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This chapter presents some recent advances in the area of computer vision. The various stages involved in the area of image processing and their interpretation are described. The first step is that of image registration. That is, the overlay two or more images of the same scene. These are taken from different viewpoints, at different times or possibly by different sensors. The next phase is image...
A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper,...
Mathematical morphology offers a set of powerful tools for image processing and analysis. From a practical perspective, the expected results of many morphological operators can be intuitively explained in terms of geometrical and topological characteristics of the images. From a formal perspective, mathematical morphology is based on complete lattices, which provides a solid theoretical framework...
Object recognition is a complex and challenging problem. It involves examining many different hypothesis in terms of the object class, position, scale, pose, etc., but the main trend in computer vision systems is to lazily rely on the brute force capacity of computers, that is to explore every possibilities indifferently. Sadly, in many case this scheme is way too slow for real-time or even practical...
The vast growth of image databases creates many challenges for computer vision applications, for instance image retrieval and object recognition. Large variation in imaging conditions such as illumination and geometrical properties (including scale, rotation, and viewpoint) gives rise to the need for invariant features; i.e. image features should have minimal differences under these conditions. Local...
This chapter considers how visual perception can be used to advantage in image analysis. The key to the solution to this problem was first pointed out by J.H. Poincaré in 1893 in his representation of the results of G.T. Fechner’s 1860 psychophysics experiments with sensation sensitivity in lifting small weights. The focus of Fechner’s experiments was on sensation sensitivity. By contrast, the focus...
Magnetic resonance imaging provides a comprehensive and non-invasive view of the structural features of living tissue at very high resolution (typically on the 1-2 mm scale). A variety of pulse sequences have been developed that provide quantitative information regarding the structural features of a variety of tissue classes, providing details that are extremely beneficial in a clinical setting. Unlike...
The problem considered in this paper is how to detect similarities in the content of digital images, useful in image retrieval and in the solution of the image correspondence problem, i.e., to what extent does the content of one digital image correspond to content of other digital images. The solution to this problem stems from a recent extension of J.H. Poincaré’s representative spaces from 1895...
Image matching and retrieval is one of the most important areas of computer vision. The key objective of image matching is detection of near-duplicate images. This chapter discusses an extension of this concept, namely, the retrieval of near-duplicate image fragments. We assume no a’priori information about visual contents of those fragments. The number of such fragments in an image is also unknown...
Feature extraction and selection has always been an interesting issue for pattern recognition tasks. There have been numerous feature schemes proposed and empirically validated for image scene and object categorization problems, no matter it is for general-purposed applications such as image retrieval, or for specific domains such as medical image analysis. On the other hand, there are few attempts...
Curve parametrization is the task of determining the parameters of a general curve equation describing a structure in an image (or a surface in a higher-dimensional dataset). A common example is the widespread use of the Hough transform to determine parameters of straight lines in an image. Moment-based methods offer an attractive alternative to Hough-type methods for this task, especially as the...
Colour palettes are used for representing image data using a limited number of colours. As the image quality directly depends on the chosen colours in the palette, deriving algorithms for colour palette design is a crucial task. In this chapter we show how computational intelligence approaches can be employed for this task. In particular, we discuss the use of generic optimisation techniques such...
Mean shift techniques have been demonstrated to be capable of estimating the local density gradients of similar image pixels. These gradient estimates are iteratively performed so that for all pixels similar pixels in corresponding images can be identified. In this chapter, we show how the application of a mean shift process can lead to improved image segmentation performance. We present several mean...
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