Showing papers on "Feature extraction published in 1992"

Recognizing human action in time-sequential images using hidden Markov model

Abstract: A human action recognition method based on a hidden Markov model (HMM) is proposed. It is a feature-based bottom-up approach that is characterized by its learning capability and time-scale invariability. To apply HMMs, one set of time-sequential images is transformed into an image feature vector sequence, and the sequence is converted into a symbol sequence by vector quantization. In learning human action categories, the parameters of the HMMs, one per category, are optimized so as to best describe the training sequences from the category. To recognize an observed sequence, the HMM which best matches the sequence is chosen. Experimental results for real time-sequential images of sports scenes show recognition rates higher than 90%. The recognition rate is improved by increasing the number of people used to generate the training data, indicating the possibility of establishing a person-independent action recognizer. >

Feature extraction from faces using deformable templates

Abstract: We propose a method for detecting and describing features of faces using deformable templates. The feature of interest, an eye for example, is described by a parameterized template. An energy function is defined which links edges, peaks, and valleys in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image by altering its parameter values to minimize the energy function, thereby deforming itself to find the best fit. The final parametr values can be used as descriptors for the feature. We illustrate this method by showing deformable templates detecting eyes and mouths in real images. We demonstrate their ability for tracking features.

Feature selection and feature extraction for text categorization

Abstract: The effect of selecting varying numbers and kinds of features for use in predicting category membership was investigated on the Reuters and MUC-3 text categorization data sets. Good categorization performance was achieved using a statistical classifier and a proportional assignment strategy. The optimal feature set size for word-based indexing was found to be surprisingly low (10 to 15 features) despite the large training sets. The extraction of new text features by syntactic analysis and feature clustering was investigated on the Reuters data set. Syntactic indexing phrases, clusters of these phrases, and clusters of words were all found to provide less effective representations than individual words.

A feature based approach to face recognition

Abstract: A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model is biologically motivated, and the locations of the features often correspond to salient facial features such as the eyes, nose, etc. Topological graphs are used to represent relations between features, and a simple deterministic graph-matching scheme that exploits the basic structure is used to recognize familiar faces from a database. Each of the stages in the system can be fully implemented in parallel to achieve real-time recognition. Experimental results for a 128*128 image with very little noise are evaluated. >

Pattern recognition and image preprocessing

Abstract: Pattern recognition: supervised and unsupervised learning in pattern recognition nonparametric decision theoretic classification nonparametric (distribution-free) training of discriminant functions statistical discriminant functions clusteringanalysis and unsupervised learning dimensionality reduction and feature selection. Neural networks for pattern recognition: multilayer perception radial basis function networks hamming net and Kohonen self-organizing feature map the Hopfield model.Data preprocessing for pictorial pattern recognition: preprocessing in the spatial domain pictorial data preposessing and shape analysis transforms and image processing in the transform doamin wavelets and wavelet transforms. Applications: exemplaryapplications. Practical concerns of image processing and pattern recognition: computer system architectures for image processing and pattern recognition. Appendices: digital images image model and discrete mathematics digital image fundamentals matrixmanipulation Eigenvectors and Eigenvalves of an operator notation.

Face recognition based on depth and curvature features

Abstract: Face recognition from a representation based on features extracted from range images is explored. Depth and curvature features have several advantages over more traditional intensity-based features. Specifically, curvature descriptors have the potential for higher accuracy in describing surface-based events, are better suited to describe properties of the face in areas such as the cheeks, forehead, and chin, and are viewpoint invariant. Faces are represented in terms of a vector of feature descriptors. Comparisons between two faces is made based on their relationship in the feature space. The author provides a detailed analysis of the accuracy and discrimination of the particular features extracted, and the effectiveness of the recognition system for a test database of 24 faces. Recognition rates are in the range of 80% to 100%. In many cases, feature accuracy is limited more by surface resolution than by the extraction process. >

A computational vision approach to image registration

Abstract: A computational vision approach is presented for the estimation of 2-D translation, rotation, and scale from two partially overlapping images. The approach results in a fast method that produces good results even when large rotation and translation have occurred between the two frames and the images are devoid of significant features. An illuminant direction estimation method is first used to obtain an initial estimation of camera rotation. A small number of feature points are then located, using a Gabor wavelet model for detecting local curvature discontinuities. An initial estimate of scale and translation is obtained by pairwise matching of the feature points detected from both frames. Finally, hierarchical feature matching is performed to obtain an accurate estimate of translation, rotation and scale. A method for error analysis of matching results is also presented. Experiments with synthetic and real images show that this algorithm yields accurate results when the scale of the images differ by up to 10%, the overlap between the two frames is as small as 23%, and the camera rotation between the two frames is significant. Experimental results and applications are presented. >

Genetic algorithms as a tool for feature selection in machine learning

Abstract: An approach being explored to improve the usefulness of machine learning techniques for generating classification rules for complex, real-world data is described. The approach involves the use of genetic algorithms as a front end to a traditional rule induction system in order to identify and select the best subset of features to be used by the rule induction system. This approach has been implemented and tested on difficult texture classification problems. The results are encouraging and indicate that there are significant advantages to the approach in this domain. >

Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers

Abstract: The classification and recognition of two-dimensional patterns independently of their position, orientation, and size by using high-order networks are discussed. A method is introduced for reducing and controlling the number of weights of a third-order network used for invariant pattern recognition. The method leads to economical networks that exhibit high recognition rates for translated, rotated, and scaled, as well as locally distorted, patterns. The performance of these networks at recognizing types and handwritten numerals independently of their position, size, and orientation is compared with and found superior to the performance of a layered feedforward network to which image features extracted by the method of moments are presented as input. >

Human facial feature extraction for face interpretation and recognition

Abstract: This paper presents facial features extraction algorithms which can be used for automated visual interpretation and recognition of human faces. It is possible to capture the contours of eye and mouth by deformable template model because of their analytically describable shapes. However, the shapes of eyebrow, nostril and face are difficult to model using a deformable template. They are extracted by using an active contour model, the snake. >

Classification trees with neural network feature extraction

Abstract: The ideal use of small multilayer nets at the decision nodes of a binary classification tree to extract nonlinear features is proposed. The nets are trained and the tree is grown using a gradient-type learning algorithm in the multiclass case. The method improves on standard classification tree design methods in that it generally produces trees with lower error rates and fewer nodes. It also reduces the problems associated with training large unstructured nets and transfers the problem of selecting the size of the net to the simpler problem of finding a tree of the right size. An efficient tree pruning algorithm is proposed for this purpose. Trees constructed with the method and the CART method are compared on a waveform recognition problem and a handwritten character recognition problem. The approach demonstrates significant decrease in error rate and tree size. It also yields comparable error rates and shorter training times than a large multilayer net trained with backpropagation on the same problems. >

A face recognition method using higher order local autocorrelation and multivariate analysis

Abstract: Proposes a face recognition method which is characterized by structural simplicity, trainability and high speed. The method consists of two stages of feature extractions: first, higher order local autocorrelation features which are shift-invariant and additive are extracted from an input image; then those features are linearly combined on the basis of multivariate analysis methods so as to provide new effective features for face recognition in learning from examples. >

At the frontiers of OCR

Abstract: It is argued that it is time for a major change of approach to optical character recognition (OCR) research. The traditional approach, focusing on the correct classification of isolated characters, has been exhausted. The demonstration of the superiority of a new classification method under operational conditions requires large experimental facilities and databases beyond the resources of most researchers. In any case, even perfect classification of individual characters is insufficient for the conversion of complex archival documents to a useful computer-readable form. Many practical OCR tasks require integrated treatment of entire documents and well-organized typographic and domain-specific knowledge. New OCR systems should take advantage of the typographic uniformity of paragraphs or other layout components. They should also exploit the unavoidable interaction with human operators to improve themselves without explicit 'training'. >

Adaptive fuzzy leader clustering of complex data sets in pattern recognition

Abstract: A modular, unsupervised neural network architecture that can be used for clustering and classification of complex data sets is presented. The adaptive fuzzy leader clustering (AFLC) architecture is a hybrid neural-fuzzy system that learns online in a stable and efficient manner. The system used a control structure similar to that found in the adaptive resonance theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two-stage process: a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid position from fuzzy C-means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The AFLC algorithm is applied to the Anderson iris data and laser-luminescent finger image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. >

Performance evaluation of scene registration and stereo matching for cartographic feature extraction

Abstract: Three major areas in the development of competent 3-D scene interpretation system are discussed. First, the importance of accurate automatic scene registration and the difficulty in automated extraction and matching of scene reference points are described. Second, the authors describe two stereo matching algorithms, S1, which is an area-based matcher previously used in the SPAM system, and S2, which is a feature-based matching algorithm based on hierarchical waveform matching. Third, the authors introduce several performance evaluation metrics that made it possible to measure the quality of the overall scene recovery, the building disparity estimate, and the quality and sharpness of the building delineations. Such manually generated scene reference models are critical for understanding strengths and weaknesses of various matching algorithms and in the incremental development of improvements to existing algorithms. Experiments were performed on difficult examples of aerial imagery. >

A neural network based hybrid system for detection, characterization, and classification of short-duration oceanic signals

Abstract: A comprehensive classifier system is presented for short-duration oceanic signals obtained from passive sonar, which exhibit variability in both temporal and spectral characteristics even in signals obtained from the same source. Wavelet-based feature extractors are shown to be superior to the more commonly used autoregressive coefficients and power spectral coefficients for describing these signals. A variety of static neural network classifiers are evaluated and are shown to compare favorably with traditional statistical techniques for signal classification. The focus is on those networks that are able to time-out irrelevant input features and are less susceptible to noisy inputs, and two new neural-network-based classifiers are introduced. Methods for combining the outputs of several classifiers to yield a more accurate labeling are proposed and evaluated. These methods lead to higher classification accuracy and provide a mechanism for recognizing deviant signals and false alarms. Performance results are given for signals in the DARPA standard data set I. >

Model-based multi-sensor data fusion

Abstract: The authors describe an algorithm for implementing a multisensor system in a model-based environment with consideration of the constraints. Based on an environment model, geometric features and constraints are generated from a CAD model database. Sensor models are used to predict sensor response to certain features and to interpret raw sensor data. A constrained MMS (minimum mean squared) estimator is used to recursively predict, match, and update feature location. The effects of applying various constraints in estimation were shown by simulation system mounted on a robot arm for localization of known object features. >

Features Extraction and Analysis Methods for Sequences of Ultrasound Images

Abstract: Our principal motivation is to study time sequences of echocardiographic raw data to track specific anatomical structures. First, we show that the image processing can make direct use of the audio signal data, avoiding loss of information and yielding optimal results.

Morphological multiscale image segmentation

Abstract: This paper deals with a morphological approach to the problem of unsupervised image segmentation. The proposed technique relies on a multiscale approach which allows a hierarchical processing of the data ranging from the most global scale to the most detailed one. At each scale, the algorithm relies on four steps: preprocessing, feature extraction, decision and quality estimation. The goal of the preprocessing step is to simplify the original signal which is too complex to be processed at once. Morphological filters by reconstruction are very attractive for this purpose because they simplify without corrupting the contour information. The feature extraction intends to extract the pertinent parameters for assessing the degree of homogeneity of the regions. To this goal, morphological techniques extracting flat or contrasted regions are very efficient. The decision step defines precisely the contours of the regions. This decision is achieved by a watershed algorithm. Finally, the quality estimation is used to compute the information that has to be further processed by the next scale to improve the segmentation result. The estimation is based on a region modeling procedure. The resulting segmentation is very robust and can deal with very different types of images. Moreover, the first levels give segmentation results with a few regions; but precisely located contours.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Feature extraction using an unsupervised neural network

Abstract: A novel unsupervised neural network for dimensionality reduction that seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursuit methods is discussed. This leads to a new statistical insight into the synaptic modification equations governing learning in Bienenstock, Cooper, and Munro (BCM) neurons (1982). The importance of a dimensionality reduction principle based solely on distinguishing features is demonstrated using a phoneme recognition experiment. The extracted features are compared with features extracted using a backpropagation network.

Analog VLSI circuits for stimulus localization and centroid computation

Abstract: An analog aggregation network that extracts the position of a stimulus in a sensory field is presented. This network is integrated with photodiodes in a VLSI circuit that performs stimulus localization through the computation of the centroid of a visual image. In this implementation, bipolar transistors and global subtraction are used to produce a high-precision centroid implementation. Theory for the localization of a bright visual stimulus is developed, and the theoretical predictions are compared to experimental data taken from the 160×160-pixel centroid circuit. Finally, the applications of these circuits to more complex feature extraction and to sensorimotor feedback systems are discussed.

Feature classification using supervised statistical pattern recognition

Abstract: Feature classification using a novel supervised statistical pattern recognition approach is described. A tree-like hierarchical decomposition of n-dimensional feature space is created off-line from an image processing system. The hierarchical tree is created through a minimax-type decompositional segregation of n-dimensional feature vectors of different feature classifications within the corresponding feature space. Each cell preferably contains feature vectors of only one feature classification, or is empty, or is of a predefined minimum cell size. Once created, the hierarchical tree is made available to the image processing system for real-time defect classification of features in a static or moving pattern. Each feature is indexed to the classification tree by locating its corresponding feature vector in the appropriate feature space cell as determined by a depth-first search of the hierarchical tree. The smallest leaf node which includes that feature vector provides the statistical information on the vector's classification.

Method and apparatus for performing mapping-type analysis including use of limited electrode sets

Abstract: A system (10) is disclosed that includes a monitor (16) for use in performing 12-lead electrocardiographic (ECG) and body surface mapping (BSM) analyses on a patient (12) with a single ten-electrode cable set (14). The monitor receives nine leads of data from the electrode cable set and initially preprocesses it to simplify further analysis. Then the data is transformed to produce a spatial distribution relative to the patient's chest, representative of the data that would be collectible with a 192-electrode set. Feature extraction techniques are also described for use in evaluating transformed, as well as conventional, BSM data with respect to clinically evaluated populations. In that regard, features of interest are extracted from the data and statistically analyzed to detect select cardiac conditions.

High confidence personal identification by rapid video analysis of iris texture

Abstract: The concept of using the iris of the eye as a kind of optical fingerprint for personal identification is discussed. By isolating and encoding the visible texture of the iris from a video image into a multiscale sequence of quadrature 2D Gabor coefficients, a 256-byte iris code has been developed that uniquely identifies any individual, with astronomic confidence levels. Following image analysis, the critical problem of pattern recognition is transformed essentially into a statistical test of independence on the real and imaginary parts of the complex 2D Gabor coefficients. Statistical decision theory permits rigorous execution of identification decisions from comparisons of iris codes, at the rate of 4000 per second, including computation of the confidence associated with each identification decision. Such a recognition system has been developed using a conventional zoom videocamera. In tests to date, the system has never failed to identify any enrolled individual correctly. >

Decision boundary feature extraction for neural networks

Abstract: The authors propose a novel feature extraction method for neural networks. The method is based on the decision boundary feature extraction algorithm. It has been shown that all the necessary features for classification can be extracted from the decision boundary. To apply the method, the authors first define the decision boundary in neural networks. Next, they propose a procedure for extracting all the necessary features for classification from the decision boundary. The proposed algorithm preserves the characteristics of neural networks, which can define an arbitrary decision boundary. Experiments show promising results. >

Three-Dimensional Object Recognition from Range Images

Abstract: 1 Introduction.- 1.1 Computer Vision.- 1.2 Three-Dimensional Object Recognition.- 1.2.1 Representation.- 1.2.2 Indexing.- 1.2.3 Constraint Propagation and Constraint Satisfaction.- 1.3 Common Goals of Three-Dimensional Object Recognition Systems.- 1.4 Qualitative Features.- 1.4.1 Study of Qualitative Properties in Low-level Vision Processes.- 1.4.2 Qualitative Features in Object Recognition.- 1.5 The Scope and Outline of the Book.- I Fundamentals of Range Image Processing and Three-Dimensional Object Recognition.- 2 Range Image Sensors and Sensing Techniques.- 2.1 Range Image Forms.- 2.2 Classification of Range Sensors.- 2.2.1 Radar Sensors.- 2.2.2 Triangulation Sensors.- 2.2.3 Sensors based on Optical Interferometry.- 2.2.4 Sensors Based on Focusing Techniques.- 2.2.5 Sensors Based on Fresnel Diffraction.- 2.2.6 Tactile Range Sensors.- 3 Range Image Segmentation.- 3.1 Mathematical Formulation of Range Image Segmentation.- 3.2 Fundamentals of Surface Differential Geometry.- 3.3 Surface Curvatures.- 3.4 Range Image Segmentation Techniques.- 3.4.1 Edge-based Segmentation Techniques.- 3.4.2 Region-based Segmentation Techniques.- 3.4.3 Hybrid Segmentation Techniques.- 3.5 Summary.- 4 Representation.- 4.1 Formal Properties of Geometric Representations.- 4.2 Wire-Frame Representation.- 4.3 Constructive Solid Geometry (CSG) Representation.- 4.4 Qualitative Representation using Geons.- 4.5 Aspect Graph Representation.- 4.6 EGI Representation.- 4.7 Representation Using Generalized Cylinders.- 4.8 Superquadric Representation.- 4.9 Octree Representation.- 4.10 Summary.- 5 Recognition and Localization Techniques.- 5.1 Recognition and Localization Techniques-An Overview.- 5.2 Interpretation Tree Search.- 5.3 Hough Clustering.- 5.4 Matching of Relational Structures.- 5.5 Geometric Hashing.- 5.6 Iterative Model Fitting.- 5.7 Indexing and Qualitative Features.- 5.8 Vision Systems as Coupled Systems.- 5.8.1 Object-Oriented Representation for Coupled Systems.- 5.8.2 Object-Oriented Representation for 3-D Object Recognition.- 5.8.3 Embedding Parallelism in an Object-Oriented Coupled System.- 5.9 Summary.- II Three-Dimensional Object Recognition Using Qualitative Features.- 6 Polyhedral Object Recognition.- 6.1 Preprocessing and Segmentation.- 6.1.1 Plane Fitting to Pixel Data.- 6.1.2 Clustering in Parameter Space.- 6.1.3 Post Processing of Clustering Results.- 6.1.4 Contour Extraction and Classification.- 6.1.5 Computation of Edge Parameters.- 6.2 Feature Extraction.- 6.3 Interpretation Tree Search.- 6.3.1 Pose Determination.- 6.3.2 Scene Interpretation Hypothesis Verification.- 6.4 Generalized Hough Transform.- 6.4.1 Feature Matching.- 6.4.2 Computation of the Transform.- 6.4.3 Pose Clustering.- 6.4.4 Verification of the Pose Hypothesis.- 6.5 Experimental Results.- 6.6 Summary.- 7 Recognition of Curved Objects.- 7.1 Representation of Curved Surfaces.- 7.1.1 Extraction of Surface Curvature Features from Range Images.- 7.2 Recognition Using a Point-Wise Curvature Description.- 7.2.1 Object Recognition Using Point-Wise Surface Matching.- 7.3 Recognition Using Qualitative Features.- 7.3.1 Cylindrical and Conical Surfaces.- 7.3.2 The Recognition Process Using Qualitative Features.- 7.3.3 Localization of a Cylindrical Surface.- 7.3.4 Localization of a Conical Surface.- 7.3.5 Localization of a Spherical Surface.- 7.3.6 An Experimental Comparison.- 7.4 Recognition of Complex Curved Objects.- 7.5 Dihedral Feature Junctions.- 7.5.1 Types of Dihedral Feature Junctions.- 7.5.2 Matching of Dihedral Feature Junctions.- 7.5.3 Pose Determination.- 7.5.4 Pose Clustering.- 7.6 Experimental Results.- 7.7 Summary.- III Sensitivity Analysis and Parallel Implementation.- 8 Sensitivity Analysis.- 8.1 Junction Matching and Pose Determination.- 8.2 Sensitivity Analysis.- 8.3 Qualitative Features.- 8.4 The Generalized Hough Transform.- 8.4.1 The Generalized Hough Transform in the Absence of Occlusion and Sensor Error.- 8.4.2 The Generalized Hough Transform in Presence of Occlusion and Sensor Error.- 8.4.3 Probability of Spurious Peaks in the Generalized Hough Transform.- 8.5 The Use of Qualitative Features in the Generalized Hough Transform.- 8.5.1 Reduction in the Search Space of Scene Interpretations due to Qualitative Features.- 8.5.2 Reducing the Effect of Smearing in Parameter Space using Qualitative Features.- 8.5.3 The Probability of Random Peaks in the Weighted Generalized Hough Transform.- 8.5.4 Determination of ?k(x), pk(x) and P(k).- 8.6 Weighted Generalized Hough Transform.- 9 Parallel Implementations of Recognition Techniques.- 9.1 Parallel Processing in Computer Vision.- 9.1.1 Parallel Architectures.- 9.1.2 Parallel Algorithms.- 9.2 The Connection Machine.- 9.2.1 System Organization.- 9.2.2 Performance Specifications.- 9.3 Object Recognition on the Connection Machine.- 9.3.1 Feature Extraction.- 9.3.2 Localization of Curved Surfaces.- 9.3.3 Computation of Dihedral Feature Junctions.- 9.3.4 Matching and Pose Computation.- 9.3.5 Pose Clustering.- 9.4 Object Recognition on the Hypercube.- 9.4.1 Scene Description.- 9.4.2 Model Data.- 9.4.3 Scene Feature Data.- 9.4.4 Pruning Constraints.- 9.4.5 Localization.- 9.5 Mapping the Interpretation Tree on the Hypercube.- 9.5.1 Breadth-First Mapping of the Interpretation Tree.- 9.5.2 Depth-First Mapping of the Interpretation Tree.- 9.5.3 Depth-First Mapping of the Interpretation Tree with Load Sharing.- 9.5.4 Experimental Results.