Techniques for cloud forecast using configuration engine, segment cloud coverage determination engine, and trained forecast models are described herein. The disclosed techniques include generating a configuration mask based on a type of an optical component for capturing a plurality of sky images and determining cloud coverages of a plurality of corresponding segments by applying the configuration mask and a segment cloud coverage determination algorithm to the plurality of images. The disclosed techniques include training a plurality of forecast models for forecasting cloud coverages of the plurality of corresponding segments using the determined cloud coverages. The plurality of forecast models correlate cloud coverages of the plurality of corresponding segments at a time point with a cloud coverage of any particular segment at a later time point. The plurality of trained forecast models generate data indicative of cloud coverages of the plurality corresponding segments at a future time point.

Embodiments of this disclosure provide a recognition method and apparatus for false detection of an abandoned object and an image processing device. The method includes: performing foreground detection on a current frame image, so as to obtain a foreground image of the current frame image, the foreground image comprising an abandoned object foreground image; clustering contours in the abandoned object foreground image into a group, and take the group as a candidate region of an abandoned object; and comparing the candidate region of the abandoned object with a reference background image, judging whether the candidate region of the abandoned object is matched with the reference background image, and a match, deeming that the abandoned object is false detection.

A circuitry of an image processing device divides a first image into a plurality of regions, extracts a feature point from each of the regions, tracks the feature point among a plurality of images to detect a motion vector, estimates a notable target of the first image, calculates the priority level of setting of a tracking feature point for each of the regions for tracking motion of the notable target, and sets the tracking feature point to any of the regions based on the priority level.

Disclosed are a method and apparatus for determining a given variation of a form used by a filled in instance of that type of form from amongst a number of form templates. The given instance is aligned to each of the variants or form templates. The result of the alignment includes a series of key points that did not match up well (bad key points). The bad key points are taken from the form templates. Then, a set of pixel patches from around each of the bad key points of the form templates are extracted. The pixel patches are individually compared to corresponding pixel patches of the instance. The comparison generates a match score. The form template having the greatest match score is the correct form template.

A detector (110) for determining at least one material property of at least one object (112) is proposed. The detector (110) comprises at least one projector (116) configured for illuminating the object (112) with at least one illumination pattern (118) comprising a plurality of illumination features (120); at least one first camera (122) having at least one first sensor element, wherein the first sensor element has a matrix of first optical sensors, the first optical sensors each having a light-sensitive area, wherein each first optical sensor is designed to generate at least one sensor signal in response to an illumination of its respective light-sensitive area by a reflection light beam propagating from the object (112) to the first camera (122), wherein the first camera (122) is configured for imaging at least one first reflection image comprising a plurality of first reflection features generated by the object (112) in response to illumination by the illumination features (120), wherein the first camera (122) is arranged such that the first reflection image is imaged under a first direction of view to the object (112); at least one second camera (124) having at least one second sensor element, wherein the second sensor element has a matrix of second optical sensors, the second optical sensors each having a light-sensitive area, wherein each second optical sensor is designed to generate at least one sensor signal in response to an illumination of its respective light-sensitive area by a reflection light beam propagating from the object (112) to the second camera (124), wherein the second camera (124) is configured for imaging at least one second reflection image comprising a plurality of second reflection features generated by the object (112) in response to illumination by the illumination feature (120), wherein the second camera (124) is arranged such that the second reflection image is imaged under a second direction of view to the object (112), wherein the first direction of view and the second direction of view differ; at least one evaluation device (126) configured for evaluating the first reflection image and the second reflection image, wherein the evaluation comprises matching the first reflection features and the second reflection features and determining a combined material property of matched pairs of first and second reflection features by analysis of their beam profiles.

Various embodiments of the teachings herein include a computer-implemented method for generating an identification data set for an electronic module having a substrate, a joint, and an electronic component. An example method includes: providing a digital and/or X-ray recording of at least a partial region of the module; and generating the identification data set based on at least a portion of the recording, the portion including the joint. The identification data set identifies at least one of a number, an arrangement, and/or a size of gas inclusions in the joint.

The present application relates to method and device for determining image similarity that includes: dividing a target image into multiple regions based on positions of pixels relative to a reference point in the target image, and dividing a reference image into multiple regions based on positions of pixels relative to a reference point in the reference image; determining, based on feature points in the target image and feature points in the reference image as well as the regions obtained by dividing the target image and the regions obtained by dividing the reference image, similarity between a distribution of the feature points in the target image and a distribution of the feature points in the reference image. According to the method of the present application, the similarity is described more reasonably.

The present disclosure accurately detects the position of a shelf label disposed on a display shelf. A shelf label detection device may be provided with a shelf label position correction unit and a shelf label position identification unit. The shelf label position correction unit corrects a manual shelf label position set including a shelf label position that has been set in advance for a reference camera image, using an automatic shelf label position set which includes a shelf label position detected from a monitoring camera image using image recognition, thereby generating a corrected shelf label position set. The shelf label position identification unit uses the corrected shelf label position set to identify the shelf label position in the monitoring camera image.

An apparatus includes a display control unit, a receiving unit, an adjusting unit, and a determination unit. The display control unit is configured to display an image showing a result of detection of a defect from a captured image of a structure on a display device. The receiving unit is configured to receive an operation to specify part of the displayed image as a first region and an operation to give an instruction to correct at least part of the detection data corresponding to the first region. The adjusting unit is configured to adjust a parameter to be applied to the first region according to the instruction. The determination unit is configured to determine one or more second regions to which the adjusted parameter is to be applied from a plurality of segmented regions of the image.

A system and method detect a local change to a region of an object using digital fingerprints of the object acquired at a reference time and at a test time. The two digital fingerprints may be used to authenticate the object, a match density is calculated from a comparison of corresponding portions of the digital fingerprints and the match density is compared to a threshold so that when the match density is below the threshold, a region where a component on the object has been added, subtracted, repositioned, substituted, or altered is identified.