Abstract: The road obstacle detection device includes a semantic label estimation unit that estimates a semantic label for each pixel of an image using a classifier learned in advance and generates a semantic label image, an original image estimation unit for reconstruction of the original image from the semantic label image, a difference calculating unit for calculating a difference between the original image and the reconstructed image from the original image estimation unit as a calculation result, and a road obstacle detection unit for detecting a road obstacle based on the calculation result.

Abstract: One aim of the present invention is to treat or prevent chronic kidney disease. Provided is a treatment or prophylactic pharmaceutical composition for chronic kidney disease, that contains an SGLTl-inhibiting compound or a pharmaceutically acceptable salt thereof.

Abstract: A road obstacle detection device which uses a pre-learned first identifier to associate a semantic label with each pixel of an image, uses a pre-learned second identifier to estimate a statistical distribution of a semantic label of a predetermined region of interest of the image from a statistical distribution of a semantic label of a peripheral region that surrounds the region of interest, and uses the statistical distribution of the semantic label associated with the region of interest and the statistical distribution of the semantic label estimated for the region of interest to estimate a likelihood that an object is a road obstacle.

Abstract: In a road obstacle detection device, a first derivation unit derives, for each of a plurality of local regions, a probability that a local region is the road, such that the probability is higher as the ratio of a road region in the local region is higher; and a second derivation unit derives a probability that a target local region is not a previously decided normal physical body, and derives a probability that a road obstacle exists at the target local region, based on the derived probability that the target local region is not the normal physical body and a probability that a peripheral local region is the road, the peripheral local region being a local region at a periphery of the target local region, the probability that the peripheral local region is the road being derived by the first derivation unit.

Abstract: An image processing device includes a processor including hardware, the processor being configured to: generate a semantic label image by estimating a semantic label for each pixel of an input image by using a discriminator trained in advance; generate a restored image by estimating an original image from the semantic label image; calculate a first difference between the input image and the restored image; and update an estimation parameter for estimating the semantic label or an estimation parameter for estimating the original image based on the first difference.

Abstract: An on-road obstacle detection device that includes: a memory; and a processor, the processor being connected to the memory and being configured to: assign a semantic label to each pixel in an image using a first discriminator that has been pre-trained using images in which an on-road obstacle is not present; and detect an on-road obstacle based on a probability density of the semantic label assigned.

Abstract: The road obstacle detection device includes a semantic label estimation unit that estimates a semantic label for each pixel of an image using a classifier learned in advance and generates a semantic label image, an original image estimation unit for reconstruction of the original image from the semantic label image, a difference calculating unit for calculating a difference between the original image and the reconstructed image from the original image estimation unit as a calculation result, and a road obstacle detection unit for detecting a road obstacle based on the calculation result.

Abstract: A road obstacle detection device which uses a pre-learned first identifier to associate a semantic label with each pixel of an image, uses a pre-learned second identifier to estimate a statistical distribution of a semantic label of a predetermined region of interest of the image from a statistical distribution of a semantic label of a peripheral region that surrounds the region of interest, and uses the statistical distribution of the semantic label associated with the region of interest and the statistical distribution of the semantic label estimated for the region of interest to estimate a likelihood that an object is a road obstacle.

Abstract: Provided is a road obstacle detection method including: a region segmentation step of segmenting an input image into a plurality of local regions; and a likelihood calculation step of calculating a probability of presence of the road obstacle in a target local region based on a probability that the target local region is not a normal object set in advance and a degree of visual saliency defined by a relationship between a surrounding local region and the target local region, wherein the degree of the visual saliency is calculated to be larger as a probability that the surrounding local region is a road is higher and calculated to be larger as a difference in visual characteristics between the target local region and the surrounding local region is larger.

Abstract: A region-of-interest detection apparatus for improving detection accuracy of a region of interest in a case where a region of interest overlaps a background region, or in a case where feature amounts of a region of interest and a background region are similar to each other calculates feature amounts of regions where partial regions and a background region set in an input image overlap each other, and based on the calculated feature amounts and a feature amount of each position in the input image, calculates a foreground level of the position in the input image. Then, the region-of-interest detection apparatus detects a region of interest from the input image based on the calculated foreground level and a saliency of the position.

Abstract: A degree-of-saliency map generated from an input image is divided into a plurality of partial areas, and the degree of nonuniformity is calculated from the distribution characteristics of the degrees of saliency of the partial areas. Whether a main subject is present in the input image is judged based on the calculated degree of nonuniformity.

Abstract: An object detection apparatus first sets a first partial region having a preset size and a second partial region in a given point (pixel) in an input image. In addition, the object detection apparatus calculates a first information amount in the second partial region, and sets a third partial region based on the size of the first information amount. Furthermore, the object detection apparatus calculates a score based on a salience degree that is based on a difference in statistical feature amount distribution between the first partial region and the second partial region, and based on an information amount of feature amount in the third partial region. Lastly, the object detection apparatus detects a main object by calculating scores on the respective points in the image and applying a predetermined statistical process to the scores.

Abstract: A degree-of-saliency map generated from an input image is divided into a plurality of partial areas, and the degree of nonuniformity is calculated from the distribution characteristics of the degrees of saliency of the partial areas. Whether a main subject is present in the input image is judged based on the calculated degree of nonuniformity.

Abstract: A degree-of-saliency map generated from an input image is divided into a plurality of partial areas, and the degree of nonuniformity is calculated from the distribution characteristics of the degrees of saliency of the partial areas. Whether a main subject is present in the input image is judged based on the calculated degree of nonuniformity.

Abstract: A region-of-interest detection apparatus for improving detection accuracy of a region of interest in a case where a region of interest overlaps a background region, or in a case where feature amounts of a region of interest and a background region are similar to each other calculates feature amounts of regions where partial regions and a background region set in an input image overlap each other, and based on the calculated feature amounts and a feature amount of each position in the input image, calculates a foreground level of the position in the input image. Then, the region-of-interest detection apparatus detects a region of interest from the input image based on the calculated foreground level and a saliency of the position.

Abstract: An image processing device includes: an extraction unit that performs a convolution processing and a pooling processing on information of an input image including an image of a person and extracts a feature from the input image to generate a plurality of feature maps; a first fully connected layer that outputs first fully connected information generated by connecting the plurality of feature maps; a second fully connected layer that connects the first fully connected information and outputs human body feature information indicating a predetermined feature of the person; and a third fully connected layer that connects the first fully connected information or the human body feature information to output behavior recognition information indicating a probability distribution of a plurality of predetermined behavior recognition labels.

Abstract: A behavior identification apparatus comprises an acquiring unit that acquires occupant information on the occupant in the vehicle, from each frame image of the moving image; a first calculating unit that calculates, for each frame image of the moving image, a first feature value, which is a feature value based on the occupant information; a second calculating unit that calculates a second feature value, which is a feature value generated by connecting the first feature values for the frame images in a predetermined period; and an identifying unit that identifies the behavior of the occupant in the vehicle using a classifier which is learned in advance so as to determine, from the second feature value, a probability distribution of behavior labels in a predetermined period, and the second feature value calculated by the second feature value calculating unit.

Abstract: Provided is a behavior recognition apparatus, including a detection unit configured to detect, based on a vehicle interior image obtained by photographing a vehicle interior, positions of a plurality of body parts of a person inside a vehicle in the vehicle interior image; a feature extraction unit configured to extract a rank-order feature which is a feature based on a rank-order of a magnitude of a distance between parts obtained by the detection unit; and a discrimination unit configured to discriminate a behavior of an occupant in the vehicle using a discriminator learned in advance and the rank-order feature extracted by the feature extraction unit. Also provided is a learning apparatus to learn the discrimination unit.

Abstract: A region-of-interest detection apparatus for improving detection accuracy of a region of interest in a case where a region of interest overlaps a background region, or in a case where feature amounts of a region of interest and a background region are similar to each other calculates feature amounts of regions where partial regions and a background region set in an input image overlap each other, and based on the calculated feature amounts and a feature amount of each position in the input image, calculates a foreground level of the position in the input image. Then, the region-of-interest detection apparatus detects a region of interest from the input image based on the calculated foreground level and a saliency of the position.

Abstract: Provided is a road obstacle detection method including: a region segmentation step of segmenting an input image into a plurality of local regions; and a likelihood calculation step of calculating a probability of presence of the road obstacle in a target local region based on a probability that the target local region is not a normal object set in advance and a degree of visual saliency defined by a relationship between a surrounding local region and the target local region, wherein the degree of the visual saliency is calculated to be larger as a probability that the surrounding local region is a road is higher and calculated to be larger as a difference in visual characteristics between the target local region and the surrounding local region is larger.