Abstract: A moving body control apparatus for controlling a moving body includes: an acquisition device that acquires a control command for the moving body and an image of a view in the traveling direction of the moving body; and an information processing device that uses a machine learning model to output a control parameter for controlling the moving body, using the control command and the image acquired by the acquisition device as inputs.

Abstract: A neural network according to the present disclosure includes: an input layer to which input information is input; a plurality of blocks to be used to extract a feature volume from the input information; and an output layer from which the feature volume extracted is output. Each of the plurality of blocks includes: a residual block formed by combining one or more first convolutional layers and a skip connection which is a connection that bypasses the one or more first convolutional layers; and a connection block which includes at least a second convolutional layer and equalizes output of the one or more first convolutional layers and output of the skip connection.

Abstract: A collation device includes: a storage that stores a search target image showing a search target; an acquisition unit that acquires a captured image generated by an imaging device; and a controller that determines whether the search target is included in the captured image. The controller searches out a collation area in the captured image, the collation area including a subject similar to at least a part of the search target, and selects, depending on the collation area, at least one of a whole of the search target image and a part of the search target image, and collates the selected at least one with the captured image so as to determine whether the search target is included in the captured image.

Abstract: A moving body control apparatus for controlling a moving body includes: an acquisition device that acquires a control command for the moving body and an image of a view in the traveling direction of the moving body; and an information processing device that uses a machine learning model to output a control parameter for controlling the moving body, using the control command and the image acquired by the acquisition device as inputs.

Abstract: A collation device includes: a storage that stores a search target image showing a search target; an acquisition unit that acquires a captured image generated by an imaging device; and a controller that determines whether the search target is included in the captured image. The controller searches out a collation area in the captured image, the collation area including a subject similar to at least a part of the search target, and selects, depending on the collation area, at least one of a whole of the search target image and a part of the search target image, and collates the selected at least one with the captured image so as to determine whether the search target is included in the captured image.

Abstract: A measurement-target-selecting device that is capable of estimating a face shape with high precision and at low computational time. In this device, a face texture assessment value calculating part (103) calculates a face texture assessment value representing a degree of match between an input face image and the texture of a face shape candidate, a facial-expression-change-likelihood-calculating part (104) calculates a first likelihood between a face shape constituting a reference and a face shape candidate, a correlation assessment part (105); calculates a first correlation assessment value representing the strength of a correlation between the face texture assessment value and the first likelihood, and a selection part (107) selects from among the plurality of face shape candidates as a measurement target a face shape candidate having a first correlation assessment value that is lower than a first threshold.

Abstract: Device (10) comprises: a comparison object pixel acquisition unit (433) which acquires pixel values of a plurality of comparison object pixels which, when each pixel of an image is designated as a pixel of interest, and a ring-shaped region with the pixel of interest being designated as the center thereof being designated a vicinity region, said comparison object pixels are included in the vicinity region; a pixel difference calculation unit (434) which calculates the difference between the pixel value of the pixel of interest and the pixel values of each comparison object pixel; and a local binary pattern generation unit (435) which generates a local binary pattern for each pixel. A plurality of vicinity regions are present for each pixel of interest, and the distance of the vicinity regions are established on the basis of the spatial frequency characteristics of a lens with which the image is photographed.

Abstract: Provided is a feature extraction device including a sub region setting unit (433) which sets a plurality of sub regions with respect to a pixel of interest, and a binary pattern generation unit (434) which generates a local binary pattern indicating pixel value comparison results relative to each sub region with respect to each pixel of interest. The sub region setting unit (433) sets (436) a region constituted by a plurality of pixels including a pixel separated from the pixel of interest as a sub region, and the binary pattern generation unit (434) calculates (437) a representative value for each sub region and generates (439) a local binary pattern indicating whether or not the difference (438) between the representative value and the value of the pixel of interest is equal to or larger than a predetermined threshold value.

Abstract: A detection device capable of reliably detecting an object to be detected. An intersection region pattern setting unit (106) sets a configuration pattern of a first intersection region pattern group in sequence for each unit image pair. Each intersection region pattern is defined by set image information which denotes locations and sizes of regions (where n is a natural number greater than 1) within respective unit images (e.g., unit image plane coordinates), as well as whether each region is set within either or both of a first unit image and a second unit image. A detection unit (108) detects the object to be detected, based on a total feature value relating to each configuration pattern of the first intersection region pattern group, computed by a feature value computation unit (107), and a strong identification apparatus configured from a plurality of weak identification apparatuses and stored in an identification apparatus storage unit (112).

Abstract: Provided is a feature extraction device whereby it is possible, while using local binary patterns, to extract image features with which object detection which is robust against disparities in a photographic environment is possible. A feature extraction unit (440) comprises: a binary pattern generation unit (443) which generates, for each of all pixels or partial pixels in an image, local binary patterns which denote, by bit values, whether the difference in pixel values between the pixel and the surrounding adjacent pixels is greater than or equal to a threshold value; a weighting generation unit (444) which determines, for each generated local binary pattern, a weighting according to the pixel value difference; and a histogram generation unit (445) which applies the determined weightings to the corresponding local binary patterns and generates a histogram which denotes the distribution of the local binary patterns which are generated from the image.

Abstract: Device (10) comprises: a comparison object pixel acquisition unit (433) which acquires pixel values of a plurality of comparison object pixels which, when each pixel of an image is designated as a pixel of interest, and a ring-shaped region with the pixel of interest being designated as the center thereof being designated a vicinity region, said comparison object pixels are included in the vicinity region; a pixel difference calculation unit (434) which calculates the difference between the pixel value of the pixel of interest and the pixel values of each comparison object pixel; and a local binary pattern generation unit (435) which generates a local binary pattern for each pixel. A plurality of vicinity regions are present for each pixel of interest, and the distance of the vicinity regions are established on the basis of the spatial frequency characteristics of a lens with which the image is photographed.

Abstract: A measurement-target-selecting device that is capable of estimating a face shape with high precision and at low computational time. In this device, a face texture assessment value calculating part (103) calculates a face texture assessment value representing a degree of match between an input face image and the texture of a face shape candidate, a facial-expression-change-likelihood-calculating part (104) calculates a first likelihood between a face shape constituting a reference and a face shape candidate, a correlation assessment part (105); calculates a first correlation assessment value representing the strength of a correlation between the face texture assessment value and the first likelihood, and a selection part (107) selects from among the plurality of face shape candidates as a measurement target a face shape candidate having a first correlation assessment value that is lower than a first threshold.

Abstract: Provided is a feature extraction device including a sub region setting unit (433) which sets a plurality of sub regions with respect to a pixel of interest, and a binary pattern generation unit (434) which generates a local binary pattern indicating pixel value comparison results relative to each sub region with respect to each pixel of interest. The sub region setting unit (433) sets (436) a region constituted by a plurality of pixels including a pixel separated from the pixel of interest as a sub region, and the binary pattern generation unit (434) calculates (437) a representative value for each sub region and generates (439) a local binary pattern indicating whether or not the difference (438) between the representative value and the value of the pixel of interest is equal to or larger than a predetermined threshold value.

Abstract: Provided is a feature extraction device whereby it is possible, while using local binary patterns, to extract image features with which object detection which is robust against disparities in a photographic environment is possible. A feature extraction unit (440) comprises: a binary pattern generation unit (443) which generates, for each of all pixels or partial pixels in an image, local binary patterns which denote, by bit values, whether the difference in pixel values between the pixel and the surrounding adjacent pixels is greater than or equal to a threshold value; a weighting generation unit (444) which determines, for each generated local binary pattern, a weighting according to the pixel value difference; and a histogram generation unit (445) which applies the determined weightings to the corresponding local binary patterns and generates a histogram which denotes the distribution of the local binary patterns which are generated from the image.

Abstract: A detection device capable of reliably detecting an object to be detected. An intersection region pattern setting unit (106) sets a configuration pattern of a first intersection region pattern group in sequence for each unit image pair. Each intersection region pattern is defined by set image information which denotes locations and sizes of regions (where n is a natural number greater than 1) within respective unit images (e.g., unit image plane coordinates), as well as whether each region is set within either or both of a first unit image and a second unit image. A detection unit (108) detects the object to be detected, based on a total feature value relating to each configuration pattern of the first intersection region pattern group, computed by a feature value computation unit (107), and a strong identification apparatus configured from a plurality of weak identification apparatuses and stored in an identification apparatus storage unit (112).