Abstract: According to one embodiment, a misalignment measuring apparatus includes: an input circuit; a storage medium; a first circuit configured to, in a first calibration pattern, calculate a second misalignment amount; a second circuit configured to, using a first image of a second calibration pattern, calculate a third misalignment amount; a third circuit configured to calculate a coefficient indicating; and a fourth circuit configured to, using a second image corresponding to the first and second patterns, calculate a third center position of a third contour and calculate the first misalignment amount between the first pattern and the second pattern based on the fourth misalignment amount and the coefficient.

Abstract: A platform, device and process for capturing images of the surface of a gaming table and determining the quantity, identity, and arrangement of chips bet at a gaming table. Image data is captured corresponding to the one or more chips positioned in at least one betting area on a gaming surface of the respective gaming table and the data is processed to filter out the background, establish a two dimensional grid of points of interests and corresponding histograms for classifying the one or more chips through identifying a dominant classification of each row in the grid of points of interests.

Abstract: A semiconductor wafer fault analysis system includes: a database to store a first reference map, which is classified as a first fault type, and a second reference map, which is classified as a second fault type; a first auto-encoder/decoder to remove a noise corresponding to the first fault type from the first reference map to generate a first pre-processed reference map; a second auto-encoder/decoder to remove a noise corresponding to the second fault type from the second reference map to generate a second pre-processed reference map; and a fault type analyzer. The database is updated based on the first and second pre-processed reference maps, and the fault type analyzer is to classify a fault type of a target map based on the updated database. The target map is generated by measuring a target wafer.

Abstract: A method of verifying optical proximity effect correction includes generating a design pattern layout including a target pattern, generating a correction pattern layout from the design pattern layout by performing optical proximity effect correction, generating a contour image including an image pattern using the correction pattern layout, detecting a defect pattern from the image pattern of the contour image, and correcting the correction pattern layout using data of the defect pattern. Detecting the defect pattern includes acquiring position data of a center of gravity of the target pattern, acquiring position data of a center of gravity of the image pattern, and determining whether the image pattern is a defect pattern by comparing a defect pattern detection reference with a distance between the center of gravity of the target pattern and the center of gravity of the image pattern.

Abstract: An electronic component evaluation method is provided for evaluating an electronic component. The electronic component has a lower surface facing a mounting substrate, an upper surface having a flat main surface, and a plurality of mounting terminals configured to be mounted on the mounting substrate. The method includes the steps of: obtaining terminal position information of the plurality of mounting terminals; generating a reference plane including at least three of the plurality of mounting terminals in response to the terminal position information; and detecting a height of at least one of the upper surface or the lower surface relative to the reference plane.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for detecting objects located in an area of interest. In accordance with one embodiment, a method is provided comprising: receiving, via an interface provided through a general instance on a cloud environment, imaging data comprising raw images collected on the area of interest; upon receiving the images: activating a central processing unit (CPU) focused instance on the cloud environment and processing, via the image, the raw images to generate an image map of the area of interest; and after generating the image map: activating a graphical processing unit (GPU) focused instance on the cloud environment and performing object detection, via the image, on a region within the image map by applying one or more object detection algorithms to the region to identify locations of the objects in the region.

Abstract: An action recognition method and apparatus related to artificial intelligence and include extracting a spatial feature of a to-be-processed picture, determining a virtual optical flow feature of the to-be-processed picture based on the spatial feature and X spatial features and X optical flow features in a preset feature library, where the X spatial features and the X optical flow features include a one-to-one correspondence, determining a first type of confidence of the to-be-processed picture in different action categories based on similarities between the virtual optical flow feature and Y optical flow features, where each of the Y optical flow features in the preset feature library corresponds to one action category, X and Y are both integers greater than 1, and determining an action category of the to-be-processed picture based on the first type of confidence.

Abstract: A visual search method, a visual search device, and an electrical device are provided. The method includes determining a first object in a visual search process. A power consumption of the first object in the visual search process is greater than a power consumption of a second object in the visual search process. The first object includes at least one of a program and an algorithm and the second object includes at least one of a program and an algorithm. The method further includes performing a visual search through an optimized object of the first object. At least one of a calculating speed of the optimized object is greater than a calculating speed of the first object and a total calculating time of the optimized object within a unit time is less than a total calculating time of the first object within the unit time.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for processing a video. The method may include: acquiring object regions obtained by performing object detection on a target video frame, a type of an object in each of the object regions being a preset type; determining, for an object region in the acquired object regions, in response to determining that the object region satisfies a preset condition, that the object region is a non-ROI; using an object region other than the non-ROI in the object regions of the target video frame as a ROI; and acquiring a quantization parameter change corresponding to each ROI, and encoding the target video frame based on the quantization parameter change.

Abstract: Systems and methods for down-scaling are provided. In one example, a method for processing image data includes determining a plurality of output pixel locations using a position value stored by a position register, using the current position value to select a center input pixel from the image data and selecting an index value, selecting a set of input pixels adjacent to the center input pixel, selecting a set of filtering coefficients from a filter coefficient lookup table using the index value, filtering the set of source input pixels to apply a respective one of the set of filtering coefficients to each of the set of source input pixels to determine an output value for the current output pixel at the current position value, and correcting chromatic aberrations in the set of source input pixels.

Abstract: According to various embodiments of the present invention, an electronic device comprises: a memory including instructions and a training database, which includes data, on at least one object, acquired on the basis of an artificial intelligence algorithm; at least one sensor; and a processor connected to the at least one sensor and the memory, wherein the processor can be configured to execute the instructions in order to acquire data on a designated area including the at least one object by using the at least one sensor, identify location information and positioning information on the at least one object on the basis of the training database, and transmit a control signal for picking the at least one object to a picking tool related to the electronic device on the basis of the identified location information and positioning information.

Abstract: An automatic detection method and an automatic detection system for detecting any crack on wafer edges are provided. The automatic detection method includes the following steps. Several wafer images of several wafers are obtained. The wafer images are integrated to create a templet image. Each of the wafer images is compared with the templet image to obtain a differential image. Each of the differential images is binarized. Each of the differential images which are binarized is de-noised. Whether each of the differential images has an edge crack is detected according to pattern of each of the differential images which are de-noised.

Abstract: This disclosure describes techniques to detect an object. The techniques include operations comprising: receiving an image captured by overhead camera; identifying a region of interest (ROI) of a plurality of regions within the image; selecting an object classifier from a plurality of object classifiers based on a position of the identified ROI relative to the overhead camera; and applying the selected object classifier to the identified ROI; and detecting presence of the object within the ROI in response to applying the selected object classifier to the identified ROI.

Abstract: System, processes and devices for monitoring betting activities using bet recognition devices and a server. Each bet recognition device has an imaging component for capturing image data for a gaming table surface. The bet recognition device receives calibration data for calibrating the bet recognition device. A server processor coupled to a data store processes the image data received from the bet recognition devices over the network to detect, for each betting area, a number of chips and a final bet value for the chips.

Abstract: An encoding method, playing method and apparatus for image stabilization of panoramic video, and a method for evaluating image stabilization algorithm are provided. The image stabilization method for the panoramic video is applicable to an electronic apparatus including a processor. In the method, a plurality of image frames of a panoramic video is captured, and each image frame is transformed into a plurality of projection frames on a plurality of faces of a cubemap. Then, variations of triaxial displacements and attitude angles between the projection frames transformed onto each of the faces and adjacent in time are calculated. The variations of triaxial displacements and attitude angles are smoothed and recorded as movement information. While playing the panoramic video, the panoramic video is corrected by the movement information and played. Thus, it is possible to reduce the amount of calculation required for the stabilization calculations on the captured video.

Abstract: Techniques to facilitate compression of depth data and real-time reconstruction of high-quality light fields. A parameter space of values for a line, pairs of endpoints on different sides of the line, and a palette index for each pixel of a pixel tile of a depth image is sampled. Values for the line, the pairs of endpoints, and the palette index that minimize an error are determined and stored.

Abstract: An intelligent item receptacle, system, and method for providing supplemental media content in response to depositing an item having media indicia disposed thereon. An intelligent item receptacle includes an item receptacle securely enclosing an interior volume, an item sensor configured to detect an inserted item, a camera within the interior volume configured to capture an image of the item, and an output configured to play audio or visual content to bystanders outside the item receptacle. Processing circuitry within the item receptacle analyzes the image to determine if the media indicia are present on the item and, if present, causes the output to play the audio or visual content associated with the media indicia.

Abstract: A system is provided and generally includes a server and an associate computing device. The server may receive location video data comprising at least one image from a camera directed to a location, such as a designated area of a parking lot. The server may detect one or more objects in the image, and determine a bounding box based on the detected objects. The server may also determine a confidence value corresponding to the bounding box. The confidence value may be determined based on an area of the image defined by the bounding box and a predefined class. The server may then generate an arrival message based on the determined confidence value, and transmit the arrival message to the associate computing device.

Abstract: Disclosed are a method and device for determining an icon position. The method includes: detecting a target object in a target image and determining the reference position of the target object in the target image, and detecting a salient position in the target image, thereby obtaining the reference position of a key target or object in the target image, and a salient position possibly requiring more attention in the target image; and selecting, according to the distance between the reference position or salient position and preset candidate positions, an icon position from the candidate positions.

Abstract: The present disclosure relates to computer-implemented systems and methods for detecting a feature-of-interest in a video. In one implementation, a computer-implemented system may include a discriminator network and a generative network. The discriminator network may include a perception branch and an adversarial branch, the perception branch being configured to output detections of the feature-of-interest in the video. The generative network may be configured to receive detections of the feature-of-interest from the perception branch of the discriminator network and generate artificial representations of the feature-of-interest based on the detections from the perception branch.