A three-dimensional model generation method executed by an information processing device includes: obtaining images generated by shooting a subject from respective viewpoints; searching for a similar point that is similar to a first point in a first image among the images, from second points in a search area in a second image different from the first image, the search area being provided based on the first point; calculating an accuracy of a search result of the searching, using degrees of similarity between the first point and the respective second points; and generating a three-dimensional model using the search result and the accuracy.

An analysis device includes a processor configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process has been executed, while gradually changing recognition accuracy of the images generated by the generative model on which the first learning process has been executed, to desired recognition accuracy; acquire each piece of information on back-error propagation calculated by executing the image recognition process, for the images with each level of the recognition accuracy generated through a course of the second learning process; and generate evaluation information indicating each of image parts that cause erroneous recognition at each level of the recognition accuracy, based on the acquired each piece of the information on the back-error propagation.

An analysis device includes a processor configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process has been executed, while gradually changing recognition accuracy of the images generated by the generative model on which the first learning process has been executed, to desired recognition accuracy; acquire each piece of information on back-error propagation calculated by executing the image recognition process, for the images with each level of the recognition accuracy generated through a course of the second learning process; and generate evaluation information indicating each of image parts that cause erroneous recognition at each level of the recognition accuracy, based on the acquired each piece of the information on the back-error propagation.

The invention relates to a method in the preparation of samples for microscopic examination onto which a coverslip is applied. The method is notable for the fact that the coverslipping quality is checked automatically and at least partly optically. The invention further relates to an apparatus for carrying out the method, and to an apparatus for checking the coverslipping quality of samples onto which a coverslip is applied.

Methods and systems are disclosed for cross-validating a second sensor with a first sensor. Cross-validating the second sensor may include obtaining sensor readings from the first sensor and comparing the sensor readings from the first sensor with sensor readings obtained from the second sensor. In particular, the comparison of the sensor readings may include comparing state information about a vehicle detected by the first sensor and the second sensor. In addition, comparing the sensor readings may include obtaining a first image from the first sensor, obtaining a second image from the second sensor, and then comparing various characteristics of the images. One characteristic that may be compared are object labels applied to the vehicle detected by the first and second sensor. The first and second sensors may be different types of sensors.

Methods and systems are disclosed for cross-validating a second sensor with a first sensor. Cross-validating the second sensor may include obtaining sensor readings from the first sensor and comparing the sensor readings from the first sensor with sensor readings obtained from the second sensor. In particular, the comparison of the sensor readings may include comparing state information about a vehicle detected by the first sensor and the second sensor. In addition, comparing the sensor readings may include obtaining a first image from the first sensor, obtaining a second image from the second sensor, and then comparing various characteristics of the images. One characteristic that may be compared are object labels applied to the vehicle detected by the first and second sensor. The first and second sensors may be different types of sensors.

A processor unit configured to identify blocks of a frame of a video sequence to be excluded from a motion-compensated operation, the processor unit comprising: a frame processor configured to process pixel values of a first frame to characterise blocks of one or more pixels of the first frame as representing at least a portion of a graphic object; a frame-difference processor configured to determine difference values between blocks of the first frame and corresponding blocks of a second frame, and to process said difference values to characterise blocks of the first frame as representing an image component that is static between the first and second frames; a block identifier configured to identify blocks of the first frame as protected blocks in dependence on blocks characterised as: (i) representing at least a portion of a graphic object; and (ii) representing an image component that is static between the first and second frames, wherein the identified protected blocks are to be excluded from the motion compensated operation.

A processor unit configured to identify blocks of a frame of a video sequence to be excluded from a motion-compensated operation, the processor unit comprising: a frame processor configured to process pixel values of a first frame to characterise blocks of one or more pixels of the first frame as representing at least a portion of a graphic object; a frame-difference processor configured to determine difference values between blocks of the first frame and corresponding blocks of a second frame, and to process said difference values to characterise blocks of the first frame as representing an image component that is static between the first and second frames; a block identifier configured to identify blocks of the first frame as protected blocks in dependence on blocks characterised as: (i) representing at least a portion of a graphic object; and (ii) representing an image component that is static between the first and second frames, wherein the identified protected blocks are to be excluded from the motion compensated operation.

There is provided a method of examination of a semiconductor specimen and a system thereof. The method comprises: using a trained Deep Neural Network (DNN) to process a fabrication process (FP) sample, wherein the FP sample comprises first FP image(s) received from first examination modality(s) and second FP image(s) received from second examination modality(s) which differs from the first examination modality(s), and wherein the trained DNN processes the first FP image(s) separately from the second FP image(s); and further processing by the trained DNN the results of such separate processing to obtain examination-related data specific for the given application and characterizing at least one of the processed FP images. When the FP sample further comprises numeric data associated with the FP image(s), the method further comprises processing by the trained DNN at least part of the numeric data separately from processing the first and the second FP images.

There is provided a method of examination of a semiconductor specimen and a system thereof. The method comprises: using a trained Deep Neural Network (DNN) to process a fabrication process (FP) sample, wherein the FP sample comprises first FP image(s) received from first examination modality(s) and second FP image(s) received from second examination modality(s) which differs from the first examination modality(s), and wherein the trained DNN processes the first FP image(s) separately from the second FP image(s); and further processing by the trained DNN the results of such separate processing to obtain examination-related data specific for the given application and characterizing at least one of the processed FP images. When the FP sample further comprises numeric data associated with the FP image(s), the method further comprises processing by the trained DNN at least part of the numeric data separately from processing the first and the second FP images.