Nec Corporation

National Cancer Center

Nec C Orporation

Other

Hitoshi Imaoka

Masahiro Saikou

Kenichi Kamijo

Ikuma Takahashi

Motoyasu Okutsu

Maki Sano

Masayoshi Yamada

Chiemi Tanaka

Yutaka Saito

Kimiyasu Takoh

Ryuji Hamamoto

Keiichi Chono

Takafumi Koshinaka

Takayuki Arakawa

Satoshi Nakazawa

Atsushi Marugame

Kenji Okajima

Shin Ishiguro

Motoaki Shimizu

Hideki Irisawa

Fumihiro Adachi

Masayuki Yuguchi

Masanobu Miyasita

Shouhei Yano

An information processing apparatus () detects an abnormal region () from a moving image frame (). The abnormal region () is a region that is estimated to represent an abnormal part inside a body of a subject. The information processing apparatus () generates and outputs output information based on the number of detected abnormal regions ().

Information processing apparatus, control method, and program

An information processing apparatus () detects an abnormal region () in each of a plurality of video frames () constituting video data () in which the inside of a body is captured. The information processing apparatus () outputs output information related to the detected abnormal region (). Herein, the information processing apparatus () starts outputting of the output information when a detection score based on a proportion of the number of the video frames () in which the abnormal region () is detected becomes equal to or more than a first threshold value in a state where outputting of the output information is not being performed. Further, the information processing apparatus () ends outputting of the output information when the detection score becomes equal to or less than a second threshold value in a state where outputting of the output information is being performed.

Information processing apparatus, control method, and non-transitory storage medium

An information processing apparatus () detects an abnormal region () from a predetermined range () of a video frame (). The information processing apparatus () determines whether a predetermined condition is satisfied in a case where the abnormal region () is detected from the predetermined range () of a certain video frame () and the abnormal region () is not detected from the predetermined range () of a predetermined video frame () generated later than the video frame (). In a case where the predetermined condition is not satisfied, the information processing apparatus () performs a predetermined notification.

Information processing apparatus, control method and program

An image diagnosis apparatus () performs a diagnosis assistance process using images (moving image) generated by an endoscope or the like. The image diagnosis apparatus () includes a first processing unit () and a second processing unit (). The first processing unit () executes preprocessing of deciding whether or not a tumor judgment process is necessary with respect to each of a plurality of input images. The second processing unit () performs the tumor judgment process with respect to the input image for which it is decided that 'the tumor judgment process is necessary' in the preprocessing executed by the first processing unit ().

Image diagnosis apparatus, image diagnosis method, and program

A personal authentication device includes: acoustic signal transmission meansfor transmitting a first acoustic signal to a part of a head of a user; acoustic signal observation meansfor observing a second acoustic signal which is an acoustic signal after the first acoustic signal propagates through the part of the head; acoustic property calculation meansfor calculating an acoustic property from the first acoustic signal and the second acoustic signal; and user identification meansfor identifying the user, based on the acoustic property or a feature value extracted from the acoustic property and relating to the user.

Personal authentication device, personal authentication method, and personal authentication program using acoustic signal propagation

A personal authentication system according to the present invention includes a matrix generation unit, a feature extraction unit, a feature transformation unit, a processing unit, and a data matching unit. The matrix generation unit classifies a plurality of feature amounts of face image data, which are recorded in advance, into classes and calculates as a mapping matrix, coefficients of a linear discriminant equation which uses the recorded plurality of feature amounts as variables. The feature extraction unit extracts a first feature amount from first face image data and extracts a second feature amount from second face image data. The feature transformation unit, by using the mapping matrix, transforms the first feature amount into a first transformed feature amount and transforms the second feature amount into a second transformed feature amount. The processing unit calculates as a similarity, a normalized correlation value between the first transformed feature amount and the second transformed feature amount. The data matching unit judges that the first face image data and the second face image data are image data of same person when the similarity exceeds a predetermined threshold.

Personal authentication system and personal authentication method

A terminal has a visual axis by using which a user points to a target object. An information generating server acquires an internal image or an external image of the target object pointed by a user, and generates a display image which is displayed on the terminal.

Image providing system and image providing method

[PROBLEMS] To provide a feature extracting method for quickly extracting a feature while preventing lowering of the identification performance of the kernel judgment analysis, a feature extracting system, and a feature extracting program. [MEANS FOR SOLVING PROBLEMS] Judgment feature extracting device () computes an interclass covariance matrix SB and an intraclass covariance matrix SW about a learning face image prepared in advance, determines optimum vectors &#x3b7;, &#x3b3; which maximizes the ratio of the interclass covariance to the intraclass covariance, derives a conversion formula for converting an inputted frequency feature vector x into a frequency feature vector y in a judgment space, and extracts judgment features of a face image for record and a face image for check by using a restructured conversion formula. Similarity computing device () computes the similarity by comparing the judgment features. Check judging device judges whether or not the persons are the same by comparing the similarity with a threshold.

Coefficient determining method, feature extracting method, system, and program, and pattern checking method, system, and program

A 3D shape estimation system has a storage device, a relative shape analysis module, a feature point location search module and an absolute shape analysis module. The storage device stores first and second learning data which represent illumination bases and 3D shapes of objects, respectively. The relative shape analysis module calculates an 'illumination basis' of an object based on a 2D image of the object and the first learning data, calculates a 'relative shape function' that is partial differential of a &#x201c;shape function&#x201d; indicating a 3D shape of the object from the illumination basis, and outputs a relative shape data indicating the relative shape function. The feature point location search module extracts a plurality of feature points from the input 2D face image based on the 2D image and the relative shape data, and outputs a feature point location data indicating locations of the feature points. The absolute shape analysis module receives the relative shape data and the feature point location data, converts the relative shape function into the shape function by referring to the second learning data and the locations of the feature points, and outputs a 3D absolute shape data indicating the shape function.

Three-dimensional shape estimation system and image generation system

Feature decision means () decides a set of features appropriate for pattern identification from a plenty of feature candidates generated by feature candidate generation means () by using learning patterns stored in learning, pattern storage means (). The feature decision means () successively decides features according to a reference of information maximization under the condition that the decided feature is known while adding an effective noise to the learning pattern and performs information amount calculation approximately and at a high speed while merging the learning patterns into a set of N elements when required. As a result, it is possible to automatically create a feature set appropriate for pattern identification of a high performance without requiring enormous learning. Moreover, by using a transition table () containing transitions between sets, it is possible to perform pattern judgment with a high efficiency.

Growing community of inventors

Tokyo, Japan

Hitoshi Imaoka