Abstract: According to one embodiment, a system includes a portable device, a plurality of second image capturing units, an extraction unit, a calculation unit, and a specifying unit. The portable device includes a first image capturing unit that captures a first image of a person, and a sending unit that sends the first image. The plurality of second image capturing units captures second images. The extraction unit extracts a first feature of the first image and a second feature of each of the second images. The calculation unit calculates a similarity based on the first feature and the second feature of the each of the second images. If the similarity for a second image is larger than a predetermined threshold, the specifying unit specifies a second image capturing unit that has captured the second image, among the plurality of second image capturing units.

Abstract: An image processing method includes: retaining marker information including markers, each of the markers having a type which is shape, pattern or color; acquiring images in which at least one of the markers is caught; referring the marker information to detect the type and a position of the marker caught in the image; and dividing the image into a plurality of divided areas on the basis of the positions of one or more markers in the image, the plurality of divided areas having no common area and each including at least one type of the marker.

Abstract: A first extracting unit extracts partial images from a learning image. A first calculator calculates a feature amount of the partial image. A retrieving unit retrieves objects included in the partial image, and gives, as a label, a vector to the feature amount. The vector represents relative positions between a first position in the partial image and each object included in the partial image. A voting unit generates a voting histogram for each partial image. A learning unit divides the feature amount of each partial image into clusters to reduce variation of the corresponding voting histogram, so as to learn a regression model representing a relationship between the feature amount of the partial image and the relative position of the object included in the partial image. A first predicting unit predicts, for each cluster, a representative label from the label given to the feature amount belonging to the cluster.

Abstract: According to one embodiment, an information associating apparatus includes an acquisition unit configured to acquire an acquisition image taken by a camera and individual identification information of the camera, an analysis unit configured to analyze attribute information of the camera from an attribute information expression body shown in the acquisition image, a detection unit configured to detect an pattern representing a specific direction recorded in the acquisition image and an association unit configured to form a pair by associating the individual identification information and the attribute information based on a detection result concerning the specific direction calculated from the pattern.