Abstract: A measurement device is configured to: calculate first contour data of a container in an empty state at a first time; calculate second contour data indicating a surface contour of an object at a second time in execution of a scooping operation by the container; rotate the second contour data, based on differential information indicating a difference between second posture data of a working gear 4 at the second time and first posture data of the working gear 4 at the first time; specify a region defined by supplemental contour data for supplementing the surface contour of the object contained in the container in the execution of the scooping operation, the rotated second contour data, and the first contour data; and calculate, based on the specified region, a first volume indicating a volume of the object contained in the container.

Abstract: A measurement device is configured to calculate, based on second measurement data provided by a distance detector, second contour data indicating a surface contour of an object contained in the container at a second time after a first time; calculate differential information indicating a difference between first posture data and second posture data which is posture data provided by a posture detector at the second time; rotate, based on the differential information, the second contour data in a three-dimensional coordinate space of the distance detector; and specify a region defined by the rotated second contour data and the first contour data, and calculate, based on the specified region, a volume of the object contained in the container at the second time.

Abstract: An endoscopic image diagnosis support system (100) includes: a memory (10) that stores learning images pre-classified into pathological types; and a processor (20) that, given an endoscopic image, performs feature value matching between an image of an identification target region in the endoscopic image and the learning images, to identify the pathological types in the identification target region.

Abstract: An endoscopic image diagnosis support system (100) includes: a memory (10) that stores learning images pre-classified into pathological types; and a processor (20) that, given an endoscopic image, performs feature value matching between an image of an identification target region in the endoscopic image and the learning images, to identify the pathological types in the identification target region.

Abstract: There is provided a golf club head capable of reliably preventing a backspin rate from varying in rainfall. A plurality of fine grooves are formed on a face surface 2. When observing a cross-sectional surface perpendicular to the face surface 2 involving a vertical line passing through the center G of gravity of the golf club head 1 with the golf club head 1 set at preset loft and lie angles, an average width of the fine grooves 4 in the cross-sectional surface is 100 ?m or less and an average pitch thereof in the cross-sectional surface is 100 ?m or less. Hence, the discharge of water can be accelerated by a capillary phenomenon, making it possible to reliably prevent a backspin rate of a golf ball from varying in rainfall.

Abstract: An image lens distortion correcting method having step (A) for printing an arbitrary image I1 in a computer; step (B) for photographing the printed image I1 with a camera having a lens distortion and obtaining a photographed image I2 in the computer; step (C) for obtaining a parameter &thgr; such that the image I1 is artificially distorted with the parameter &thgr; and an obtained distorted image I1ud equals with the photographed image 2; and step (D) for using the obtained parameter &thgr; to correct the image photographed by the camera. Thereby, (1) no correspondence point needs to be given, (2) no special tool or object is used, and (3) all points on the image are used, so that an internal parameter can automatically be obtained with high precision, and image lens distortion can be corrected with high precision.

Abstract: There is disclosed an image lens distortion correcting method comprising: an image printing step (A) for printing an arbitrary image I1 in a computer; an image pickup step (B) for photographing the printed image I1 with a camera having a lens distortion and obtaining a photographed image I2 in the computer; a parameter estimating step (C) for obtaining a parameter &thgr; such that the image I1 is artificially distorted with the parameter &thgr; and an obtained distorted image I1ud equals with the photographed image I2; and an image correcting step (D) for using the obtained parameter &thgr; to correct the image photographed by the camera. Thereby, (1) no correspondence point needs to be given, (2) any special tool or object is not used, and (3) all points on the image are used, so that an internal parameter can automatically be obtained with high precision, and an image lens distortion can be corrected with high precision.