Abstract: In an object detecting method according to an embodiment, external reference points are set in external space of a model of an object and an internal reference point is set in internal space of the model. A table is stored in which feature quantities on a local surface of the model are associated with positions of the external reference points and the internal reference point. The feature quantity on the local surface of the model is calculated, and the position of the reference point whose feature quantity is identical to the calculated feature quantity is acquired from the table and is converted into a position in a real space. When the converted position is outside the object, the position is excluded from information for estimation and the position and the attitude of the object are estimated.

Abstract: In an object detecting method according to an embodiment, external reference points are set in external space of a model of an object and an internal reference point is set in internal space of the model. A table is stored in which feature quantities on a local surface of the model are associated with positions of the external reference points and the internal reference point. The feature quantity on the local surface of the model is calculated, and the position of the reference point whose feature quantity is identical to the calculated feature quantity is acquired from the table and is converted into a position in a real space. When the converted position is outside the object, the position is excluded from information for estimation and the position and the attitude of the object are estimated.

Abstract: In a broadly-applicable face image extraction device and method for defining a face by position and size in target images varied in type for face image extraction at high speed, an edge extraction part 1 extracts an edge part from a target image and generates an edge image. A template storage part 2 previously stores a template composed of a plurality of concentric shapes varied in size. A voting result storage part 3 has voting storage regions for each size of the concentric shapes of the template so as to store the result obtained by voting processing carried out by a voting part 4. The voting part 4 carries out the voting processing utilizing the template at each pixel in the edge image, and stores the result obtained thereby in the corresponding voting storage region. After the voting processing, an analysis part 5 performs cluster evaluation based on the voting results stored in the voting storage regions, and then defines the face in the target image by position and size.

Abstract: In a broadly-applicable face image extraction device and method for defining a face by position and size in target images varied in type for face image extraction at high speed, an edge extraction part 1 extracts an edge part from a target image and generates an edge image. A template storage part 2 previously stores a template composed of a plurality of concentric shapes varied in size. A voting result storage part 3 has voting storage regions for each size of the concentric shapes of the template so as to store the result obtained by voting processing carried out by a voting part 4. The voting part 4 carries out the voting processing utilizing the template at each pixel in the edge image, and stores the result obtained thereby in the corresponding voting storage region. After the voting processing, an analysis part 5 performs cluster evaluation based on the voting results stored in the voting storage regions, and then defines the face in the target image by position and size.