According to one aspect of the presently disclosed subject there is provided a device configured figured to allow illumination of light towards a tunable filter, each time towards a different portion thereof, and detect the optical response, (e.g., transmission or reflection from the portion that is illuminated with light). By detecting optical response of isolated illuminations towards different portions of the tunable filter each time, the state of the tunable filter at the illuminated portion, e.g. the optical gap between a movable member and a stationary member of the tunable filter, can be determined. By monitoring different portions of the tunable filter, the general state of the tunable filter is determined. For example, the general state of the tunable filter may be determined based on the optical gaps at different portions of the tunable filter while the actuation parameters are maintained unchanged.

A method of determining axial alignment of a lower limb includes determining a three-dimensional volume based on a scan of a lower limb of a patient, identifying a level of hip version by referring to a first axis of symmetry of a femoral neck and a second axis of symmetry of a distal portion of a femur, wherein hip version is defined by an angle between the first axis of symmetry and the second the second axis of symmetry, identifying a level of tibial torsion by referring to a third axis of symmetry of a proximal portion of a tibia and a fourth axis of symmetry of a distal portion of the tibia, wherein tibial torsion is defined by an angle between the third axis of symmetry and the fourth axis of symmetry, and providing the level of hip version and the level of tibial torsion to a user.

A method of determining axial alignment of a lower limb includes determining a three-dimensional volume based on a scan of a lower limb of a patient, identifying a level of hip version by referring to a first axis of symmetry of a femoral neck and a second axis of symmetry of a distal portion of a femur, wherein hip version is defined by an angle between the first axis of symmetry and the second the second axis of symmetry, identifying a level of tibial torsion by referring to a third axis of symmetry of a proximal portion of a tibia and a fourth axis of symmetry of a distal portion of the tibia, wherein tibial torsion is defined by an angle between the third axis of symmetry and the fourth axis of symmetry, and providing the level of hip version and the level of tibial torsion to a user.

The learning device 1A includes an acquiring means 23A, a conversion means 24A, and a learning means 25A. The acquiring means 23A is configured to acquire a combination of a first label that is a unique label for each feature point of an object and a feature point image in which a feature point corresponding to the first label is imaged. The conversion means 24A is configured to convert the first label to a second label that is set to a same label for feature points of the object with at least one of a congruence relation in appearance or a mirror symmetry relation to one another. The learning means 25A is configured to learn an inference engine based on the second label, the feature point image, and correct answer data regarding a position of the feature point, the inference engine being configured to perform an inference on the position of the feature point included in an image that is inputted to the inference engine.

The learning device 1A includes an acquiring means 23A, a conversion means 24A, and a learning means 25A. The acquiring means 23A is configured to acquire a combination of a first label that is a unique label for each feature point of an object and a feature point image in which a feature point corresponding to the first label is imaged. The conversion means 24A is configured to convert the first label to a second label that is set to a same label for feature points of the object with at least one of a congruence relation in appearance or a mirror symmetry relation to one another. The learning means 25A is configured to learn an inference engine based on the second label, the feature point image, and correct answer data regarding a position of the feature point, the inference engine being configured to perform an inference on the position of the feature point included in an image that is inputted to the inference engine.

The present invention relates to an inspection system for confirming whether a capsule is defective and, more particularly, a large quantity of capsules flowing in from the outside is randomly supplied to a moving part by means of a hopper part, the moving part transfers, to a discharge part by means of a plurality of rollers, the large quantity of capsules fed by means of the hopper part so that a controller can distinguish between the good-quality and defective capsules on the basis of an image captured by means of a vision part and discharge same, a gap is formed between the rollers so that the plurality of fed capsules are randomly loaded and horizontally aligned in the gap, and the vision part can capture still images of the rotating capsules that are horizontally aligned and loaded in the gap, while the rollers rotate/stop through a driving part according to the repeated going/stopping of the moving part.

The present invention relates to a regularity measurement system and method for seat arrangement, the regularity measurement system includes: a content capturing mechanism, which is located on the casing of a mobile terminal, and is used for performing image content capturing actions on the layout scene of a conference room facing it, so as to obtain the corresponding layout scene image, wherein the content capturing mechanism of the mobile terminal is facing to the center position of the conference table to be analyzed; a classification and recognition device, which is used for matching the layout scene image with the shape of each type of reference seat to obtain the image area where each seat object in the layout scene image is located and the corresponding seat type. The seat arrangement regularity measurement system and method of the present invention are reasonably designed and widely used.

An information processing device 4 includes a feature point acquiring means 41A and a label determining means 43A. The feature point acquiring means 41A is configured to acquire, based on a captured image “Im” captured by an imaging unit 15A, a combination of positions of feature points of a symmetrical object with a symmetry and second labels defined to integrate or change first labels that are unique labels of the feature points based on the symmetry of the symmetrical object. The label determination means 43A is configured to determine a first label to be assigned to each of the feature points based on additional information to break the symmetry and the second labels.

An information processing device 4 includes a feature point acquiring means 41A and a label determining means 43A. The feature point acquiring means 41A is configured to acquire, based on a captured image “Im” captured by an imaging unit 15A, a combination of positions of feature points of a symmetrical object with a symmetry and second labels defined to integrate or change first labels that are unique labels of the feature points based on the symmetry of the symmetrical object. The label determination means 43A is configured to determine a first label to be assigned to each of the feature points based on additional information to break the symmetry and the second labels.

The present invention relates to a method and apparatus for evaluating inspiration-level quality of a chest radiographic image, wherein the method includes extracting a lung region from a chest radiographic image, detecting a rib cage from the extracted lung region, analyzing a degree of inspiration when the chest radiographic image is captured, and evaluating quality of the chest radiographic image. It is possible for the present invention to be applied to other embodiments.