There are included a probe that can be arranged in an imaging field of view, a horizontal drive section for causing the probe to contact a side surface of a workpiece on a stage, a display section for displaying a model image, a contact position designation section for receiving designation of contact target position information in the model image, a characteristic amount information setting section for setting characteristic amount information, a measurement setting information storage section for storing a plurality of pieces of contact target position information and the characteristic amount information, and a measurement control section for identifying a position and an attitude of the workpiece from a workpiece image by using the characteristic amount information, for identifying a plurality of contact target positions on the side surface of the workpiece where the probe should contact, based on the identified position and the identified attitude of the workpiece.

In one embodiment, a method includes receiving an image associated with an object in an environment, the image being captured by sensors associated with a vehicle, generating a feature representation of the image, determining a potential ground control point associated with the object based on the feature representation of the image, determining a predetermined location reading based on the potential ground control point, calculating a differential relative to the predetermined location reading based on the potential ground control point, and determining a location of the vehicle based on the differential and the predetermined location reading based on the potential ground control point.

Image data having text associated with a plurality of text-field types is received, the image data including target image data and context image data. The target image data including target text associated with a text-field type. The context image data providing a context for the target image data. A trained neural network that is constrained to a set of characters for the text-field type is applied to the image data. The trained neural network identifies the target text of the text-field type using a vector embedding that is based on learned patterns for recognizing the context provided by the context image data. One or more predicted characters are provided for the target text of the text-field type in response to identifying the target text using the trained neural network.

An image evaluation device includes a determination result acquisition unit acquires a result of determining the presence or absence of a difference between an object image that is one of a plurality of images that include three or more images obtained by imaging substantially the same spatial region and each of reference images that are images other than the object image among the plurality of images and an evaluation index acquisition unit configured to acquire an evaluation index for the plurality of images on the basis of at least one of the number of determinations of the presence of the difference between the object image and each reference image and the number of determinations of the absence of the difference between the object image and each reference image.

Disclosed are methods, circuits, devices, systems and associated executable code for multi factor image feature registration and tracking, wherein utilized factors include both static and dynamic parameters within a video feed. Assessed factors may originate from a heterogeneous set of sensors including both video and audio sensors. Acoustically acquired scene information may supplement optically acquired information.

Disclosed is an image quality evaluation method for a digital pathology system according to the present invention. The image quality evaluation method includes receiving a digital slide image by an image quality evaluation unit; dividing the digital slide image into a plurality of blocks by the image quality evaluation unit; analyzing the plurality of blocks to extract a foreground; calculating a blur for the extracted foreground; calculating brightness distortion for the extracted foreground; calculating contrast distortion for the extracted foreground; and evaluating the overall quality of the digital slide image using the blur, the brightness distortion, and the contrast distortion by the image quality evaluation unit.

This application relates to a target detection method performed at a computer device. The method includes: obtaining a to-be-detected image; extracting a first image feature and a second image feature corresponding to the to-be-detected image; performing dilated convolution to the second image feature, to obtain a third image feature corresponding to the to-be-detected image; performing classification and regression to the first image feature and the third image feature, to determine candidate position parameters corresponding to a target object in the to-be-detected image and degrees of confidence corresponding to the candidate position parameters; and selecting a valid position parameter from the candidate position parameters according to their corresponding degrees of confidence, and determining a position of the target object in the to-be-detected image according to the valid position parameter. The solutions in this application can improve robustness and consume less time.

A method for detecting a static logo of a video, an electronic apparatus and a storage medium. The method includes: calculating a pixel grayscale flag value, an edge gradient flag value and an edge direction flag value; calculating, in a preset neighborhood centered on the pixel at each pixel position of the current video frame, a first local confidence degree of the pixel grayscale flag value, a second local confidence degree of the edge gradient flag value, and a third local confidence degree of the edge direction flag value respectively; calculating a contribution score of each local confidence degree and a total contribution score of each pixel position; and gathering the total contribution score of each pixel position of the current video frame, and determining a static logo in the current video frame according to the total contribution score of each pixel position.

A method for detecting a static logo of a video, an electronic apparatus and a storage medium. The method includes: calculating a pixel grayscale flag value, an edge gradient flag value and an edge direction flag value; calculating, in a preset neighborhood centered on the pixel at each pixel position of the current video frame, a first local confidence degree of the pixel grayscale flag value, a second local confidence degree of the edge gradient flag value, and a third local confidence degree of the edge direction flag value respectively; calculating a contribution score of each local confidence degree and a total contribution score of each pixel position; and gathering the total contribution score of each pixel position of the current video frame, and determining a static logo in the current video frame according to the total contribution score of each pixel position.

Systems, methods, and other embodiments described herein relate to improving the classification of objects depicted in a scene. In one embodiment, a method includes generating, using an ontological detector, a type classification of a detected object according to a detector ontology of known classes. The detected object is represented as segmented data from sensor data about a surrounding environment. The method includes, in response to determining that the type classification specifies an unknown class that is not defined in the detector ontology, annotating the segmented data as unknown. The method includes providing the segmented data to specify that the type classification for the detected object is unknown.