An apparatus for detecting an interfacing region in a depth image detects the interfacing region based on a depth of a first region and a depth of a second region which is an external region of the first region in a depth image.

An article pickup apparatus configured so as to measure surface positions of articles randomly piled on the three-dimensional space using a three-dimensional measurement instrument to acquire position information of three-dimensional points, determine a connected set made by connecting three-dimensional points present in the vicinity of each other among the three-dimensional points, and identify a position and posture of an article based on the position information of three-dimensional points belonging to the connected set. The posture of the article is identified by calculating a main component direction of the connected set by applying main component analysis to the three-dimensional points belonging to the connected set and identifying the posture of the article based on the main component direction.

An image processing apparatus includes a corresponding region connecting unit that connects regions that depict the same target between a series of images captured in time series, thereby sets at least one connected region; a connected region feature data calculation unit that calculates feature data of the connected region; a digest index value calculation unit that calculates a digest index value corresponding to a degree at which the target depicted in the series of images is aggregated in each image of the series of images, based on the feature data; and a digest image detector that detects a digest image based on the digest index value.

Systems and methods for extracting text from images rendered on a display screen, the method comprising capturing a color image rendered on a display screen; and transforming the color image to binary color image, preserving text-like graphic components and filtering out non-text-like graphical components. The transforming comprises scanning one or more areas of the color image; and detecting continuous bi-tonal regions in the scanned one or more areas, wherein the continuous bi-tonal regions have large variances.

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.

Systems and methods are disclosed for background modeling in a computer vision system for enabling foreground object detection. A video acquisition model receives video data from a sequence of frames. A fit test module identifies a foreground object from the video data and defines a foreground mask representative of the identified foreground object. A foreground-aware background estimation module defines a first background model from the video data and then further defines an updated background model from an association of a current frame of the video data, the first background model and the foreground mask.

Various aspects of a method and system for detection of surgical gauze during anatomical surgery are disclosed herein. In accordance with an embodiment of the disclosure, the method is implementable in an image-processing engine, which is communicatively coupled to an image-capturing device that captures one or more video frames. The method includes the determination of a set of pixel characteristics based on color filtering of at least a portion of a video frame. Thereafter, one or more blocks of pixels of a portion of a surgical gauze are detected in the video frame based on the set of pixel characteristics. Further, additional pixels that correspond to a remaining portion of the surgical gauze are identified based on a plurality of metrics. The surgical gauze is recognized in the video frame based on the detection of the one or more blocks of pixels and the identification of the additional pixels.

The present disclosure relates to a target object tracking method and apparatus, an electronic device, and a storage medium. The method includes: obtaining a first reference image of a target object; determining time information and location information of the target object in an image to be analyzed according to the first reference image, the image to be analyzed including the time information and the location information; determining a trajectory of the target object according to the time information and the location information of the target object; and generating tracking information for tracking the target object according to the trajectory of the target object. Embodiments of the present disclosure obtain highly-accurate tracking information of the target object according to the trajectory of the target object determined in the image to be analyzed by using the first reference image of the target object, such that the success rate of target object tracking is improved.

Systems and methods for determining a dominant color in a digital image are provided and include dividing pixels of the digital image into pixel groups, with pixels in a first pixel group being closer to a center of the digital image than pixels in a second pixel group. Pixels in the first and second pixel groups having a chroma value greater than a predetermined chroma value threshold and a lightness greater than a low brightness threshold and less than a high brightness threshold are analyzed using a first sample rate for the first pixel group and a second sample rate for the second group. The first sample rate is greater than the second sample rate. A dominant color for the digital image is determined based on the analyzed pixels in the first and second pixel groups.

The present disclosure relates to a technology field of artificial intelligence and provides a method for selecting image samples and related equipment. The method trains an instance segmentation model with first image samples and trains a score prediction model with third image samples. An information quantum score of second image samples is calculated through the score prediction model and feature vectors extracted. The second image samples are clustered according to the feature vectors of the second image samples and sample clusters of the second image samples are obtained. Target image samples are selected from the second image samples according to the information quantum score of the second image samples and the sample clusters. Target image samples from the image samples are selected for labelling, improving an accuracy of sample selection.