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The purpose of this paper is to present near set theory using the perceptual indiscernibility and tolerance relations, to demonstrate the practical application of near set theory to the image correspondence problem, and to compare this method with existing image similarity measures.

Image‐correspondence methodologies are present in many systems that are depended on daily. In these systems, the discovery of sets of similar objects (aka, tolerance classes) stems from human perception of the objects being classified. This view of perception of image‐correspondence springs directly from Poincaré's work on visual spaces during 1890s and Zeeman's work on tolerance spaces and visual acuity during 1960s. Thus, in solving the image‐correspondence problem, it is important to have systems that accurately model human perception. Near set theory provides a framework for measuring the similarity of digital images (and perceptual objects, in general) based on features that describe them in much the same way that humans perceive objects.

The contribution of this paper is a perception‐based classification of images using near sets.

The method presented in this paper represents a new approach to solving problems in which the goal is to match human perceptual groupings. While the results presented in the paper are based on measuring the resemblance between images, the approach can be applied to any application that can be formulated in terms of sets such that the objects in the sets can be described by feature vectors.

The purpose of this paper is to demonstrate the effectiveness and advantages of using perceptual tolerance neighbourhoods in tolerance space‐based image similarity measures and its application in content‐based image classification and retrieval.

The proposed method in this paper is based on a set‐theoretic approach, where an image is viewed as a set of local visual elements. The method also includes a tolerance relation that detects the similarity between pairs of elements, if the difference between corresponding feature vectors is less than a threshold 2 (0,1).

It is shown that tolerance space‐based methods can be successfully used in a complete content‐based image retrieval (CBIR) system. Also, it is shown that perceptual tolerance neighbourhoods can replace tolerance classes in CBIR, resulting in more accuracy and less computations.

The main contribution of this paper is the introduction of perceptual tolerance neighbourhoods instead of tolerance classes in a new form of the Henry‐Peters tolerance‐based nearness measure (tNM) and a new neighbourhood‐based tolerance‐covering nearness measure (tcNM). Moreover, this paper presents a side – by – side comparison of the tolerance space based methods with other published methods on a test dataset of images.

The purpose of this paper is to report upon research into developing a biologically inspired target‐tracking system (TTS) capable of acquiring quality images of a known target type for a robotic inspection application.

The approach used in the design of the TTS hearkens back to the work on adaptive learning by Oliver Selfridge and Chris J.C.H. Watkins and the work on the classification of objects by Zdzislaw Pawlak during the 1980s in an approximation space‐based form of feedback during learning. Also, during the 1980s, it was Ewa Orlowska who called attention to the importance of approximation spaces as a formal counterpart of perception. This insight by Orlowska has been important in working toward a new form of adaptive learning useful in controlling the behaviour of machines to accomplish system goals. The adaptive learning algorithms presented in this paper are strictly temporal difference methods, including Q‐learning, sarsa, and the actor‐critic method. Learning itself is considered episodic. During each episode, the equivalent of a Tinbergen‐like ethogram is constructed. Such an ethogram provides a basis for the construction of an approximation space at the end of each episode. The combination of episodic ethograms and approximation spaces provides an extremely effective means of feedback useful in guiding learning during the lifetime of a robotic system such as the TTS reported in this paper.

It was discovered that even though the adaptive learning methods were computationally more expensive than the classical algorithm implementations, they proved to be more effective in a number of cases, especially in noisy environments.

The novelty associated with this work is the introduction of an approach to adaptive adaptive learning carried out within the framework of ethology‐based approximation spaces to provide performance feedback during the learning process.

In the last four years, since Volume I of this Bibliography first appeared, there has been an explosion of literature in all the main functional areas of business. This wealth of material poses problems for the researcher in management studies — and, of course, for the librarian: uncovering what has been written in any one area is not an easy task. This volume aims to help the librarian and the researcher overcome some of the immediate problems of identification of material. It is an annotated bibliography of management, drawing on the wide variety of literature produced by MCB University Press. Over the last four years, MCB University Press has produced an extensive range of books and serial publications covering most of the established and many of the developing areas of management. This volume, in conjunction with Volume I, provides a guide to all the material published so far.

Communications regarding this column should be addressed to Mrs. Cheney, Peabody Library School, Nashville, Term. 37203. Mrs. Cheney does not sell the books listed here. They are available through normal trade sources. Mrs. Cheney, being a member of the editorial board of Pierian Press, will not review Pierian Press reference books in this column. Descriptions of Pierian Press reference books will be included elsewhere in this publication.

Aarhus Kommunes Biblioteker (Teknisk Bibliotek), Ingerslevs Plads 7, Aarhus, Denmark. Representative: V. NEDERGAARD PEDERSEN (Librarian).

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.