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# IEEE Transactions on Pattern Analysis and Machine Intelligence

## Filter Results

Displaying Results 1 - 19 of 19
• ### [Front cover]

Publication Year: 1980, Page(s): c1
| PDF (719 KB)
• ### List of Contributors

Publication Year: 1980, Page(s): nil1
| PDF (252 KB)
• ### [Breaker page]

Publication Year: 1980, Page(s): nil1
| PDF (252 KB)
• ### The Relationship of the Bayes Risk to Certain Separability Measures in Normal Classification

Publication Year: 1980, Page(s):97 - 100
Cited by:  Papers (3)
| | PDF (1467 KB)

For the problem of classifying an element (e.g., an unknown pattern) into one of two given categories where the associated observables are distributed according to one of two known multivariate normal populations having a common covariance matrix, it is shown that the minimum Bayes risk is a strict monotonic function of certain separability or statistical distance measures regardless of the a prio... View full abstract»

• ### Locally Trained Piecewise Linear Classifiers

Publication Year: 1980, Page(s):101 - 111
Cited by:  Papers (54)  |  Patents (1)
| | PDF (3046 KB)

We describe a versatile technique for designing computer algorithms for separating multiple-dimensional data (feature vectors) into two classes. We refer to these algorithms as classifiers. Our classifiers achieve nearly Bayes-minimum error rates while requiring relatively small amounts of memory. Our design procedure finds a set of close-opposed pairs of clusters of data. From these pairs the pro... View full abstract»

• ### A Structural Model of Shape

Publication Year: 1980, Page(s):111 - 126
Cited by:  Papers (68)
| | PDF (3724 KB)

Shape description and recognition is an important and interesting problem in scene analysis. Our approach to shape description is a formal model of a shape consisting of a set of primitives, their properties, and their interrelationships. The primitives are the simple parts and intrusions of the shape which can be derived through the graph-theoretic clustering procedure described in [31]. The inte... View full abstract»

• ### Three-Dimensional Moment Invariants

Publication Year: 1980, Page(s):127 - 136
Cited by:  Papers (177)  |  Patents (4)
| | PDF (1860 KB)

Recognition of three-dimensional objects independent of size, position, and orientation is an important and difficult problem of scene analysis. The use of three-dimensional moment invariants is proposed as a solution. The generalization of the results of two-dimensional moment invariants which had linked two-dimensional moments to binary quantics is done by linking three-dimensional moments to te... View full abstract»

• ### Use of Fuzzy Algorithms for Phonetic and Phonemic Labeling of Continuous Speech

Publication Year: 1980, Page(s):136 - 148
Cited by:  Papers (28)  |  Patents (7)
| | PDF (3828 KB)

A model for assigning phonetic and phonemic labels to speech segments is presented. The system executes fuzzy algorithms that assign degrees of worthiness to structured interpretations of syllabic segments extracted from the signal of a spoken sentence. The knowledge source is a series of syntactic rules whose syntactic categories are phonetic and phonemic features detected by a precategorical and... View full abstract»

• ### Pattern-Based Interactive Diagnosis of Multiple Disorders: The MEDAS System

Publication Year: 1980, Page(s):148 - 160
Cited by:  Papers (51)  |  Patents (1)
| | PDF (4833 KB)

A knowledge-based interactive sequential diagnostic system is introduced which provides for diagnosis of multiple disorders in several body systems. The knowledge base consists of disorder patterns in a hierarchical structure that constitute the background medical information required for diagnosis in the domain under consideration (emergency and critical care medicine, in our case). Utilizing thi... View full abstract»

• ### Pattern Recognition as Conceptual Morphogenesis

Publication Year: 1980, Page(s):161 - 165
Cited by:  Papers (6)
| | PDF (1139 KB)

Pattern recognition is our mental activity in which we formulate, select, modify, and adjust our concepts or our frames of reference so that we can see a form'' in objects. We can tentatively formulate this viewpoint as the principle of minimum entropy (or entropy-like function). Different algorithms in clustering and pattern recognition are given new interpretations in the light of this unifyin... View full abstract»

• ### Digital Image Enhancement and Noise Filtering by Use of Local Statistics

Publication Year: 1980, Page(s):165 - 168
Cited by:  Papers (1019)  |  Patents (38)
| | PDF (3148 KB)

Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in re... View full abstract»

• ### Model-Based Texture Measures

Publication Year: 1980, Page(s):169 - 171
Cited by:  Papers (4)
| | PDF (1937 KB)

Many measures have been developed to quantify the structural properties of image texture. This correspondence explores their applicability to textures generated by several standard models. View full abstract»

• ### On the Problems of Constructing Minimal Realizations for Two-Dimensional Filters

Publication Year: 1980, Page(s):172 - 176
Cited by:  Papers (11)
| | PDF (972 KB)

The input-output behavior of a two-dimensional linear filter is defined by a formal power series in two variables. If the power series is rational the dynamics of the filter is described by updating equations on finite dimensional local state spaces. The class of realizations considered in this paper is constituted by doubly indexed dynamical systems of reduced structure. The construction of the c... View full abstract»

• ### The Approximation of Image Blur Restoration Filters by Finite Impulse Responses

Publication Year: 1980, Page(s):176 - 180
Cited by:  Papers (5)
| | PDF (1556 KB)

Image blur can often be modeled by a linear spatially invariant, symmetric point spread function. For this class of functions, several restoration filters are known in the literature. The approximation of their frequency transfer functions (ftf's) by the ftf's of small finite impulse response (FIR) filters has been studied. Accurate approximations will be possible by 9 Ã 9 FIR's with 8-bit elemen... View full abstract»

• ### The Sensitivity of the Modified Viterbi Algorithm to the Source Statistics

Publication Year: 1980, Page(s):181 - 185
Cited by:  Papers (9)
| | PDF (986 KB)

The modified Viterbi algorithm is a powerful, and increasingly used, tool for using contextual information in text recognition in its various forms. As yet, no known studies have been published concerning its robustness with respect to source statistics. This paper describes experiments performed to determine the sensitivity of the algorithm to variations in source statistics. The results of the e... View full abstract»

• ### Image Segmentation with Directed Trees

Publication Year: 1980, Page(s):185 - 191
Cited by:  Papers (29)
| | PDF (5387 KB)

This correspondence presents a simple algorithm to detect and label homogeneous areas in an image, using directed trees for region labeling. The scheme constructs directed trees with the image points as nodes, guided by an edge value computed at every point. These directed trees segment the image into disjoint regions. Because of a valley seeldng property of the tree construction procedure, the bo... View full abstract»

Publication Year: 1980, Page(s): 192
| PDF (1958 KB)
• ### List of Contributors

Publication Year: 1980, Page(s): nil2
| PDF (158 KB)
• ### [Front cover]

Publication Year: 1980, Page(s): c2
| PDF (379 KB)

## Aims & Scope

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Sven Dickinson
University of Toronto
e-mail: sven@cs.toronto.edu