Showing papers on "Feature vector published in 1992"

A survey of image registration techniques

Abstract: Registration is a fundamental task in image processing used to match two or more pictures taken, for example, at different times, from different sensors, or from different viewpoints. Virtually all large systems which evaluate images require the registration of images, or a closely related operation, as an intermediate step. Specific examples of systems where image registration is a significant component include matching a target with a real-time image of a scene for target recognition, monitoring global land usage using satellite images, matching stereo images to recover shape for autonomous navigation, and aligning images from different medical modalities for diagnosis.Over the years, a broad range of techniques has been developed for various types of data and problems. These techniques have been independently studied for several different applications, resulting in a large body of research. This paper organizes this material by establishing the relationship between the variations in the images and the type of registration techniques which can most appropriately be applied. Three major types of variations are distinguished. The first type are the variations due to the differences in acquisition which cause the images to be misaligned. To register images, a spatial transformation is found which will remove these variations. The class of transformations which must be searched to find the optimal transformation is determined by knowledge about the variations of this type. The transformation class in turn influences the general technique that should be taken. The second type of variations are those which are also due to differences in acquisition, but cannot be modeled easily such as lighting and atmospheric conditions. This type usually effects intensity values, but they may also be spatial, such as perspective distortions. The third type of variations are differences in the images that are of interest such as object movements, growths, or other scene changes. Variations of the second and third type are not directly removed by registration, but they make registration more difficult since an exact match is no longer possible. In particular, it is critical that variations of the third type are not removed. Knowledge about the characteristics of each type of variation effect the choice of feature space, similarity measure, search space, and search strategy which will make up the final technique. All registration techniques can be viewed as different combinations of these choices. This framework is useful for understanding the merits and relationships between the wide variety of existing techniques and for assisting in the selection of the most suitable technique for a specific problem.

The feature selection problem: traditional methods and a new algorithm

Abstract: For real-world concept learning problems, feature selection is important to speed up learning and to improve concept quality We review and analyze past approaches to feature selection and note their strengths and weaknesses We then introduce and theoretically examine a new algorithm Rellef which selects relevant features using a statistical method Relief does not depend on heuristics, is accurate even if features interact, and is noise-tolerant It requires only linear time in the number of given features and the number of training instances, regardless of the target concept complexity The algorithm also has certain limitations such as nonoptimal feature set size Ways to overcome the limitations are suggested We also report the test results of comparison between Relief and other feature selection algorithms The empirical results support the theoretical analysis, suggesting a practical approach to feature selection for real-world problems

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. >

Texture features for classification of ultrasonic liver images

Abstract: The classification of ultrasonic liver images is studied, making use of the spatial gray-level dependence matrices, the Fourier power spectrum, the gray-level difference statistics, and the Laws texture energy measures. Features of these types are used to classify three sets of ultrasonic liver images-normal liver, hepatoma, and cirrhosis (30 samples each). The Bayes classifier and the Hotelling trace criterion are employed to evaluate the performance of these features. From the viewpoint of speed and accuracy of classification, it is found that these features do not perform well enough. Hence, a new texture feature set (multiresolution fractal features) based on multiple resolution imagery and the fractional Brownian motion model is proposed to detect diffuse liver diseases quickly and accurately. Fractal dimensions estimated at various resolutions of the image are gathered to form the feature vector. Texture information contained in the proposed feature vector is discussed. A real-time implementation of the algorithm produces about 90% correct classification for the three sets of ultrasonic liver images. >

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. >

Computational strategies for object recognition

Abstract: This article reviews the available methods for automated identification of objects in digital images. The techniques are classified into groups according to the nature of the computational strategy used. Four classes are proposed: (1) the simplest strategies, which work on data appropriate for feature vector classification, (2) methods that match models to symbolic data structures for situations involving reliable data and complex models, (3) approaches that fit models to the photometry and are appropriate for noisy data and simple models, and (4) combinations of these strategies, which must be adopted in complex situations. Representative examples of various methods are summarized, and the classes of strategies with respect to their appropriateness for particular applications.

A non-linear projection method based on Kohonen's topology preserving maps

Abstract: A nonlinear projection method is presented to visualize high-dimensional data as a 2D image. The proposed method is based on the topology preserving mapping algorithm of Kohonen. The topology preserving mapping algorithm is used to train a 2D network structure. Then the interpoint distances in the feature space between the units in the network are graphically displayed to show the underlying structure of the data. Furthermore, we present and discuss a new method to quantify how well a topology preserving mapping algorithm maps the high-dimensional input data onto the network structure. This is used to compare our projection method with a well-known method of Sammon (1969). Experiments indicate that the performance of the Kohonen projection method is comparable or better than Sammon's method for the purpose of classifying clustered data. Its time-complexity only depends on the resolution of the output image, and not on the size of the dataset. A disadvantage, however, is the large amount of CPU time required. >

Speech recognition apparatus having a speech coder outputting acoustic prototype ranks

Abstract: A speech coding and speech recognition apparatus. The value of at least one feature of an utterance is measured over each of a series of successive time intervals to produce a series of feature vector signals. The closeness of the feature value of each feature vector signal to the parameter value of each of a set of prototype vector signals is determined to obtain prototype match scores for each vector signal and each prototype vector signal. For each feature vector signal, first-rank and second-rank scores are associated with the prototype vector signals having the best and second best prototype match scores, respectively. For each feature vector signal, at least the identification value and the rank score of the first-ranked and second-ranked prototype vector signals are output as a coded utterance representation signal of the feature vector signal, to produce a series of coded utterance representation signals. For each of a plurality of speech units, a probabilistic model has a plurality of model outputs, and output probabilities for each model output. Each model output comprises the identification value of a prototype vector and a rank score. For each speech unit, a match score comprises an estimate of the probability that the probabilistic model of the speech unit would output a series of model outputs matching a reference series comprising the identification value and rank score of at least one prototype vector from each coded utterance representation signal in the series of coded utterance representation signals.

Speech recognition apparatus using syntactic and semantic analysis

Abstract: A speech recognition apparatus comprises a speech input unit for receiving an input speech signal, analyzing it, and outputting a speech feature parameter series, a speech recognition unit for extracting a speech feature vector from the parameter series, and matching it with a plurality of predetermined words to output a series of word candidates used as keywords, a syntactic analysis unit for analyzing the series of the word candidates as the keywords according to syntactic limitation, and generating a sentence candidate.

Speech recognizer having a speech coder for an acoustic match based on context-dependent speech-transition acoustic models

Abstract: A speech coding apparatus compares the closeness of the feature value of a feature vector signal of an utterance to the parameter values of prototype vector signals to obtain prototype match scores for the feature vector signal and each prototype vector signal. The speech coding apparatus stores a plurality of speech transition models representing speech transitions. At least one speech transition is represented by a plurality of different models. Each speech transition model has a plurality of model outputs, each comprising a prototype match score for a prototype vector signal. Each model output has an output probability. A model match score for a first feature vector signal and each speech transition model comprises the output probability for at least one prototype match score for the first feature vector signal and a prototype vector signal. A speech transition match score for the first feature vector signal and each speech transition comprises the best model match score for the first feature vector signal and all speech transition models representing the speech transition. The identification value of each speech transition and the speech transition match score for the first feature vector signal and each speech transition are output as a coded utterance representation signal of the first feature vector signal.

Method for recognizing patterns in time-variant measurement signals

Abstract: In automatic speech recognition, confusion easily arises between phonetically similar words (for example, the German words "zwei" and "drei") in the case of previous recognition systems. Confusion of words which differ only in a single phoneme (for example, German phonemes "dem" and "den") occurs particularly easily with these recognition systems. In order to solve this problem, a method for recognizing patterns in time-variant measurement signals is specified which permits an improved discrimination between such signals by reclassifying in pairs. This method combines the Viterbi decoding algorithm with the method of hidden Markov models, the discrimination-relevant features being examined separately in a second step after the main classification. In this case, different components of feature vectors are weighted differently, it being the case that by contrast with known approaches these weightings are performed in a theoretically based way. The method is suitable, inter alia, for improving speech-recognizing systems.

Recognition of Arabic characters

Abstract: A statistical approach for the recognition of Arabic characters is introduced As a first step, the character is segmented into primary and secondary parts (dots and zigzags) The secondary parts of the character are then isolated and identified separately, thereby reducing the number of classes from 28 to 18 The moments of the horizontal and vertical projections of the remaining primary characters are then calculated and normalized with respect to the zero-order moment Simple measures of the shape are obtained from the normalized moments A 9-D feature vector is obtained for each character Classification is accomplished using quadratic discriminant functions The approach was evaluated using isolated, handwritten, and printed characters from a database established for this purpose The results indicate that the technique offers better classification rates in comparison with existing methods >

Temporal decorrelation method for robust speaker verification

Abstract: A speaker voice verification system uses temporal decorrelation linear transformation and includes a collector for receiving speech inputs from an unknown speaker claiming a specific identity, a word-level speech features calculator operable to use a temporal decorrelation linear transformation for generating word-level speech feature vectors from such speech inputs, word-level speech feature storage for storing word-level speech feature vectors known to belong to a speaker with the specific identity, a word-level speech feature vectors received from the unknown speaker with those received from the word-level speech feature storage, and speaker verification decision circuitry for determining, based on the similarity score, whether the unknown speaker's identity is the same as that claimed The word-level vector scorer further includes concatenation circuitry as well as a word-specific orthogonalizing linear transformer Other systems and methods are also disclosed

The Meta-Pi network: building distributed knowledge representations for robust multisource pattern recognition

Abstract: The authors present the Meta-Pi network, a multinetwork connectionist classifier that forms distributed low-level knowledge representations for robust pattern recognition, given random feature vectors generated by multiple statistically distinct sources. They illustrate how the Meta-Pi paradigm implements an adaptive Bayesian maximum a posteriori classifier. They also demonstrate its performance in the context of multispeaker phoneme recognition in which the Meta-Pi superstructure combines speaker-dependent time-delay neural network (TDNN) modules to perform multispeaker /b,d,g/ phoneme recognition with speaker-dependent error rates of 2%. Finally, the authors apply the Meta-Pi architecture to a limited source-independent recognition task, illustrating its discrimination of a novel source. They demonstrate that it can adapt to the novel source (speaker), given five adaptation examples of each of the three phonemes. >

Identification of change structure in statistical process control

Abstract: In order to diagnose properly quality problems that occur in manufacturing the diagnostician, be it human or computer, must be privy to various sources of information about the process and its behaviour This paper describes how neural networks and Bayesian discriminant function techniques can be used to provide knowledge of how a product characteristic changed, ie shift in mean or variability, when so noted by the control chart application Such information is useful because there usually exists some underyling knowledge about the physical phenomena in question that relates the behaviour of the observed characteristic to its processing variables When a change in the process is detected by the appropriate statistical method, a feature vector of process-related statistics is used to identify the change structure as a shift in mean or variance This paper addresses various issues concerned with this problem, namely: process change detection, feature vector selection, training patterns, and error rates S

Method and apparatus for determining and organizing feature vectors for neural network recognition

Abstract: A pattern recognition method and apparatus utilizes a neural network to recognize input images which are sufficiently similar to a database of previously stored images. Images are first processed and subjected to a Fourier transform which yields a power spectrum. An in-class to out-of-class study is performed on a typical collection of images in order to determine the most discriminatory regions of the Fourier transform. A feature vector consisting of the (most discriminatory) information from the power spectrum of the Fourier transform of the image is formed. Feature vectors are input to a neural network having preferably two hidden layers, input dimensionality of the number of elements in the feature vector and output dimensionality of the number of data elements stored in the database. Unique identifier numbers are preferably stored along with the feature vector. Application of a query feature vector to the neural network results in an output vector. The output vector is subjected to statistical analysis to determine if a sufficiently high confidence level exists to indicate a successful identification whereupon a unique identifier number may be displayed.

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.

Self-organizing pattern classification neural network system

Abstract: A self-organizing pattern classification neural network system includes means for receiving incoming pattern of signals that were processed by feature extractors that extract feature vectors from the incoming signal. These feature vectors correspond to information regarding certain features of the incoming signal. The extracted feature vectors then each pass to separate self-organizing neural network classifiers. The classifiers compare the feature vectors to templates corresponding to respective classes and output the results of their comparisons. The output from the classifier for each class enter a discriminator. The discriminator generates a classification response indicating the best class for the input signal. The classification response includes information indicative of whether the classification is possible and also includes the identified best class. Lastly, the system includes a learning trigger for transferring a correct glass signal to the self-organizing classifiers so that they can determine the validity of their classification results.

Off-line signature verification using directional PDF and neural networks

Abstract: The first stage of a complete automatic handwritten signature verification system (AHSVS) is described in this paper Since only random forgeries are taken into account in this first stage of decision, the directional probability density function (PDF) which is related to the overall shape of the handwritten signature has been taken into account as feature vector Experimental results show that using both directional PDFs and the completely connected feedforward neural network classifier are valuable to build the first stage of a complete AHSVS >

Human face recognition method based on the statistical model of small sample size

Abstract: Automatic recognition of human faces is a frontier topic in computer vision. In this paper, a novel recognition approach to human faces is proposed, which is based on the statistical model in the optimal discriminant space. Singular value vector has been proposed to represent algebraic features of images. This kind of feature vector has some important properties of algebraic and geometric invariance, and insensitiveness to noise. Because singular value vector is usually of high dimensionality, and recognition model based on these feature vectors belongs to the problem of small sample size, which has not been solved completely, dimensionality compression of singular value vector is very necessary. In our method, an optimal discriminant transformation is constructed to transform an original space of singular value vector into a new space in which its dimensionality is significantly lower than that in the original space. Finally, a recognition model is established in the new space. Experimental results show that our method has very good recognition performance, and recognition accuracies of 100 percent are obtained for all 64 facial images of 8 classes of human faces.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Continuous parameter hidden Markov model approach to automatic handwriting recognition

Abstract: A computer-based system and method for recognizing handwriting. The present invention includes a pre-processor, a front end, and a modeling component. The present invention operates as follows. First, the present invention identifies the lexemes for all characters of interest. Second, the present invention performs a training phase in order to generate a hidden Markov model for each of the lexemes. Third, the present invention performs a decoding phase to recognize handwritten text. Hidden Markov models for lexemes are produced during the training phase. The present invention performs the decoding phase as follows. The present invention receives test characters to be decoded (that is, to be recognized). The present invention generates sequences of feature vectors for the test characters by mapping in chirographic space. For each of the test characters, the present invention computes probabilities that the test character can be generated by the hidden Markov models. The present invention decodes the test character as the recognized character associated with the hidden Markov model having the greatest probability.

Formulation of a multivalued recognition system

Abstract: A recognition system based on fuzzy set theory and approximate reasoning that is capable of handling various imprecise input patterns and providing a natural decision system is described. The input feature is considered to be of either quantitative form, linguistic form, mixed form, or set form. The entire feature space is decomposed into overlapping subdomains depending on the geometric structure and the relative position of the pattern classes found in the training samples. Uncertainty (ambiguity) in the input statement is managed by providing/modifying membership values to a great extent. A relational matrix corresponding to the subdomains and the pattern classes is used to recognize the samples. The system uses L.A. Zadeh's (1977) compositional rule of inference and gives a natural (linguistic) multivalued output decision associated with a confidence factor denoting the degree of certainty of a decision. The effectiveness of the algorithm is demonstrated for some artificially generated patterns and for real-life speech data. >

Signal-processing arrangement for classifying objects on the basis of signals from sensors

Abstract: In a signal processing arrangement for classifying objects on the basis of signals from a plurality of different sensors each of the signals from the sensors is applied to a pair of neural networks. One neural network of each pair processes predetermined characteristics of the object and the other neural network processes movement or special data of the object such that these networks provide detection, identification and movement information specific for the sensors. Feature vectors formed from this information specific for the sensors are applied to a neural network for determining the associations of the identification and movement information. The information obtained by this network is applied together with the feature vectors to a network for identifying and classifying the object. The information from the association and identification networks, respectively, are supplied together with the information specific for the sensors to an expert system which, by using further knowledge about data and facts of the potential objects, makes final decisions and conclusions for identification.

Keyword/non-keyword classification in isolated word speech recognition

Abstract: A two-pass classification system and method that post-processes HMM scores with additional confidence scores to derive a value that may be applied to a threshold on which a keyword verses non-keyword determination may be based. The first stage comprises Generalized Probabilistic Descent (GPD) analysis which uses feature vectors of the spoken words and the HMM segmentation information (developed by the HMM detector during processing) as inputs to develop a first set of confidence scores through a linear combination (a weighted sum) of the feature vectors of the speech. The second stage comprises a linear discrimination method that combines the HMM scores and the confidence scores from the GPD stage with a weighted sum to derive a second confidence score. The output of the second stage may then be compared to a predetermined threshold to determine whether the spoken word or words include a keyword.

Feature extraction and classification algorithms for high-dimensional data

Abstract: In this research, feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next a novel approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A novel characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. 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; (2) it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, we study some problems encountered in analyzing high dimensional data and propose possible solutions. We first recognize the increased importance of the second order statistics in analyzing high dimensional data. By investigating the characteristics of high dimensional data, we suggest the reason why the second order statistics must be taken into account in high dimensional data. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. We propose a method to visualize statistics using a color code. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

A speech coding apparatus having speaker dependent prototypes generated from nonuser reference data

Abstract: A speech coding apparatus and method for use in a speech recognition apparatus and method. The value of at least one feature of an utterance is measured during each of a series of successive time intervals to produce a series of feature vector signals representing the feature values. A plurality of prototype vector signals, each having at least one parameter value and a unique identification value are stored. The closeness of the feature vector signal is compared to the parameter values of the prototype vector signals to obtain prototype match scores for the feature value signal and each prototype vector signal. The identification value of the prototype vector signal having the best prototype match score is output as a coded representation signal of the feature vector signal. Speaker-dependent prototype vector signals are generated from both synthesized training vector signals and measured training vector signals. The synthesized training vector signals are transformed reference feature vector signals representing the values of features of one or more utterances of one or more speakers in a reference set of speakers. The measured training feature vector signals represent the values of features of one or more utterances of a new speaker/user not in the reference set.