There is provided a computer-implemented method and system (100) for determining regions of hyperdense lung parenchyma in an image of a lung. The system (100) comprises a memory (106) comprising instruction data representing a set of instructions and a processor (102) configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor (102), cause the processor (102) to locate a vessel in the image, determine a density of lung parenchyma in a region of the image that neighbours the located vessel, and determine whether the region of the image comprises hyperdense lung parenchyma based on the determined density, hyperdense lung parenchyma having a density greater than −800 HU.

Some embodiments described herein relate to a method that includes separating an analyte-containing sample via electrophoresis in a capillary. The capillary is loaded with a chemiluminescence agent, such as luminol, that is configured to react with the analyte (e.g., HRP-conjugated proteins) to produce a signal indicative of a concentration and/or quantity of analyte at each location along the length of the capillary. A first image of the capillary containing the analytes and the chemiluminescence agent is captured over a first period of time. A second image of the capillary containing the analytes and the chemiluminescence agent is captured over a second, longer, period of time. A concentration and/or quantity of a first population of analytes at a first location is determined using the first image, and a concentration and/or quantity of a second population of analytes at a second location is determined using the second image.

Some embodiments provide image evaluation systems and methods, comprising: a plurality of camera systems distributed about a retail facility and each of the plurality of camera systems; and an image processing system configured to receive multiple images over time, process each image comprising: determine, from pixel data, a gradient amplitude and directional component; determine a histogram curve from the gradient amplitudes as a function of the directional component of the pixel data, and identify a key direction relative to a maximum accumulation of the gradient amplitudes; for the gradient amplitudes, of the pixel data, having a corresponding directional component that is within a direction threshold of the key direction, identify a number of local maxima corresponding to the key direction; and determine a quantity of items of the product corresponding to a quantity of the number of local maxima.

A method for locating a target (C) relative to a vehicle (V), the target including at least one three-dimensional external component (Cs) presenting a quadratic surface, the method being implemented by a computer and including capturing an image of the target comprising an area of interest in which the external component of the target is visible in the form of an elliptical contour, for each of a plurality of pixels of the area of interest in the image, calculating an intensity gradient of the image around the pixel, and determining, based on the intensity gradient, the parameters of a line presenting a direction normal to the gradient at the pixel, selecting, among the set of determined lines, a set of lines that are tangents to the elliptical contour, and estimating the parameters of the elliptical contour on the image based on the parameters of the tangents to the elliptical contour.

A skin detection and evaluation method includes: obtaining a face image of a user; detecting a skin problem that appears in the face image; prompting in a first interface, the user that the skin problem appears on a face, wherein the first interface comprises the face image; and displaying a second interface in response to a first operation performed by the user in the first interface, wherein the second interface comprises a first facial simulated image obtained after the skin problem is aged; or displaying a third interface in response to a second operation performed by the user in the first interface, wherein the third interface comprises a second facial simulated image obtained after the skin problem is de-aged.

This specification describes systems and methods for refining point cloud data. Methods can include receiving point cloud data for a physical space, iteratively selecting points along an x, y, and z dimension, clustering the selected points into 2D histograms, determining a slope value for each 2D histogram, and removing, based on the slope value exceeding a predetermined value, points from the point cloud data. Methods can also include iteratively voxelizing each 2D histogram into predetermined mesh sizes, summating points in each voxelized 2D histogram, removing, based on determining the summation is below a predetermined sum value, points from the point cloud data, keeping, based on determining that a number of points in each voxelized 2D histogram exceeds a threshold value, a center point, selecting, for each histogram, a point, identifying, nearest neighbors in the point cloud data, removing the identified nearest neighbors from the data, and returning remaining points.

An electronic device is provided. The electronic device includes a display, a processor functionally connected with the display, and a memory functionally connected with the processor. The memory stores instructions configured to, when executed, enable the processor to display a first image through the display, display one or more second images through the display while displaying the first image, select a third image from among the one or more second images, identify a value of at least one property of the third image, generate a filter for applying the value of the at least one property to an image, apply the value of the at least one property to the first image using the filter, display the first image, to which the value of the at least one property is applied, through the display, and store the filter in the memory.

The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.

In an embodiment a method for detecting a presence of at least one object in a field of view of a time of flight sensor includes successively generating, by the time of flight sensor, histograms, each histogram comprising several classes associating a number of photons detected at a given acquisition period, adding several successively generated histograms so as to obtain a summed histogram and analyzing the summed histogram to detect the presence of at least one object in the field of view of the time of flight sensor.

The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not. Patches that do not include features of interest, or include disqualifying features, can be disqualified from further analysis. Relevant patches can analyzed and stored with various feature parameters.