Kabushiki Kaisha Toshiba

Quoc Viet Pham

Nao Mishima

Tatsuo Kozakaya

Toshiaki Nakasu

Osamu Yamaguchi

Tomoki Watanabe

Isao Mihara

Mayumi Yuasa

Tomokazu Kawahara

Masayuki Maruyama

Shojun Nakayama

Yusuke Hosoya

An image processing apparatus according to an embodiment includes a hardware processor. The processor receives input of an image and receives input of a query including one or more words. The processor calculates a word heat map for each of the words. The word heat map indicates a degree of relation between the word and each of subregions included in the image. The processor calculates weight of each of the words. The processor calculates, on the basis of the word heat maps and the weights, a query heat map indicating a degree of relation between the query and each of the subregions included in the image. The processor detects an image region from the image on the basis of the query heat map and outputs the image region. The image region corresponds to an object related to the query.

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

According to one embodiment, a case query apparatus includes a processing circuit. The processing circuit acquires a query condition represented by a query target. The processing circuit acquires a meta query condition represented by a description concerning a viewpoint to focus on when querying a case similar to the query condition. The processing circuit calculates a similarity degree between the query condition and each of a plurality of reference cases represented by a query target. The processing circuit queries a similar reference case similar to the query condition from a viewpoint of the meta query condition, among the plurality of reference cases, based on the similarity degree. The processing circuit presents a query result on the similar reference case.

Case query apparatus acquires query condition and meta query condition to calculate a similarity degree between the query condition and reference cases, case query method and a non-transitory computer readable storage medium

According to one embodiment, a state determination apparatus includes a processor. The processor acquires a targeted image. The processor acquires a question concerning the targeted image and an expected answer to the question. The processor generates an estimated answer estimated with respect to the question concerning the targeted image using a trained model trained to estimate an answer based on a question concerning an image. The processor determines a state of a target for determination in accordance with a similarity between the expected answer and the estimated answer.

State determination apparatus and image analysis apparatus

According to one embodiment, an image analysis device includes one or more processors configured to receive input of an image; calculate feature amount information indicating a feature of a region of the image; recognize a known object from the image on the basis of the feature amount information, the known object being registered in learning data of image recognition; recognize a generalization object from the image on the basis of the feature amount information, the generalization object being generalizable from the known object; and output output information on an object identified from the image as the known object or the generalization object.

Object identification image device, method, and computer program product

According to an embodiment, an image analysis device includes one or more processors. The one or more processors configured to: calculate a feature map of a target image; calculate context score information representing context of each pixel in the target image on the basis of the feature map; calculate shape score information representing a shape of an object in at least one region included in the target image on the basis of the feature map; correct the shape score information in the at least one region using the context score information in a corresponding region; and output the corrected shape score information in the at least one region.

Image analysis device, neural network device, learning device and computer program product

According to an embodiment, an information processing apparatus includes a memory and processing circuitry. The processing circuitry configured to acquire an input image captured by an image-capturing device installed in a specific location. The processing circuitry configured to perform adaptation processing of adapting an estimation model, which is used for detecting positions or the number of objects contained in an image, to the specific location by sequentially selecting a parameter of the estimation model from a lower level toward a higher level, and by modifying the selected parameter in such a manner to reduce an estimation error in the positions or the number of the objects contained in the input image. The processing circuitry configured to acquire a termination condition for the adaptation processing. The processing circuitry configured to terminate the adaptation processing when the termination condition is satisfied.

Information processing apparatus, method and computer program product

According to an embodiment, an image analyzer includes a set unit, a first calculator, a second calculator, a divider, a third calculator, and a specifier. The set unit sets multiple partial regions in the image. The first calculator calculates an optical flow in a partial region. The second calculator calculates the density of an object included in the partial region. The divider divides the image into the multiple blocks. The third calculator calculates, for a block, a statistical amount that is derived from the density and the moving amount of the object derived from the optical flow for the partial region included in the block. The specifier specifies the block, out of the blocks, as an unsteady block that is in an unsteady state on the basis of the statistical amount.

Image analyzer, image analysis method, computer program product, and image analysis system

An image processing method includes: retaining marker information including markers, each of the markers having a type which is shape, pattern or color; acquiring images in which at least one of the markers is caught; referring the marker information to detect the type and a position of the marker caught in the image; and dividing the image into a plurality of divided areas on the basis of the positions of one or more markers in the image, the plurality of divided areas having no common area and each including at least one type of the marker.

Image processing apparatus for dividing an image based on markers, method thereof and program therefor

According to an embodiment, a density measuring device includes a first calculator, a second calculator, and a first generator. The first calculator calculates, from an image including objects of a plurality of classes classified according to a predetermined rule, for each of a plurality of regions formed by dividing the image, density of the objects captured in the region. The second calculator calculates, from the density of the objects captured in each of the regions, likelihood of each object class captured in each of the regions. The first generator generates density data, in which position corresponding to each of the regions in the image is assigned with the density of the object class having at least the higher likelihood than the lowest likelihood from among likelihoods calculated for object classes captured in the corresponding region.

Density measuring device, density measuring method, and computer program product

A first extracting unit extracts partial images from a learning image. A first calculator calculates a feature amount of the partial image. A retrieving unit retrieves objects included in the partial image, and gives, as a label, a vector to the feature amount. The vector represents relative positions between a first position in the partial image and each object included in the partial image. A voting unit generates a voting histogram for each partial image. A learning unit divides the feature amount of each partial image into clusters to reduce variation of the corresponding voting histogram, so as to learn a regression model representing a relationship between the feature amount of the partial image and the relative position of the object included in the partial image. A first predicting unit predicts, for each cluster, a representative label from the label given to the feature amount belonging to the cluster.

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Kanagawa, Japan

Quoc Viet Pham