shape to maximize contact area.

This chapter reviews and discusses various aspects of texture analysis. The concentration is on the various methods of extracting textural features from images. The geometric, random field, fractal, and signal processing models of texture are presented. The major classes of texture processing problems such as segmentation, classification, and shape from texture are discussed. The possible application areas of texture such as automated inspection, document processing, and remote sensing are summarized. A bibliography is provided at the end for further reading.