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: An event detection apparatus includes an input unit configured to input a plurality of time-sequential images, a first extraction unit configured to extract sets of first image samples according to respective different sample scales from a first time range of the plurality of time-sequential images based on a first scale parameter, a second extraction unit configured to extract sets of second image samples according to respective different sample scales from a second time range of the plurality of time-sequential images based on a second scale parameter, a dissimilarity calculation unit configured to calculate a dissimilarity between the first and second image samples based on the sets of the first and second image samples, and a detection unit configured to detect an event from the plurality of time-sequential images based on the dissimilarity.

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: 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 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 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: An information processing apparatus comprising a setting unit configured to set a plurality of local regions on an image; an extraction unit configured to extract feature amounts from the respective local regions; a calculation unit configured to calculate dissimilarities between the local regions based on probability densities for the respective feature amounts; and an integration unit configured to integrate the plurality of local regions as region groups based on the dissimilarities.

Abstract: An image processing device clips a plurality of partial images from an image, and calculates difference values of pixel values between a target pixel and surrounding pixels surrounding the target pixel in the partial images. Then, the image processing device decides parameter values corresponding to respective surrounding pixels based on identification information that identifies the partial image from among the plurality of the partial images and the difference values, and calculates a feature amount corresponding to the target pixel using the parameter value decided. Then, the image processing device obtains a feature amount of the partial images by calculating the feature amount with the above-described steps, using each pixel in the partial images as the target pixels.

Abstract: An information processing apparatus comprising a setting unit configured to set a plurality of local regions on an image; an extraction unit configured to extract feature amounts from the respective local regions; a calculation unit configured to calculate dissimilarities between the local regions based on probability densities for the respective feature amounts; and an integration unit configured to integrate the plurality of local regions as region groups based on the dissimilarities.

Abstract: An image processing device clips a plurality of partial images from an image, and calculates difference values of pixel values between a target pixel and surrounding pixels surrounding the target pixel in the partial images. Then, the image processing device decides parameter values corresponding to respective surrounding pixels based on identification information that identifies the partial image from among the plurality of the partial images and the difference values, and calculates a feature amount corresponding to the target pixel using the parameter value decided. Then, the image processing device obtains a feature amount of the partial images by calculating the feature amount with the above-described steps, using each pixel in the partial images as the target pixels.

Abstract: A bucket elevator type continuous ship unloader is used for both a sidewise shovelling operation and a catenary cleaning up operation. In this ship unloader, a bucket chain is wound around sprockets respectively provided at the front and rear ends of a horizontal frame and a sprocket provided above the rear sprocket such that it encircles the sprockets in an L-shaped form. The bucket chain is made loosened or taut by the upward or downward movement of the horizontal frame with respect to the upper sprocket. The horizontal frame is provided with a horizontal rail with which a roller provided on each bucket is fitted when the bucket chain is stretched tautly. During the sidewise shovelling operation, the buckets run while being guided by the rail. In consequence, the force acting on the bucket during the shovelling in the vertical direction as well as in a direction transverse to the direction of movement of the bucket is exerted on the rail.

Abstract: An apparatus for stacking signatures or the like includes a guide plate having a predetermined angle of inclination and a conveyor for successively loading signatures onto the upper end of the guide plate from the upper side of the latter. The foremost end of a stack of signatures which are being stacked on the guide plate is supported by a first support member which gradually lowers through a section from the signature loading position to an intermediary position, and is then supported by a second support member through a section from the intermediary position to an unloading position. The rearmost end of the stack of signatures is supported by a third support member through the entire section from the loading position to the unloading position. For this purpose, the first, second and third support members are repeatedly moved along the guide plate in relation to each other.