A method includes capturing a raw image from a semiconductor wafer, using graphic data system (GDS) information corresponding to the wafer to assign a measurement box in the raw image, performing a distance measurement on a feature of the raw image in the measurement box, and performing a manufacturing activity based on the distance measurement.

A method and a system of object contours comparisons are described. A first tree structure that connects all points of a set of points is determined such that a length of the first tree structure is smaller than a length of any other possible tree structures for connecting all points. Based on the first tree structure, a first subset of the set of points is determined such that it includes points ordered to form a path that satisfies a selection criterion for representing a first contour of a first object. From the first subset of points and a second set of points that forms a contour of a second object, a set of points of interest is determined. The first object and the second object are compared based on characteristics of the shape indicative of a difference between the first and the second contours formed by the points of interest.

Disclosed herein are systems and methods for selecting re-usable car wheels, comprising, receiving one or more images of an evaluated used car wheel, transforming one or more of the images to multi-dimension feature vectors, mapping the evaluated used car wheel to one of a plurality of used car wheels by applying one or more trained ML models to classify the multi-dimension feature vector. The one or more trained ML models are trained using a plurality of multi-dimension feature vectors each created based on a plurality of images of a plurality of used car wheels, computing a price value of the mapped used car wheel based on a plurality of sale transactions of used car wheels such as the mapped used car wheel, and generating instructions to indicate whether the evaluated used car wheel is re-usable or not based on comparison between the computed price value and a price threshold.

Disclosed are a method and apparatus for recognizing a landmark in a panoramic image. The method includes steps of performing projection transformation on the panoramic image so as to generate a projection image; conducting semantic segmentation on the projection image so as to determine a landmark region and a road surface region; correcting distortion in the landmark region so as to produce a corrected landmark region; and recognizing the landmark in the corrected landmark region.

A system and method for matching a pattern may include generating a set of model descriptors representative of segment features of a first imaged object. A set of query descriptors representative of segment features of a second imaged object may be generated. A first model segment may be selected to align with the query segments to determine if any of the query segments correspond with the first model segment based on respective model descriptors and query descriptors. An hypothesis may be generated by computing a transformation to match the selected first model descriptor with a query descriptor. The hypothesis may be validated by a model fitting algorithm when comparing other transformed model descriptors with other query descriptors. Based on a consensus value from the model fitting algorithm, a determination as to whether a pattern match exists between the first and second images may be made.

Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

A method of detecting concealed objects using a thermal imager includes obtaining an output comprising a plurality of pixels representing a person, analyzing each pixel matching a contour of the person and excluding any pixel within a blob bounding box of the person, and determining whether a pixel address is represented in a pixel map. In addition, the method includes comparing a value of each remaining pixel to an allowable minimum threshold value representing a lower pre-defined body temperature, and comparing the value of each remaining pixel greater than or equal to the allowable minimum threshold value to an upper allowable threshold value representing an upper pre-defined body temperature. The method also includes excluding any of the remaining pixels within a range between the lower and upper pre-defined body temperatures to define final set of pixels and calculating a pixel difference to indicate a severity of the difference.

Examples for determining a confidence level associated with image segmentation are disclosed. A confidence level associated with a collective image segmentation result can be determined by generating multiple individual segmentation results each from the same image data. These examples can then aggregate the individual segmentation results to form the collective image segmentation result and measure the spread of each individual segmentation result from the collective image segmentation result. The measured spread of each individual segmentation result can then be used to determine the confidence level associated with the collective image segmentation result. This can allow a confidence level associated with the collective image segmentation result to be determined. This confidence level may be determined without needing a ground truth to compare to the collective image segmentation result.

Approaches are described for shape-based graphics search. Each graphics object of a set of graphics objects is analyzed. The analyzing includes determining an outline of the graphics object from graphics data that forms the graphics object. The outline of the graphics object is sampled resulting in sampled points that capture the outline of the graphics object. A shape descriptor of the graphics object is determined which captures local and global geometric properties of the sampled points. Search results of a search query are determined based on a comparison between a shape descriptor of a user identified graphics object and the shape descriptor of at least one graphics object of the set of graphics objects. At least one of the search results can be presented on a user device associated with the search query.

An information processing apparatus (100) determines, by referring to a storage unit that stores therein contour data of a plurality of objects, whether a plurality of pieces of contour data associated with a contour of a subject included in a captured image. The information processing apparatus (100) acquires, when a determination result is affirmative, by referring to the storage unit, a plurality of pieces of region data associated with the plurality of pieces of corresponding contour data associated with the contour of the subject and specifies, based on the plurality of pieces of acquired region data, an object associated with the subject from among the plurality of objects.