Abstract: A bicycle component is provided other than a rear derailleur and a drive unit. The bicycle component includes an electrical part, a rechargeable power source and a non-contact charging portion. The rechargeable power source is electrically connected to the electrical part. The non-contact charging portion is configured to wirelessly receive external electric power and to supply the external electric power to the rechargeable power source. A non-contact charging method is also provided for charging the rechargeable power source of the bicycle component.

Abstract: A positive electrode active material for a secondary battery includes a center part and a covering part. The center part includes a layered rock-salt lithium-nickel composite oxide. The covering part covers a surface of the center part and includes a boron compound. The positive electrode active material has a crystallite size of a (104) plane that is greater than or equal to 40.0 nm and less than or equal to 74.5 nm. The crystallite size is calculated by X-ray diffractometry and Scherrer equation. The positive electrode active material has a specific surface area that satisfies a condition represented by ?0.0160×Z+1.72?A??0.0324×Z+2.94 where Z is the crystallite size (nm), and A is the specific surface area (m2/g). The specific surface area is measured by BET specific surface area measurement method.

Abstract: A similar case image search method performed by a computer, the method includes: extracting a lung field area from a medical image and identifying a contour of the lung field area including a chest wall and a mediastinum; identifying a position at which the chest wall and the mediastinum are internally divided and dividing the lung field area into a central area and a peripheral area based on a shape of the lung field area; counting the number of pixels indicating lesions in each of the divided central area and peripheral area; and identifying a similar case image corresponding to similarity level of the number of pixels indicating lesions by referring to a storage unit that stores the number of pixels indicating lesions in each of the areas.

Abstract: A non-transitory recording medium stores a program causing a computer to execute a process including determining a reference value on a decision surface classifying a group of image data represented by a plurality of types of feature quantities into two classes in a feature quantity space, based on a feature quantity of given image data that is closest to the decision surface and is classified into one of the classes of the group of image data in the feature quantity space, plotting and displaying pieces of image data of the group of image data along at least one axis indicating a degree of separation of each of the pieces of image data relative to the reference value, modifying, upon receiving a single piece of image data identified from the plotted and displayed pieces of image data, the decision surface based on the identified single piece of image data.

Abstract: An image retrieval apparatus that retrieves a candidate medical image in diagnosis of diffuse lung disease based on a position of an abnormal shadow in an organ region in a target medical image, the apparatus includes a memory, and a processor coupled to the memory and configured to map the organ region in the target medical image to an image having a predetermined shape to make it identifiable whether the abnormal shadow is distributed over a first portion in the organ region or a second portion in the organ region, occurrence portions of the abnormal shadow within the first portion and the second portion are organizationally different, and calculate a position of the abnormal shadow after the mapping in the image having the predetermined shape.

Abstract: A non-transitory computer-readable recording medium storing an image interpretation support program that causes a computer to execute a process, the process including generating first data indicating a first internal structure of a predetermined object, based on a first cross-sectional image group acquired with respect to the predetermined object; detecting a structural change of the first internal structure from a second internal structure of the predetermined object, based on second data indicating the second internal structure and the generated first data, the second data being generated based on a second cross-sectional image group acquired at a past time; identifying a new cross-section with respect to the predetermined object based on the structural change; generating a cross-sectional image of the predetermined object with respect to the new cross-section, based on the first cross-sectional image group; and displaying the generated cross-sectional image together with first information indicating the de

Abstract: There is provided a distance measuring apparatus including a storing unit configured to store a first image and a second image to be compared with one another; and a calculating unit configured to extract a plurality of feature points from each of the first image and the second image, extract a combination of feature points with high stability of feature values amongst the plurality of feature points in the first image and a combination of feature points with high stability of feature values amongst the plurality of feature points in the second image, and output, as bit strings, the feature values associated with the combination extracted from the first image and the feature values associated with the combination extracted from the second image.

Abstract: An image similarity determination apparatus includes a memory and a processor configured to acquire a first image and a second image, perform selection of a first group and a second group from a plurality of feature points included in the first image and perform selection of a third group and a fourth group from a plurality of feature points included in the second image, calculate feature quantity for each feature point included in the first group and the third group on the basis of luminance and calculate feature quantity for each feature point included in the second group and the fourth group on the basis of hue, and determine similarity between the first image and the second image on the basis of both first comparison of the first group with the third group and second comparison of the second group with the fourth group.

Abstract: A computation unit calculates luminance differences of individual pixel pairs in a feature area and calculates, based thereon, a local feature value formed from bit values respectively corresponding to the pixel pairs. Specifically, the computation unit calculates a specific luminance difference for a specific pixel pair corresponding to a specific bit value and then compares the result with a specified range including a zero point of luminance difference. Then a first value is assigned to the specific bit value when the specific luminance difference is greater than the upper bound of the specified range. A second value is assigned to the same when the specific luminance difference is smaller than the lower bound of the specified range. A predetermined one of the first and second values is assigned to the same when the specific luminance difference falls in the specified range.

Abstract: A similar case image search method performed by a computer, the method includes: extracting a lung field area from a medical image and identifying a contour of the lung field area including a chest wall and a mediastinum; identifying a position at which the chest wall and the mediastinum are internally divided and dividing the lung field area into a central area and a peripheral area based on a shape of the lung field area; counting the number of pixels indicating lesions in each of the divided central area and peripheral area; and identifying a similar case image corresponding to similarity level of the number of pixels indicating lesions by referring to a storage unit that stores the number of pixels indicating lesions in each of the areas.

Abstract: An image similarity determination apparatus includes a memory and a processor configured to acquire a first image and a second image, perform selection of a first group and a second group from a plurality of feature points included in the first image and perform selection of a third group and a fourth group from a plurality of feature points included in the second image, calculate feature quantity for each feature point included in the first group and the third group on the basis of luminance and calculate feature quantity for each feature point included in the second group and the fourth group on the basis of hue, and determine similarity between the first image and the second image on the basis of both first comparison of the first group with the third group and second comparison of the second group with the fourth group.

Abstract: A non-transitory recording medium stores a program causing a computer to execute a process including determining a reference value on a decision surface classifying a group of image data represented by a plurality of types of feature quantities into two classes in a feature quantity space, based on a feature quantity of given image data that is closest to the decision surface and is classified into one of the classes of the group of image data in the feature quantity space, plotting and displaying pieces of image data of the group of image data along at least one axis indicating a degree of separation of each of the pieces of image data relative to the reference value, modifying, upon receiving a single piece of image data identified from the plotted and displayed pieces of image data, the decision surface based on the identified single piece of image data.

Abstract: There is provided a distance measuring apparatus including a storing unit configured to store a first image and a second image to be compared with one another; and a calculating unit configured to extract a plurality of feature points from each of the first image and the second image, extract a combination of feature points with high stability of feature values amongst the plurality of feature points in the first image and a combination of feature points with high stability of feature values amongst the plurality of feature points in the second image, and output, as bit strings, the feature values associated with the combination extracted from the first image and the feature values associated with the combination extracted from the second image.

Abstract: A non-transitory computer-readable recording medium storing an image interpretation support program that causes a computer to execute a process, the process including generating first data indicating a first internal structure of a predetermined object, based on a first cross-sectional image group acquired with respect to the predetermined object; detecting a structural change of the first internal structure from a second internal structure of the predetermined object, based on second data indicating the second internal structure and the generated first data, the second data being generated based on a second cross-sectional image group acquired at a past time; identifying a new cross-section with respect to the predetermined object based on the structural change; generating a cross-sectional image of the predetermined object with respect to the new cross-section, based on the first cross-sectional image group; and displaying the generated cross-sectional image together with first information indicating the de

Abstract: An image processor includes: a memory; and a processor, wherein the processor is configured to: extract an arc area in an image captured by an image sensor; and determine whether a portion that protrudes from an arc in the arc area satisfies a first reference regarding a shape and detect the portion as a colony candidate when it is determined that the portion satisfies the first reference.

Abstract: An image retrieval apparatus that retrieves a candidate medical image in diagnosis of diffuse lung disease based on a position of an abnormal shadow in an organ region in a target medical image, the apparatus includes a memory, and a processor coupled to the memory and configured to map the organ region in the target medical image to an image having a predetermined shape to make it identifiable whether the abnormal shadow is distributed over a first portion in the organ region or a second portion in the organ region, occurrence portions of the abnormal shadow within the first portion and the second portion are organizationally different, and calculate a position of the abnormal shadow after the mapping in the image having the predetermined shape.

Abstract: An extraction device includes a processor that executes a procedure. The procedure includes: from captured images obtained by capturing a sports game having a match style of attack toward a goal provided in territory of an opposing team, identifying a portion of captured images in which the captured images change over time toward a direction of one or other goal; and extracting a scene in which one or other team is attacking based on the portion of captured images.

Abstract: A computation unit calculates luminance differences of individual pixel pairs in a feature area and calculates, based thereon, a local feature value formed from bit values respectively corresponding to the pixel pairs. Specifically, the computation unit calculates a specific luminance difference for a specific pixel pair corresponding to a specific bit value and then compares the result with a specified range including a zero point of luminance difference. Then a first value is assigned to the specific bit value when the specific luminance difference is greater than the upper bound of the specified range. A second value is assigned to the same when the specific luminance difference is smaller than the lower bound of the specified range. A predetermined one of the first and second values is assigned to the same when the specific luminance difference falls in the specified range.

Abstract: A memory stores area information indicating a pitcher area in which a pitcher is presumed to appear in an image that represents a pitching scene. A processor detects an edge pixel from a difference image that represents a difference between a first image at a first time and a second image at a second time that are included in a video, and identifies an edge area in which a plurality of edge pixels are aligned in a prescribed direction in the difference image. Then, the processor determines that the difference image represents the pitching scene on the basis of a size of the edge area included in the pitcher area indicated by the area information in the difference image and on the basis of a size of the edge area included in a prescribed area in the difference image.

Abstract: This invention discloses an image retrieval apparatus. The image retrieval apparatus comprises an unlabelled image selector for selecting one or more unlabelled image(s) from an image database; and a main learner for training in each feedback round of the image retrieval, estimating relevance of images in the image database and a user's intention, and determining retrieval results, wherein the main learner makes use of the unlabelled image(s) selected by the unlabelled image selector in the estimation. In addition, the image retrieval apparatus may also include an active selector for selecting, in each feedback round and according to estimation results of the main learner, one or more unlabelled image(s) from the image database for the user to label.