Omron Corporation

Tsinghua University

Xiang Ruan

Huchuan Lu

Takayoshi Yamashita

Lu Huchuan

Naru Yasuda

Kiyoaki Tanaka

Niloy Jyoti Mitra

Tomoyoshi Aizawa

Motoo Yamamoto

Yanping Lu

Guo-Xin Zhang

Ming-Ming Cheng

Shi-Min Hu

Tong Na

Zhang Lihe

Yang Chuan

Xiaohui Li

Lihe Zhang

Na Tong

An object recognition device includes an acquisition unit configured to acquire a recognition target image that serves as an object to be recognized; a retrieval unit configured to search an image database storing a plurality of image data in association with tag information and retrieve a similar image that matches the recognition target image; and a recognition unit configured to recognize the object included in the recognition target image on the basis of tag information associated with a similar image obtained by the retrieval unit. The recognition may select the tag information that appears most frequently among the tag information associated with the similar images as a recognition result. The recognition unit may also compute a tag information reliability score from the similar image in the retrieval result and recognize an object taking into account said reliability score.

Object recognition device, object recognition method, and program for recognizing an object in an image based on tag information

A visual attention detector includes a feature extraction unit configured to extract a spatiotemporal feature from a local region in a video; a hashing unit configured to convert a spatiotemporal feature value for the local region into a hash value, and to select a training value mapped to the hash using a hash table; and an attention measure determining unit configured to determine an attention measure on the basis of the distance between a spatiotemporal feature value for the local region and the selected training value such that the larger the distance the larger the attention measure.

Visual attention detector and visual attention detection method

A saliency information acquisition device has a local saliency acquisition unit configured to calculate a saliency measure for each pixel in an input image on the basis of information obtained from a local region surrounding each pixel, a candidate-region setting unit configured to set a plurality of candidate regions in the input image, a global saliency acquisition unit configured to calculate a saliency measure for each candidate region in the plurality of candidate regions on the basis of information including a local saliency feature representing an attribute of the saliency measure for each pixel within a candidate region, and a global feature representing an attribute of the candidate regions in relation to the entire input image, and an integration unit configured to combine the saliency measure for each candidate region in the plurality of candidate regions obtained by the global saliency acquisition unit to generate saliency information.

Saliency information acquisition device and saliency information acquisition method

An image processing apparatus has an image acquisition unit that acquires an image that is to be subjected to processing, a learning sample extraction unit that extracts data of a plurality of learning samples from the image, a classifier learning unit that performs learning of a plurality of classifiers using the plurality of learning samples, a strong classifier generation unit that generates a strong classifier by combining the plurality of learned classifiers, and a saliency map generation unit that generates a saliency map of the image using the strong classifier.

Image processing apparatus and image processing method

An image processing device and a method performed by a computer, the image processing device comprising: a processing unit configured to operate as an image acquisition unit configured to acquire an image; a similarity map generator configured to calculate, with a region constructed with one or a plurality of pixels in the image as a constituent unit, a first similarity map based on a first algorithm, the first similarity map representing a degree of similarity between the region and a marginal region of the image, and calculate a second similarity map based on a second algorithm, the second similarity map representing a degree of similarity between the region and the marginal region of the image; and a saliency map generator configured to integrate the first similarity map and the second similarity map to generate a saliency map.

Image processing device and method, and computer readable medium

An image processing device has an affinity calculating unit that handles each pixel in an image or each region including a plurality of pixels joined together, as a unit component, and determines a first affinity between each of the unit components and one of the unit components which is located in a periphery region of the image, a foreground region identifying unit that identifies, as a foreground region, a region made up of one or more of the unit components whose first affinities calculated by the affinity calculating unit are lower than a preset threshold, and a saliency measure output unit that determines a second affinity between each of the unit components in the image and the foreground region identified by the foreground region identifying unit, and outputs the second affinity as a saliency measure for each unit component.

Image processing device, image processing method, program, and recording medium

An attribute of image data can accurately be discriminated. An image attribute discrimination apparatus includes a heterogeneous region extracting unit that specifies a heterogeneous region from image data. The heterogeneous region includes a heterogeneous matter whose attribute is different from that of a content originally produced by the image data. An image attribute discrimination apparatus further includes a scene discrimination unit that discriminates the attribute of the image data based on a feature quantity extracted from a pixel group except each pixel in the heterogeneous region in each pixel of the image data.

Image attribute discrimination apparatus, attribute discrimination support apparatus, image attribute discrimination method, attribute discrimination support apparatus controlling method, and control program

An image processing device configured to detect a salient region from an image has a pixel small-region image generating unit that generates a pixel small-region image using, as a unit, a pixel small region made of pixels adjacent to one another and whose luminance values or chromaticity are similar, from the image, a prior probability calculator that calculates prior probability of likelihood of the salient region for each of the pixels of the image, a region generating unit that generates a salient-region-containing region having high possibility of containing a salient region on the basis of a corner point extracted from the image, and a likelihood calculator.

Image processing device, image processing method, image processing control program, and recording medium

An image processing method includes a segmentation step that segments an input image into a plurality of regions by using an automatic segmentation algorithm, and a computation step that calculates a saliency value of one region of the plurality of segmented regions by using a weighted sum of color differences between the one region and all other regions. Accordingly, it is possible to automatically analyze visual saliency regions in an image, and a result of analysis can be used in application areas including significant object segmentation, object recognition, adaptive image compression, content-aware image resizing, and image retrieval.

Image processing method and image processing device

An image processing device for identifying a characteristic of an eye from a face image comprising: a first differentiation unit configured to differentiate an eye region in at least a vertical direction of the eye to obtain a first luminance gradient; a first edge extraction unit configured to extract a first edge point according to the first luminance gradient; and a curve identification unit configured to identify a curve, which is a B-spline curve or a Bezier curve expressed by a control point and both end points and fits to the first edge point, as a curve expressing an upper-eyelid or lower-eyelid outline, the end points being an inner corner point of eye and a tail point of eye, by voting for the control point that is a voting target with respect to the first edge point using the Hough transform.

Image processing device and image processing method

An image processing device for identifying a characteristic of an eye from a face image comprising: a first differentiation unit configured to differentiate an eye region in a crosswise direction of the eye to obtain a first luminance gradient; a first edge extraction unit configured to extract a first and a second edge points; a voting unit configured to vote for an ellipse; a pupil outline identification unit configured to identify an ellipse expressing a pupil outline; a second differentiation unit configured to obtain a second luminance gradient by differentiating in a vertical direction; a second edge extraction unit configured to extract a third edge point; a curve identification unit configured to identify a curve that fits to the third edge point as a curve expressing an eyelid outline; and a pupil region identification unit configured to identify a pupil region based on the identified ellipse and the identified curve.

Image processing device and image processing method for identifying a pupil region

A gesture recognition apparatus is caused to correctly recognize start and end of a gesture without use of special unit by a natural manipulation of a user and low-load processing for the gesture recognition apparatus. The gesture recognition apparatus that recognizes the gesture from action of a recognition object taken in a moving image includes: a gravity center tracking unit that detects a specific subject having a specific feature from the moving image; a moving speed determining unit that computes a moving speed per unit time of the specific subject; a moving pattern extracting unit that extracts a moving pattern of the specific subject; and a start/end judgment unit that discriminates movement of the specific subject as an instruction (such as an instruction to start or end gesture recognition processing) input to the gesture recognition apparatus when the moving speed and the moving pattern satisfy predetermined conditions.

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Otsu, Japan

Xiang Ruan