Even if an object to be detected is not remarkable in images, and the input includes images including regions that are not the object to be detected and have a common appearance on the images, a region indicating the object to be detected is accurately detected. A local feature extraction unit 20 extracts a local feature of a feature point from each image included in an input image set. An image-pair common pattern extraction unit 30 extracts, from each image pair selected from images included in the image set, a common pattern constituted by a set of feature point pairs that have similar local features extracted by the local feature extraction unit 20 in images constituting the image pair, the set of feature point pairs being geometrically similar to each other. A region detection unit 50 detects, as a region indicating an object to be detected in each image included in the image set, a region that is based on a common pattern that is omnipresent in the image set, of common patterns extracted by the image-pair common pattern extraction unit 30.

A method for image segmentation includes (a) clustering, based upon k-means clustering, pixels of an image into first clusters, (b) outputting a cluster map of the first clusters (c) re-clustering the pixels into a new plurality of non-disjoint pixel-clusters, and (d) classifying the non-disjoint pixel-clusters in categories, according to a user-indicated classification. Another method for image segmentation includes (a) forming a graph with each node of the graph corresponding to a first respective non-disjoint pixel-cluster of the image and connected to each terminal of the graph and to all other nodes corresponding to other respective non-disjoint pixel-clusters that, in the image, are within a neighborhood of the first respective non-disjoint pixel-cluster, (b) setting weights of connections of the graph according to a user-indicated classification in categories respectively associated with the terminals, and (c) segmenting the image into the categories by cutting the graph based upon the weights.

A method for automated routing of pictures taken on mobile electronic devices to a digital picture frame including a camera integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from photo collections of community members. The integrated camera is used to automatically determine an identity of a frame viewer and can capture gesture-based feedback. The displayed photos are automatically shown and/or changed according to the detected viewers. The photos can be filtered and cropped at the receiver side. Clustering photos by content is used to improve display and to respond to photo viewer desires.

A method for compressing a digital representation of a stimulus includes encoding the digital representation as a feature vector within a feature space. The method also includes multiplying the feature vector with a Jacobian that maps the feature space to a non-Euclidean perceptual space according to a perceptual system that is capable of perceiving the stimulus. This multiplication generates a perceptual vector within the non-Euclidean perceptual space. The method also includes applying an update operator to the perceptual vector to move the perceptual vector in the perceptual space to an updated vector such that the updated vector has a lower entropy than the perceptual vector. The method also includes rounding the updated vector into a compressed vector that is smaller than the feature vector.

The present disclosure is directed to systems and methods for classifying biomedical images. A feature classifier may generate a plurality of tiles from a biomedical image. Each tile may correspond to a portion of the biomedical image. The feature classifier may select a subset of tiles from the plurality of tiles by applying an inference model. The subset of tiles may have highest scores. Each score may indicate a likelihood that the corresponding tile includes a feature indicative of the presence of the condition. The feature classifier may determine a classification result for the biomedical image by applying an aggregation model. The classification result may indicate whether the biomedical includes the presence or lack of the condition.

A method and a computing device for clustering phishing web resources based on images of visual content thereof are provided. The method comprises: receiving references to a plurality of phishing web resources; generating, for a given phishing web resource of the plurality of phishing web resources, at least one image of a visual content of the given phishing web resource; analyzing the at least one image associated with the given phishing web resource, the analyzing comprising identifying contours of elements of the visual content of the given phishing web resource within the at least one image; conducting pairwise comparison between the contours associated with the given phishing web resource and contours of stored clusters of visual content images; and storing, in a database, data indicative of an association between the given phishing web resource and a respective cluster of the at least one image.

A computer-implemented method for ascertaining an output signal, which characterizes an object detection of an object of an image. The method includes: ascertaining a plurality of object detections with respect to the image; ascertaining a graph based on the plurality of object detections, object detections of the plurality of object detections being characterized by nodes of the graph and overlaps between two object detections each being characterized by edges of the graph; ascertaining a cluster of the graph based on the nodes and on the edges of the graph with the aid of a density-based clustering method; ascertaining an object detection based on the cluster and providing the object detection in the output signal.

The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations. The operations include extracting one or more image characterization metrics from respective ones of a plurality of digitized images within an imaging data set. The plurality of digitized images have batch effects. The operations further include identifying a plurality of batch effect groups of the digitized images using the one or more image characterization metrics, and dividing the plurality of batch effect groups between a training set and/or a validation set. The training set and/or the validation set include some of the plurality of digitized images associated with respective ones of the plurality of batch effect groups.

A method for automated routing of pictures taken on mobile electronic devices to a digital picture frame including a camera integrated with the frame, and a network connection module allowing the frame for direct contact and upload of photos from electronic devices or from photo collections of community members. The integrated camera is used to automatically determine an identity of a frame viewer and can capture gesture-based feedback. The displayed photos are automatically shown and/or changed according to the detected viewers. The photos can be filtered and cropped at the receiver side. Clustering photos by content is used to improve display and to respond to photo viewer desires.

A method of determining the position of one or more components of a landing gear assembly of an aircraft is disclosed including scanning the one or more components with a lidar system to generate a set of position data points, each position data point comprising a set of three orthogonal position values. The position data points are partitioned into one or more clusters using a distance metric. Each cluster is determined to represent a component of the landing gear assembly. The position of the components are then determined from the position data points in the clusters. The value of the distance metric for a first position data point and a second position data point is representative of the difference between a first position value of the three orthogonal position values of the first position data point and the corresponding first position value of the three orthogonal position values of the second position data point.