The present disclosure relates to systems and methods for selecting a best facial image of a target human face. The methods may include determining whether a candidate facial image is obtained before a time point in a time period threshold, wherein the candidate facial image has a greatest quality score of the target human face among a plurality of facial images of the target human face; in response to a determination that the candidate facial image is obtained before the time point, determining the candidate facial image as the best facial image of the target human face; and storing the best facial image together with a face ID and the greatest quality score of the target human face in a face log.

A method for calibrating a photodetector array supplying a video stream includes: a determination step, wherein an offset table is determined for each current image of the video stream based on at least two corrections from among the following: a first correction from a comparison of the current image to a corresponding predetermined reference table; a second correction from a calculation of a column error of the current image; and a third correction from a high-pass temporal filtering of the video stream; and a calculation step, wherein a current value of an offset table, equal to a sum between a previous value of the offset table and a weighted sum of at least two corrections, is calculated, with each coefficient of the offset table being associated with a respective photodetector of the array.

In various embodiments, a training application generates a perceptual video model. The training application computes a first feature value for a first feature included in a feature vector based on a first color component associated with a first reconstructed training video. The training application also computes a second feature value for a second feature included in the feature vector based on a first brightness component associated with the first reconstructed training video. Subsequently, the training application performs one or more machine learning operations based on the first feature value, the second feature value, and a first subjective quality score for the first reconstructed training video to generate a trained perceptual quality model. The trained perceptual quality model maps a feature value vector for the feature vector to a perceptual quality score.

A method of enhancing an x-ray image is disclosed. The method involves obtaining an input image based on a source x-ray image of an object. Compositional information representing physical characteristics of the object is also obtained. An image enhancement process is applied to the input image to generate a processed image. Application of the image enhancement process is controlled by one or more parameters determined in dependence on the compositional information. An output image is then provided based on the processed image.

A video processing apparatus configured to process a stream of video surveillance data, wherein the video surveillance data includes metadata associated with video data, the metadata describing at least one object in the video data. The apparatus comprises means for applying an image assessment algorithm to generate a reliability score for the metadata, and associating the reliability score with the metadata. The image assessment algorithm generates the reliability score based on an assessment of the image quality of the video data to which the metadata relates to indicate a likelihood that the metadata accurately describes the object. An image enhancement module applies image enhancement to video data if the reliability score of metadata associated with the video data indicates a low likelihood that the metadata accurately describes the object.

An image processing method to provide a final panoramic image of at least a portion of a head of a patient, wherein a plurality of different provisional panoramic images are calculated from captured frame data sets through the variation of a reconstruction parameter; the provisional panoramic images are scanned for recognizable structures; the imaging quality of the recognizable structures is determined; the variation of the at least one reconstruction parameter for the calculation of different provisional panoramic images of those frame data sets which have recognizable structures with the highest imaging quality is determined; and with reference to the determined variation of the reconstruction parameter of step a final panoramic image is calculated. A computer-readable storage medium comprising instructions which cause a computer to perform the method and an imaging system having such a storage medium are also described.

The present disclosure provides a method for incrementing a sample image, an electronic device, and a computer readable storage medium. A specific implementation comprises: acquiring a first convolutional feature of an original sample image; determining, according to a region generation network and the first convolutional feature, a candidate region and a first probability that the candidate region contains a target object; determining a target candidate region from the candidate region based on the first probability, and mapping the target candidate region back to the original sample image to obtain an intermediate image; and performing image enhancement processing on a portion of the intermediate image corresponding to the target candidate region and/or performing image blur processing on a portion of the intermediate image corresponding to a non-target candidate region to obtain an incremental sample image.

A method for generating a parallax-tolerant panoramic image includes obtaining a point cloud captured by a depth sensor, the point cloud representing a support structure bearing a set of objects; obtaining a set of images of the support structure and the set of objects, the set of images captured by an image sensor from a plurality of positions alongside a length of the support structure; generating a mesh structure using the point cloud, the mesh structure including a plurality of patches and representing a surface of the support structure and the set of objects; for each patch in the mesh structure, selecting an image from the set of images and projecting the selected image to the mesh patch; and generating an orthographic projection of the mesh structure onto a shelf plane of the support structure.

Disclosed herein are methods of providing an image storage service, computer-readable recording mediums, and/or computing devices. The method of providing the image storage service includes selecting image data in a first format, determining an initial compression parameter for converting the selected image data in the first format into a second format, obtaining primary image data in the second format by transcoding the selected image data in the first format based on the initial compression parameter, searching for a desired compression parameter based on whether image quality of the primary image data satisfies a criterion, obtaining final image data in the second format by transcoding the selected image data in the first format based on the desired compression parameter, and storing final image data in the second format in the memory.

An inspection apparatus includes a specimen stage configured to retain a specimen, at least three imaging devices arranged in a triangular array positioned above the specimen stage, each of the at least three imaging devices configured to capture an image of the specimen, one or more sets of lights positioned between the specimen stage and the at least three imaging devices, and a control system in communication with the at least three imaging devices.