Abstract: A posture estimation system for estimating a posture of an object is provided, which includes: a plurality of three-dimensional cameras taking images of the object from different angles; an estimation section estimating, by using two-dimensional images respectively acquired by the plurality of three-dimensional cameras, a position of a predetermined section of the object on the two-dimensional images; a determination section determining reliability of depth information on the position estimated by the estimation section based on a change over time of the depth information; and a calculation section calculating the position of the predetermined section of the object in consideration of a determination result of the determination section.

Abstract: A picking system is provided, which is capable of picking up an object even when the object is not registered in advance. The picking system includes: a picking device holding the object; an RGB-D camera acquiring three-dimensional point cloud data of the object to be picked up by the picking device; and a control device controlling the picking device based on a detection result by the RGB-D camera. The control device generates a geometric model of the object by combining simple geometric primitives while referring to the three-dimensional point cloud data, and calculates a holding position of the object for the picking device based on the geometric model.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A region-of-interest extraction device is provided with an extraction unit configured to extract one or a plurality of local regions from an input image; a retrieval unit configured to search an image database storing a plurality of images and retrieve an image matching a local region for each of the local regions extracted by the extraction unit; and a relevance score determination unit configured to determine a relevance score for each of the local regions on the basis of the retrieval result from the retrieval unit.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An event detection apparatus determines the occurrence of an abnormal event based on input data without predefining and learning normal or abnormal patterns. A first data obtaining unit obtains first data. A feature quantity classifying unit obtains a feature quantity corresponding to the first data, generates a plurality of clusters for classifying the obtained feature quantity, and classifies the feature quantity into a corresponding one of the clusters. A learning unit learns a plurality of identifiers using feature quantities classified into the clusters. A second data obtaining unit obtains second data. An identifier unit inputs a feature quantity corresponding to the second data into the plurality of learned identifiers, and receives an identification result from each identifier. A determination unit determines whether the second data includes an identification target event based on the obtained identification result.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed are an image processing device, an image processing method, and a non-transitory computer-readable medium that obtain a contour image in which noise edges are eliminated from local edge information and a contour of an important object is enhanced. The image processing device includes a local contour extraction unit which generates a local edge image and a global contour extraction unit which generates a global edge image. The image processing device generates a contour image by preparing the local edge image and obtaining the weighting sum of the local edge image and the global edge image.

Abstract: 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.