A method may include obtaining an image representing a region of interest (ROI) of an object. The ROI may include two or more sub-regions. The method may include determining an average value of quantitative indexes associated with elements in the image corresponding to a first region of the ROI. The method may include determining, for each of the two or more sub-regions of the ROI, a threshold based on the average value; identifying target elements in the image based on the thresholds of the two or more sub-regions. The method may include assigning a presentation value to each of at least some of the target elements based on the average value and the quantitative index of the each target element. The method may include generating a presentation of the image based on the presentation values.

A time sequenced series of optical images of a patient is obtained at a rate faster than cardiac frequency, wherein the time sequenced series of images capture one or more physical properties of intrinsic contrast. A cross-correland signal from the patient is obtained. A cross-correlated wavelet transform analysis is applied to the time sequenced series of optical images to yield a spatiotemporal representation of cardiac frequency phenomena. The cross-correlated wavelet transform analysis comprises performing a wavelet transform on the time-sequenced series of optical images to obtain a wavelet transformed signal, cross-correlating the wavelet transformed signal with the cross-correland signal to obtain a cross-correlated signal, filtering the cross-correlated signal at cardiac frequency to obtain a filtered signal, and performing an inverse wavelet transform on the filtered signal to obtain a spatiotemporal representation of the time sequenced series of optical images. Images of the cardiac frequency phenomena are generated.

A system and method for digitally grading collectible trading cards on a predefined standard scale. The collectible trading cards are graded using their images. First, an image is converted to grayscale image. The grayscale image is subjected to a set of algorithms, such as edge detection algorithm, threshold inversion algorithm, wavelet transform algorithm, corner detection algorithm, color filtering algorithm, and an image sharpen algorithm to obtain respective image features as outputs. The output can be processed using a bag of visual words computer vision model to obtain quantitative data. The quantitative data can then be processed using a pre-trained machine learning model to obtain a grade for the collectible trading card.

Methods, systems, and devices for equipment reading in a factory or plant environment are described, including: capturing an image of an environment including a measurement device; detecting a target region included in the image, the target region including at least a portion of the measurement device; determining identification information associated with the measurement device based on detecting the target region; and extracting measurement information associated with the measurement device based on detecting the target region. In some aspects, detecting the target region may include: providing the image to a machine learning network; and receiving an output from the machine learning network in response to the machine learning network processing the image based on a detection model, the output including the target region.

A method may include obtaining an image representing a region of interest (ROI) of an object. The ROI may include two or more sub-regions. The method may include determining an average value of quantitative indexes associated with elements in the image corresponding to a first region of the ROI. The method may include determining, for each of the two or more sub-regions of the ROI, a threshold based on the average value; identifying target elements in the image based on the thresholds of the two or more sub-regions. The method may include assigning a presentation value to each of at least some of the target elements based on the average value and the quantitative index of the each target element. The method may include generating a presentation of the image based on the presentation values.

A method, apparatus, and a non-transitory computer-readable storage medium for sub-band image reconstruction. The method may include obtaining an image captured by a camera. The method may also obtain a transform image based on the image captured by the camera. The transform image may be in a transform domain. The method may further obtain decomposed image components of the transform image. The decomposed image components may include a low frequency component and at least one high frequency component. The method may also obtain a reconstructed image based on at least two neural networks processing the decomposed image components in the transform domain

A multi-camera vehicle vision system and method. In one embodiment a map is generated about a moving vehicle. Frames of image data are provided with a series of cameras extending along a surface of the vehicle. The image data frames are processed to identify an object of interest. An object of interest is classified among a set of object types and location of an identified object of interest is determined. Object type and location information is provided to a control unit spaced apart from the cameras via a data link. Road map data is generated to illustrate changes in position of the moving vehicle along a roadway based on data other than the image data provided by the cameras. A display of the road map data is generated with the object type and location information overlaid on the road map data to indicate object location relative to the vehicle.

Systems and methods for automated imaging and evaluation of image based features are disclosed herein. Method for automated imaging and evaluation of image based features can include receiving time-lapse images of at least one human embryo contained in a multi-well culture dish that can have a plurality of micro-wells. Image based features can be automatically generated from the time-lapse images of the human embryo. The image based features, which can include a cavitation feature, can be inputted into a classifier. The classifier can automatically and directly generate a viability prediction with the classifier from the image-based features.

The present invention relates to a method for generating a result image. The method comprises the steps of: acquiring a STED image of a sample, the STED image comprising pixels; calculating Fourier coefficients of arrival times for the pixels of the image, resulting in real coefficients representing a first image and in imaginary coefficients representing a second image; deriving an intensity image from the STED image; applying a spatial filter to the first image, the second image and the intensity image, resulting in a filtered first image, a filtered second image, and filtered intensity image, respectively; calculating an image G based on the filtered first image and the filtered intensity image; calculating an image S based on the filtered second image and the filtered intensity image; and calculating a result image based on the image G and the image S.

A method of processing an image of a scene including a road acquired by a vehicle-mounted camera to generate boundary data indicative of a boundary of an image region which represents an unoccupied area of the road, comprising: generating (S10) an LL sub-band image of an N.sup.th level of an (N+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image N times, where N is an integer equal to or greater than one; generating (S20) a sub-band image of an (N+1).sup.th level by high-pass filtering the LL sub-band image of the N.sup.th level, and down-sampling a result of the high-pass filtering, such that the sub-band image of the (N+1).sup.th level has a pixel region having substantially equal pixel values representing the unoccupied area of the road in the image; and generating (S30) the boundary data by determining a boundary of the pixel region.