Abstract: Devices, systems, and methods obtain one or more training images; obtain a test image; select one or more associated pixels in the training images for a target pixel in the training images; calculate respective value relationships between a value of the target pixel and respective values of the associated pixels in the training images; select one or more associated pixels in the test image for a target pixel in the test image; and detect an anomaly in the target pixel in the test image based on the respective value relationships between the value of the target pixel and the respective values of the associated pixels in the training images and on respective value relationships between a value of the target pixel and respective values of the associated pixels in the test image.

Abstract: Devices, systems, and methods obtain an image; perform at least one nonlinear locally-adaptive mapping on the image, thereby generating a revised image; generate an edge-enhanced image based on the revised image; and perform dynamic contrast stretching on the edge-enhanced image, thereby generating an enhanced image.

Abstract: Devices, systems, and methods obtain training images; generate image-alignment data based on the training images; cluster the training images based at least in part on the image-alignment data, thereby generating clusters of training images; and select one or more representative images from the training images based on the clusters of training images.

Abstract: Devices, systems, and methods obtain a first image, obtain a second image, calculate respective distances between a histogram from a patch in the first image to respective histograms from patches in the second image, and identify a patch in the second image that is most similar to the patch in the first image based on the respective distances.

Abstract: Devices, systems, and methods obtain respective corresponding feature-detection scores for a plurality of areas in an image; calculate respective corresponding sorting scores for at least some areas of the plurality of areas; for the at least some areas of the plurality of areas, arrange the corresponding feature-detection scores in order of the corresponding sorting scores, thereby generating an order of sorted feature-detection scores; and assign respective detection scores to the at least some areas based on the order of sorted feature-detection scores and on three or more of the following: the respective corresponding feature-detection scores of the areas, a spectral threshold, a spatial threshold, and a neighborhood kernel.

Abstract: Devices, systems, and methods obtain a reference image; obtain a test image; globally align the test image to the reference image; select subfields in the test image; align the subfields in the test image with respective areas in the reference image; warp the test image based on the aligning of the subfields; select anchor points in the reference image; select anchor-edge points in the reference image; realign the subfields in the warped test image with respective areas in the reference image based on the anchor points in the reference image and on the anchor-edge points in the reference image; and warp the warped test image based on the realigning of the subfields.

Abstract: Retrieval of images of objects from a large-scale database of object images, based on a query image. The database may, for example, contain images of objects such as faces, vehicles, people and luggage. Semantic attributes such as doors or windows in the case of vehicles are used as high level semantic cues to determine identities of objects in the images. Salient visual characteristics of the images are labeled with attribute information, and a transformation is learned so as to transform the labeled visual characteristics into a discrimination vector that discriminates between the labels. A similarity metric is learned using the discrimination vectors, such that different images depicting the same object are determined to be close while those having different objects are determined to be far apart. Candidates are retrieved based on a query image, and a re-ranking step may be applied to improve results. Validation experiments are described.

Abstract: A system and a method for slug edge detection in a microchannel of a microfluidic device is provided. Specifically, the system comprises an image sensor in communication with the microchannel. The microchannel has at least two fluid slugs each of which has a marker of different color providing color gradient across the edge between the adjacent fluid slugs. An edge score function is generated for each channel segmentation dividing the microchannel into tvv?O segments at a specific location along the microchannel. The edge score function is proportional to a between class variance for intensity values associated with the tvvo selected channel segments. The edge location is determined as the location along the channel defining one of the channel segmentations based at least in part on the edge score function.

Abstract: A system and a method for slug edge detection in a microchannel of a microfluidic device is provided. Specifically, the system comprises an image sensor in communication with the microchannel. The microchannel has at least two fluid slugs each of which has a marker of different color providing color gradient across the edge between the adjacent fluid slugs. An edge score function is generated for each channel segmentation dividing the microchannel into two segments at a specific location along the microchannel. The edge score function is proportional to a between class variance for intensity values associated with the two selected channel segments. The edge location is determined as the location along the channel defining one of the channel segmentations based at least in part on the edge score function.

Abstract: Devices, systems, and methods obtain a first image, obtain a second image, calculate respective distances between a histogram from a patch in the first image to respective histograms from patches in the second image, and identify a patch in the second image that is most similar to the patch in the first image based on the respective distances.

Abstract: The present invention relates to a method and system for determining Copy Number Variations (CNVs) in a genomic test sample including target amplicons and a reference amplicons. Specifically, nucleic acid melting curves are generated for the test sample. A mathematical model is fitted to each of the nucleic acid melting curves to separate target and reference melting reactions within the measured nucleic acid melting curve. The fitting parameters of the mathematical model are calculated. A CNV of the test sample is determined based on the fitting parameters of the mathematical model corresponding to the target and reference melting reactions.

Abstract: The present invention relates to methods and systems for the analysis of nucleic acids present in biological samples, and more specifically, relates to clustering melt curves derived from high resolution thermal melt analysis performed on a sample of nucleic acids, the resulting clusters being usable, in one embodiment, for analyzing the sequences of nucleic acids and to classify their genotypes that are useful for determining the identity of the genotype of a nucleic acid that is present in a biological sample.

Abstract: The present invention relates to methods for the analysis of nucleic acids present in biological samples, and more specifically to normalize a high resolution melt curve to assist in the identification of one or more properties of the nucleic acids. The present invention provides methods and systems that incorporate a background identification algorithm according to invention principles using raw melt curve data to identify reactions that are unrelated actual DNA melt reactions. Furthermore, a web-based application for analyzing experimental data is provided. The raw experimental data obtained from a variety of instruments is processed and analyzed on a server and presented to a user through a user interface (UI).

Abstract: Devices, systems, and methods for automatic genotyping obtain high-resolution melt data from a test sample defining a melting curve for a target nucleic acid in the test sample; obtain high-resolution melt data from a control sample defining a melting curve for a wild type of the target nucleic acid in the control sample; calculate melting curve derivatives of the melting curves for the test sample and the control sample, respectively, wherein each melting curve derivative represents a negative derivative of a fluorescence emitted from a nucleic acid sample as a function of temperature affecting nucleic acid denaturation; calculate parameters defining differences between features of the test sample and the control sample melting curve derivatives; and assign a genotype to the test sample based on a comparison of the calculated parameters to predetermined thresholds and boundaries defining genotypes.

Abstract: SIFT features are selected from an input image. A SIFT procedure is applied to the input image to obtain candidate keypoints. For each candidate keypoint, there is calculation of a first Laplacian value (Lu) for pixels in an upper Scale Space and a second Laplacian value (Ll) for pixels in a lower Scale Space, based on the position of the candidate keypoint. A candidate keypoint is discarded if a Laplacian value Lc of the keypoint position is less than or equal to either of Lu or Ll. In the case that the candidate keypoint is not discarded, the candidate keypoint's Laplacian strength (Ls) is calculated, based on a relative change in Laplacian value from Lc to Lu and from Lc to Ll. One or more candidate keypoints are selected as SIFT features based on the corresponding Laplacian strength.

Abstract: Systems and methods for learning a high-level visual vocabulary generate inter-visual-word relationships between a plurality of visual words based on visual word-label relationships, map the visual words to a vector space based on the inter-visual word relationships, and generate high-level visual words in the vector space.

Abstract: SIFT features are selected from an input image. A SIFT procedure is applied to the input image to obtain candidate keypoints. For each candidate keypoint, there is calculation of a first Laplacian value (Lu) for pixels in an upper Scale Space and a second Laplacian value (Ll) for pixels in a lower Scale Space, based on the position of the candidate keypoint. A candidate keypoint is discarded if a Laplacian value Lc of the keypoint position is less than or equal to either of Lu or Ll. In the case that the candidate keypoint is not discarded, the candidate keypoint's Laplacian strength (Ls) is calculated, based on a relative change in Laplacian value from Lc to Lu and from Lc to Ll. One or more candidate keypoints are selected as SIFT features based on the corresponding Laplacian strength.

Abstract: Systems and methods for displaying an object determine a display time of a first object, wherein the display time of the first object is determined based on one or more attributes of objects in a collection of objects, generate a display that includes at least part of the first object in a first portion of the display for the display time of the first object, wherein the display is configured for rendering on a display device, and remove the first object from the first portion of the display after the display time of the first object has elapsed.

Abstract: Systems, devices, and methods for generating an image representation obtain a set of low-level features from an image; generate a high-dimensional generative representation of the low-level features; generate a lower-dimensional representation of the low-level features based on the high-dimensional generative representation of the low-level features; generate classifier scores based on classifiers and on one or more of the high-dimensional generative representation and the lower-dimensional representation, wherein each classifier uses the one or more of the high-dimensional generative representation and the lower-dimensional representation as an input, and wherein each classifier is associated with a respective label; and generate a combined representation for the image based on the classifier scores and the lower-dimensional representation.

Abstract: Systems and methods for generating visual words define initial inter-visual word relationships between a plurality of visual words; define visual word-image relationships between the plurality of visual words and a plurality of images; define inter-image relationships between the plurality of images; generate revised inter-visual word relationships in a vector space based on the initial inter-visual word relationships, the inter-image relationships, and the visual word-image relationships; and generate higher-level visual words in the vector space based on the revised inter-visual word relationships.