Matsushita Electric Industrial Co., Ltd.

Panasonic Corporation

Ajinomoto Co., Ltd.

Kenji Nagao

Masaki Souma

Mihoko Shimano

Etsuko Nagao, Legal Representative

Megumi Yamaoka

Mihoko Kunii

Taro Imagawa

Masahiro Iwasaki

Shin Yamada

Tomonobu Naruoka

Mitsuaki Inaba

Mika Hamada

Yuichi Kobayakawa

Kanno: Yuji

Kenichi Udea

Asuka Seki

Robert Reed

Keisuke Hayata

Susan Gingrich

Natsumi Nishikata

Toshiaki Akimoto

Ryuta Itoh

Masaki Soma

Described herein are nutritional compositions, such as ketogenic compositions, leucine-enriched amino acid mixtures, etc., useful, for example, for dietary management of intractable epilepsy. In some embodiments, the disclosure relates to methods of inhibiting epileptic seizures in a subject comprising administering to the subject a leucine-enriched amino acid mixture.

Leucine-enriched ketogenic formulations

The present invention provides a food intake regulating agent which improves anorexia and overeating and is safe for ingestion for a long term. The present invention relates to a food intake regulating agent containing 3 kinds of amino acids: threonine, methionine and tryptophan, as active ingredients.

Nutrition composition

A normal line information estimation device for estimating normal line information of an object from an image of the object. The normal line information estimation device includes an illumination direction estimation section for estimating an illumination direction with respect to the object in the image from a luminance value of the image; a normal line information estimation section for estimating the normal line information of the object in the image based on the illumination direction; and a specular reflection component estimation section for estimating a specular reflection component of the image from the normal line information of the object and the illumination direction with respect to the object. The normal line information estimation section estimates the normal line information again from the image from which a specular reflection component has been removed in the specular reflection component section.

Normal line information estimation device, registered image group formation device and image collation device, and normal line information estimation method

A parameter estimation method for estimating a parameter by estimating maximum a posteriori probability for input data. Operation for the input data is expressed by an inner product for the input data and the inner product is replaced by a Kernel function. By using the calculation result of the kernel function, a parameter is estimated. The method includes a step (offline operation) for learning a correlation between a plurality of learning input data in which a parameter to be estimated is known and the parameter corresponding to each of the learning input data; and a step (online operation) for estimating the parameter for the estimating input data in which a parameter to be estimated is unknown, by using the learned correlation.

Parameter estimation method, parameter estimation device and collation method

The present invention provides a human detection device which detects a human contained in a moving picture, and includes the following: a spatiotemporal volume generation unit which generates a three-dimensional spatiotemporal image in which frame images that make up the moving picture in which a human has been filmed are arranged along a temporal axis; a spatiotemporal fragment extraction unit which extracts a real image spatiotemporal fragment, which is an image appearing in a cut plane or cut fragment when the three-dimensional spatiotemporal image is cut, from the generated three-dimensional spatiotemporal image; a human body region movement model spatiotemporal fragment output unit which generates and outputs, based on a human movement model which defines a characteristic of the movement of a human, a human body region movement spatiotemporal fragment, which is a spatiotemporal fragment obtained from a movement by the human movement model; a spatiotemporal fragment verification unit which verifies between a real image spatiotemporal fragment and a human body region movement model spatiotemporal fragment; and an attribute output unit which outputs a human attribute which includes the presence/absence of a human in the moving picture, based on that verification result.

Human detection device and human detection method

The present invention enables estimation of desired parameters with less computation cost and with high precision by inputting first training vectors generated from observation patterns and second training vectors generated from estimation targets in order to learn the correlation between observation patterns as inputs and patterns of the estimation targets such that desired outputs are assumed from the inputs, calculating the auto-correlation information of the two training vectors, and cross-correlation information of an average vector, the first training vectors and second training vectors, and using the information, obtaining probable expectation values based on the Bayes theory of the estimation targets with respect to an input pattern.

Parameter estimation apparatus and data matching apparatus

According to a disclosed method, an image is learned beforehand, and an image of an object to be recognized is entered, then this object is recognized. An image including an object to be learned is entered, and it is divided into partial images. Further classifying into plural classes, a matrix for feature extraction is calculated in each class. A feature is calculated by using this matrix for feature extraction, and stored. Consequently, an image including an object to be recognized is entered, and it is divided into partial images. From the partial images, the feature of the object of recognition is calculated by using the obtained matrix for feature extraction, and the similarity measure of the both is calculated by using the data of the stored feature and the feature of the object of recognition, and the object is recognized.

Method of object recognition, apparatus of the same and recording medium therefor

An object recognizing apparatus is provided which is capable of precisely recognizing an object in an input image with the use of a corresponding learning image even when a local-segment of the input image coincides with a learning-local-segment of another similar learning image. The apparatus comprises (1) image dividing means for dividing an input image, which is received from image input means, into local-segments, (2) similar-local-segment extracting means for extracting a similar learning-local-segment to the local-segment of the input image from a learning image database, (3) object position estimating means for estimating the position of an object to be identified in the input image from the coordinates of the local-segment and the coordinates of the learning-local-segment corresponding to the local-segment, (4) counting means for counting the local-segments coincide with their corresponding learning-local-segments, and (5) object determining means for judging that the object to be identified is present when a result of counting is greater than a predetermined number. Consequently, the object and its position in any input image can be detected at higher accuracy.

Apparatus and method for image recognition

Based on image data taken and acquired by any one of a plurality of cameras, a predetermined object included in the image is recognized by pattern matching. A plurality of databases associated with a plurality of cameras is provided and a database to be used is changed according to the camera to be used. The plurality of databases can also be changed according to conditions such as weather and environment.

Method and apparatus for object recognition using a plurality of cameras and databases

Apparatus and method for recognizing an object and determining its position and shape

In the present invention, model data groups each comprised of a plurality of items of model data are classified according to predetermined item and are thereby classified into classes each having a smaller distribution than a category that an application requires, a representative value of each class is compared with input data, a class with the representative value having a highest degree of similarity with the input data is determine, a category to which the class belongs is obtained, and thus the category to which the input data belongs is determined.

Data classifying apparatus and material recognizing apparatus

In an apparatus for detecting a human face from an input image, candidate areas for an eye and candidate areas for an interval between eyes are detected from the image, an area of the interval between eyes is determined from the positional relationship between the candidate areas for an eye and candidate area for an interval between eyes, and a face area is determined so that the face area includes the area of the interval between eyes.

Method for detecting a human face and an apparatus of the same

With respect to two pattern sets obtained on different conditions, a feature-extraction matrix, which maximizes between-class scatter and minimizes within-class scatter, is found respectively. A first feature amount is calculated using one of the feature-extraction matrices. The first feature amount and the two matrices are retained in a referential database. A pattern is determined to a second feature amount&#x2014;extracted by applying another feature-extraction matrix to a pattern input&#x2014;by extracting a most similar element out of the first feature amount retained in the referential database.

Pattern recognition method, pattern check method and pattern recognition apparatus as well as pattern check apparatus using the same methods

A plurality of teaching identification face image patterns of teaching persons are obtained in a first pattern obtaining process using an image scanner, and a plurality of teaching video face image patterns of the teaching persons are obtained in a second pattern obtaining process using a video camera. A feature extraction matrix, which minimizes an overlapping area between a pattern distribution of the teaching identification face image patterns and a perturbation distribution between a group of teaching identification face image patterns and a group of teaching video face image patterns, is calculated. In cases where a feature extraction using the feature extraction matrix is performed for referential face image patterns of registered persons obtained in the first pattern obtaining process, referential feature pattern vectors independent of any pattern obtaining process are obtained. When an input face image pattern of a specific person obtained in the second pattern obtaining process is received, the feature extraction is performed for the input face image pattern to obtain an input feature pattern vector independent of any pattern obtaining process. Therefore, the specific person can be recognized as a specific registered person by selecting a specific referential feature pattern vector most similar to the input feature pattern vector.

METHOD AND APPARATUS FOR RECOGNIZING IMAGE PATTERN, METHOD AND APPARATUS FOR JUDGING IDENTITY OF IMAGE PATTERNS, RECORDING MEDIUM FOR RECORDING THE PATTERN RECOGNIZING METHOD AND RECORDING MEDIUM FOR RECORDING THE PATTERN IDENTITY JUDGING METHOD

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Kanagawa, Japan

Kenji Nagao