Abstract: An image recognition apparatus detects a specific object image from an image to be processed, calculates a coincidence degree between an object recognizability state of the object image and that of an object in registered image information, and calculates a similarity between the image feature of the object image and the image feature in the registered image information. Based on the similarity and coincidence degree, the image recognition apparatus recognizes whether the object of the object image is that of the registered image information. When the similarity is lower than the first threshold and the coincidence degree is equal to or higher than the second threshold, the image recognition apparatus recognizes that the object of the object image is different from that of the registered image information.

Abstract: A subject tracking apparatus which performs subject tracking based on the degree of correlation between a reference image and an input image is disclosed. The degree of correlation between each of a plurality of reference images based on images input at different times, and the input image is obtained. If the maximum degree of correlation between a reference image based on a first input image among the plurality of reference images and the input image is equal to or higher than a threshold, a region with a maximum degree of correlation with a first reference image is determined as a subject region. Otherwise, a region with a maximum degree of correlation with a reference image based on an image input later than the first input image is determined as a subject region.

Abstract: A subject tracking apparatus extracts a subject region which is similar to a reference image on the basis of a degree of correlation with the reference image for tracking a predetermined subject from images supplied in a time series manner. Further, the subject tracking apparatus detects the position of the predetermined subject in the subject region on the basis of the distribution of characteristic pixels representing the predetermined subject contained in the subject region, and corrects the subject region so as to reduce a shift in position of the predetermined subject in the subject region. Moreover, the corrected subject region is taken as the result of tracking the predetermined subject, and the reference image is updated with the corrected subject region as the reference image to be used for the next supplied image.

Abstract: Provided is a waterproof structure including: a first casing component member 107; a second casing component member 110 that is fitted to the first casing component member 107 and constitutes a casing; an annular groove portion 71 formed in the first casing component member 107; and an annular seal member 110c that is provided on the second casing component member 110 and inserted into the groove portion 71. A cross section of the seal member 110c includes: a base portion BH; a trunk portion RH formed substantially perpendicular from the base portion BH; and a valve portion VH formed to protrude in a triangular shape from one side surface on a tip side of the trunk portion, and in a fitted state, the trunk portion and the valve portion are housed in an enclosed space formed by the base portion and the groove portion.

Abstract: An image recognition apparatus detects a specific object image from an image to be processed, calculates a coincidence degree between an object recognisability state of the object image and that of an object in registered image information, and calculates a similarity between the image feature of the object image and the image feature in the registered image information. Based on the similarity and coincidence degree, the image recognition apparatus recognizes whether the object of the object image is that of the registered image information. When the similarity is lower than the first threshold and the coincidence degree is equal to or higher than the second threshold, the image recognition apparatus recognizes that the object of the object image is different from that of the registered image information.

Abstract: A subject tracking apparatus which performs subject tracking based on the degree of correlation between a reference image and an input image is disclosed. The degree of correlation between each of a plurality of reference images based on images input at different times, and the input image is obtained. If the maximum degree of correlation between a reference image based on a first input image among the plurality of reference images and the input image is equal to or higher than a threshold, a region with a maximum degree of correlation with a first reference image is determined as a subject region. Otherwise, a region with a maximum degree of correlation with a reference image based on an image input later than the first input image is determined as a subject region.

Abstract: A subject tracking apparatus extracts a subject region which is similar to a reference image on the basis of a degree of correlation with the reference image for tracking a predetermined subject from images supplied in a time series manner. Further, the subject tracking apparatus detects the position of the predetermined subject in the subject region on the basis of the distribution of characteristic pixels representing the predetermined subject contained in the subject region, and corrects the subject region so as to reduce a shift in position of the predetermined subject in the subject region. Moreover, the corrected subject region is taken as the result of tracking the predetermined subject, and the reference image is updated with the corrected subject region as the reference image to be used for the next supplied image.

Abstract: Face regions are detected from a captured image, and a weight of each detected face region is computed based on a size and/or a position of the detected face region. Then a previous priority ranking weight is computed based on a priority ranking determined in previous processing. A priority of the face region is computed from the weight and the previous priority ranking weight. For example, if the continuous processing number exceeds the threshold the priority ranking weight is reduced. After the processing is completed for all face regions, a priority ranking of each face region is determined based on the priority computed for each face region.