Showing papers on "Feature extraction published in 1969"

Feature extraction: A survey

Abstract: A survey of computer algorithms and philosophies applied to problems of feature extraction and pattern recognition in conjunction with image analysis is presented. The main emphasis is on usable techniques applicable to practical image processing systems. The various methods are discussed under the broad headings of microanalysis and macroanalysis.

Visual Feature Extraction by a Multilayered Network of Analog Threshold Elements

Abstract: A new type of visual feature extracting network has been synthesized, and the response of the network has been simulated on a digital computer. This research has been done as a first step towards the realization of a recognizer of handwritten characters. The design of the network was suggested by biological systems, especially, the visual systems of cat and monkey. The network is composed of analog threshold elements connected in layers. Each analog threshold element receives inputs from a large number of elements in the neighbouring layers and performs its own special functions. It takes care of one restricted part of the photoreceptor layer, on which an input pattem is presented, and it responds to one particular feature of the input pattem, such as brightness contrast, a dot in the pattern, a line segment of a particular orientation, or an end of the line. This means that the network performs parallel processing of the information. With the propagation of the information through the layered network, the input pattern is successively decomposed into dots, groups of line segments of the same orientation, and the ends of these line segments.

Experiments with "Characteristic Loci" for Recognition of Handprinted Characters

Abstract: Recognition experiments have been performed on handprinted characters using a set of features which has not been applied previously to handprinting. Glucksman's "characteristic loci" were utilized in experiments with the well-known Highleyman data, as well as samples generated at Stanford Research Institute and Honeywell. Two recognition algorithms were tested. Results on numeric samples compare favorably with those of other investigators despite the small dimensionality of the feature vector. On the constrained Honeywell samples, recognition rates exceeding 98 percent were achieved using the simpler algorithm. With alphabetic samples, some problems remain in resolving persistent ambiguities, and methods for attacking these problems are considered.

Feature Extraction on Binary Patterns

Abstract: The objects and methods of automatic feature extraction on binary patterns are briefly reviewed. An intuitive interpretation for geometric features is suggested whereby such a feature is conceived of as a cluster of component vectors in pattern space. A modified version of the Isodata or K-means clustering algorithm is applied to a set of patterns originally proposed by Block, Nilsson, and Duda, and to another artificial alphabet. Results are given in terms of a figure-of-merit which measures the deviation between the original patterns and the patterns reconstructed from the automatically derived feature set.

Feature selection for pattern recognition systems

Abstract: Publisher Summary The major problem of pattern recognition is essentially the discrimination of the input data between statistical populations via the search for features among members of a population. This chapter presents several approaches for the extraction of features in pattern recognition systems. The design of pattern recognition systems generally involves several major problem areas. The first problem is concerned with the representation of input data which can be measured from the objects of a pattern class. This is the sensing problem. The second problem is concerned with the selection of characteristic features or attributes from the received input data. This is often referred to as the feature extraction or selection problem. The third problem deals with the determination of optimum decision procedures which are needed in the process of identification and classification. This is the optimum decision problem. In solving the feature selection problem and the optimum decision problem, a set of parameters to be estimated and optimized is generally involved. This gives rise to the parameter estimation problem. The selection of features has been recognized as an important process in a pattern recognition system. When the complete set of discriminatory features for each pattern class can be determined from the measurement, the recognition and classification of the patterns will present no problem and automatic classification may be reduced to a simple matching procedure.

Geometrical pattern feature extraction by projection on Haar orthonormal basis

Abstract: The c l a s s i f i c a t i o n o f a s e t o f p a t t e r n s i s a p rob lem t h a t appears i n v e r y many f i e l d s . I n g e n e r a l , the number o f p o s s i b l e c l asses i s unknown. To d e f i n e a d i s t a n c e ( o r s i m i l a r i t y ) m a t r i x on the s e t o f p a t t e r n s , we must summarize the a v a i l a b l e da te i n the fo rm o f a f i n i t e s e t o f f e a t u r e s w i t h an i n f o r m a t i o n l o s s as sma l l as p o s s i b l e . To e v a l u a t e d i s t a n c e c o e f f i c i e n t s , the b e s t i s t o p r o j e c t t he p a t t e r n on a t o t a l o r thono rma l b a s i s ; on the c o n d i t i o n of choos ing a base match ing the p a t t e r n p r o p e r t i e s wh ich concern t he c l a s s i f i c a t i o n prob lem to be s o l v e d . In the case o f geomet r i c p a t t e r n s where the d i s c o n t i n u i t i e s p l a y a n e s s e n t i a l r o l e , H a a r ! s d i s c o n t i n u o u s f u n c t i o n s appears to be v e r y p r o m i s i n g as shown in t h e g i v e n examples. Morever , Haa r ' s f u n c t i o n s are w e l l adapt e d t o d i g i t a l c o m p u t a t i o n .

Nonorthogonal projections for feature extraction in pattern recognition

Abstract: It is known that R linearly separable classes of multi-dimensional pattern vectors can always be represented in a feature space of at most R dimensions. An approach is developed which can frequently be used to find a non-orthogonal transformation to project the patterns into a feature space of considerably lower dimensionality. Examples involving classification of handwritten and printed digits are used to illustrate the technique.

Application of the Karhunen-Loeve Expansion to Feature Extraction and Classification,

Abstract: : The Karhunen-Loeve expansion has been used previously to extract important features for representing samples taken from a given distribution. A method is developed herein to use the Karhunen-Loeve expansion to extract features relevant to classification of a sample taken from one of two pattern classes. Numerical examples are presented to illustrate the technique. Also, it is shown that the properties of the proposed technique can be applied into two classes without a priori knowledge of the class. (Author)

Classification of electro-encephalograms with pattern recognition algorithms.

Abstract: : The report proposes a mathematical model for making decisions about the condition of a subject from EEG date and algorithms for implementing the model. Pattern recognition methods are combined with the experience of a practicing electroencephalographer to balance the availability of mathematical models, computational feasibility, and experience. The aim of the model building is to produce a computationally feasible algorithm for a digital computer that generates a chart showing the condition of the subject as a function of time. The report gives preliminary results on feature extraction. In its present version, the pattern recognizer treats feature extraction and pattern classification distinctly and limits learning to transition probabilities of the Markov chain. The decision procedure that is outlined is applicable to any model that defines discrete states and permits Markovian movement between states. (Author)