Abstract: An abnormality detection device calculates high-dimensional feature values from real world data; projects, by using a matrix generated from real world data collected for learning so as to project the feature values onto an abnormality discriminant space, feature values calculated from real world data which is input for inspection onto an abnormality discriminant space, the abnormality discriminant space being used for determining presence or absence of an abnormality; judges the presence or absence of an abnormality from a distribution in the abnormality discriminant space; identifies, if an abnormality is found and if the abnormality is a known abnormality, the content of the abnormality from a trend of a distribution in the abnormality discriminant space and presenting the content of the abnormality; and visually displays a distribution in the abnormality discriminant space as a detection result of an abnormality.

Abstract: An abnormality detection device calculates high-dimensional feature values from real world data; projects, by using a matrix generated from real world data collected for learning so as to project the feature values onto an abnormality discriminant space, feature values calculated from real world data which is input for inspection onto an abnormality discriminant space, the abnormality discriminant space being used for determining presence or absence of an abnormality; judges the presence or absence of an abnormality from a distribution in the abnormality discriminant space; identifies, if an abnormality is found and if the abnormality is a known abnormality, the content of the abnormality from a trend of a distribution in the abnormality discriminant space and presenting the content of the abnormality; and visually displays a distribution in the abnormality discriminant space as a detection result of an abnormality.

Abstract: A region segmented image data creating system for histopathological images is provided. The region segmented image data creating system is capable of creating region segmented image data required to generating a region segmented image. A first bi-level image data creating section 12 creates first bi-level image data, in which nucleus regions can be discriminated from other regions, from histopathological image data. A second bi-level image data creating section 14 creates second bi-level image data, in which a background regions can be discriminated from other regions, from the histopathological image data. A three-level image data creating section 15 clarifies cytoplasm regions by computing a negative logical addition of the first bi-level image data and the second bi-level image data, and to create three-level image data as the region segmented image data.

Abstract: A region segmented image data creating system for histopathological images is provided. The region segmented image data creating system is capable of creating region segmented image data required to generating a region segmented image. A first bi-level image data creating section 12 creates first bi-level image data, in which nucleus regions can be discriminated from other regions, from histopathological image data. A second bi-level image data creating section 14 creates second bi-level image data, in which a background regions can be discriminated from other regions, from the histopathological image data. A three-level image data creating section 15 clarifies cytoplasm regions by computing a negative logical addition of the first bi-level image data and the second bi-level image data, and to create three-level image data as the region segmented image data.

Abstract: An image data processing system has a learning storage apparatus that stores projection matrixes obtained by canonical correlation analysis so as to derive, based on at least one of an image feature and a word feature, a latent variable as an abstract concept used for associating an image with a word corresponding thereto and that further stores information required for obtaining the latent variable acquired by use of the projection matrixes, a probability of occurrence of an arbitrary image feature from a certain latent variable and a probability of occurrence of an arbitrary word feature from a certain latent variable. In this way, a probability of the image feature and word feature being simultaneously outputted can be easily and quickly determined, thereby executing a high-speed annotation or retrieval with high precision.

Abstract: A tracing device and a tracing method for realizing a real-time trace of a moving object such as a person from a time-varying image by method of a light processing load, and for learning features through the trace thereby to realize a trace of higher precision. The tracing device divides time-varying data inputted into partial images (in a strip or rectangle shape), and compares the background image containing no tracing object thereby with the current image thereby to extract the divided image having the tracing object. In order to discriminate the tracing object, moreover, the tracing device calculates the color high-level local self-correlated data, from which the features of color and shape can be extracted all at once, and performs the discrimination on the basis of a distance from the featuring data of the tracing object registered. The tracing device updates the background image and the registered featuring data into the latest ones.

Abstract: An abnormal action detector is provided for accurately detecting the position of an object together with an abnormal action using a cubic higher-order local auto-correlation feature. The abnormal action detector comprises a computer which generates inter-frame differential data from moving image data, extracts cubic higher-order local auto-correlation feature data on a pixel-by-pixel basis, adds the feature data for pixels within a predetermined range including each of pixels, calculates an index indicative of abnormality of the added feature data with respect to a subspace indicative of a normal action, determines an abnormality when the index is larger than a predetermined value, and outputs the position of a pixel at which the abnormality is determined. The computer further finds a subspace which exhibits a normal action from past feature data in accordance with a principal component analysis approach.

Abstract: An image data processing system has a learning storage apparatus that stores projection matrixes obtained by canonical correlation analysis so as to derive, based on at least one of an image feature and a word feature, a latent variable as an abstract concept used for associating an image with a word corresponding thereto and that further stores information required for obtaining the latent variable acquired by use of the projection matrixes, a probability of occurrence of an arbitrary image feature from a certain latent variable and a probability of occurrence of an arbitrary word feature from a certain latent variable. In this way, a probability of the image feature and word feature being simultaneously outputted can be easily and quickly determined, thereby executing a high-speed annotation or retrieval with high precision.

Abstract: An object enumerating apparatus comprises means for generating and binarizing inter-frame differential data from moving image data representative of a photographed object under detection, means for extracting feature data from a plurality of the inter-frame binary differential data directly adjacent to each other on a pixel-by-pixel basis through cubic higher-order local auto-correlation, means for calculating a coefficient of each factor vector from a factor matrix comprised of a plurality of factor vectors previously generated through learning using a factor analysis and arranged for one object under detection, and the feature data, and means for adding a plurality of the coefficients for one object under detection, and rounding off the sum to the decimal point to the closest integer representative of a quantity.

Abstract: An abnormal area detecting apparatus is provided for detecting the presence or absence and the position of abnormality with high accuracy using higher-order local auto-correlation feature. The abnormal area detecting apparatus comprises means for extracting feature data from image data on a pixel-by-pixel basis through higher-order local auto-correlation; means for adding the feature data extracted by the feature data extracting means for pixels within a predetermined range including each of pixels spaced apart by a predetermined distance; means for calculating an index indicative of abnormality of feature data with respect to a subspace indicative of a normal area; means for determining an abnormality based on the index; and means for outputting a pixel position at which an abnormal is determined. The apparatus may extract a plurality of higher-order local auto-correlation feature data which differ in displacement width.

Abstract: A tracing device and a tracing method for realizing a real-time trace of a moving object such as a person from a time-varying image by method of a light processing load, and for learning features through the trace thereby to realize a trace of higher precision. The tracing device divides time-varying data inputted into partial images (in a strip or rectangle shape), and compares the background image containing no tracing object thereby with the current image thereby to extract the divided image having the tracing object. In order to discriminate the tracing object, moreover, the tracing device calculates the color high-level local self-correlated data, from which the features of color and shape can be extracted all at once, and performs the discrimination on the basis of a distance from the featuring data of the tracing object registered. The tracing device updates the background image and the registered featuring data into the latest ones.

Abstract: An abnormal action detecting device and method for detecting an abnormal action from a moving picture. An abnormal action detecting device (11) creates from-to-frame difference data from moving picture data inputted from a video camera (10), extracts feature data from three-dimensional data composed of frame-to-frame difference data by using a stereoscopic high-order local cross-correlation, computes the distance between a partial space based on a main component vector determined by a main component analysis technique from the past feature data and the latest feature data, and judges that an action is abnormal if the distance is greater than a predetermined value. By learning a normal action as a partial space and detecting an abnormal action as a deviation from the normal one, for example, even if several persons are present in the screen, an abnormal action of a person can be detected. The computational complexity is low and the real-time processing is possible.

Abstract: An abnormal action detector is provided for accurately detecting the position of an object together with an abnormal action using a cubic higher-order local auto-correlation feature. The abnormal action detector comprises a computer which generates inter-frame differential data from moving image data, extracts cubic higher-order local auto-correlation feature data on a pixel-by-pixel basis, adds the feature data for pixels within a predetermined range including each of pixels, calculates an index indicative of abnormality of the added feature data with respect to a subspace indicative of a normal action, determines an abnormality when the index is larger than a predetermined value, and outputs the position of a pixel at which the abnormality is determined. The computer further finds a subspace which exhibits a normal action from past feature data in accordance with a principal component analysis approach.

Abstract: An adaptive learning type general purpose image measurement and recognition method includes the steps of extracting a large number of basic initial features having values which are invariant to parallel displacement of an object to be caught in an image frame and which have additivity with respect to the image frame and performing statistical feature extraction having a learning function on the basis of a multivariate analysis method applied to the extracted initial features to thereby adaptively enable use for various types of measurement.

Abstract: An adaptive learning type general purpose image measurement and recognition method includes the steps of extracting a large number of basic initial features having values which are invariant to parallel displacement of an object to be caught in an image frame and which have additivity with respect to the image frame, and performing statistical feature extraction having a learning function on the basis of a multivariate analysis method applied to the extracted initial features to thereby adaptively enable use for various types of measurement.

Abstract: In an antilock brake control system, a predetermined ratio of a pressure reduction threshold value to a pseudo vehicular body speed is varied in accordance with predetermined sections of a range of the pseudo vehicular body speed.

Abstract: An image processor which can accurately and promptly obtain information about a ventricle of a living body, such as the boundary diagram, the volume, the centroid movement view and a three-dimensional view. An X-ray projection of a part to be diagnosed is quantized. The boundary and centroid of the part are obtained from the optimal ternary data using the variance of the gray-level of the image and the separation degree of a histogram. The volume and three-dimensional view of the part are obtained from this data using the gray-level method.

Abstract: A method for character reading requiring no character segmentation, which method effects required character recognition by the steps of subjecting a given string of character patterns to continuous scanning to produce either a local feature vector at each of the intersections of rows and columns of character patterns or a global feature vector for each of the columns formed in consequence of the scanning, linearly consolidating either or both of the feature vectors to obtain a lower dimensional vector in new feature axes and continuously matching the vectors with the standard ones set in advance.