Abstract: An image processing apparatus comprising: a processor and a memory connected to or incorporated in the processor, in which the processor acquires an analysis target image in which a plurality of contiguous target objects of the same type appear, receives an input of a marker indicating positions of the target objects in the analysis target image, generates a marker position display map indicating a position of the marker in the analysis target image, inputs the analysis target image and the marker position display map to a semantic segmentation model, and outputs, from the semantic segmentation model, an output image in which the target objects are identified.

Abstract: A processor is configured to convert a size of a target image to derive a size-converted image, segment the size-converted image into regions of at least one class by using a segmentation model constructed by machine-learning a neural network to derive a plurality of class images in which a pixel value of each pixel represents class-likeness for the at least one class, convert a size of at least one class image into the size of the target image to derive at least one converted class image, and segment the target image based on a pixel value in each pixel of the at least one converted class image.

Abstract: A processor uses a semantic segmentation model that has been trained using an annotation image in which a first pixel corresponding to at least any one of one point corresponding to an object, a plurality of discrete points corresponding to a plurality of objects, or a line corresponding to an object having a line structure is set as a first pixel value and a second pixel other than the first pixel is set as a second pixel value different from the first pixel value, the model having been trained by assigning a greater weight to the first pixel than to the second pixel to calculate a loss, inputs an image to the model and outputs a feature amount map having a feature amount related to the one point, etc. in the image from the model, and identifies the one point, etc. in the image based on the map.

Abstract: A processor is configured to: acquire training data that consists of a learning expression medium and a correct answer label for at least one of a plurality of types of classes included in the learning expression medium; input the learning expression medium to a neural network such that probabilities that each class included in the learning expression medium will be each of the plurality of types of classes are output; integrate the probabilities that each class will be each of the plurality of types of classes on the basis of classes classified by the correct answer label of the training data; and train the neural network on the basis of a loss derived from the integrated probability and the correct answer label of the training data.

Abstract: A processor uses a semantic segmentation model that has been trained using an annotation image in which a first pixel corresponding to at least any one of one point corresponding to an object, a plurality of discrete points corresponding to a plurality of objects, or a line corresponding to an object having a line structure is set as a first pixel value and a second pixel other than the first pixel is set as a second pixel value different from the first pixel value, the model having been trained by assigning a greater weight to the first pixel than to the second pixel to calculate a loss, inputs an image to the model and outputs a feature amount map having a feature amount related to the one point, etc. in the image from the model, and identifies the one point, etc. in the image based on the map.

Abstract: A processor extracts, from an image including a plurality of structures that spatially continuously present and whose corresponding labels have a hierarchy, respective key points of the plurality of structures in association with labels of a first layer; uses the key points as nodes to derive a graph structure in which the labels of the first layer are associated with the nodes; and associates the nodes with labels of a second layer lower than the first layer by analyzing the graph structure.

Abstract: An image processing apparatus has at least one processor. The at least one processor is configured to perform division processing of dividing an image into a plurality of partial images, the division processing being processing of dividing the image such that each of the plurality of partial images has an overlapping area that overlaps another partial image adjacent to the partial image at an edge part of the partial image; perform, individually for each of the plurality of partial images, calculation processing of calculating numerical values for respective pixels of the partial image, the numerical values being for use in class classification and each being related to a probability of a corresponding pixel among the pixels belonging to a specific class; and perform integration processing of integrating the numerical values individually calculated for the plurality of partial images to achieve a size which the image had before the division processing.

Abstract: The present technology relates to an information processing apparatus capable of more easily determining a maintenance timing by using event data. The information processing apparatus includes a state estimation unit that estimates a state of a grindstone by using event data supplied from an event sensor that outputs, as event data, a temporal change of an electrical signal obtained by photoelectrically converting an optical signal, and outputs a result of the estimation. The present technology can be applied to, for example, an information processing system or the like that tells a maintenance timing of a grindstone of a machine tool.

Abstract: A processor is configured to: acquire training data that consists of a learning expression medium and a correct answer label for at least one of a plurality of types of classes included in the learning expression medium; input the learning expression medium to a neural network such that probabilities that each class included in the learning expression medium will be each of the plurality of types of classes are output; integrate the probabilities that each class will be each of the plurality of types of classes on the basis of classes classified by the correct answer label of the training data; and train the neural network on the basis of a loss derived from the integrated probability and the correct answer label of the training data.

Abstract: An image processing apparatus comprising: a processor and a memory connected to or incorporated in the processor, in which the processor acquires an analysis target image in which a plurality of contiguous target objects of the same type appear, receives an input of a marker indicating positions of the target objects in the analysis target image, generates a marker position display map indicating a position of the marker in the analysis target image, inputs the analysis target image and the marker position display map to a semantic segmentation model, and outputs, from the semantic segmentation model, an output image in which the target objects are identified.

Abstract: A processor uses a semantic segmentation model that has been trained using an annotation image in which a first pixel corresponding to at least any one of one point corresponding to an object, a plurality of discrete points corresponding to a plurality of objects, or a line corresponding to an object having a line structure is set as a first pixel value and a second pixel other than the first pixel is set as a second pixel value different from the first pixel value, the model having been trained by assigning a greater weight to the first pixel than to the second pixel to calculate a loss, inputs an image to the model and outputs a feature amount map having a feature amount related to the one point, etc. in the image from the model, and identifies the one point, etc. in the image based on the map.

Abstract: A processor is configured to convert a size of a target image to derive a size-converted image, segment the size-converted image into regions of at least one class by using a segmentation model constructed by machine-learning a neural network to derive a plurality of class images in which a pixel value of each pixel represents class-likeness for the at least one class, convert a size of at least one class image into the size of the target image to derive at least one converted class image, and segment the target image based on a pixel value in each pixel of the at least one converted class image.

Abstract: A processor is configured to: reduce a target image to derive a reduced image; extract a region of a target structure from the reduced image to derive a reduced structure image including the region of the target structure; extract a corresponding image corresponding to the reduced structure image from the target image; and input the corresponding image and the reduced structure image into an extraction model constructed by machine-learning a neural network to extract a region of the target structure included in the corresponding image from the extraction model.

Abstract: A processor extracts, from an image including a plurality of structures that spatially continuously present and whose corresponding labels have a hierarchy, respective key points of the plurality of structures in association with labels of a first layer; uses the key points as nodes to derive a graph structure in which the labels of the first layer are associated with the nodes; and associates the nodes with labels of a second layer lower than the first layer by analyzing the graph structure.

Abstract: A processor determines an exponent common to a plurality of numerical values, determines a mantissa for each of the plurality of numerical values based on the determined exponent, and performs four arithmetic operations using a sign, the determined exponent, and the determined mantissa.

Abstract: The present technology relates to an image processing apparatus and method, and a program that are capable of more facilitating editing. The image processing apparatus includes: an operation detection unit configured to detect an operation input by an operation unit; and a display control unit configured to output, to an immersive presentation device, part or entirety of a spherical image on which an image of the operation unit is superimposed, as a presentation image, to cause the immersive presentation device to display the presentation image, the display control unit being configured to control, in a case where the operation input has been detected, the display of the presentation image such that the operation input is reflected. The present technology is applicable to an editing system.

Abstract: The present technology relates to an image processing apparatus and method that are capable of more facilitating editing. The image processing apparatus includes an operation detection unit that detects an operation input by an operation unit and a display control unit that outputs, to an immersive presentation device, part or entirety of a spherical image on which an image of the operation unit is superimposed, as a presentation image, to cause the immersive presentation device to display the presentation image, the display control unit being configured to control, in a case where the operation input has been detected, the display of the presentation image such that the operation input is reflected, in which the operation detection unit detects coordinates at a position pointed by a pointer of the operation unit, in the spherical image. The present technology is applicable to an editing system that edits spherical images.

Abstract: The present technology relates to an image processing apparatus and method, and a program that are capable of more facilitating editing. The image processing apparatus includes: an operation detection unit configured to detect an operation input by an operation unit; and a display control unit configured to output, to an immersive presentation device, part or entirety of a spherical image on which an image of the operation unit is superimposed, as a presentation image, to cause the immersive presentation device to display the presentation image, the display control unit being configured to control, in a case where the operation input has been detected, the display of the presentation image such that the operation input is reflected. The present technology is applicable to an editing system.

Abstract: The medical image processing apparatus includes a medical image acquisition unit that acquires a medical image; and a lesion detection unit that detects a lesion region in the medical image. The lesion detection unit includes a first identifier that identifies a lesion region candidate in the medical image and a second identifier that identifies whether the lesion region candidate identified by the first identifier is a blood vessel region, and detects the lesion region candidate that is not identified as the blood vessel region by the second identifier as the lesion region.

Abstract: The present technology relates to an image processing apparatus and method, and a program that are capable of more facilitating editing. The image processing apparatus includes: an operation detection unit configured to detect an operation input by an operation unit; and a display control unit configured to output, to an immersive presentation device, part or entirety of a spherical image on which an image of the operation unit is superimposed, as a presentation image, to cause the immersive presentation device to display the presentation image, the display control unit being configured to control, in a case where the operation input has been detected, the display of the presentation image such that the operation input is reflected, in which the operation detection unit detects coordinates at a position pointed by a pointer of the operation unit, in the spherical image. The present technology is applicable to an editing system that edits spherical images.