A high efficiency method of processing images to provide perceptual high-contrast output. Pixel intensities are calculated by a weighted combination of a fixed number of static bounding rectangle sizes. This is more performant than incrementally growing the bounding rectangle size and performing expensive analysis on resultant histograms. To mitigate image artifacts and noise, blurring and down-sampling are applied to the image prior to processing.

In one embodiment, a method includes obtaining an image comprising a plurality of pixels, determining, for a particular pixel of the plurality of pixels, a gradient value, classifying, based on the gradient value, the particular pixel into a flat class or one of a plurality of edge classes, and denoising the particular pixel based on the classification.

A solution for non-invasive determination of supraphysiological body joint kinematics. The solution obtains external images related to a test procedure of the body joint and performs image analysis on the obtained images to define a pattern of a plurality of spatial points in a region of interest. Each individual spatial point is defined by a unique pattern of neighboring surrounding pixels in each image, and the pattern is part of a high-contrast speckle pattern applied to the body joint. The solution identifies displacements of the spatial points in subsequently obtained images by tracing a location of the unique pattern of neighboring pixels in each image in relation to a base image of the body joint, calculates deformation measures from the displacements of the plurality of spatial points, and obtains deformation measures of a reference body joint. The solution compares the deformation measures and determines supraphysiological body joint kinematics from the comparison.

A method for detecting a field navigation line after ridge sealing of crops includes the following steps. A field crop image is acquired. Image color space transformation, image binaryzation, longitudinal integration, neighborhood setting and region integration calculation are sequentially performed on the field crop image to obtain a crop row image. Detections of an initial middle ridge, a left ridge and a right ridge are performed on the crop row image to obtain center lines of the initial middle ridge, left ridge and right ridge. Center lines of a left (right) crop row are established by using an area 1 between the center lines of the left (right) ridge and the initial middle ridge. A center line model of a middle ridge is established by using an area 0 between the center lines of the left and right crop rows, namely a navigation line of a field operation machine.

An image processing apparatus obtains a read image of a document including a handwritten character, generates a first image formed by pixels of the handwritten character by extracting the pixels of the handwritten character from pixels of the read image using a first learning model for extracting the pixels of the handwritten character, estimates a handwriting area including the handwritten character using a second learning model for estimating the handwriting area, and performs handwriting OCR processing based on the generated first image and the estimated handwriting area.

A method and an apparatus for recognizing a subtitle region, a device, and a storage medium are provided, relating to the field of computer vision technologies of artificial intelligence. The method includes: recognizing a video to obtain n candidate subtitle regions, the candidate subtitle regions being regions in which text contents are displayed in the video, and n being a positive integer; and screening the n candidate subtitle regions according to a subtitle region screening policy to obtain the subtitle region, the subtitle region screening policy being used for determining a candidate subtitle region in which text contents have a repetition rate being lower than a repetition rate threshold and have a longest total display duration as the subtitle region. By using the method and apparatus, device, and system, labor resources required for subtitle region recognition can be saved.

Body and hand correlation method, apparatus, device, storage medium and computer program are provided. Method includes: in an image of which an image content includes a body to be correlated and a hand to be correlated, a first correlation probability between a body detection box of the body and a hand detection box of the hand is determined; a second correlation probability between the body and a wrist key point in a key point is determined based on the key point in the body detection box, the key point including the wrist key point and elbow key point belonging to the same arm; a third correlation probability between the hand detection box and the wrist key point is determined based on the wrist key point and elbow key point; and a correlation degree between the body and the hand is determined based on the first, second, and third correlation probabilities.

Provided are a method and apparatus for generating ultra-high-quality digital data. A method for generating digital data includes identifying information on a target disposed at a specific position, controlling a movement of a first device to photograph two-dimensional image data for a partial region of the identified target, determining whether the two-dimensional image data for the partial region of the target photographed through a camera mounted on the first device corresponds to an entire region of the target, and controlling a movement of a second device loaded with the target when the two-dimensional image data for the partial region of the photographed target does not correspond to the entire region of the target.

An item recognition system uses a top camera and one or more peripheral cameras to identify items. The item recognition system may use image embeddings generated based on images captured by the cameras to generate a concatenated embedding that describes an item depicted in the image. The item recognition system may compare the concatenated embedding to reference embeddings to identify the item. Furthermore, the item recognition system may detect when items are overlapping in an image. For example, the item recognition system may apply an overlap detection model to a top image and a pixel-wise mask for the top image to detect whether an item is overlapping with another in the top image. The item recognition system notifies a user of the overlap if detected.

Systems (100) and methods (1000) for operating a shoulder Speaker Microphone (“SM”) device coupled to a radio. The methods comprising: using a speaker and a microphone of the shoulder SM device to facilitate auditory communications to and from a user of the radio; activating a thermal imaging device integrated with the shoulder SM device when the shoulder SM device resides at a location of an incident event; capturing thermal images by the thermal imaging device of the shoulder SM device; and performing operations by the shoulder SM device to cause the thermal images to be streamed over a network.