Abstract: According to one embodiment, an image encoding method includes selecting a motion reference block from an encoded pixel block to which an inter prediction is applied. The method includes selecting one or more available blocks from the motion reference block. The method includes selecting a selection block from the available blocks. The method includes generating a predicted image of the encoding target block using motion information of the selection block. The method includes encoding a prediction error between the predicted image and an original image. The method includes encoding selection information specifying the selection block by referring to a code table decided according to a number of the available blocks.

Abstract: According to an embodiment, an encoding device includes an index setting unit and an encoding unit. The index setting unit generates a common index in which reference indices of one or more reference images included in a first index and a second index are sorted in a combination so as not to include a same reference image in accordance with a predetermined scanning order. The first index representing a combination of the one or more reference images referred to by a first reference image. The second index representing a combination of the one or more reference images referred to by a second reference image. The encoding unit encodes the common index.

Abstract: According to one embodiment, an image encoding method includes selecting a motion reference block from an encoded pixel block to which an inter prediction is applied. The method includes selecting one or more available blocks from the motion reference block. The method includes selecting a selection block from the available blocks. The method includes generating a predicted image of the encoding target block using motion information of the selection block. The method includes encoding a prediction error between the predicted image and an original image. The method includes encoding selection information specifying the selection block by referring to a code table decided according to a number of the available blocks.

Abstract: According to an embodiment, an encoding device includes an index setting unit and an encoding unit. The index setting unit generates a common index in which reference indices of one or more reference images included in a first index and a second index are sorted in a combination so as not to include a same reference image in accordance with a predetermined scanning order. The first index representing a combination of the one or more reference images referred to by a first reference image. The second index representing a combination of the one or more reference images referred to by a second reference image. The encoding unit encodes the common index.

Abstract: According to an embodiment, an encoding device includes a deriving unit and an encoding unit. The deriving unit is configured to derive a first reference value based on fixed point precision representing roughness of a weighting factor that is used for multiplying a reference image. The encoding unit is configured to encode a first difference value that is a difference value between the weighting factor and the first reference value and the fixed point precision. The weighting factor is included in a first range of predetermined bit precision having the first reference value at approximate center. The first difference value is in the predetermined range.

Abstract: A treatment system of embodiments includes an imaging system including one or more radiation sources and a plurality of detectors, a first acquirer, a second acquirer, a first deriver, a second deriver, and a calibrator. The radiation sources radiate radiation to an object in a plurality of different directions. The plurality of detectors detect the radiation at different positions. The first acquirer acquires images based on the radiation. The second acquirer acquires position information of a first imaging device in a three-dimensional space. The first deriver derives the position of the object in the images. The second deriver derives the position of a second imaging device in the three-dimensional space based on the position of the object in the images, the position of the first imaging device, and the like. The calibrator performs calibration of the imaging system based on a derivation result of the second deriver.

Abstract: A medical image processing device includes a first position acquirer, a first converter, a first likelihood image generator, and a learner. The first position acquirer is configured to acquire, as first positions, target positions in plural first images. The first converter is configured to convert the first positions to second positions by expanding movement in a second direction intersecting a first direction based on movement over time of the first positions. The first likelihood image generator is configured to generate a first likelihood image showing a distribution of likelihood of the second positions. The learner is configured to output a model using the plural first images and the first likelihood image as training data, and upon receiving part or all of a transparent image, derives a second likelihood image showing a distribution of likelihood indicating probability of the part or all of the transparent image corresponding to the second positions.

Abstract: According to an embodiment, a radiation therapy device includes an acquirer, a projection position calculator, an element projection image generator, and an element projection image synthesizer. The acquirer acquires a condition of X-ray imaging in a treatment stage and a three-dimensional image of a patient imaged before the treatment stage. The projection position calculator calculates a projection position when each of pixels included in the three-dimensional image is projected onto a two-dimensional X-ray fluoroscopic image generated in the X-ray imaging on the basis of the condition of the X-ray imaging. The element projection image generator generates an element projection image for each pixel when each of the pixels included in the three-dimensional image is projected onto the X-ray fluoroscopic image. The element projection image synthesizer performs a synthesis process for the element projection image for each pixel on the basis of the projection position to generate a reconstructed image.

Abstract: A medical image processing device includes a first image acquiring unit, a second image acquiring unit, a first likelihood distribution calculating unit, a trackability determining unit, and a tracking unit. The first image acquiring unit acquires first images which are transparent images of the patient. The second image acquiring unit acquires second images which are transparent images of the patient generated at a time different from that of the first images. The first likelihood distribution calculating unit calculates a first likelihood distribution indicating a distribution of likelihoods indicating a likeness to the object in the first images. The trackability determining unit determines whether the object is trackable on the basis of the first likelihood distribution. The tracking unit tracks the position of the object in the second images on the basis of the result of determination.

Abstract: A video encoding apparatus is a video encoding apparatus for subjecting a video image to motion compensated prediction coding, comprising an acquisition module to acquire available blocks of blocks having motion vectors from encoded blocks adjacent to a to-be-encoded block and number of the available blocks, an acquisition/selection module to select one selection block from the encoded available blocks, a selection information encoder to encode selection information specifying the selection block using a code table corresponding to the number of available blocks, and an image encoder to subject the to-be-encoded block to motion compensated prediction coding using a motion vector of the selection block.

Abstract: An encoding device that includes a deriving unit and an encoding unit. The deriving unit is configured to derive a first reference value based on fixed point precision representing roughness of a weighting factor that is used for multiplying a reference image. The encoding unit is configured to encode a first difference value that is a difference value between the weighting factor and the first reference value and the fixed point precision. The weighting factor is included in a first range of predetermined bit precision having the first reference value at approximate center. The first difference value is in the predetermined range.

Abstract: According to one embodiment, a learning apparatus includes a processor. The processor divides target data into pieces of partial data. The processor inputs the pieces of partial data into a first network model to output a first prediction result and calculates a first confidence indicating a degree of contribution to the first prediction result. The processor inputs the target data into a second network model to output a second prediction result and calculates a second confidence indicating a degree of contribution to the second prediction result. The processor updates a parameter of the first network model, based on the first prediction result, the second prediction result, the first confidence and the second confidence.

Abstract: A video encoding apparatus is a video encoding apparatus for subjecting a video image to motion compensated prediction coding, comprising an acquisition module to acquire available blocks of blocks having motion vectors from encoded blocks adjacent to a to-be-encoded block and number of the available blocks, an acquisition/selection module to select one selection block from the encoded available blocks, a selection information encoder to encode selection information specifying the selection block using a code table corresponding to the number of available blocks, and an image encoder to subject the to-be-encoded block to motion compensated prediction coding using a motion vector of the selection block.

Abstract: A method of dividing an input image signal into pixel blocks, and performing inter-prediction on the divided pixel blocks. This method includes selecting predicted motion information from a motion information buffer storing motion information in an encoded region, and predicting motion information of an encoding target block by using the predicted motion information. The method further includes acquiring representative motion information from a plurality of items of motion information in an encoded region in accordance with first information indicating a method of selecting the predicted motion information, thereby obtaining only the representative motion information.

Abstract: A learning device includes a structure search unit that searches for a first learned model structure obtained by selecting search space information in accordance with a target constraint condition of target hardware for each of a plurality of convolution processing blocks included in a base model structure in a neural network model; a parameter search unit that searches for a learning parameter of the neural network model in accordance with the target constraint condition; and a pruning unit that deletes a unit of at least one of the plurality of convolution processing blocks in the first learned model structure based on the target constraint condition and generates a second learned model structure.

Abstract: A robot system according to an embodiment includes one or more processors. The processors acquire first input data predetermined as data affecting an operation of a robot. The processors calculate a calculation cost of inference processing using a machine learning model for inferring control data used for controlling the robot, on the basis of the first input data. The processors infer the control data by the machine learning model set according to the calculation cost. The processors control the robot using the inferred control data.

Abstract: According to an embodiment, a medical image processing device includes a first image acquirer, a second image acquirer, a direction acquirer, and a movement amount calculator. The first image acquirer acquires a three-dimensional first image obtained by photographing the inside of a body of a patient. The second image acquirer acquires a three-dimensional second image of the inside of the body of the patient imaged at a timing different from that of the first image. The direction acquirer acquires information about an irradiation direction of radiation to the patient in a treatment room. The movement amount calculator outputs a movement amount signal indicating the amount of movement of the second image to be moved to align the position of the patient shown in the second image with the position of the patient shown in the first image based on the path of the radiation set in the first image and the information about the irradiation direction.

Abstract: A method of dividing an input image signal into pixel blocks, and performing inter-prediction on the divided pixel blocks. This method includes selecting predicted motion information from a motion information buffer storing motion information in an encoded region, and predicting motion information of an encoding target block by using the predicted motion information. The method further includes acquiring representative motion information from a plurality of items of motion information in an encoded region in accordance with first information indicating a method of selecting the predicted motion information, thereby obtaining only the representative motion information.

Abstract: According to one embodiment, a learning system includes a plurality of local devices and a server. Each of the local devices includes a processor. The processor of the local device selects a first parameter set from a plurality of parameters related to the local model, and transmits the first parameter set to the server. At least one of the local devices is different from other local devices in a size of the local model in accordance with a resolution of input data. The server comprises a processor. The processor of the server integrates first parameter sets acquired from the local devices and update a global model. The processor of the server transmits the second parameter set to a local device that has transmitted the corresponding first parameter set.

Abstract: A learning device according to an embodiment includes one or more hardware processors configured to function as a generation unit, an inference unit, and a training unit. The generation unit generates input data with which an error between a value output from each of one or more target nodes and a preset aimed value is equal to or less than a preset value, the target nodes being in a target layer of a plurality of layers included in a first neural network. The inference unit causes the input data to propagate in a forward direction of the first neural network to generate output data. The training unit trains a second neural network differing from the first neural network by using training data including a set of the input data and the output data.