Nec Corporation

Kosuke Yoshimi

Ryoma Oami

Hiroo Ikeda

Katsuhiko Takahashi

Takuya Ogawa

Ryo Kawai

Junko Nakagawa

Hiroshi Yamada

Yuusuke Konishi

Yusuke Konishi

Yukie Ebiyama

Yuka Ogino

Ryuichi Akashi

Ryo Yamakabe

Takashi Shibata

Masato Tsukada

An illumination control apparatuscomprises: a detection unitthat detects an iris area IA and a reflection area RA in an eye image IMG_E generated by imaging eyes of a target being illuminated with illumination light IL from an illumination apparatus, the iris area IA and the reflection area RA corresponding to an iris of the eyes and a reflection image of the illumination light respectively; and an illumination control unitthat controls the illumination apparatus based on an overlap state between the iris area and the reflection area.

Controlling illumination apparatus based on overlap state between iris area and reflection area

An image processing apparatus includes: a three-dimensional point cloud segmentation unit for segmenting a three-dimensional point cloud of an object using clustering parameters so that the smaller the distance between points in the three-dimensional point cloud is, the more likely the points are to be clustered into the same cluster; a cluster mapping unit for generating a mapped point cloud by mapping clusters of the three-dimensional point cloud to a two-dimensional image based on the correspondence between the coordinates of the three-dimensional point cloud and the coordinates of pixels of the two-dimensional image; a label matching unit for assigning labels to points in the mapped point cloud based on a target label and position information in the two-dimensional image; a matching result voting unit for voting for labels of the points; and a label determination unit for determining the label for the points based on voting results.

Determination, for each point in mapping point cloud, of one of the labels assigned to the point based on voting results of the labels

This machine-learning device is provided with: a detection unit which detects a loss of consistency with a lapse of time in a determination result for unit data, the determination result being output from a determination unit that generates a learning model to be used when performing prescribed determination for one or more pieces of the unit data that form time series data; and a selection unit which selects, on the basis of the result of detection by the detection unit, unit data to be used as teacher data when the determination unit updates the learning model, thereby efficiently raising the accuracy of the learning model when machine learning is performed on the basis of the time series data.

Machine learning device, machine learning method, and recording medium storing machine learning program

An information processing system () comprises: a distance acquisition unit () that specifies an iris area containing an iris of a target from a visible-light image of the target, and acquires an iris distance that is a distance to the iris area; an iris image acquisition unit () that acquires an iris image of the target by changing a focal length according to the iris distance; a score computing unit () that calculates a score relating to deviation of a focus in the iris image, based on the iris image; and a correlation update unit () that updates correlation between the iris distance and the focal length at a moment of acquisition of the iris image, based on the score. According to such an information processing system, since the correlation is updated with good accuracy, it is possible to acquire the appropriate iris image.

Information processing system, information processing apparatus, information processing method, and recording medium

An information processing apparatus () includes a first analyzing unit (), a second analyzing unit (), and an estimating unit (). The first analyzing unit () calculates a flow of a crowd in a capturing range of a fixed camera () using a first surveillance image (). The second analyzing unit () calculates a distribution of an attribute of objects in a capturing range of a moving camera () using a second surveillance image (). The estimating unit () estimates an attribute distribution for a range that is not included in the capturing range of the moving camera ().

Information processing apparatus, control method, and program

An image processing apparatusincludes: a detecting unitthat detects an iris area IA including an iris from a person image IMGin which at least an eye of the target person is included; an image processing unitthat converts the person image to a protected image IMGin which an iris information relating to a pattern of the iris is not obtainable by performing an image processing on at least a part of the iris area of the person image; and an adding unitthat adds meta data indicating that the image processing is performed on at least a part of the iris area to the protected image.

Image processing apparatus, image processing method and recording medium

An image processing device is provided that can easily generate label data used for an object recognition technology using three-dimensional point cloud data. The image processing device () includes a three-dimensional point cloud obtaining unit (), a two-dimensional label obtaining unit (), and a label conversion unit (). The three-dimensional point cloud obtaining unit () obtains three-dimensional point cloud data that represents a three-dimensional point cloud of an object. The two-dimensional label obtaining unit () obtains two-dimensional label data corresponding to a two-dimensional image of the object. The label conversion unit () associates the two-dimensional label data with the three-dimensional point cloud data, and converts the two-dimensional label data into three-dimensional point cloud label data that indicates a label of the three-dimensional point cloud.

Associating two dimensional label data with three-dimensional point cloud data

A method of training a model includes generating processed data by analyzing an input related to an image using a pre-trained model to detect an object in the input. The method includes generating moving area detection data by analyzing the input to determine whether any areas within the input exhibit movement. The method includes generating temporal inconsistency data by analyzing the processed data to determine whether any areas within the processed data exhibit a temporal inconsistency. The method includes identifying whether the input contains an error candidate based on the moving area detection data and the temporal inconsistency data. The method includes receiving confirmation information regarding whether the error candidate is an undetected object within the input. The method includes updating the pre-trained model in response to receiving the confirmation information indicating that the error candidate is the undetected object within the input.

Labelling training method and system for implementing the same

A surveillance system () includes an area information acquisition unit (), a request information provision unit (), and a participation consent reception unit (). The area information acquisition unit () acquires information of a surveillance-desired area. The request information provision unit () provides participation request information for surveillance that is conducted by using an image capturing unit of a portable terminal in the surveillance-desired area. The participation consent reception unit () receives participation consent as a response to the participation request information, from the portable terminal.

Surveillance system, surveillance network construction method, and program

An object detection apparatusis an apparatus for detecting an object in a fish-eye image. The object detection apparatusincludes a normalized image acquisition unitconfigured to acquire a normalized image obtained by normalizing a fish-eye image in which an object appears; a position detecting unitconfigured to detect position coordinates of the object in the normalized image; and a determination unitconfigured to determine a positional relationship between the object and the object detection apparatususing the position coordinates of the object in the normalized image.

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Tokyo, Japan

Kosuke Yoshimi