A machine may be configured to access an image of an item described by a description of the item. The machine may determine an image quality score of the image based on an analysis of the image. A request for search results that pertain to the description may be received by the machine, and the machine may present a search result that references the item's image, based on its image quality score. Also, the machine may access images of items and descriptions of items and generate a set of most frequent text tokens included in the item descriptions. The machine may identify an image feature exhibited by an item's image and determine that a text token from the corresponding item description matches one of the most frequent text tokens. A data structure may be generated by the machine to correlate the identified image feature with the text token.

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.

In an example, a method includes, by one or more processors, acquiring a dot pattern having a distribution of dots. A greyscale image comprising a plurality of pixels each associated with a greyscale value may be derived from the dot pattern. Deriving the greyscale image may comprise, for each pixel, determining the closest dot to the pixel and associating a distance of the closest dot from the pixel with the pixel, and assigning a greyscale value to each pixel, wherein the greyscale value is based on the distance associated with that pixel.

Provided is an image processing device with which it is possible to automate the setting of a detection parameter that is suitable for obtaining a desired detection result. An image processing device is provided with: an object detection unit for detecting the image of an object from input image data using a detection parameter; a detection rate calculation unit for comparing the result of detection by the object detection unit with information that represents a desired detection result so as to calculate at least one of a non-detection rate and a false detection rate in object detection by the objection detection unit; an objective function value calculation unit for calculating the value of an objective function, the input variable of which is at least one of the non-detection rate and the false detection rate; and a detection parameter search unit for performing a search of the detection parameter by changing the value of the detection parameter and repeating object detection, calculation of at least one of the non-detection rate and the false detection rate, and calculation of the value of the objective function until the value of the objective function satisfies a prescribed condition or the number of times a search of the detection parameter is performed reaches a prescribed count.

An article of manufacture may include a matrix barcode on a physical medium and associated with an environment. The matrix barcode may include a plurality of colors, where at least one of the plurality of colors is a least prevalent color, of a plurality of prevalent colors in the environment.

An object detection system and method includes: an optical image sensor arranged to perform the following steps: capturing a calibration image during a calibration stage, dividing the calibration image into a plurality of quadrants, and calculating a parameter for each of the quadrants; capturing a plurality of raw images during a detection stage, dividing each image of the raw images into a plurality of quadrants, and calculating a parameter for each of the quadrants; comparing the respective parameters of each quadrant of a raw image with the respective parameters of each quadrant of the calibration image to generate a ratio value for each quadrant; and comparing the ratio value of each quadrant with a predetermined threshold. When each ratio value of specific quadrants of the quadrants is greater than the predetermined threshold, object detection is confirmed.

A method for generating an image that includes at least one of a vignette effect or a grain effect corresponding to an input image may include obtaining the input image including at least one of the vignette effect or the grain effect; identifying at least one of a vignette parameter or a grain parameter of the input image; obtaining at least one of a vignette filter based on the vignette parameter or a grain layer based on the grain parameter; and generating the image that includes at least one of the vignette effect or the grain effect by applying at least one of the vignette filter or the grain layer to the image.

Aspects of the present disclosure include methods for identifying one or more components of a sample in a flow stream using a dynamic algorithm (e.g., a machine learning algorithm). Methods according to certain embodiments include detecting light from a sample having particles in a flow stream, generating a data signal of parameters of the particles from the detected light, generating an image based on the data signal, comparing the image with one or more image classification parameters and classifying one or more components of the image using a dynamic algorithm that updates the image classification parameters based on the classified components in the image. Systems and integrated circuit devices programmed for practicing the subject methods, such as on a flow cytometer, are also provided.

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.

A computer-implemented method for identifying a user, the method using a computing device comprising an illumination source that, when activated, emits visible light, the method comprising taking two images of a scene potentially comprising a living body part carrying a biometric characteristic, wherein a first image is taken without the illumination source being activated and the second image is taken with the illumination source being activated, transferring the first image and the second image to a neural network and processing, by the neural network the first image and the second image, wherein the processing comprises comparing the first image and the second image, thereby determining whether the first image and the second image are images of a living body part, the method further comprising, if it is determined that the first image and the second image are images of a living body part, performing an identification algorithm to find a biometric characteristic for identifying the user and, if it is determined that the first image and the second image are not images of a living body part, not performing the identification algorithm.