Abstract: An object of the present invention is to provide a water level measurement device and a shoreline extraction method each of which is capable of stably measuring the water level. A pixel selection unit (11) selects a pixel of interest (302) from a designated area (301) designated from a captured image (300), and an identification image extraction unit (12) extracts identification images (303), (304) each coming in contact with the pixel of interest (302). The identification unit (13) calculates an identification strength indicating a degree to which an area corresponding to each of the identification images (303), (304) is a water area, on a basis of the result of machine learning related to identification between the water area and a non-water area.

Abstract: The navigation apparatus calculates second navigation parameters of a mobile object corresponding to data retrieved by a data retrieval unit by using a neural network that calculates first navigation parameters of the mobile object with the data used for calculating the first navigation parameters of the mobile object as an input.

Abstract: An attitude estimation apparatus (30) includes: a data acquiring unit (32) for acquiring observation data; a range-measurement point coordinate calculator (33A) for calculating geodetic coordinates of a range-measurement point in a geodetic coordinate system using positioning data, ranging data, actual attitude data, and an estimated value of an unknown parameter representing an attitude of a flying object; an image coordinate calculator (33B) for calculating coordinates of a projected point by performing projection transformation from the geodetic coordinate system to an image coordinate system on the geodetic coordinates; an image matching processor (34) for detecting image coordinates of the range-measurement point appearing in a first captured image by executing image matching between the first captured image and a second captured image selected from captured images; and an attitude estimator (35) for correcting the estimated value such that a magnitude of a difference between the coordinates of the proje

Abstract: An image feature map generating unit (3) generates, on the basis of feature amounts extracted from a plurality of images successively captured by a camera (109), an image feature map which is an estimated distribution of the object likelihood on each of the images. An object detecting unit (4) detects an object on the basis of the image feature map generated by the image feature map generating unit (3).

Abstract: A processor outputs laser irradiation location identifying pattern information for displaying a laser irradiation location identifying pattern from a laser measuring device to a display screen of a display. The processor receives reflection intensity value information including reflection intensity values of laser light, and determines a laser irradiation location on the display screen of the display on the basis of the reflection intensity values included in the reflection intensity value information, the reflection intensity value information being outputted from the laser measuring device having received the laser light emitted from the laser measuring device and reflected from the laser irradiation location identifying pattern displayed on the display screen of the display. The processor outputs camera calibration pattern information for displaying a camera calibration pattern on the display screen of the display.

Abstract: An object of the present invention is to provide a water level measurement device and a shoreline extraction method each of which is capable of stably measuring the water level. A pixel selection unit (11) selects a pixel of interest (302) from a designated area (301) designated from a captured image (300), and an identification image extraction unit (12) extracts identification images (303), (304) each coming in contact with the pixel of interest (302). The identification unit (13) calculates an identification strength indicating a degree to which an area corresponding to each of the identification images (303), (304) is a water area, on a basis of the result of machine learning related to identification between the water area and a non-water area.

Abstract: The learning model building unit (2) builds a learning model for a convolution neural network, by extracting characteristics of abnormality included in a sample image from the convolution neural network using a kernel having a shape corresponding to the shape of the abnormality included in the sample image and by learning the extracted characteristics.

Abstract: The present invention is provided with: a coordinates calculation unit (161) that, on the basis of data related to distance measurement points (P) indicating the distance to and the angle of a respective plurality of distance measurement points (P) and the coordinates of a laser beam irradiation reference point, and on the basis of the attitude angle of an aircraft (2), calculates coordinates of each of the distance measurement points (P) on a corresponding image among a plurality of images; a feature point extraction unit (162) that extracts feature points for each of the images; a distance calculation unit (163) that calculates, for each of the distance measurement points (P), the distance to a nearby feature point among the feature points extracted by the feature point extraction unit (162) on the basis of the coordinates calculated in the corresponding image by the coordinates calculation unit (161); and a necessity determination unit (166) that deletes unnecessary data from the data related to the distan

Abstract: On the basis of monitoring information acquired by a monitoring device (4), a control device (6) determines a capacity and a position of a net (3), a winding device (2) adjusts the capacity of the net (3) to the capacity determined by the control device (6), and an underwater moving device (5) moves the net (3) to the position determined by the control device (6).

Abstract: An attitude estimation apparatus (30) includes: a data acquiring unit (32) for acquiring observation data; a range-measurement point coordinate calculator (33A) for calculating geodetic coordinates of a range-measurement point in a geodetic coordinate system using positioning data, ranging data, actual attitude data, and an estimated value of an unknown parameter representing an attitude of a flying object; an image coordinate calculator (33B) for calculating coordinates of a projected point by performing projection transformation from the geodetic coordinate system to an image coordinate system on the geodetic coordinates; an image matching processor (34) for detecting image coordinates of the range-measurement point appearing in a first captured image by executing image matching between the first captured image and a second captured image selected from captured images; and an attitude estimator (35) for correcting the estimated value such that a magnitude of a difference between the coordinates of the proje

Abstract: An information processing unit (32) collects accident information from a vehicle-mounted device (2) existing in a periphery of a site of an accident indicated by accident information stored in a storage unit (31), by controlling a communication unit (30).

Abstract: A return point acquiring unit refers to both a first optical flow from an image to an image, and a second optical flow from the image to the image, to acquire the coordinates of a return point which is an end point of the second optical flow. An accuracy calculating unit calculates the accuracy of the optical flows on the basis of the difference between the coordinates of a start point of the first optical flow and the coordinates of the return point acquired by the return point acquiring unit.

Abstract: An image feature map generating unit (3) generates, on the basis of feature amounts extracted from a plurality of images successively captured by a camera (109), an image feature map which is an estimated distribution of the object likelihood on each of the images. An object detecting unit (4) detects an object on the basis of the image feature map generated by the image feature map generating unit (3).