The primary problem dealt with in this paper is the following. Given some description of a visual object, find that object in an actual photograph. Part of the solution to this problem is the specification of a descriptive scheme, and a metric on which to base the decision of "goodness" of matching or detection.

The Representation and Matching of Pictorial Structures

: The field of picture processing by computer is reviewed from a technique-oriented standpoint. Only the processing of given pictures (as opposed to computer-synthesized pictures) is considered. Specific areas covered include: (a) Pictures as information sources and their efficient encoding; (b) Approximation of pictures - sampling and quantization techniques; (c) Position-invariant operations on pictures and their implementation (digital, electro-optical, optical); applications to matched filtering (template matching), spatial frequency filtering and image restoration, measurement of image quality, and image enhancement ('smoothing' and 'sharpening'); (d) Picture properties (linear; local and 'textural'; random) useful for pictorial pattern recognition; (e) 'Figure extraction' from pictures; figure properties (topology, size, shape); (f) Picture description and 'picture languages.' (Author)

Picture processing by computer

The pattern deformational model proposed by Tsai and Fu [11] is extended so that numerical attributes and probability or density distributions can be introduced into primitives and relations in a nonhierarchical relational graph. Conventional graph isomorphisms are then generalized to include error-correcting capability for matching deformed patterns represented by such attributed relational graphs. An ordered-search algorithm is proposed for determining error-correcting isomorphisms. Finally, a pattern classification approach using graph isomorphisms is described, which can be considered as a combination of structural and statistical techniques.

http://www.asia.edu.tw/Main_pages/about/Former_president_TsaiWH_publications/Journal%20Paper%20PDFs/Tsai_&_Fu_IEEETSMC_1979.pdf

Error-Correcting Isomorphisms of Attributed Relational Graphs for Pattern Analysis

: A technique for recognizing systems of lines is presented, in which the heuristic of the problem is not embedded in the recognition algorithm but is expressed in a figure of merit. A multistage decision process is then able to recognize in the input picture the optimal system of lines according to the given figure of merit. Due to the global approach, greater flexibility and adequacy in the particular problem is achieved. The relation between the structure of the figure of merit and the complexity of the optimization process is then discussed. (Modified author abstract)

On the optimal detection of curves in noisy pictures

This paper presents a method for detecting edges and contours in noisy pictures. The properties of an edge are embedded in a figure of merit and the edge detection problem becomes the problem of minimizing the given figure of merit. This problem can be represented as a shortest path problem on a graph and can be solved using well-known graph search algorithms. The relations between this representation of the minimization problem and a dynamic programming approach are discussed, showing that the graph search method can lead to substantial improvements in computing time. Moreover, if heuristic search methods are used, the computing time will depend on the amount of noise in the picture. Some experimental results are given; these show how various information about the shape of the contour of an object can be embedded in the figure of merit, thus allowing the extraction of contours from noisy pictures and the separation of touching objects.

Vladimir Kovalevsky

Papers

Sequential optimization in pattern recognition and pattern description.