Techniques are described for selective extraction of color attributes from digital images that overcome the challenges experienced in conventional systems for color extraction. In an implementation, a user applies a region selector to a source image to select a portion of the source image for color attribute extraction. A graphics editing system identifies a selected region of the source image as well as visual objects of the source image included as part of the selected region. The graphics editing system iterates through the selected visual objects and extracts color attributes from the visual objects, such as color values, patterns, gradients, gradient stops, opacity, color area, and so forth. The graphics editing system then generates a color palette that includes the extracted color attributes, and the color palette is able to be utilized for various image editing tasks, such as digital image creation and transformation.

Methods of autonomous diagnostic verification and detection of defects in the optical components of a vision-based inspection system are provided. The method includes illuminating a light panel with a first light intensity pattern, capturing a first image of the first light intensity pattern with a sensor, illuminating the light panel with a second light intensity pattern different than the first light intensity pattern, capturing a second image of the second light intensity pattern with the sensor, comparing the first image and the second image to generate a comparison of images, and identifying defects in the light panel or the sensor based upon the comparison of images. Systems adapted to carry out the methods are provided as are other aspects.

A distance measurement accuracy is improved without increasing power consumption of an image processing apparatus that performs distance-measuring processing. In one embodiment, an image processing apparatus for calculating distance information on an image has a reliability calculation unit 113 configured to calculate reliability in accordance with contrast for each pixel of the image and a distance calculation unit 116 configured to calculate distance information on each of the pixels based on reliability of each of the pixels. The distance calculation unit 116 calculates the distance information about a second pixel group whose reliability is lower than that of a first pixel group by using a collation area whose size is larger than a predetermined size in a range in which an amount of calculation in a case where a collation area of the predetermined size is used for all the pixels of the image is not exceeded.

A structure that forms a visual representation may include a first outer layer, a second outer layer, and an interlayer being disposed between the first outer layer and the second outer layer. The interlayer may have a first side adjacent to the first outer layer and a second side adjacent to the second outer layer. The interlayer includes a plurality of cuts extending from the first side of the interlayer towards the second side of the interlayer. Each of the plurality of cuts may have an angle with respect to a plane formed by a surface of the first side of the interlayer. Each angle for at least a portion of the plurality of cuts is based on one or more pixel values of at least one image that forms the basis of the visual representation.

A microscope system is provided with a microscope that acquires images at least at a first magnification and a second magnification higher than the first magnification, and a processor. The processor is configured to specify a type of a container in which a specimen is placed, and when starting observation of the specimen placed in the container at the second magnification, the processor is configured to specify an observation start position by performing object detection according to the type of container on a first image that includes the container acquired by the microscope at the first magnification, and control a relative position of the microscope with respect to the specimen such that the observation start position is contained in a field of view at the second magnification of the microscope.

The present disclosure is related to an optical encoder which is configured to provide precise coding reference data by feature recognition technology. To apply the present disclosure, it is not necessary to provide particular dense patterns on a working surface. The precise coding reference data can be generated by detecting surface features of the working surface.

The present disclosure relates to systems, methods, and non-transitory computer readable media for selecting hyper-parameter sets by utilizing a modified Bayesian optimization approach based on a combination of accuracy and training efficiency metrics of a machine learning model. For example, the disclosed systems can fit accuracy regression and efficiency regression models to observed metrics associated with hyper-parameter sets of a machine learning model. The disclosed systems can also implement a trade-off acquisition function that implements an accuracy-training efficiency balance metric to explore the hyper-parameter feature space and select hyper-parameters for training the machine learning model considering a balance between accuracy and training efficiency.

The present invention discloses a clustering algorithm-based multi-parameter cumulative calculation method for lower limb vascular calcification indexes, including the following steps: firstly carrying out super-pixel segmentation of a CT image, and enabling calcified spots in the CT image to be segmented in each super-pixel region; after the super-pixel segmentation is accomplished, extracting a brightness characteristic value of a super-pixel region where the calcified spots are located by using a Lab color space, and performing edge detection and contour extraction on the calcified spots in the image; and after edge detection and contour extraction, fitting the calcified spots in the image by using a segmented ellipse, and extracting the area of the calcified spots after optimizing an ellipse contour.

The disclosure relates to systems, methods and computer readable media for providing network-based identification, generation and management of a unique damage (finger) print of vehicle(s) by geodetic mapping of stable key points onto a ground truth 3D model of the vehicle, and vehicle parts—identified from the raw images using supervised and unsupervised machine learning. Specifically, the disclosure relates to System and methods for the generation of unique damage print on a vehicle that is obtained from captured images of the damaged vehicle, photogrammetrically localized to a specific vehicle part, and the computer programs enabling the method, the damage print configured to be used, for example, in fraud detection in insurance claims.

The disclosure relates to systems, methods and computer readable media for providing network-based identification, generation and management of a unique damage (finger) print of vehicle(s) by geodetic mapping of stable key points onto a ground truth 3D model of the vehicle, and vehicle parts—identified from the raw images using supervised and unsupervised machine learning. Specifically, the disclosure relates to System and methods for the generation of unique damage print on a vehicle that is obtained from captured images of the damaged vehicle, photogrammetrically localized to a specific vehicle part, and the computer programs enabling the method, the damage print configured to be used, for example, in fraud detection in insurance claims.