Kabushiki Kaisha Toshiba

Asahi Fiber Glass Company, Limited

Tokyo Eletec Corporation

Susumu Kubota

Satoshi Ito

Tomohiro Nakai

Yasuhiro Taniguchi

Tatsuo Kozakaya

Toshimitsu Kaneko

Kenichi Maeda

Tomoki Watanabe

Hiroaki Nakai

Tomokazu Kawahara

Nobuyuki Matsumoto

Toshiyuki Ono

Yasunori Taguchi

Takuma Yamamoto

Yasukazu Okamoto

Hiromitsu Nakayama

Yuji Yokoo

Masayoshi Hoshino

Naoki Kawamura

Tsuyoshi Nakano

Kazuhiro Kitamura

According to an embodiment, a collation device includes a hardware processor configured to: generate, based at least in part on input data, an input vector comprising input data features indicating features of the input data, the input data features comprising D number of features, D being an integer equal to or larger than two; and generate first specification information that specifies d selected features among the input data features of the input vector, based at least in part on a plurality of reference vectors and the input vector, the plurality of reference vectors each comprising reference features in the same form as the input vector, the reference features comprising the D number of features, d being an integer equal to or larger than one and smaller than D.

Collation device, collation method, and computer program product

According to an embodiment, a computing device includes a processing circuitry. The processing circuitry receives an input of tensor data. The processing circuitry sets a window in the tensor data. The processing circuitry compares, for each pair of coordinates in the tensor data within the window, a pixel value at the pair of coordinates with one or more thresholds, and selects a weight value corresponding to a comparison result. The processing circuitry adds the weight values selected for the respective pairs of coordinates to obtain a cumulative value. The processing circuitry derives a value based at least in part on the cumulative value.

Tensor image mapping device, method, and computer program product

According to an embodiment, an apparatus includes a calculator, an pixel evaluator, an accumulator, and an area evaluator. The calculator is configured to calculate a feature of an image for each pixel in image data. The pixel evaluator is configured to produce a score that evaluates the feature for each pixel. The accumulator is configured to calculate, for each pixel, a cumulative score obtained by accumulating all scores in an area including a minor angle formed by a half line in a first direction from the each pixel position and another half line in a second direction from the each pixel position. The area evaluator is configured to calculate an evaluation value that is a total of the scores in a quadrilateral area enclosed by two lines of the first direction and two lines of the second direction based on the cumulative scores at pixel positions at vertexes of the quadrilateral area.

Image evaluation apparatus, image evaluation method, and computer program product

According to an embodiment, a computing apparatus includes a memory, and a processor. The memory stores N first vectors in a d-dimensional binary vector space consisting of binary values. The processor acquires a second vector in the d-dimensional binary vector space. The processor extracts M first vectors having a distance from the second vector satisfying a first condition out of the N first vectors, and calculate a distribution of distances of the M first vectors from the second vector. The processor acquires a first kernel function per a first distance between the M first vectors and the second vector in a first range. The processor generates a second kernel function based on the distribution and the first kernel functions. The processor calculates an occurrence probability of the second vector in the N first vectors based on the second kernel function.

Apparatus, method, and computer program product for computing occurrence probability of vector

According to an embodiment, a feature point detection device includes a generator to generate a K-class classifier and perform, for T times, an operation in which a first displacement vector is obtained that approximates D number of initial feature points of each training sample classified on a class-by-class basis to true feature points; a calculator to calculate, from the first displacement vectors, second displacement label vectors each unique to one second displacement vector, and a second displacement coordinate vector common to the second displacement vectors; a classifier to apply the K-class classifiers to the input image and obtain a second displacement label vector associated with a class identifier output from each K-class classifier; an adder to add up the second displacement label vectors; and a detector to detect D number of true feature points based on the initial feature points, the added label vector, and the second displacement coordinate vector.

Feature point detection device, feature point detection method, and computer program product

A recognition device includes a storage unit, an acquiring unit, a first calculator, a second calculator, a determining unit, and an output unit. The storage unit stores multiple training patterns each belonging to any one of multiple categories. The acquiring unit acquires a recognition target pattern to be recognized. The first calculator calculates, for each of the categories, a distance histogram representing distribution of the number of training patterns belonging to the category with respect to distances between the recognition target pattern and the training patterns belonging to the category. The second calculator analyzes the distance histogram of each of the categories to calculate confidence of the category. The determining unit determines a category of the recognition target pattern from the multiple categories by using the confidences. The output unit outputs the category of the recognition target pattern.

Recognition device, method, and computer program product

According to an embodiment, an image processing device includes a generator and a processor. The generator is configured to generate, from a plurality of unit images in which points on an object are imaged by an imaging unit at different positions according to distances between the imaging unit and the positions of the points on the object, a refocused image focused at a predetermined distance. The processor is configured to perform blurring processing on each pixel of the refocused image according to an intensity corresponding to a focusing degree of the pixel of the refocused image.

Image processing device, image processing method, and computer program product

According to one embodiment, a feature extraction device includes an obtaining unit that obtains image data having a plurality of pixels. The device includes a pixel feature calculation unit that calculates first pixel features and second pixel features of each of the pixels, which are different from each other, and a classification unit that classifies a pair of pixels by using the first features for at least some of the plurality of pixels. The device includes a co-occurrence frequency calculation unit that calculates a co-occurrence frequency representing a frequency of co-occurrence of the second pixel features of the first pixel and the second pixel features of the second pixel for the set for which a result of the classification by the classification unit is consistent.

Feature extraction device

According to an embodiment, a recognition device includes a generation unit to select, plural times, groups each including learning samples from a storage unit, learn a classification metric for classifying the groups selected in each selection, and generate an evaluation metric including the classification metrics; a transformation unit to transform a first feature value of an image including an object into a second feature value using the evaluation metric; a calculation unit to calculate similarities of the object to categories in a table using the second feature value and reference feature values; and a registration unit to register the second feature value as the reference feature value in the table associated with the category of the object and register the first feature value as the learning sample belonging to the category of the object in the storage unit. The generation unit performs the generation again.

Recognition device, recognition method, and computer program product

According to an embodiment, a control apparatus controls a touch panel includes a first resistive film includes a first terminal and a second terminal and a second resistive film includes a third terminal and a fourth terminal, the first and second resistive films being laid on top of each other with a gap between the resistive films. The control apparatus comprises an application unit, a measurement unit and a calculation unit. The application unit is configured to apply a voltage between the first terminal and the second terminal at a first timing. The measurement unit is configured to measure a voltage of each of the third and fourth terminals at the first timing. The calculation unit is configured to derive a positional relationship between two points at which the touch panel has been touched based on the voltages of the third and fourth terminals measured at the first timing.

Touch panel apparatus, control apparatus and control method

According to one embodiment, a three-dimensional object determining apparatus includes: a detecting unit configured to detect a plurality of feature points of an object included in an image data that is acquired; a pattern normalizing unit configured to generate a normalized pattern that is normalized by a three-dimensional model from the image data using the plurality of feature points; an estimating unit configured to estimate an illumination direction in which light is emitted to the object in the image data from the three-dimensional model and the normalized pattern; and a determining unit configured to determine whether or not the object in the image data is a three-dimensional object on the basis of the illumination direction.

Three-dimensional object determining apparatus, method, and computer program product

It is an object of the present invention to uniformly disperse fibrous material such as short glass fibers so as to be distributed on a collection conveyor, without using compressed air for dispersion of the fibrous material. The present invention provides a method for collecting fibrous material, wherein the fibrous material fiberized by a spinner of a fiberizing unit is dispersed by a hollow bucket disposed just under the spinner, so as to be collected on a collection conveyor disposed below the hollow bucket, comprising:

Method and apparatus for collecting fibrous material

A linear transformation matrix calculating apparatus linearly transforms a plurality of dictionary subspaces which belong to respective categories by a linear transformation matrix respectively, selects a plurality of sets of two dictionary subspaces from the plurality of linearly transformed dictionary subspaces, calculates a loss function using similarities among the selected sets of dictionary subspaces respectively, calculates a differential parameter obtained by differentiating the loss function by the linear transformation matrix, calculates a new linear transformation matrix from the differential parameter and the linear transformation matrix by Deepest Descent Method, and updates the new linear transformation matrix as the linear transformation matrix used in the linear transformation unit.

Linear transformation matrix calculating apparatus, method thereof and program thereof

A disparity profile indicating a relation between a perpendicular position on time series images and a disparity on a target monitoring area based on an arrangement of a camera is calculated. Processing areas are set, by setting a height of each of the processing areas using a length at the bottom of the image obtained by converting a reference value of a height of an object according to the profile, while setting a position of each bottom of processing areas on the image. An object having a height higher than a certain height with respect to the monitoring area, unify an object detection result in each processing area according to the disparity of the object, and detect the object of the whole monitoring area from each processing area is detected. Position and speed for the object detected by the object primary detection unit are estimated.

Apparatus and method for detecting object

An apparatus of detecting an object on a road surface includes a stereo set of video cameras mounted on a vehicle to produce right and left images, a storage to store the right and left images, a parameter computation unit to compute a parameter representing road planarity constraint based on the images of the storage, a corresponding point computation unit to compute correspondence between a first point on one of the right and left images and a second point on the other, which corresponds to the first point, based on the parameter, an image transformation unit to produce a transformed image from the one image using the correspondence, and a detector to detect an object having a dimension larger than a given value in a vertical direction with respect to the road surface, using the correspondence and the transformed image.

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Tokyo, Japan

Susumu Kubota