A path and/or orientation of at least a portion of a handheld sporting implement swung by an athlete is tracked using two or more cameras. At least two sets of video images of the handheld sporting implement being swung are obtained using at least two different cameras having different positions. Motion regions within video images are identified, and candidate locations in 2D space of an identifiable portion (e.g., a head) of the handheld sporting implement is/are identified within the motion region(s). Based thereon, a probable location in 3D space of the identifiable portion is identified, for each of a plurality of instants during which the handheld sporting implement was swung. A piecewise 3D trajectory of at least the identifiable portion (e.g., the head) of the sporting implement is approximated from the probable locations in 3D space of the head for multiple instants during which the sporting implement was swung.

Systems and methods for performing face recognition and image searching are provided. A system for face recognition and image searching includes an ingestion system, a search system, a user device, and a database of galley files that include feature vectors. The ingestion system crawls the internet starting with a seed URL to scrape image files and generate feature vectors. Feature vectors of images input by a user may be compared by the search system to feature vectors in the gallery files. A method for generating feature vectors includes landmark detection, component aligning, texture mapping, vector computation, comparing cluster centers defined by vectors stored in a database with vectors generated based on an input image, linear discriminant analysis, and principal component analysis.

Systems, devices, media, and methods are presented for segmenting an image of a video stream with a client device, identifying an area of interest, generating a modified area of interest within one or more image, identifying a first set of pixels and a second set of pixels, and modifying a color value for the first set of pixels.

A method for performing cellular classification includes generating a plurality of local dense Scale Invariant Feature Transform (SIFT) features based on a set of input images and converting the plurality of local dense SIFT features into a multi-dimensional code using a feature coding process. A first classification component is used to generate first output confidence values based on the multi-dimensional code and a plurality of global Local Binary Pattern Histogram (LBP-H) features are generated based on the set of input images. A second classification component is used to generate second output confidence values based on the plurality of LBP-H features and the first output confidence values and the second output confidence values are merged. Each of the set of input images may then be classified as one of a plurality of cell types using the merged output confidence values.

A method for processing images includes: determining an image attribute of each image block of a plurality of image blocks in a to-be-processed image, determining a filter model corresponding to each of the image blocks based on the image attribute of each of the image blocks, and acquiring a filtered image of the to-be-processed image by filtering each of the image blocks by the filter model corresponding to each of the image blocks.

A method may include obtaining an image including a face. The method may further include determining at least one time domain feature related to the face in the image and at least one frequency domain information related to the face in the image. The method may further include evaluating the quality of the image based on the at least one time domain feature and the frequency domain information.

The present invention belongs to the technical field of petroleum exploitation engineering, and discloses a 3D modeling method for cementing hydrate sediment based on a CT image. Indoor remolding rock cores or in situ site rock cores without hydrate can be scanned by CT; a sediment matrix image stack and a pore image stack are obtained by gray threshold segmentation; then, a series of cementing hydrate image stacks with different saturations are constructed through image morphological processing of the sediment matrix image stack such as dilation, erosion and image subtraction operation; and a series of digital rock core image stacks of the cementing hydrate sediment with different saturations are formed through image subtraction operation and splicing operation to provide a relatively real 3D model for the numerical simulation work of the basic physical properties of a reservoir of natural gas hydrate.

Embodiments of the present application provide an anti-counterfeiting face detection method, device and multi-lens camera, wherein the anti-counterfeiting face detection method comprises: acquiring a depth image, an infrared image and an RGB image by using a TOF camera and an RGB camera; analyzing the RGB image through a preset face detection algorithm to determine an RGB face region of a face in the RGB image and position information of the RGB face region; determining a depth face region of the face in the depth image and an infrared face region of the face in the infrared image based on the position information of the RGB face region; determining that the face passes the detection when the depth face region, the infrared face region and the RGB face region meet corresponding preset rules respectively. In the anti-counterfeiting face detection method in the embodiment of the present application, the detection of a living body face can be completed without the cooperation of the user performing corresponding actions, which can save the detection time and provide good user experience.

A CMOS image sensor for a code reader in an optical code recognition system incorporates a digital processing circuit that applies a calculation process to the capture image data as said data acquired by the sequential readout circuit of the sensor, in order to calculate a macro-image from the capture image data, which corresponds to location information of code(s) in the capture image, and transmit this macro-image in the image frame following the capture image data, in the footer of the frame.

In an image comparison method, an original reference image and an original test image are obtained. The original reference image and the original test image are binarized to obtain a reference binary image and a test binary image. The reference binary image and the test binary image are detected edges to obtain a reference edge image and a test edge image. A morphological expansion is performed on the reference edge image to obtain an expanded reference edge image. An OR operation is performed on the extended reference edge image and the test edge image to obtain an extended test edge image. An XOR operation is performed on the expanded reference edge image and the expanded test edge image. The method improves the accuracy of image comparison.