Visual pattern recognition by moment invariants
TL;DR: It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished and it is indicated that generalization is possible to include invariance with parallel projection.
Abstract: In this paper a theory of two-dimensional moment invariants for planar geometric figures is presented. A fundamental theorem is established to relate such moment invariants to the well-known algebraic invariants. Complete systems of moment invariants under translation, similitude and orthogonal transformations are derived. Some moment invariants under general two-dimensional linear transformations are also included. Both theoretical formulation and practical models of visual pattern recognition based upon these moment invariants are discussed. A simple simulation program together with its performance are also presented. It is shown that recognition of geometrical patterns and alphabetical characters independently of position, size and orientation can be accomplished. It is also indicated that generalization is possible to include invariance with parallel projection.
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TL;DR: A review of recent as well as classic image registration methods to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas.
6,842 citations
Cites background or methods from "Visual pattern recognition by momen..."
...Considering the most often assumed deformations, Hu [93] introduced moment invariants to the similarity transform....
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...For translated and rotated images, Goshtasby [66] proposed to calculate the moment invariants [93] from the circular-shaped windows and then to apply the CC criterion on the moment window representation....
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TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
Abstract: Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.
6,447 citations
TL;DR: A view-based approach to the representation and recognition of human movement is presented, and a recognition method matching temporal templates against stored instances of views of known actions is developed.
Abstract: A view-based approach to the representation and recognition of human movement is presented. The basis of the representation is a temporal template-a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image sequence. Using aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: The first value is a binary value indicating the presence of motion and the second value is a function of the recency of motion in a sequence. We then develop a recognition method matching temporal templates against stored instances of views of known actions. The method automatically performs temporal segmentation, is invariant to linear changes in speed, and runs in real-time on standard platforms.
2,932 citations
Cites background from "Visual pattern recognition by momen..."
...We will show examples of how the two images together provide better discrimination than either alone....
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01 Dec 1993
TL;DR: The geometric, random field, fractal, and signal processing models of texture are presented and major classes of texture processing such as segmentation, classification, and shape from texture are discussed.
Abstract: This chapter reviews and discusses various aspects of texture analysis. The concentration is o 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 pro lems such as segmentation, classification, and shape from texture are discussed. The possible applic tion areas of texture such as automated inspection, document processing, and remote sensing a summarized. A bibliography is provided at the end for further reading.
2,257 citations
Cites background from "Visual pattern recognition by momen..."
...Moments of area of the Voronoi polygons serve as a useful set of features reflect both the spatial distribution and shapes of the tokens in the textured image order moments of area of a closed region with respect to a token with coo nates are defined as [55]:...
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30 Aug 2004
TL;DR: artificial neural networks, artificial neural networks , مرکز فناوری اطلاعات و اصاع رسانی, کδاوρزی
Abstract: artificial neural networks , artificial neural networks , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی
2,254 citations
References
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01 Jan 1961
TL;DR: The problems of heuristic programming can be divided into five main areas: Search, Pattern-Recognition, Learning, Planning, and Induction as discussed by the authors, and the most successful heuristic (problem-solving) programs constructed to date.
Abstract: The problems of heuristic programming-of making computers solve really difficult problems-are divided into five main areas: Search, Pattern-Recognition, Learning, Planning, and Induction. A computer can do, in a sense, only what it is told to do. But even when we do not know how to solve a certain problem, we may program a machine (computer) to Search through some large space of solution attempts. Unfortunately, this usually leads to an enormously inefficient process. With Pattern-Recognition techniques, efficiency can often be improved, by restricting the application of the machine's methods to appropriate problems. Pattern-Recognition, together with Learning, can be used to exploit generalizations based on accumulated experience, further reducing search. By analyzing the situation, using Planning methods, we may obtain a fundamental improvement by replacing the given search with a much smaller, more appropriate exploration. To manage broad classes of problems, machines will need to construct models of their environments, using some scheme for Induction. Wherever appropriate, the discussion is supported by extensive citation of the literature and by descriptions of a few of the most successful heuristic (problem-solving) programs constructed to date.
1,318 citations
TL;DR: Two neural mechanisms are described which exhibit recognition of forms which are independent of small perturbations at synapses of excitation, threshold, and synchrony, and are referred to partiular appropriate regions of the nervous system, thus suggesting experimental verification.
753 citations
01 Mar 1955
TL;DR: Questions such as: If the machines can speak, will they squawk when you ask them to divide by zero?
Abstract: Everyone likes to speculate, and recently there has been a lot of talk about reading machines and hearing machines. We know it is possible to simulate speech. This raises lots of interesting questions such as: If the machines can speak, will they squawk when you ask them to divide by zero? And can two machines carry on an intelligent conversation, say in Gaelic? And, of course, there is the expression "electronic brain" and the question, Do machines think? These questions are more philosophical than technical and I am going to duck them.
118 citations
01 Dec 1959
TL;DR: This paper describes a demonstration of the recognition of thirty-four alpha-numeric characters through the use of the IBM 704 EDPM.
Abstract: This paper describes a demonstration of the recognition of thirty-four alpha-numeric characters. The IBM 704 EDPM was used as a tool to study the method which led to this demonstration. The Generalized Scanner was used as an input transducer for this study.
43 citations
TL;DR: A geometrical approach is taken where membership in classes is regarded measurable by metrics with which a set of points, representing different members of the same class, may be brought "close" to one another.
Abstract: This paper presents an approach to the general problem of recognition of membership in classes which are known only from a set of their examples. A geometrical approach is taken where membership in classes is regarded measurable by metrics with which a set of points, representing different members of the same class, may be brought "close" to one another. For the case where classes are Gaussian processes, the method described herein and that of decision theory are found to agree. A practical application of the method to the automatically "learned" recognition of spoken numerals is described.
30 citations