Methods, systems and computer program products for flagging abnormal videos are provided. Aspects include training an image recognition model based on a plurality of images that depict one or more of a plurality of subjects. Aspects also include generating a normal subject relationship graph representing normal relationships between the plurality of subjects by applying the image recognition model to a plurality of training videos and a test subject relationship graph representing test relationships between subjects depicted in a test video by applying the image recognition model to the test video. Each normal relationship is associated with a strength value. Responsive to determining that a difference between a strength value associated with a first normal relationship and a strength value associated with a corresponding first test relationship exceeds a predetermined threshold, aspects include flagging the test video as being abnormal.

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 data partitioning system receives an input dataset for e-commerce products, each sample containing attributes and associated values for each product including at least an image; represents each sample as a node on a graph to provide a graph of nodes for the dataset; measures a relative similarity distance between each pair of nodes based on comparing at least image values for the attributes; determines for each pair of nodes whether they are related if the similarity distance between them is below a defined threshold, and if related, generate an edge between them on the graph; group the connected nodes into a first or a second group such that the grouped nodes have no edges connecting them to nodes in the other group and have a shortest relative similarity distance with each other. The groups are used as training dataset and testing data sets for a supervised machine learning classifier.

Disclosed are systems and methods for automatic selection of canonical digital images from a large corpus of digital images, such as the corpus of digital images available on the web, for an entity, such as and without limitation a person, a point of interest, object, etc. The automated, unsupervised approach for selecting a diverse set of high quality, canonical digital images, is well suited for processing a large corpus of digital images. A set of canonical digital images identified for an entity can be retrieved in response to a digital image request for digital images depicting the entity.

This application discloses a method and apparatus for segmenting point cloud data, a storage medium and an electronic device. The method includes: obtaining target point cloud data by scanning target objects around a vehicle with laser lines; clustering the target point cloud data to obtain a plurality of first datasets, wherein feature points represented by point cloud data within each of the plurality of first datasets are fitted on one segmented line segment, each feature point being a point on a respective target object; and combining the plurality of first datasets according to distances between the corresponding plurality of segmented line segments to obtain a plurality of second datasets, wherein each second dataset includes at least one of the plurality of first datasets. This application resolves a technical problem of relatively low efficiency of point cloud segmentation in the related art.

According to an aspect of an embodiment, operations may comprise receiving a 3D point cloud representation of a region comprising points, with each point of the 3D point cloud representation associated with a normal value of a surface corresponding to the point, storing a set of discretized normal values, for each point of the 3D point cloud representation, associating the point with one of the discretized normal values in the set of discretized normal values by mapping the normal value associated with the point to the one of the discretized normal values in the set of discretized normal values, and storing a compressed octree representation comprising nodes, with at least a subset of the nodes of the compressed octree representation storing an index value identifying a discretized normal value for points of the 3D point cloud representation represented by the node.

A computer implemented method for generating a one-to-one mapping between a first spatial point set and a second spatial point set in nD comprising receiving a first and a second spatial point sets in nD and a plurality of candidate correspondences; computing conflict lists for the candidate correspondences; generating one or more MatchPairs between the first and the second point sets using the Cartesian Products of the plurality of candidate correspondences; computing local distance measures for the MatchPairs; converting the local distance measures to weights; computing conflict lists for pairs of the MatchPairs by taking the bitwise UNIONs of their candidate correspondences' unit conflict lists; computing correspondence lists for the MatchPairs; computing compatibilities between pairs of the MatchPairs by examining for each pair of said pairs the bitwise AND of one's correspondence list and the other's conflict list; constructing an undirected graph with its nodes corresponding to the MatchPairs, its edges representing the compatibilities, and its graph vertices assigned the weights; computing a maximum-weight clique of the graph; and merging the maximum-weight clique to generate the one-to-one mapping.

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.

An image processing device includes a processor including hardware, the processor being configured to: acquire an input image; detect an arc curve in the input image; determine a change in a luminance of a pixel in a direction from an inner side toward an outer side of the arc curve; detect, as a representative bubble region, a region including the arc curve involving a change determined to be an increase in the luminance in the direction from the inner side toward the outer side; and extract a bubble region, based on the representative bubble region.

A facial stroking detection method includes a detecting step and a determining step. The detecting step includes a pre-processing step, a feature extracting step and a feature selecting step. In the pre-processing step, an image is captured by an image capturing device, and the image is pre-processed so as to obtain a post-processing image. In the feature extracting step, a plurality of image features are extracted from the post-processing image so as to form an image feature set. In the feature selecting step, a determining feature set is formed by selecting a part of the image features from the image feature set and entered into a classifier. In the determining step, wherein the classifier provides a determining result according to the determining feature set.