The present disclosure discloses methods and apparatuses for wave recognition, and unmanned aerial vehicles. The method includes: extracting a first image acquired by an image acquisition apparatus at a first moment and a second image acquired by the image acquisition apparatus at a second moment; extracting a target region in each of the first image and the second image; comparing feature information of the target region in the first image with feature information of the target region in the second image; and determining whether the target region is a wave according to a result of the comparing of the feature information. The methods and apparatuses for wave recognition, and unmanned aerial vehicles recognize a wave in an image based on the change of feature information of a target region in the image at different times.

A method clusters each character on a document into one of a plurality of clusters based on widths of at least a portion of the characters on the document and measures distances between characters on the document. A threshold for each of the plurality of clusters is calculated based on at least a portion of the distances between characters in each cluster. The method then segments characters into units using the thresholds for the plurality of clusters. A distance between two characters in the document is compared to a threshold for a cluster to classify the two characters as being part of a unit when the distance is less than the threshold and not being part of the unit when the distance is greater than the threshold. Then, the method performs a recognition process on the document using the units.

A method of indexing and searching for video content is provided. For each frame of a first plurality of frames, a first global feature and a first plurality of local features may be identified. The first plurality of local features may be clustered around a first plurality of cluster centers. The first plurality of local features may be converted into a first plurality of binary signatures. An index that maps the first plurality of cluster centers and the first plurality of binary signatures to the first plurality of frames may be generated. A search request associated with a second video may be received and its direct and indirect features may be identified. The identified features of the second video may be compared against the index and a candidate video may be selected as a result of the search request.

A text extraction computing method that comprises calculating an estimated character pixel height of text from a digital image. The method may scale the digital image using the estimated character pixel height and a preferred character pixel height. The method may binarizes the digital image. The method may remove distortions using a neural network trained by a cycle GAN on a set of source text images and a set of clean text images. The set of source text images and clean text images are unpaired. The source text images may be distorted images of text. Calculating the estimated character pixel height may include summarizing the rows of pixels into a horizontal projection, and determining a line-repetition period from the projection, and quantifying the portion of the line-repetition period that corresponds to the text as the estimated character pixel height. The method may extract characters from the digital image using OCR.

Techniques for processing images of documents captured using a mobile device are provided. The images can include different sides of a document from a mobile device for an authenticated transaction. In an example implementation, a method incudes inspecting the images to detect a feature associated with a first side of the document. In response to determining an image is the first side of the document, a type of content is selected to be analyze on the image of the first side and one or more of regions of interests (ROIs) are identified on the image of the first side that are known to include the selected type of content. A process can include receiving a sub-image of the image of the first side from the preprocessing unit, and performing content detection test on the sub-image.

A device for collecting personal data from a user includes a processor, a sensing device, a document scanner, and a scene camera. The sensing device is configured to capture a biometric of user. The document scanner is configured to produce a visual representation of an identifying document that includes personally identifying information. The scene camera monitors and captures a video of an uninterrupted area surrounding the kiosk including the user, the sensing device, and the document scanner. The processor transmits the captured video to a remote station that can send a signal back to the device.

In an image processing method (18), for images (9) in a image sequence (8), in each case a position indication (23) of a center (24) of the image content (10) of individual images (9) is calculated in a completely computer-implemented and/or hardware-implemented, statistical evaluation method (20). The center (24) is defined by a circle section (62) which is described or characterized by a separation line (12) between the image content (10) and a periphery (11) which is supplementary to the image content (10) in the image (9) or complementary therewith.

Methods, systems, apparatus and articles of manufacture are disclosed herein to improve image classification with boundary-bitmaps. An example disclosed apparatus includes a silhouette engine to identify a foreground silhouette within the image, generate a bounding box based on borders of the foreground silhouette, and generate an encoded silhouette matrix which identifies cells of a foreground and cells of a background, a convolution cell selector to convolve the encoded silhouette matrix to generate a convoluted bitmap matrix, and a filter cell selector to improve image classification efficiency by identifying eligible blocks of the convoluted bitmap matrix by retaining first respective cells of the convoluted bitmap matrix that satisfy a cell retention threshold, and removing second respective cells of the convoluted bitmap matrix that do not satisfy the cell retention threshold.

The feature extraction device according to one aspect of the present disclosure comprises: a reliability determination unit that determines a degree of reliability with respect to a second region, which is a region that has been extracted as a foreground region of an image and is within a first region that has been extracted from the image as a partial region containing a recognition subject, said degree of reliability indicating the likelihood of being the recognition subject; a feature determination unit that, on the basis of the degree of reliability, uses a first feature which is a feature extracted from the first region and a second feature which is a feature extracted from the second region to determine a feature of the recognition subject; and an output unit that outputs information indicating the determined feature of the recognition subject.

This disclosure provides a pupil localization method, a device, an apparatus and a storage medium. The method comprises: preprocessing a pupil image; generating a first projection curve and a second projection curve on horizontal and vertical axes of two-dimensional coordinate axes respectively according to the preprocessed image; determining a first pair of dividing points and a second pair of dividing points on the first projection curve and the second projection curve respectively according to a pre-configured threshold; and determining center coordinates of the pupil according to the first pair of dividing points and the second pair of dividing points. This disclosure obtains projection curves through a simple operation of the preprocessed image on the two-dimensional coordinate axes, and then intercepts the dividing point on the projection curves according to the preconfigured threshold to determine the center coordinates of the pupil.