The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Color and exposure matching for systems, such as a videoconferencing endpoint, that have overlapping camera fields of view. The geometric relationships between the overlapping cameras are used to determine correction processing. For each camera, histograms are developed for the overlapping cameras. A dynamic threshold is determined for each histogram. Using the dynamic threshold, peak detection is performed on each histogram. Using the geometric relationships, expected histogram relationships are determined. The actual histogram relationships are compared to the expected relationships, with further processing based on the correctness of the comparison. In some of the cases of further processing, peaks of the histograms are compared to find matching and non-matching peaks. Various ratios of pixels in the various peaks are used to determine needed changes to respective cameras. Incremental changes to camera outputs are provided and accumulated so that overall changes can be provided to adjust the output of the respective cameras.

Color and exposure matching for systems, such as a videoconferencing endpoint, that have overlapping camera fields of view. The geometric relationships between the overlapping cameras are used to determine correction processing. For each camera, histograms are developed for the overlapping cameras. A dynamic threshold is determined for each histogram. Using the dynamic threshold, peak detection is performed on each histogram. Using the geometric relationships, expected histogram relationships are determined. The actual histogram relationships are compared to the expected relationships, with further processing based on the correctness of the comparison. In some of the cases of further processing, peaks of the histograms are compared to find matching and non-matching peaks. Various ratios of pixels in the various peaks are used to determine needed changes to respective cameras. Incremental changes to camera outputs are provided and accumulated so that overall changes can be provided to adjust the output of the respective cameras.

A signal recognizing apparatus detects a target traffic light from an image showing surroundings of a host vehicle and generates traffic light detecting information including a traffic light image. The apparatus recognizes a lighting state of the target traffic light and specifies a positional relationship between the host vehicle and the target traffic light on the basis of position information of the host vehicle and the traffic light detecting information, and determines a center of a lighting area corresponding to a lighting portion of the target traffic light in the traffic light image, and performs a predetermined conversion process on the traffic light image on the basis of the positional relationship in order to compare with the lighting pattern information, and recognizes the lighting state by comparing the center of the lighting area in a traffic light image with the lighting pattern information.

An image processing system and a processing method of video stream are provided. The first image is obtained according to a parameter. The deformation correction procedure is performed on the first image, and the second image is generated. The identification detection procedure is performed on the second image, and a detected result is generated. Control information is generated according to the detected result. The parameter is adjusted according to the control information, and a third image is generated. The second image and the third image are output. Therefore, the subsequent application could be enhanced through the dual image outputs.

Systems and methods are provided for processing and extracting content from an image captured using a mobile device. In one embodiment, an image is captured by a mobile device and corrected to improve the quality of the image. The corrected image is then further processed by adjusting the image, identifying the format and layout of the document, binarizing the image and extracting the content using optical character recognition (OCR). Multiple methods of image adjusting may be implemented to accurately assess features of the document, and a secondary layout identification process may be performed to ensure that the content being extracted is properly classified.

A method for identifying authenticity of an object, the method includes maintaining, in an identification server system, a reference image of an original object, the reference image and provided to represent all equivalent original objects, receiving, in the identification server system, one or more input images of the object to be identified, and generating, by the identification server system, a target image from the one or more input images. The method further includes aligning, by the identification server system, the target image with the reference image and analysing, by the identification server system, the target image in relation to the aligned reference image for identifying authenticity of the object.

A method and an apparatus for obtaining 3D information of a vehicle are provided. The method includes: first determining a body boundary line of a first vehicle, and then determining an observation angle and/or an orientation angle of the first vehicle based on the body boundary line.

A system for collecting data for training a computer vision model for shape estimation includes: an imaging system configured to capture one or more images; and a processing system including a processor and memory storing instructions that, when executed by the processor, cause the processor to: receive one or more input images from the imaging system; estimate a pose of an object depicted in the one or more images; render a shape estimate from a 3-D model of the object posed in accordance with the pose of the object; and generate a data point of a training dataset, the data point including one or more images based on the one or more input images and a label corresponding to the one or more images, the label including the shape estimate.

Real-time evaluation and enhancement of image quality prior to capturing an image of a document on a mobile device is provided. An image capture process is initiated on a mobile device during which a user of the mobile device prepares to capture the image of the document, utilizing hardware and software on the mobile device to measure and achieve optimal parameters for image capture. Feedback may be provided to a user of the mobile device to instruct the user on how to manually optimize certain parameters relating to image quality, such as the angle, motion and distance of the mobile device from the document. When the optimal parameters for image capture of the document are achieved, at least one image of the document is automatically captured by the mobile device.