Kabushiki Kaisha Toshiba

Toshiba Elevator Kabushiki Kaisha

Kentaro Yokoi

Shuhei Noda

Sayumi Kimura

Hiroshi Sukegawa

Tomoki Watanabe

Toshio Sato

Satoshi Tamura

Satoshi Ito

Akio Okazaki

Jun Ogata

Junichi Nakamura

Yasuhiro Taniguchi

Yoshihiko Suzuki

Masahiro Yamamoto

Ryuzo Okada

Mayumi Yuasa

Tsukasa Ike

Yasuhiro Aoki

Bjorn Stenger

Tomokazu Kawahara

Kenji Kimiyama

Hironori Dobashi

Yukari Murata

Nobuhisa Kishikawa

According to one embodiment, an elevator passenger detection system comprises an image capturing unit, a detection area setting unit, and a detection processing unit. The image capturing unit captures an image in a predetermined range including a vicinity of a doorway at which a door is opened or closed, from an inside of a car. The detection area setting unit sets a detection area on a front return panel provided on at least one of sides of a doorway of the car, on the captured image obtained by the image capturing unit. The detection processing unit detects presence of a passenger or an object, based on the image in the detection area set by the detection area setting unit.

Elevator passenger detection system

According to one embodiment, a user detection system includes a camera, a boundary detector, a user detector and a controller. The camera is installed in a peripheral region of a door, and captures a running region when opening or closing the door and a region near the door. The boundary detector detects a boundary between a first structure and a second structure in the region near the door based on an image captured by the camera. The user detector detects whether there is a user in the running region based on a result detected by the boundary detector. The controller controls an open/close operation of the door based on a result detected by the user detector.

User detection system and image processing device

According to one embodiment, an image detection system includes an imaging apparatus, an edge extraction unit, a specific-edge extraction unit, a candidate area extraction unit, and a to-be-detected area determination unit. The edge extraction unit extracts edges each representing a boundary line between areas with different features. The specific-edge extraction unit extracts specific edges including at least remaining edges present in background image and input image. The candidate area extraction unit extracts candidate areas for a preset area to be detected. The to-be-detected area determination unit determines the area to be detected from candidate areas extracted.

Image detection device for detecting shadow or light based on detected edges

According to one embodiment, an elevator system includes a camera, a passenger detection module and a controller. The camera is capable of imaging a particular range in a direction from a vicinity of a door of a car to a hall when the car arrives at the hall. The passenger detection module detects presence or absence of a passenger who intends to get into the car by focusing on movement of a movable body within a specific area by using a plurality of time-series continuous images which are captured by the camera. The controller controls an opening/closing operation of the door based on a detection result of the passenger detection module. A width of the area is being set greater than at least a width of the door.

Elevator system

An embodiment of a vehicle monitoring apparatus has a determiner and a reader. The determiner determines whether or not a specific site of a vehicle exists at least in an area of an image, the image imaged by an imager, the area being one of multiple areas into which the image is divided by dividing line that are substantially orthogonal to a direction of travel of the vehicle. The reader reads information on a license plate mounted to the vehicle from the area in case that the determiner determines the specific site of the vehicle is determined to exist in the area.

Vehicle monitoring apparatus and vehicle monitoring method

A disclosure describes a learning image collection apparatus includes an acquisition unit, an extraction unit, a calculation unit, and a selection unit. The acquisition unit acquires an image including a target object. The extraction unit extracts, from the image, a plurality of candidate areas being candidates for the target object. The calculation unit calculates one of a first degree of similarity, a second degree of similarity, and a third degree of similarity, the first degree of similarity being a degree of similarity between one of the candidate areas and a predetermined area, the second degree of similarity being a degree of similarity between a size of the target object and a predetermined size, the third degree of similarity being a degree of similarity between the plurality of candidate areas. The selection unit selects one of the candidate areas as a target object area including the target object.

Learning image collection apparatus, learning apparatus, and target object detection apparatus

A feature extraction apparatus includes a pixel feature calculator configured to calculate a plurality of pixel features for each of pixels included in a plurality of pieces of image data; a co-occurrence frequency calculator configured to calculate co-occurrence frequencies of the pixel features by comparing the pixel features among corresponding pixels in the pieces of image data; and a co-occurrence frequency output unit configured to output the co-occurrence frequencies.

Method and apparatus for calculating features of image data

A feature extracting apparatus includes a pixel feature calculator that calculates a pixel feature for each pixel of image data; an area setting unit configured to set a plurality of areas in the image data; a coordinate mapping unit configured to map a first coordinate in one of the plurality of areas onto a second coordinate in at least one of the other plurality of areas; and a co-occurrence matrix calculator configured to calculate a co-occurrence matrix for each of the plurality of areas, the co-occurrence matrix being frequency of combinations of the pixel features at the first coordinate and the pixel feature at the second coordinates.

Method and apparatus for calculating pixel features of image data

An apparatus for a user to interface with a control object apparatus by a posture or a motion of the user's physical part. An image input unit inputs an image including the user's physical part. A gesture recognition unit recognizes the posture or the motion of the user's physical part from the image. A control unit controls the control object apparatus based on an indication corresponding to the posture or the motion. A gesture information display unit displays an exemplary image of the posture or the motion recognized for the user's reference to indicate the control object apparatus.

Interface apparatus and interface method

A device includes a first change detection unit which detects a change region distribution based on the texture information; a second change detection unit which detects a change region distribution based on a background differential method; a texture detection unit, a change-detection-unit weighting storage unit, and a change detection result integration unit, wherein while applying a firm texture differential method to the illumination change as much as possible, a detection unit based on a background differential method is applied to a region to which the texture differential method is not favorably applicable.

Change region detection device and change region detecting method

When authentication data of a person O to be authenticated is registered as dictionary data, this authentication data of the person to be authenticated is acquired and collated with the registered dictionary data. In accordance with the collation result, the dictionary data is updated.

Person recognition apparatus

An image input section inputs an image of a letter P and stores the same into an image memory. A stamp detecting section detects a stamp by a luminance projection value based on the image of the letter stored in the image memory, determining that an area is not an external area of the stamp and determining the area as an internal area of the stamp if the length of the area is smaller than a stamp internal gap permissible threshold value used for determining a stamp internal gap even in a case where the area which is partly determined as an external area of the stamp is present in an area which is determined as the internal area of the stamp affixed to the letter P based on the thus derived projection value.

Stamp detecting device, stamp detecting method, letter processing apparatus and letter processing method

A recognition apparatus of this invention for recognizing sorting information as a character string written within a cellophane region of a paper-like material extracts edge components according to a differential image based on an image of light and shade of the paper-like material, determines a cellophane frame based on the edge components and recognizes the character string within the thus determined cellophane frame as an address. Thus, the cellophane region in which the address used as the sorting information is written can be correctly extracted and occurrence of erroneous recognition of the sorting information and erroneous sorting of the paper-like material can be prevented.

Growing community of inventors

Kanagawa, Japan

Kentaro Yokoi