Nec Corporation

Nec C Orporation

Kyota Higa

Kota Iwamoto

Toshiyuki Nomura

Emi Kitagawa

Ruihan Bao

Yuzo Senda

Hideaki Sato

Takami Sato

Keishi Ohashi

Wataru Hattori

Ryota Mase

Masumi Ishikawa

Katsumi Kikuchi

Hiroyoshi Miyano

Takayuki Arakawa

Azusa Sawada

Yasuyuki Mitsui

Yasunori Babazaki

In a foreground extraction apparatus, an extraction result generation unit performs a foreground extraction using a plurality of foreground extraction models for an input image, and generates foreground extraction results. A selection unit selects one or more foreground extraction models among the plurality of foreground extraction models using respective foreground results acquired by the plurality of foreground extraction models. A foreground region generation unit extracts each foreground region based on the input image using the selected one or more foreground extraction models.

Foreground extraction apparatus, foreground extraction method, and recording medium

In an image processing apparatus, a foreground extraction unit extracts each foreground from input images, and generates a foreground extraction result. A movement trajectory feature extraction unit tracks each foreground based on the foreground extraction result, and extracts a movement trajectory feature of the foreground. The area variation feature extraction unit extracts an area variation feature representing a temporal area variation of each foreground. A foreground center estimation unit estimates a center of each foreground using the movement trajectory feature and the area variation feature.

Image processing apparatus, image processing method, learning apparatus, learning method and recording medium

A teaching data extending device includes: a relationship acquiring unit that obtains a relationship between a plurality of features included in each of a plurality of teaching data; a feature selecting unit that selects any one or more of the plurality of features based on the relationship; and a teaching data extending unit that generates, for one or more teaching data, new teaching data in which a value of the feature selected by the feature selecting unit is replaced with a value of the feature in another teaching data classified in a same class.

Teaching data extending device, teaching data extending method, and program

In an object identification apparatus, a foreground extraction unit performs a foreground extraction with respect to input images, and generates a foreground extraction result. A state extraction unit extracts a state for each foreground based on the foreground extraction result. An identification model selection unit selects one or more identification models based on the extracted state for each foreground by using a selection model. An identification unit identifies a moving object included in the input images using the selected identification model.

Identification apparatus, object identification method, learning apparatus, learning method, and recording medium

A change detection meansdetects an area of change in a product shelf on which a product has been arranged, on the basis of a captured image of the product. A classification meansclassifies the change in the product shelf in the detected area of change. A shopping list generation meansgenerates a shopping list for a customer on the basis of the classification of the change in the product shelf and of shelving information on the product shelf.

Self-checkout system, purchased product management method, and purchased product management program

An abnormality detection apparatus () includes an acquisition unit () that acquires input data, an abnormality degree computation unit () that has a discriminative model for computing an abnormality degree of input data, inputs the acquired input data to the discriminative model, and thereby computes an abnormality degree of the input data, a normality degree computation unit () that has a normality model for computing a normality degree of input data, inputs the input data to the normality model, and thereby computes a normality degree of the input data, a determination unit () that has a determination model for performing determination relating to an abnormality level of input data, inputs the abnormality degree and the normality degree to the determination model, and thereby performs determination relating to an abnormality level of the input data, and an output unit () that outputs output information based on a result of the determination.

Abnormality detection apparatus, control method, and program

Disclosed is a display condition analysis device which is capable of analyzing the display conditions of products. This display condition analysis device is provided with: a product recognition means for recognizing, from a display image taken of products on display, the products in the display image; and a display condition analysis means for analyzing, on the basis of the positions of the recognized products, the display conditions of the products on display.

Display condition analysis device, display condition analysis method, and program recording medium

An inspection apparatus () detects an inspection object () from first image data () in which the inspection object () is included. The inspection apparatus () generates second image data () by performing a geometric transform on the first image data () in such a way that a view of the detected inspection object () becomes a view satisfying a predetermined reference. In an inference phase, the inspection apparatus () detects, by using an identification model for detecting an abnormality of the inspection object (), an abnormality of the inspection object () included in the second image data (). Further, in a learning phase, the inspection apparatus () learns, by using the second image data (), an identification model for detecting an abnormality of the inspection object ().

Inspection apparatus, control method, and program

An apparatus of this invention is directed to an information processing apparatus that effectively counts, on a type basis, articles of a plurality of types displayed in a depth direction on a display shelf. The information processing apparatus includes a display count acquirer that acquires display count information of articles using article presence/absence sensors provided on the display shelf on which the articles are placed, an article identifier that acquires article identification information capable of identifying the types of articles based on an image acquired by capturing the display shelf, and a display recognizer that recognizes, based on the display count information and the article identification information, display count of each type of the articles.

Article management system, information processing apparatus, and control method and control program of information processing apparatus

An image processing device has: a change detecting unit configured to, based on a captured image in which an product to be monitored is shown, detect a change in display status of the product; and a display detecting unit configured to detect that the product is returned to a different place from a place where the product has been picked up based on the change in display status of the product detected by the change detecting unit and on a person included in the captured image.

Image recognition device for detecting a change of an object, image recognition method for detecting a change of an object, and image recognition system for detecting a change of an object

A product analysis systemincludes a detection means, a classification means, and a specification means. The detection meansdetects an area of change in a product shelf from a video of the product shelf. The classification meansclassifies the change in the product shelf in the detected area of change. The specification meansspecifies the frequency at which a customer was interested in but did not purchase a product on the basis of the classification of the change.

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Tokyo, Japan

Kyota Higa