Abstract: An object of the present invention is to provide a cellulose acetate with excellent biodegradability in seawater. A cellulose acetate having a total degree of acetyl substitution of not greater than 2.7, a ratio ? of a sum of a degree of acetyl substitution at the 2-position and a degree of acetyl substitution at the 3-position to a degree of acetyl substitution at the 6-position in the total degree of acetyl substitution of not less than 2.0, and a sulfate component amount of greater than 20 ppm and not greater than 400 ppm.

Abstract: A subject tracking device includes a first histogram generation unit configured to generate a first histogram representing an appearance frequency of an image feature quantity of a subject region, for each segment and for each auxiliary segment of the image feature quantity, a second histogram generation unit configured to generate, for each candidate region, a second histogram representing an appearance frequency of the image feature quantity of an image of a candidate region for each segment and for each auxiliary segment of the image feature quantity, a difference derivation unit configured to compare the first histogram with the second histogram for each segment and for each auxiliary segment, and derive, for each candidate region, a total difference value of the appearance frequencies of the image feature quantities of the first histogram and the second histogram; and a region selection unit configured to select the candidate region having a minimum total difference value from among the candidate regions

Abstract: A behavior classifier includes a coordinate estimation unit that receives a plurality of frames in chronological order as an input to estimate a keypoint coordinate group for each of the plurality of frames and to generate a chronological keypoint coordinate group for the plurality of input frames; a matrix generation unit that generates a trajectory matrix and generates a trajectory matrix group in which trajectory matrices are collected for all of keypoints defined for the object; and a behavior classification unit that classifies a behavior of the object based on the trajectory matrix group. Each of the plurality of frames captures an object as a moving image. The keypoint coordinate group is a set of two-dimensional coordinates of a keypoint defined for the object. The chronological keypoint coordinate group is a set of the keypoint coordinate groups arranged in chronological order.

Abstract: An object-specific keypoint separation apparatus includes: an inference execution unit configured to receive a captured image capturing an object as an input and use a pre-trained model that has been trained in order to output a plurality of first maps and a plurality of second maps generated from the input captured image to output the plurality of first maps and the plurality of second maps, the plurality of first maps storing, for keypoints of the object. a distance from a first keypoint only around a second keypoint, and the plurality of second maps representing a heat map configured to have a peak at coordinates at which the keypoint of the object appears; and an object-specific keypoint separation unit configured to separate the keypoints for each object based on the plurality of first maps and the plurality of second maps output from the inference execution unit.

Abstract: An object-specific keypoint separation apparatus includes: an inference execution unit configured to receive a captured image capturing an object as an input and use a pre-trained model that has been trained in order to output a plurality of first maps and a plurality of second maps generated from the input captured image to output the plurality of first maps and the plurality of second maps, the plurality of first maps storing a vector describing a connection relationship of a keypoint of the object only around the keypoint, and the plurality of second maps representing a heat map configured to have a peak at coordinates at which the keypoint of the object appears; a map correction unit configured to correct the plurality of second maps using the plurality of first maps and the plurality of second maps; an upsampling unit configured to upsample the plurality of first maps; and an object-specific keypoint separation unit configured to separate keypoints for each object based on the plurality of upsampled firs

Abstract: A learning device includes: a time-series information generation unit that obtains a first image group including a plurality of successive time-series images including a reference image and generates first time-series information based on a difference between the reference image and each of the images in the first image group other than the reference image; and a first learning unit that performs machine learning using the reference image and the first time-series information, thereby obtaining a first learning result used for estimating depth information on a target image, which is an image to be processed, and silhouette information on a subject captured in the target image based on the target image and second time-series information generated from a second image group including a plurality of successive time-series images including the target image.

Abstract: A weight mask generation device includes: a displacement amount derivation unit that derives displacement amounts of feature points in an entire region of a face shape; a separation unit that derives separated displacement amounts, each of which is a displacement amount of each feature point for every deformation pattern of a partial region of the face shape, based on the displacement amounts of the feature points in the entire region of the face shape and deformation degrees for every deformation pattern of the partial regions of the face shape; a normalization unit that derives normalized and separated displacement amounts, which are obtained by normalizing the separated displacement amounts, for every deformation pattern of the partial regions of the face shape; and a weight value generation unit that generates weight values of the feature points in the entire region of the face shape based on the normalized and separated displacement amounts for every deformation pattern of the partial regions of the face

Abstract: A subject tracking device includes a first histogram generation unit configured to generate a first histogram representing an appearance frequency of an image feature quantity of a subject region, for each segment and for each auxiliary segment of the image feature quantity, a second histogram generation unit configured to generate, for each candidate region, a second histogram representing an appearance frequency of the image feature quantity of an image of a candidate region for each segment and for each auxiliary segment of the image feature quantity, a difference derivation unit configured to compare the first histogram with the second histogram for each segment and for each auxiliary segment, and derive, for each candidate region, a total difference value of the appearance frequencies of the image feature quantities of the first histogram and the second histogram; and a region selection unit configured to select the candidate region having a minimum total difference value from among the candidate regions

Abstract: A learning device includes: a time-series information generation unit that obtains a first image group including a plurality of successive time-series images including a reference image and generates first time-series information based on a difference between the reference image and each of the images in the first image group other than the reference image; and a first learning unit that performs machine learning using the reference image and the first time-series information, thereby obtaining a first learning result used for estimating depth information on a target image, which is an image to be processed, and silhouette information on a subject captured in the target image based on the target image and second time-series information generated from a second image group including a plurality of successive time-series images including the target image.

Abstract: Setting a reference pixel signal from a decoded pixel signal in intra-prediction in which a prediction signal is generated in the same frame, acquiring prediction mode identification information that identifies an intra-prediction mode, generating a prediction signal based on the reference pixel signal and the prediction mode identification information, making a determination regarding correction of the prediction signal using a reference pixel having a shorter distance from a pixel that is a prediction target in the intra-prediction mode identified by the prediction mode identification information, and correcting the generated prediction signal according to a result of the determination are included in order to provide an image encoding method and an image decoding method capable of improving subjective image quality and coding efficiency using a closer decoded pixel in an intra-prediction process.

Abstract: The computational complexity of a denoising filter is reduced while suppressing a reduction in coding efficiency. In a video encoding/decoding apparatus which uses a loop filter, a deviation degree calculating unit calculates the degree of deviation between a denoising target pixel and a surrounding pixel using a decoded image. A template shape setting unit limits a template shape so that its size is proportional to the degree of deviation relative to the maximum value of the degree of deviation in the decoded image. When noise of the target pixel is removed, weighted in accordance with the degree of similarity between a template of the target pixel and a template of each of search points in a search shape and the weighted sum of pixel values at the search points, the loop filter performs template matching using the limited template shape and removes noise of the target pixel.

Abstract: In an image processing apparatus, an edge direction detection unit calculates an edge direction using a denoising target image. A search shape setting unit sets a search shape in which the number of search points along the direction perpendicular to the edge direction is smaller than the number of search points along the edge direction, using the calculated edge direction as an index used for limiting the search shape. When noise of a target pixel is removed with a weight in accordance with the degree of template similarity between a template for the target pixel and a template for each of search points within a search shape and the weighted sum of pixel values at the search points, a filter execution unit executes a process of performing template matching on only search points within the set search shape and removing the noise of the target pixel.

Abstract: An automatic producing method for a predicted value generation procedure that predicts a value of an encoding target pixel by using a previously-decoded pixel. A parent population is generated by randomly producing predicted value generation procedures each of which is indicated by a tree structure, and a plurality of predicted value generation procedures are selected as parents from the parent population. One or more predicted value generation procedures are generated as children based on a predetermined tree structure developing method which subjects the selected predicted value generation procedures to a development where an existing predicted value generation function can be an end node of a tree.

Abstract: Setting a reference pixel signal from a decoded pixel signal in intra-prediction in which a prediction signal is generated in the same frame, acquiring prediction mode identification information that identifies an intra-prediction mode, generating a prediction signal based on the reference pixel signal and the prediction mode identification information, making a determination regarding correction of the prediction signal using a reference pixel having a shorter distance from a pixel that is a prediction target in the intra-prediction mode identified by the prediction mode identification information, and correcting the generated prediction signal according to a result of the determination are included in order to provide an image encoding method and an image decoding method capable of improving subjective image quality and coding efficiency using a closer decoded pixel in an intra-prediction process.

Abstract: The computational complexity of a denoising filter is reduced while suppressing a reduction in coding efficiency. In an image processing apparatus, an edge direction detection unit calculates an edge direction using a denoising target image. A search shape setting unit sets a search shape in which the number of search points along the direction perpendicular to the edge direction is smaller than the number of search points along the edge direction, using the calculated edge direction as an index used for limiting the search shape. When noise of a target pixel is removed with a weight in accordance with the degree of template similarity between a template for the target pixel and a template for each of search points within a search shape and the weighted sum of pixel values at the search points, a filter execution unit executes a process of performing template matching on only search points within the set search shape and removing the noise of the target pixel.

Abstract: The computational complexity of a denoising filter is reduced while suppressing a reduction in coding efficiency. In a video encoding/decoding apparatus which uses a loop filter, a deviation degree calculating unit calculates the degree of deviation between a denoising target pixel and a surrounding pixel using a decoded image. A template shape setting unit limits a template shape so that its size is proportional to the degree of deviation relative to the maximum value of the degree of deviation in the decoded image. When noise of the target pixel is removed, weighted in accordance with the degree of similarity between a template of the target pixel and a template of each of search points in a search shape and the weighted sum of pixel values at the search points, the loop filter performs template matching using the limited template shape and removes noise of the target pixel.

Abstract: An automatic producing method for a predicted value generation procedure that predicts a value of an encoding target pixel by using a previously-decoded pixel. A parent population is generated by randomly producing predicted value generation procedures each of which is indicated by a tree structure, and a plurality of predicted value generation procedures are selected as parents from the parent population. One or more predicted value generation procedures are generated as children based on a predetermined tree structure developing method which subjects the selected predicted value generation procedures to a development where an existing predicted value generation function can be an end node of a tree.

Abstract: This invention discloses a method of manufacturing a product of one-piece construction wherein silicone rubber has been made to adhere, characterized in that a substrate is immersed in a primer comprising:(A) A silicon compound containing at least 1 organic group with an aliphatic unsaturated bond and at least 1 hydrolyzable group bonded to a silicon atom,(B) A silicon compound containing at least 1 organic group with an epoxy group, and at least 1 hydrolyzable group bonded to a silicon atom,(C) Platinum or a platinum compound,(D) An organic solvent,and after drying, the substrate and silicone rubber are heated together with pressure contact.