Nec Corporation

Nec C Orporation

Soma Shiraishi

Kota Iwamoto

Yu Nabeto

Katsumi Kikuchi

Takami Sato

Hideo Yokoi

Tsugunori Takata

Emi Kitagawa

Eiji Muramatsu

Tetsuo Inoshita

Jun Kobayashi

Haruna Watanabe

Takashi Shibata

Hiroshi Yamada

Shoji Yachida

Hideaki Sato

Shinya Yamasaki

Jun Piao

Azusa Sawada

Yasunori Babazaki

Katsuhiko Takahashi

Michio Nagai

Takanori Inoue

Makoto Terao

Ryosuke Sakai

Kazuki Tsuchimochi

Tomokazu Kaneko

The video processing device includes a video acquisition means, a person identification means, an importance calculation means, and an importance integration means. The video acquisition means acquires a material video. The person identification means identifies a person from the material video. The importance calculation means calculates an importance of the material video. The importance integration means integrates the importance for each person and outputs a person importance indicating an importance for each person.

Video processing device, video processing method, and recording medium

The present invention provides a processing apparatus () including: an acquisition unit () that acquires a plurality of images, captured by a plurality of cameras, of a product held by a customer with a hand; a detection unit () that detects an object from each of the plurality of images; a positional information generation unit () that generates positional information indicating a position in the image, for the each detected object; a product kind-related information generation unit () that generates product kind-related information that determines a product kind, for the each detected object; an extraction unit () that extracts a group of a plurality of objects which satisfies conditions with each other, the conditions being related to the positional information and the product kind-related information; and a product recognition result output unit () that outputs a product recognition result for the each extracted group.

Processing apparatus, processing method, and non-transitory storage medium

Provided is an object type identifying apparatus that is capable of correctly identifying the types of objects held in a hand of a person. This object type identifying apparatus is provided with: a memory storing instructions; a storage device storing information indicating a type of an object at a position of each object; and one or more processors configured to execute the instructions to: acquire a position of an object; determine whether an object is picked up or an object is placed, based on sensor information; when determined that an object is picked up, identify a type of the picked-up object, based on the position of the object acquired and information stored in the storage device; and when determined that an object is placed, update information stored in the storage device, using an image captured by a camera that captures arrangement of each object from a front side.

Object type identifying apparatus, object type identifying method, and recording medium

An image acquisition unit () acquires a recognition processing image. A recognition unit () recognizes a product in the recognition processing image based on an estimation model. A registration unit () registers a result of the recognition in recognized product information. An output unit () outputs a result of the recognition. A correction reception unit () receives an input for correcting a result of the recognition. A correction unit () changes a result of the recognition to a result of the recognition after a correction, and also stores correction information in which a result of the recognition after a correction and the recognition processing image are associated with each other. A learning unit () performs relearning by using the recognition processing image stored as the correction information and updates the estimation model, when a number of the recognition processing image stored exceeds a predetermined value.

Processing system, processing method, and non-transitory storage medium

A product detection apparatus () includes an acquisition unit (), an image processing unit (), and an output unit (). The acquisition unit () acquires a plurality of images (one example of a first image) generated by an image capture unit () in association with a date and a time when the image is generated. These plurality of images are generated at different timings from each other. The image processing unit () detects a state (hereinafter, described as a first state) in which a new product is present on a product shelf () continuously for a reference time or longer by processing the plurality of images. The output unit () performs a first output when the first state is detected. The first output indicates that a product () has been returned to the product shelf () after the product () is taken out of the product shelf () by a customer.

Product detection apparatus, product detection method, and non-transitory storage medium

A learning apparatus includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from sets of attribute-attached image data for each combination of different attributes by using the sets of attribute-attached image data to which pieces of attribute information are added. The case example storage unit calculates feature vectors from sets of case example image data, and stores the feature vectors as case examples associated with the metric space.

Learning apparatus, learning method, inference apparatus, inference method, and recording medium

An article determination apparatus () is used with two image capture units (). One image capture unit (), an article shelf (), and the other image capture unit () are arranged in this order. The acquisition unit () acquires images generated by the two image capture units (). The image processing unit () generates first size data and second size data by processing the images acquired by the acquisition unit (). The first size data and the second size data indicate the size of an area in which an article is captured in an image generated by one image capture unit () and by the other image capture unit (), respectively. The determination unit () determines that an estimation result of the article name of the article by image processing is correct when a relation between the first size data and the second size data satisfies a criterion.

Article determination using image processing

An information processing deviceX mainly includes a slow motion video generation meansX and a digest candidate generation meansX. The slow motion video generation meansX generates a slow motion video SL being video data in which a playback speed of the first candidate video data Cdis slower than a normal speed, with respect to first candidate video data Cdbeing video data corresponding to a segment of a portion selected from video material data Dm. Here, the digest candidate generation meansX generates digest candidate Cd being a candidate of a digest of the video material data Dm based on the video material data Dm and the slow motion video SL.

Information processing device, control method, and recording medium

The learning device includes a metric space learning unit and a case example storage unit. The metric space learning unit learns a metric space including feature vectors extracted from attributed image data, for each combination of different attributes, using the attributed image data to which attribute information is assigned. The case example storage unit computes the feature vector from the image data for case example to store the computed feature vector as a case example associated with the metric space, and stores additional information associated with the case example.

Object recognition device, method, and computer-readable medium

An installation assistance apparatus () includes a first image capture unit and a second image capture unit that are placed in such a way as to face each other in a plan view, an acquisition unit () acquiring a first image from the first image capture unit, a detection unit () detecting a position of at least part of the second image capture unit in the first image by processing the first image, a determination unit () determining whether the position of the second image capture unit satisfies a criterion, and an output processing unit () causing a determination result by the determination unit () to be output.

Installation assistance apparatus, installation assistance method, and non-transitory computer-readable storage medium

An image acquisition unitacquires a plurality of images. The plurality of images include an object to be inferred. An image cut-out unitcuts out an object region including the object from each of the plurality of images acquired by the image acquisition unit. An importance generation unitgenerates importance information by processing the object region cut out by the image cut-out unit. The importance information indicates the importance of the object region when an object inference model is generated, and is generated for each object region, that is, for each image acquired by the image acquisition unit. A learning data generation unitstores a plurality of object regions cut out by the image cut-out unitand a plurality of pieces of importance information generated by the importance generation unitin a learning data storage unitas at least a part of the learning data.

Generating learning data from important, cut-out object regions

The present invention provides a processing apparatus () including: a web product image acquisition unit () acquiring a web product image publicly available in a web page determined based on a predetermined search condition; a product identification information acquisition unit () acquiring product identification information of a product indicated by the web product image; and a registration unit () registering the web product image and the product identification information in association with each other in a product database.

In an object recognition apparatus, a storage unit stores a table in that a plurality of feature amounts are associated with each object having feature points of respective feature amounts. An object region detection unit detects object regions of a plurality of objects from an input image. A feature amount extraction unit extracts feature amounts of feature points from the input image. A refining unit refers to the table, and refines from all objects of recognition subjects to object candidates corresponding to the object regions based on feature amounts of feature points belonging to the object regions. A matching unit recognizes the plurality of objects by matching the feature points belonging to each of the object regions with feature points for each of the object candidates, and outputs a recognition result.

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Tokyo, Japan

Soma Shiraishi