Abstract: An encoding device and methods for point cloud encoding are disclosed. The method for encoding includes generating, using a processor of an encoder, a first frame and a second frame that include patches representing a cluster of points of three-dimensional (3D) point cloud; identifying a patch to segment in the patches of the first frame and the second frame; determining, in response to identifying the patch, a path representing a boundary between segmented regions within the patch; segmenting the patch along the path into two patches for the first frame and the second frame; encoding the first frame and the second frame to generate a compressed bitstream; and transmitting, using a communication interface operably coupled to the processor, the compressed bitstream.

Abstract: An image processing method includes obtaining an original image; partitioning the original image into a first part and a second part such that distortion of at least a part of an image in the first part of the original image is smaller than a predetermined threshold, and distortion of at least a part of an image in the second part of the original image is greater than or equal to the predetermined threshold; correcting the second part of the original image so as to obtain a distortion-corrected image corresponding to the second part; and recognizing the first part of the original image and the distortion-corrected image so as to recognize an object in the original image.

Abstract: An image processing method and device, storage medium and computer device are provided. The method includes: generating an original gray scale image of an original image; performing a histogram equalization process on the original gray scale image to obtain an equalized gray scale image; generating decision factor distribution image, wherein the decision factor distribution image includes a first marked region including a region where pixels that are adjacent in position and have standard deviations smaller than set value in the original image are located, and second marked region; obtaining final gray scale image according to original gray scale image, equalized gray scale image and decision factor distribution image. Gray scale values of pixel corresponding to second marked region and first marked region in final gray scale image are respectively gray scale values of corresponding pixel in equalized gray scale image and original gray scale image; and restoring a processed image.

Abstract: An image capturing method includes: obtaining a video frame during capturing images through a camera; extracting a target histogram feature based on the video frame; inputting the target histogram feature into an image quality recognition model which is used to determine whether an image is fuzzy based on a histogram feature, and outputting an image quality recognition result of the video frame; and in response to the image quality recognition result indicating that the video frame is fuzzy, prompting the user to clean the camera.

Abstract: A method, computer system, and a computer program product for case-adaptive image quality assessment is provided. The present invention may include detecting a current set of features in a current exam associated with a patient. The present invention may also include calculating a current set of quality measurements for the current exam based on the detected current set of features. The present invention may further include in response to determining that the calculated current set of quality measurements for the current exam is below a patient-specific image quality threshold defined by at least one prior exam associated with the patient, automatically registering a negative quality assessment for the current exam associated with the patient.

Abstract: Embodiments of object detection method, device, apparatus and a computer-readable storage medium are provided. The method can include: obtaining an enclosing frame of a target object in an input image; according to the enclosing frame, determining a reference frame from a predetermined candidate frame set comprising a plurality of candidate frames; generating a size-related feature according to a size of the reference frame and a size of the enclosing frame; and detecting an object in the input image by applying the size-related feature in a machine learning model. In an embodiment of the present application, the object detection is performed by using a feature related to an object size, that is, the prediction criterion related to the object size is added to an original feature in a machine learning model, thereby further improving the accuracy of the object detection.

Abstract: An adaptive approach to image bracket determination and a more memory-efficient approach to image fusion, which are designed to generate low noise and high dynamic range (HDR) images in a wide variety of capturing conditions, are described. An incoming preview image stream may be obtained from an image capture device. When a capture request is received, an analysis may be performed on an image from the preview image stream that has a predetermined temporal relationship to the image capture request. Based on the analysis, a set of images (and their respective capture parameters, e.g., exposure time) may be determined for the image capture device to capture. As the determined set of images are captured, they may be registered and fused in a memory-efficient manner that, e.g., places an upper limit on the overall memory footprint of the registration and fusion operations—regardless of how many images are captured in total.

Abstract: A radiographic imaging apparatus includes an image generation unit that generates radiation images, and an image processing unit that synthesizes a plurality of radiation images to generate an enhanced image. The image processing unit is configured to synthesize the plurality of radiation images by weighting each of the plurality of radiation images in accordance with a feature point distance.

Abstract: The present disclosure provides a hybrid framework-based image bit-depth expansion method and device. The invention fuses a traditional de-banding algorithm and a depth network-based learning algorithm, and can remove unnatural effects in an image flat area whilst more realistically restoring numerical information of missing bits. The method comprises the extraction of image flat areas, local adaptive pixel value adjustment-based flat area bit-depth expansion and convolutional neural network-based non-flat area bit-depth expansion. The present invention uses a learning-based method to train an effective depth network to solve the problem of realistically restoring missing bits, whilst using a simple and robust local adaptive pixel value adjustment method in an flat area to effectively inhibit unnatural effects in the flat area such as banding, a ringing and flat noise, improving subjective visual quality of the flat area.

Abstract: A similarity determination apparatus comprising a processor configured to: classify each pixel of a first medical image into at least one of a plurality of types of findings; calculate a first feature amount for each classified finding; set a weighting coefficient indicating a degree of weighting which varies depending on a size of each finding for each classified finding; derive an adjusted weighting coefficient by adjusting the weighting coefficient for each of a plurality of finding groups, into which the plurality of types of findings are classified; and derive the similarity between the first medical image and a second medical image by performing a weighting operation for the first feature amount for each finding in the first medical image and a second feature amount for each finding calculated in advance in the second medical image, for each of the finding groups on the basis of the adjusted weighting coefficient.

Abstract: Methods, systems, and apparatuses are provide to perform automatic banding correction in captured images. For example, the methods receive from a plurality of sensing elements in a sensor array, first image data captured with a first exposure parameter, and second image data captured with a second exposure parameter. The methods partition first image data and second image data and determine values for each partition. The methods compute banding errors based on the determined values of the partitions for first image data and second image data. The methods also determine a banding error correction to one or more of first image data and second image data based on the banding errors. Further, the methods perform an automatic correction of the banding errors on one or more of first image data and second image based on the banding error correction.