Abstract: The information processing device performs distillation learning of a student model using unknown data which a teacher model has not learned. The label distribution determination unit outputs an arbitrary label for the unknown data. The data generation unit outputs new generated data using an arbitrary label and unknown data as inputs. The distillation learning part performs distillation learning of the student model using the teacher model and using the generated data as an input.

Abstract: In a camera calibration apparatus (10), an acquisition unit (11) acquires a first normal vector in an image plane and a second normal vector in the image plane respectively corresponding to a first normal vector in a world coordinate space and a second normal vector in the world coordinate space which are normal vectors with respect to a reference plane in the world coordinate space and have the same length. A projective depth calculation unit (12) calculates a projective depth vector having, as vector elements, four projective depths respectively corresponding to a start point and an end point of the first normal vector in the image plane and a start point and a end point of the second normal vector in the image plane.

Abstract: The measurement system 100 includes: a measurement apparatus 20 that measures vibrations of an object 40; an imaging apparatus 30 that is located so as to capture an image of the measurement apparatus 20; and a correction processing apparatus 10. the correction processing apparatus 10 includes: a displacement calculation unit 11 that calculates a displacement of the measurement apparatus 20 based on time-series images of the measurement apparatus 20 output from the imaging apparatus 30; a movement amount calculation unit 12 that calculates an amount of movement of the measurement apparatus 20 relative to the imaging apparatus 30, based on the displacement; and a correction processing unit 13 that corrects vibrations of the object measured by the measurement apparatus 20, using the calculated amount of movement of the measurement apparatus 20.

Abstract: In order to provide a technique for improving precision of location estimation using a video, a location estimation device includes: a video processing unit that executes video processing including location estimation of an imaging unit, based on a plurality of feature points extracted from a video captured by the imaging unit and composed of a plurality of frames; and an imaging control unit that determines, on the basis of video-related information acquired in the video processing on a first frame belonging to a first group out of the plurality of frames, an exposure condition of the imaging unit in a second frame belonging to a second group different from the first group out of the plurality of frames.

Abstract: A parameter estimation apparatus 10 is an apparatus for calculating a threshold for separating a plurality of data points into outliers and inliers and estimating a parameter fitting the inliers. The parameter estimation apparatus 10 includes: a parameter estimation unit 13 configured to estimate the parameter by using the plurality of data points as input; a threshold setting unit 14 configured to calculate the threshold based on statistical information of residuals of the data points; and a convergence determination unit 15 configured to determine whether or not convergence of the estimation of the parameter is reached based on the estimated parameter and the calculated threshold, and to cause the parameter estimation unit 13 and the threshold setting unit 14 to each execute processing again if the convergence determination unit 15 determines that convergence is not reached.

Abstract: The server device receives a model information from a plurality of terminal devices, and generates an integrated model by integrating the model information received from the plurality of terminal devices. The server device generates an updated model by learning a model defined by the model information received from the terminal device of update-target using the integrated model. Then, the server device transmits the model information of the updated model to the terminal device. Thereafter, the terminal device executes recognition processing using updated model.

Abstract: In an object detection device, a plurality of object detection units output a score indicating the probability that a predetermined object exists for each partial region set with respect to inputted image data. On the basis of the image data, a weight computation unit uses weight computation parameters to compute weights for each of the plurality of object detection units, the weights being used when the scores outputted by the plurality of object detection units are merged. A merging unit merges the scores outputted by the plurality of object detection units for each partial region according to the weights computed by the weight computation unit. A loss computation unit computes a difference between a ground truth label of the image data and the scores merged by the merging unit as a loss. Then, a parameter correction unit corrects the weight computation parameters so as to reduce the computed loss.

Abstract: In an object detection device, a plurality of object detection units output a score indicating probability that a predetermined object exists, for each partial region set to image data inputted. The weight computation unit computes weights for merging the scores outputted by the plurality of object detection units, using weight calculation parameters, based on the image data. The merging unit merges the scores outputted by the plurality of object detection units, for each partial region, with the weights computed by the weight computation unit. The target model object detection unit configured to output a score indicating probability that the predetermined object exists, for each partial region set to the image data. The first loss computation unit computes a first loss indicating a difference of the score of the target model object detection unit from a ground truth label of the image data and the score merged by the merging unit.

Abstract: A road surface inspection apparatus (10) includes an image acquisition unit (110), a damage detection unit (120), and an information output unit (130). The image acquisition unit (110) acquires an image in which a road is captured. The damage detection unit (120) sets a target region in the image in image processing for detecting damage to a road, based on an attribute of the road captured in the image, and performs the image processing on the set target region. The information output unit (130) outputs position determination information allowing determination of a position of a road damage to which is detected by the image processing.

Abstract: A camera parameter estimation apparatus: takes three sets of three-dimensional coordinates pertaining to an object and two-dimensional coordinates corresponding to the three-dimensional coordinates, and transforms a coordinate system of the three-dimensional coordinates from a world coordinate system to a local coordinate system; calculates a linear transformation matrix based on a projection transformation expression from the transformed three-dimensional coordinates to the two-dimensional coordinates, calculates a coefficient of a quartic equation pertaining to any one of depths from a camera center to each three-dimensional coordinate, and calculates each depth; calculates the rotation matrix in the local coordinate system using each depth and the linear transformation matrix; calculates a translation vector in the local coordinate system from each depth based on the projection transformation expression; and calculates a rotation matrix and a translation vector in the world coordinate system by performing

Abstract: A road surface inspection apparatus (10) includes an image acquisition unit (110), a damage detection unit (120), and an output unit (130). The image acquisition unit (110) acquires an input image in which a road is captured. The damage detection unit (120) detects a damaged part of the road in the input image by using a damage determiner (122) being built by machine learning and determining a damaged part of a road. The output unit (130) outputs, to a display apparatus (30), a determination result with a certainty factor equal to or less than a reference value in a state of being distinguishable from another determination result out of one or more determination results of a damaged part of a road by the damage determiner (122).

Abstract: In a camera calibration apparatus (10), an acquisition unit (11) acquires a first normal vector in an image plane and a second normal vector in the image plane respectively corresponding to a first normal vector in a world coordinate space and a second normal vector in the world coordinate space which are normal vectors with respect to a reference plane in the world coordinate space and have the same length. A projective depth calculation unit (12) calculates a projective depth vector having, as vector elements, four projective depths respectively corresponding to a start point and an end point of the first normal vector in the image plane and a start point and a end point of the second normal vector in the image plane.

Abstract: An apparatus includes an acquisition means for acquiring the displacement quantity in a time-series manner, the displacement quantity being generated at a measurement part of a structure by the weight of a vehicle that travels on the structure, a detection means for detecting the position and the clock time of the vehicle that travels a pre-passage area of the measurement part, an estimation means for estimating the arrival time that the vehicle arrives at the measurement part, from the position of the measurement part and the position and the clock time of the vehicle, and a control means for controlling the acquisition means on the basis of the estimated arrival time.

Abstract: In a camera calibration apparatus (10), an external parameter calculation unit (13) calculates a rotation vector and a rotation matrix using the normalized start point coordinates, the normalized end point coordinates, and an “evaluation function” obtained by the normalized coordinate transformation unit (12) for each normal vector in the image plane and then outputs the calculated translation vector and rotation matrix. The normal vectors in the image plane respectively correspond to a plurality of normal vectors in a world coordinate space with respect to a reference plane in the world coordinate space. The evaluation function is for overdetermining a rotation parameter around a normal line with respect to the above reference plane in the world coordinate space.

Abstract: In a camera calibration apparatus (10), an acquisition unit (11) acquires a first normal vector in an image plane and a second normal vector in the image plane respectively corresponding to a first normal vector in a world coordinate space and a second normal vector in the world coordinate space which are normal vectors with respect to a reference plane in the world coordinate space and have the same length. A projective depth calculation unit (12) calculates a projective depth vector having, as vector elements, four projective depths respectively corresponding to a start point and an end point of the first normal vector in the image plane and a start point and a end point of the second normal vector in the image plane.

Abstract: In a calibration apparatus (10), an acquisition unit (11) acquires a plurality of positions in an image plane respectively corresponding to a plurality of body region points distributed over a whole body of a person in each of a plurality of photographed images obtained by photographing a common photographing area by a plurality of cameras at the same time arranged at positions different from each other and including images of a same person. A camera parameter calculation unit (12) calculates camera parameters of the plurality of cameras using the plurality of positions in the image plane acquired by the acquisition unit (11) as image feature points.

Abstract: An apparatus includes an acquisition means for acquiring displacement quantity in a time-series manner, the displacement quantity being generated at a measurement part of a structure by the weight of a vehicle that travels on the structure, a detection means for detecting the size of the vehicle that passes through the structure and detecting the type of the vehicle from the size, and a control means for controlling the acquisition means on the basis of the detected type of the vehicle.

Abstract: A camera parameter estimation apparatus 10 for estimating geometric parameters of a camera that has shot an image of an object and a lens distortion parameter of a lens distortion model represented by a single unknown. The camera parameter estimation apparatus 10 includes: a data obtaining unit 11 that obtains image corresponding points relating to the object and an approximation order for polynomial approximation of the lens distortion model; and a parameter estimation unit 12 that estimates, based on the image corresponding points and the approximation order, the geometric parameter and the lens distortion parameter that minimize an error function representing a specific transformation of the image corresponding points.

Abstract: A photographing guide device includes a detection means and an output means. The detection means compares a candidate image of a structure captured from a capturing candidate position in a capturing candidate direction with a registered image of the structure captured from a given capturing position in a given capturing direction and stored in advance, thereby detecting a positional difference between the capturing candidate position and the given capturing position. The output means outputs information indicating the detected positional difference.

Abstract: A vibration measurement system comprises an image capturing apparatus, a distance measuring apparatus, a sensor that outputs a signal according to an inclination of the image capturing apparatus relative to the vertical direction, and a vibration measurement apparatus. The vibration measurement apparatus includes calculating an angle formed by the normal of an image capturing surface of the image capturing apparatus and the normal of the measurement target surface that the image capturing apparatus shoots, based on the signal output by the sensor, converting the image obtained that the image capturing apparatus shoots into an image that would be obtained were the normal of the measurement target surface coincident with the normal of the image capturing surface of the image capturing apparatus, using the calculated angle, and measuring vibration of the structure, using the converted image and the measured distance from the image capturing apparatus to the measurement target surface.