Omron Corporation

Yukiko Yanagawa

Yoshihisa Minato

Yutaka Kato

Yasuyuki Ikeda

Yoshihisa Ijiri

Miharu Sakuragi

Yutaka Kiuchi

Tadashi Hyuga

Yasuyo Kotake

Manabu Kawashima

Anh Nguyen

This object detection device is provided with: an output unit; a plurality of detection units; a data acquisition unit; and an object information confirmation unit. The output unit outputs measurement light Lm towards an object. Each of the plurality of detection units detects, as a signal representing the distance to the object present in an observed area and the shape of the object, the reflected light generated as a result of the measurement light being reflected by the object. The data acquisition unit acquires detection signal waveforms W representing changes in the intensities of second signals over time. The object information confirmation unit confirms the range of the presence of the object by determining that any two or more of the detection units correspond to the same object, or correspond to different objects, on the basis of the detection signal waveforms.

Object detection device, object detection method, and program

The purpose of the present invention is to improve the speed of processing related to the presence of objects, while maintaining measurement accuracy of object detection, in an object detection device. This object detection device is provided with: an output unit; a plurality of detection units; a first data generation unit; a second data generation unit; and an information processing unit. The output unit outputs measurement light. The plurality of detection units detect reflected light. The first data generation unit generates first data. The second data generation unit generates second data by extracting, from the first data, a plurality of pieces of second position information, which are pieces of first position information that correspond to representative points expressing the presence ranges of objects. The information processing unit uses the second data to execute information processing related to the presence of the objects.

An image inspection method executed by an image inspection apparatus includes an acquisition step of acquiring an inspection target object image obtained by capturing an image of an inspection target object, a setting reading step of reading, from a storage device that stores inspection region defining information in advance, the inspection region defining information, an inspection region extraction step of extracting, as an inspection region image, a portion to be an inspection region from the inspection target object image, based on the inspection region defining information, and an inspection processing step of performing inspection on the inspection target object by analyzing the inspection region image. The inspection region defining information comprises information defining an initial contour of the inspection region and information defining a range based on the initial contour as a search range for searching a contour of the inspection region.

Image inspection method and inspection region setting method

A method for displaying a result of area extraction processing of dividing a target image into a foreground area and a background area includes a result acquisition step of acquiring a result of the area extraction processing on the target image, an extraction result image generation step of acquiring an extraction result image representing an estimated foreground area or an estimated background area estimated by the area extraction processing or a boundary between the estimated foreground area and the estimated background area, based on the acquired result, and a display step of generating a composite image by combining the extraction result image with the target image, and displaying the composite image on a display device. The extraction result image generation step includes a color extraction step and a color determination step.

Method for displaying by determined color result image of area extraction result

An area partitioning method for partitioning an image into a foreground and a background includes a foreground designation step of causing a user to designate a part of pixels in an area that should be the foreground in the image as a foreground designating pixel, an estimation step of estimating a foreground color distribution and a background color distribution based on a color of the foreground designating pixel designated by the user, and an area partition step of partitioning the image into a foreground area and a background area with the estimated foreground color distribution and the estimated background color distribution as a condition. The estimation step includes dividing the color distribution of the image into a plurality of clusters, and selecting at least one cluster having a large relationship with the foreground designating pixel designated by the user in the plurality of clusters as the foreground color distribution.

Method for partitioning area, and inspection device

An image examination method includes an acquisition step of acquiring a to-be-examined object image obtained by capturing an image of a to-be-examined object, a setting reading step of reading an examination region definition information from a storage device in which the examination region definition information is previously stored, an examination region extracting step of extracting a portion constituting the examination region as an examination region image from the to-be-examined object image based on the examination region definition information, and an examination process step of examining the to-be-examined object by analyzing the examination region image. The examination region definition information is a parameter used as a constraint condition in an optimum solution search process.

Image examination method and image examination apparatus

An inspection area setting method for setting inspection area-defining information defining an inspection area to an image inspecting device, the image inspecting device being configured to extract a portion constituting the inspection area as an inspection area image from an original image obtained by taking an image of an inspection object, and to inspect the inspection object by analyzing the inspection area image, includes an acquisition step of acquiring a sample image obtained by taking an image of a sample of the inspection object, an inspection area searching step, and a setting step.

Inspection area setting method for image inspecting device

A face collation apparatus has a storage that stores a feature quantity of at least one registrant, the feature quantity being extracted from a registration image of the registrant, a feature quantity extractor that extracts a feature quantity from a collation image of a collation object person, a score calculator that calculates a score indicating an analogy degree between the feature quantity of the registrant and the feature quantity of the collation object person, a score adjuster that adjusts the score using a score adjustment parameter so that any one of a stranger acceptance rate indicating a probability that a stranger is accepted at the time of collation, a principal rejection rate indicating a probability that a principal is rejected at the time of collation, and an equal error rate, which is a probability that the stranger acceptance rate and the principal rejection rate are equal, becomes substantially constant regardless of a registration condition and/or a collation condition, and a determination unit that determines whether the collation object person is the registrant by comparing the adjusted score and a predetermined threshold.

Face collation apparatus

A face feature value vector extracting section extracts a feature value from a registered image of a registrant to generate a face feature value vector. A virtual face feature value vector generating section converts the face feature value vector using a feature value converter which performs a conversion in a feature value space. Then, a registration information generating section generates face definition information of the registrant based on a face feature value vector before conversion and a virtual face feature value vector after conversion and registers this information in a registration information storing section.

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Nara, Japan

Yukiko Yanagawa