Showing papers on "Feature extraction published in 1993"

Comparing images using the Hausdorff distance

Abstract: The Hausdorff distance measures the extent to which each point of a model set lies near some point of an image set and vice versa. Thus, this distance can be used to determine the degree of resemblance between two objects that are superimposed on one another. Efficient algorithms for computing the Hausdorff distance between all possible relative positions of a binary image and a model are presented. The focus is primarily on the case in which the model is only allowed to translate with respect to the image. The techniques are extended to rigid motion. The Hausdorff distance computation differs from many other shape comparison methods in that no correspondence between the model and the image is derived. The method is quite tolerant of small position errors such as those that occur with edge detectors and other feature extraction methods. It is shown that the method extends naturally to the problem of comparing a portion of a model against an image. >

A review of recent texture segmentation and feature extraction techniques

Abstract: The area of texture segmentation has undergone tremendous growth in recent years. There has been a great deal of activity both in the refinement of previously known approaches and in the development of completely new techniques. Although a wide variety of methodologies have been applied to this problem, there is a particularly strong concentration in the development of feature-based approaches and on the search for appropriate texture features. In this paper, we present a survey of current texture segmentation and feature extraction methods. Our emphasis is on techniques developed since 1980, particularly those with promise for unsupervised applications.

Nuclear feature extraction for breast tumor diagnosis

Abstract: Interactive image processing techniques, along with a linear-programming-based inductive classifier, have been used to create a highly accurate system for diagnosis of breast tumors. A small fraction of a fine needle aspirate slide is selected and digitized. With an interactive interface, the user initializes active contour models, known as snakes, near the boundaries of a set of cell nuclei. The customized snakes are deformed to the exact shape of the nuclei. This allows for precise, automated analysis of nuclear size, shape and texture. Ten such features are computed for each nucleus, and the mean value, largest (or 'worst') value and standard error of each feature are found over the range of isolated cells. After 569 images were analyzed in this fashion, different combinations of features were tested to find those which best separate benign from malignant samples. Ten-fold cross-validation accuracy of 97% was achieved using a single separating plane on three of the thirty features: mean texture, worst area and worst smoothness. This represents an improvement over the best diagnostic results in the medical literature. The system is currently in use at the University of Wisconsin Hospitals. The same feature set has also been utilized in the much more difficult task of predicting distant recurrence of malignancy in patients, resulting in an accuracy of 86%.

Feature extraction based on decision boundaries

Abstract: A novel approach to feature extraction for classification based directly on the decision boundaries is proposed. It is shown how discriminantly redundant features and discriminantly informative features are related to decision boundaries. A procedure to extract discriminantly informative features based on a decision boundary is proposed. The proposed feature extraction algorithm has several desirable properties: (1) it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and (2) it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal class means or equal class covariances as some previous algorithms do. Experiments show that the performance of the proposed algorithm compares favorably with those of previous algorithms. >

Further Research on Feature Selection and Classification Using Genetic Algorithms

Abstract: This paper summarizes work on an approach that combines feature selection and data classiication using Genetic Algorithms. First, it describes our use of Genetic Algorithms combined with a K-nearest neighbor algorithm to optimize classiication by searching for an optimal feature weight-ing, essentially warping the feature space to coalesce individuals within groups and to separate groups from one another. This approach has proven especially useful with large data sets where standard feature selection techniques are computationally expensive. Second, it describes our implementation of the approach in a parallel processing environment, giving nearly linear speed-up in processing time. Third, it will summarize our present results in using the technique to discover the relative importance of features in large biological test sets. Finally, it will indicate areas for future research. 1 The Problem We live in the age of information where data is plentiful , to the extent that we are typically unable to process all of it usefully. Computer science has been challenged to discover approaches that can sort through the mountains of data available and discover the essential features needed to answer a speciic question. These approaches must be able to process large quantities of data, in reasonable time and in the presence of oisy" data i.e., irrelevant or erroneous data. Consider a typical example in biology. Researchers in the Center for Microbial Ecology (CME) have selected soil samples from three environments found in agriculture. The environments were: near the roots of a crop (rhizosphere), away from the innuence of the crop roots (non-rhizosphere), and from a fallow eld (crop residue). The CME researchers wished to investigate whether samples from those three environments could be distinguished. In particular, they wanted to see if diversity decreased in the rhizosphere as a result of the symbiotic relationship between the roots and its near-neighbor microbes, and if so in what ways. Their rst experiments used the Biolog c test as the discriminator. Biolog consists of a plate of 96 wells, with a diierent substrate in each well. These sub-strates (various sugars, amino acids and other nutrients) are assimilated by some microbes and not by others. If the microbial sample processes the substrate in the well, that well changes color which can be recorded photometrically. Thus large numbers of samples can be processed and characterized based on the substrates they can assimilate. The CME researchers applied the Biolog test to 3 sets of 100 samples …

Using range profiles as feature vectors to identify aerospace objects

Abstract: The authors use range profiles as the feature vectors for data representation, and they establish a decision rule based on the matching scores to identify aerospace objects. Reasons for choosing range profiles as the feature vectors are explained, and criteria for determining aspect increments for building the database are proposed. Typical experimental examples of the matching scores and recognition rates are provided and discussed. The results demonstrated can be used for comparison with other identification methods. >

Genetic Programming for Feature Discovery and Image Discrimination

Abstract: We apply Genetic Programming (GP) to the development of a processing tree for the classification of features extracted from images: measurements from a set of input nodes are weighted and combined through linear and nonlinear operations to form an output response. No constraints are placed upon size, shape, or order of processing within the network. This network is used to classify feature vectors extracted from IR imagery into target/nontarget categories using a database of 2000 training samples. Performance is tested against a separate database of 7000 samples. This represents a significant scaling up from the problems to which GP has been applied to date. Two experiments are performed: in the first set, we input classical "statistical" image features and minimize misclassification of target and non-target samples. In the second set of experiments, GP is allowed to form it's own feature set from primitive intensity measurements. For purposes of comparison, the same training and test sets are used to train two other adaptive classifier systems, the binary tree classifier and the Backpropagation neural network. The GP network achieves higher performance with reduced computational requirements. The contributions of GP "schemata," or subtrees, to the performance of generated trees are examined. Genetic Programming for Feature Discovery and Image Discrimination 1

Direct gray-scale extraction of features for character recognition

Abstract: A method for feature extraction directly from gray-scale images of scanned documents without the usual step of binarization is presented. This approach eliminates binarization by extracting features directly from gray-scale images. In this method, a digitized gray-scale image is treated as a noisy sampling of the underlying continuous surface and desired features are obtained by extracting and assembling topographic characteristics of this surface. The advantages and effectiveness of the approach are both shown theoretically and demonstrated through preliminary experiments of the proposed method. >

Fingerprint image analysis for automatic identification

Abstract: Most of the papers on fingerprints deal with classification of fingerprint images. Fingerprint databases being large (in the range of millions), the effort in matching of fingerprints within a class or when the class is unknown, is very significant. This requires fingerprint image analysis and extraction of the “minutiae” features, which are used for matching FPs. In this paper a scheme of preprocessing and feature extraction of fingerprint images for automatic identification is presented, which works even if the pattern class is unknown. The identification of fingerprints is based on matching the minutiae features of a given finger-print against those stored in the database. The core and delta information is used for classification and for registration while matching. These algorithms have been tested for more than 10,000 fingerprint images of different qualities. The results are manually verified and found to be very good for practical application. A few sample results are presented.

View variation of point-set and line-segment features

Abstract: The variation, with respect to view, of 2D features defined for projections of 3D point sets and line segments is studied. It is established that general-case view-invariants do not exist for any number of points, given true perspective, weak perspective, or orthographic projection models. Feature variation under the weak perspective approximation is then addressed. Though there are no general-case weak-perspective invariants, there are special-case invariants of practical importance. Those cited in the literature are derived from linear dependence relations and the invariance of this type of relation to linear transformation. The variation with respect to view is studied for an important set of 2D line segment features: the relative orientation, size, and position of one line segment with respect to another. The analysis includes an evaluation criterion for feature utility in terms of view-variation. This relationship is a function of both the feature and the particular configuration of 3D line segments. The use of this information in objection recognition is demonstrated for difficult discrimination tasks. >

Robust feature selection algorithms

Abstract: Selecting a set of features which is optimal for a given task is a problem which plays an important role in wide variety of contexts including pattern recognition, adaptive control and machine learning. Experience with traditional feature selection algorithms in the domain of machine learning leads to an appreciation for their computational efficiency and a concern for their brittleness. The authors describe an alternative approach to feature selection which uses genetic algorithms as the primary search component. Results are presented which suggested that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in compuational efficiency.

Pattern Recognition Using Invariants Defined From Higher Order Spectra- One Dimensional Inputs

Abstract: A new approach to pattern recognition using in- variant parameters based on higher order spectra is presented. In particular, invariant parameters derived from the bispec- trum are used to classify one-dimensional shapes. The bispec- trum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale and amplifi- cation invariant, as well. A minimal set of these invariants is selected as the feature vector for pattern classification, and a minimum distance classifier using a statistical distance measure is used to classify test patterns. The classification technique is shown to distinguish two similar, but different bolts given their one-dimensional profiles. Pattern recognition using higher or- der spectral invariants is fast, suited for parallel implementa- tion, and has high immunity to additive Gaussian noise. Sim- ulation results show very high classification accuracy, even for low signal-to-noise ratios.

A neural network-based methodology for pavement crack detection and classification

Abstract: This paper presents a methodology for automating the processingof highway pavement video images using an integration of artificial neural network models with conventional image-processing techniques. The methodology developed is able to classify pavement surface cracking by the type, severity, and extent of cracks detected in video images. The approach is divided into five major steps: (1) image segmentation, which involves reduction of a raw gray-scale pavement image into a binary image, (2) feature extraction, (3) decomposition of the image into tiles and identification of tiles with cracking, (4) integration of the results from step (3) and classification of the type of cracking in each image, and (5) computation of the severities and extents of cracking detected in each image. In this methodology, artificial neural network models are used in automatic thresholding of the images in stage (1) and in the classification stages (3) and (4). The results obtained in each stage of the process are presented and discussed in this paper. The research results demonstrate the feasibility of this new approach for the detection, classification, and quantification of highway pavement surface cracking.

Improvements of a lexicon directed algorithm for recognition of unconstrained handwritten words

Abstract: Discusses improvements made to a lexicon directed algorithm for recognition of unconstrained handwritten words (cursive, discrete, or mixed) such as those encountered in mail pieces. The procedure consists of binarization, pre-segmentation, intermediate feature extraction, segmentation recognition, and post-processing. The segmentation recognition and the post-processing are repeated for all lexicon words while the binarization to the intermediate feature extraction are applied once for an input word. The result of performance evaluation using large handwritten address block database is described, and algorithm improvements are described and discussed, in order to achieve higher recognition accuracy and speed. As a result the performance for lexicons of size 10, 100, and 1000 are improved to 98.01%, 95.46%, and 91.49% respectively. The processing speed for each lexicon is improved to 2.0, 2.5, and 3.5 sec/word on a SUN SPARC station 2. >

Acoustical and Environmental Robustness in Automatic Speech Recognition

Abstract: This dissertation describes a number of algorithms developed to increase the robustness of automatic speech recognition systems with respect to changes in the environment These algorithms attempt to improve the recognition accuracy of speech recognition systems when they are trained and tested in different acoustical environments, and when a desk-top microphone (rather than a close-talking microphone) is used for speech input Without such processing, mismatches between training and testing conditions produce an unacceptable degradation in recognition accuracy Two kinds of environmental variability are introduced by the use of desk-top microphones and different training and testing conditions: additive noise and spectral tilt introduced by linear filtering An important attribute of the novel compensation algorithms described in this thesis is that they provide joint rather than independent compensation for these two types of degradation Acoustical compensation is applied in our algorithms as an additive correction in the cepstral domain This allows a higher degree of integration within SPHINX, the Carnegie Mellon speech recognition system, that uses the cepstrum as its feature vector Therefore, these algorithms can be implemented very efficiently Processing in many of these algorithms is based on instantaneous signal-to-noise ratio (SNR), as the appropriate compensation represents a form of noise suppression at low SNRs and spectral equalization at high SNRs The compensation vectors for additive noise and spectral transformations are estimated by minimizing the differences between speech feature vectors obtained from a “standard” training corpus of speech and feature vectors that represent the current acoustical environment In our work this is accomplished by a minimizing the distortion of vector-quantized cepstra that are produced by the feature extraction module in SPHINX In this dissertation we describe several algorithms including the SNR-Dependent Cepstral Normalization, (SDCN) and the Codeword-Dependent Cepstral Normalization (CDCN) With CDCN, the accuracy of SPHINX when trained on speech recorded with a close-talking microphone and tested on speech recorded with a desk-top microphone is essentially the same obtained when the system is trained and tested on speech from the desk-top microphone An algorithm for frequency normalization has also been proposed in which the parameter of the bilinear transformation that is used by the signal-processing stage to produce frequency warping is adjusted for each new speaker and acoustical environment The optimum value of this parameter is again chosen to minimize the vector-quantization distortion between the standard environment and the current one In preliminary studies, use of this frequency normalization produced a moderate additional decrease in the observed error rate

A unified approach to boundary perception: edges, textures, and illusory contours

Abstract: A model consisting of a multistage system which extracts and groups salient features in the image at different spatial scales (or frequencies) is used. In the first stage, a Gabor wavelet decomposition provides a representation of the image which is orientation selective and has optimal localization properties in space and frequency. This decomposition is useful in detecting significant features such as step and line edges at different scales and orientations in the image. Following the wavelet transformation, local competitive interactions are introduced to reduce the effects of noise and changes in illumination. Interscale interactions help in localizing the line ends and corners, and play a crucial role in boundary perception. The final stage groups similar features, aiding in boundary completion. The different stages can be identified with processing by simple, complex, and hypercomplex cells in the visual cortex of mammals. Experimental results demonstrate the performance of this model in detecting boundaries (both real and illusory) in real and synthetic images. >

Feature modelling by incremental feature recognition

Abstract: A novel feature-modelling system which implements a hybrid of feature-based design and feature recognition in a single framework is described. During the design process of a part, the user can modify interactively either the solid model or the feature model of the part while the system keeps the other model 3onsistent with the changed one. This gives the user the freedom of choosing the most convenient means of expressing each required operation. The system is based on a novel feature recognizer that provides incremental feature recognition , which allows changes to a geometric model to be recognized as new or modified features while preserving previously recognized features that remain unchanged in the geometric model. Each recognizable feature type is specified by means of a feature-definition language which facilitates the addition of new feature types into the system.

Method and apparatus for converting documents between paper medium and electronic media using a user profile

Abstract: An apparatus and method for providing access to computerized information services for users unequipped with computers, particularly information services related to commercial transactions. Facsimile images of hardcopy forms are used as means for conveying transactional information. Each facsimile image contains character and data fields with information for a particular trade transaction. The system captures the facsimile image in bitmap form and converts the character and data fields into computer readable form. The system enhances each image and performs several levels of feature extraction, including character recognition techniques. To efficiently and automatically convert great document volumes, the system optimizes database lookup and human operator manual keying to assist character recognition techniques in developing accurate coded text from the features of the facsimile image. The system must produce accurate coded text strings as the system subsequently converts the coded text string into files of coded text or EDI messages for transmission over telecommunications networks to independent computer systems. Unrecognized or inaccurate characters in the coded text are impermissible in order to form valid EDI messages or coded text files. Thus, validation of the coded text is an important part of the present invention. Additionally, user services provide additional processing of data received from independent computer systems into management information of additional business value to particular system users.

Feature-based retrieval of similar shapes

Abstract: A shape similarity-based retrieval method for image databases that supports a variety of queries is proposed It is flexible with respect to the choice of feature and definition of similarity and is implementable using existing multidimensional point access methods A prototype system that handles the problems of distortion and occlusion is described Experiments with one specific point access method (PAM) are presented >

Sophisticated topology of hidden Markov models for cursive script recognition

Abstract: The paper describes an adaptation of hidden Markov models (HMM) to automatic recognition of unrestricted handwritten words. Many interesting details of a 50,000 vocabulary recognition system for US city names are described. This system includes feature extraction, classification, estimation of model parameters, and word recognition. The feature extraction module transforms a binary image to a sequence of feature vectors. The classification module consists of a transformation based on linear discriminant analysis and Gaussian soft-decision vector quantizers which transform feature vectors into sets of symbols and associated likelihoods. Symbols and likelihoods form the input to both HMM training and recognition. HMM training performed in several successive steps requires only a small amount of gestalt labeled data on the level of characters for initialization. HMM recognition based on the Viterbi algorithm runs on subsets of the whole vocabulary. >

Recovering and characterizing image features using an efficient model based approach

Abstract: The development of an efficient model-based approach to detect and characterize precisely important features such as edges, corners and vertices is discussed. The key is to propose some efficient models associated to each of these features directly from the image by searching the parameters of the model that best approximate the observed grey level image intensities. Due to the large amount of time required by a first approach that assumes the blur of the imaging acquisition system to be describable by a 2-D Gaussian filter, different solutions that drastically reduce this computational time are considered and developed. The problem of the initialization phase in the minimization process is considered, and an original and efficient solution is proposed. A large number of experiments involving real images are conducted in order to test and compare the reliability, the robustness, and the efficiency of the proposed approaches. >

The behavior-knowledge space method for combination of multiple classifiers

Abstract: A new method called the "Behavior-Knowledge Space Method" halJ been developed which can efficientlr aggregate decilJionlJ ol the individual c1alJlJifierlJ and de­ rive better relJuitlJ lor the unconlJtrained handwritten character recognition. It containlJ two IJtages: (1) the knowledge-modeling IJtage, which enroctlJ knowledge from the former behavior of classifiers and constructlJ a K-dimenlJional behavior-knowledge space; and (8) the operation stage, which is carried out lor each telJt sample, and which combines decisions generoted from individual classifiers, enters a specific unit 01 the con­ structed space, and makes a final decision by a rule which utilizelJ the knowledge inside the unit. Many important properties can be derived from this method which make it very promising.

Directional mathematical morphology and reformalized Hough transformation for the analysis of topographic maps

Abstract: One of the most difficult and important problems encountered in the automatic digitizing of graphical topographic maps is the identification and separate digitizing of different kinds of features. Essentially, in topographic maps, there are two kinds of geometric features: linear features, such as roads and railways that have an arbitrary length, and symbols, which indicate a type of building or area of land usage or even numerical information. These two types of features are extracted and recognized by using methods based on multiangled parallelism (MAP). The MAP operation method performs parallel calculation on directional feature planes. The linear features are extracted using erosion-dilation operations on the directional feature planes, and the symbols are extracted using a reformalized and parallel version of the generalized Hough transformation on the same directional planes, which is called the MAP matching method. The methods have been applied to a 1/25000 scale map. >

Preprocessing and feature extraction for a handwriting recognition system

Abstract: Offline cursive script word recognition has received increasing attention during the last years. Impressive progress has been achieved in reading isolated single characters during the last decade. Cursive script recognition still lacks a good recognition rate. Since there is a high variability in unconstrainted handwritten script words, the domain is much more difficult than single character recognition. To achieve acceptable results, the context has to be restricted by a given lexicon of all possible words. The only accessible information is the binary image of the cursive script word. Since handling of raster data is cumbersome, connectivity analysis is applied as a first processing step. Thereafter it is necessary to reduce the variability as much as possible without losing relevant information. Therefore, some normalization steps angle, rotation stroke width, and size. The normalization techniques of the authors' system and the subsequent feature extraction are presented. The proposed algorithms are every efficient because they are based on the contour information provided by connectivity analysis. >

Logo recognition using geometric invariants

Abstract: The problem of logo recognition is of great interest in the document domain, especially for databases, because of its potential for identifying the source of the document and its generality as a recognition problem. By recognizing the logo, one obtains semantic information about the document, which may be useful in deciding whether or not to analyze the textual components. A multi-level stages approach to logo recognition which uses global invariants to prune the database and local affine invariants to obtain a more refined match is presented. An invariant signature which can be used for matching under a variety of transformations is obtained. The authors provide a method of computing Euclidean invariants and show how to extend them to capture similarity, affine, and projective invariants when necessary. They implement feature detection, feature extraction, and local invariant algorithms and successfully demonstrate the approach on a small database. >

Fuzzy methods for multisensor data fusion

Abstract: This paper presents a new general framework for information fusion based on fuzzy methods. In the proposed architecture, feature extraction and feature fusion are performed by adopting a class of (possibly multidimensional) membership functions which take care of the possibility and admissibility of the feature itself. Test cases of one-dimensional and image data fusion are presented. >