Abstract: In a position measuring apparatus, a correspondence point detector detects, for each set of images at a respective one of time instants, correspondence points from the respective images of the set, where the correspondence points are points on respective image planes representing the same three-dimensional position. A projection point calculator calculates a projection point of each of the correspondence points detected at the respective time instants onto each of a plurality of common planes set at different depthwise positions in a world coordinate system using preset camera parameters. A reconstruction point calculator calculates a point at which distances to a plurality of rays each connecting the projection points of the correspondence point on a respective one of the image planes onto the plurality of common planes are minimized, as a reconstruction point representing a three-dimensional position of the correspondence point.

Abstract: A position measuring apparatus detects, from respective first and second imaging planes of first and second captured images, first and second corresponding points estimated to represent a common three-dimensional position. The apparatus calculates first to fourth projected points. Each of the first and second projected points represents a projected point of the first corresponding point on a corresponding one of the first and second common planes. Each of the third and fourth projected points represents a projected point of the second corresponding point on a corresponding one of the first and second common planes. The apparatus calculates a first beam connecting the first and second projected points, a second beam connecting the third and fourth projected points, and a point having a minimum square distance relative to each of the first and second beams as a restored point representing the three-dimensional position of the first and second corresponding points.

Abstract: A position measuring apparatus detects, from respective first and second imaging planes of first and second captured images, first and second corresponding points estimated to represent a common three-dimensional position. The apparatus calculates first to fourth projected points. Each of the first and second projected points represents a projected point of the first corresponding point on a corresponding one of the first and second common planes. Each of the third and fourth projected points represents a projected point of the second corresponding point on a corresponding one of the first and second common planes. The apparatus calculates a first beam connecting the first and second projected points, a second beam connecting the third and fourth projected points, and a point having a minimum square distance relative to each of the first and second beams as a restored point representing the three-dimensional position of the first and second corresponding points.

Abstract: A recognition task executing means 11 that provides a feature point selecting system which can select an adequate feature point matching a recognition algorithm in a recognition task executes the recognition task using an importance of each of a plurality of feature point candidates on a three-dimensional shape model for a plurality of evaluation images. A recognition error evaluating means 12 evaluates a recognition error related to all evaluation images from a difference between a recognition result of the recognition task and correct data of the recognition task for each evaluation image. A feature point importance determining means 13 sets a cost function which is represented as a function obtained by adding a restriction condition that an importance of an unimportant feature point candidate becomes close to zero, to the recognition error related to all evaluation images, and calculating the importance of each feature point candidate which minimizes a value of the cost function.

Abstract: Three-dimensional information is presented directly on the surface of a target three-dimensional object without using a display device such as a display monitor and an HUD. Spatially coded light patterns are projected from two projectors on a target object surface. The light projected from the two projectors are added up on the object surface to produce unified color and luminance. On the object surface, light is simply added up, so that a pattern whose color and brightness correspond to distance and height appears. Hence, a distance to an object or a three-dimensional shape of an object can be highlighted by the color and brightness presented on the object for perception by human beings.

Abstract: A recognition task executing means 11 that provides a feature point selecting system which can select an adequate feature point matching a recognition algorithm in a recognition task executes the recognition task using an importance of each of a plurality of feature point candidates on a three-dimensional shape model for a plurality of evaluation images. A recognition error evaluating means 12 evaluates a recognition error related to all evaluation images from a difference between a recognition result of the recognition task and correct data of the recognition task for each evaluation image. A feature point importance determining means 13 sets a cost function which is represented as a function obtained by adding a restriction condition that an importance of an unimportant feature point candidate becomes close to zero, to the recognition error related to all evaluation images, and calculating the importance of each feature point candidate which minimizes a value of the cost function.

Abstract: A feature point generation system capable of generating a feature point that satisfies a preferred condition from a three-dimensional shape model is provided. Image group generation means 31 generates a plurality of images obtained by varying conditions with respect to the three-dimensional shape model. Evaluation means 33 calculates a first evaluation value that decreases steadily as a feature point group is distributed more uniformly on the three-dimensional shape model and a second evaluation value that decreases steadily as extraction of a feature point in an image corresponding to a feature point on the three-dimensional shape model becomes easier, and calculates an evaluation value relating to a designated feature point group as a weighted sum of the respective evaluation values. Feature point arrangement means 32 arranges the feature point group on the three-dimensional shape model so that the evaluation value calculated by the evaluation means 33 is minimized.

Abstract: Three-dimensional information is presented directly on the surface of a target three-dimensional object without using a display device such as a display monitor and an HUD. Spatially coded light patterns are projected from two projectors on a target object surface. The light projected from the two projectors are added up on the object surface to produce unified color and luminance. On the object surface, light is simply added up, so that a pattern whose color and brightness correspond to distance and height appears. Hence, a distance to an object or a three-dimensional shape of an object can be highlighted by the color and brightness presented on the object for perception by human beings.

Abstract: A feature point generation system capable of generating a feature point that satisfies a preferred condition from a three-dimensional shape model is provided. Image group generation means 31 generates a plurality of images obtained by varying conditions with respect to the three-dimensional shape model. Evaluation means 33 calculates a first evaluation value that decreases steadily as a feature point group is distributed more uniformly on the three-dimensional shape model and a second evaluation value that decreases steadily as extraction of a feature point in an image corresponding to a feature point on the three-dimensional shape model becomes easier, and calculates an evaluation value relating to a designated feature point group as a weighted sum of the respective evaluation values. Feature point arrangement means 32 arranges the feature point group on the three-dimensional shape model so that the evaluation value calculated by the evaluation means 33 is minimized.