Disclosed is a method of analyzing abnormal behavior by using data imaging, including: receiving data to be analyzed as an input, wherein the data to be analyzed is related to a state of a system to be analyzed; converting the inputted data to be analyzed into image data; training a neural network unit with the converted image data as an input; and detecting or predicting abnormal behavior in the system to be analyzed, at the neural network unit, which has received the image data converted from the data to be analyzed as the input and completed training.

In some arrangements, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts. Imagery captured by conventional or plenoptic cameras can be processed (e.g., by GPUs) to derive several different perspective-transformed viewsfurther minimizing the need to manually reposition items for identification. Crinkles and other deformations in product packaging can be optically sensed, allowing such surfaces to be virtually flattened to aid identification. Piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification, and to discover locations of obscured items. Other data (e.g., including data from sensors in aisles, shelves and carts, and gaze tracking for clues about visual saliency) can be used in assessing identification hypotheses about an item. Logos may be identified and usedor ignoredin product identification. A great variety of other features and arrangements are also detailed.

Systems and methods are described for dynamically fitting a digital image based on the saliency of the image and the aspect ratio of a frame are described. The systems and methods may provide for identifying an aspect ratio of the frame, selecting a salient region of the digital image based on the aspect ratio using a saliency prediction model, and fitting the digital image into the frame so that a boundary of the frame is aligned with a boundary of the salient region.

A diagnostic system includes: a first feature extraction module configured to extract features present in an image captured by a camera of a vehicle; a second feature extraction module configured to extract features present in the image captured by the camera of the vehicle; a first feature matching module configured to match the image captured by the camera with first images stored in a database and to output one of the first images based on the matching; a second feature matching module configured to, based on a comparison of the one of the first images with the image captured by the camera, determine a score value that corresponds to closeness between the one of the first images and the image captured by the camera; and a fault module configured to selectively diagnose a fault based on the score value and output a fault indicator based on the diagnosis.

Various embodiments are generally directed to systems for summarizing data visualizations (i.e., images of data visualizations), such as a graph image, for instance. Some embodiments are particularly directed to a personalized graph summarizer that analyzes a data visualization, or image, to detect pre-defined patterns within the data visualization, and produces a textual summary of the data visualization based on the pre-defined patterns detected within the data visualization. In various embodiments, the personalized graph summarizer may include features to adapt to the preferences of a user for generating an automated, personalized computer-generated narrative. For instance, additional pre-defined patterns may be created for detection and/or the textual summary may be tailored based on user preferences. In some such instances, one or more of the user preferences may be automatically determined by the personalized graph summarizer without requiring the user to explicitly indicate them. Embodiments may integrate machine learning and computer vision concepts.

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.

The described positional awareness techniques employing visual-inertial sensory data gathering and analysis hardware with reference to specific example implementations implement improvements in the use of sensors, techniques and hardware design that can enable specific embodiments to provide positional awareness to machines with improved speed and accuracy.

An image processing method includes acquiring a set of training images, and extracting a visual feature of each training image in the set of training images. The method includes clustering the visual feature, generating a visual dictionary composed of cluster centers serving as visual words, and adding 1 to the number of the visual dictionaries. The method includes determining whether the number of the visual dictionaries is equal to a predetermined number, and outputting the predetermined number of visual dictionaries generated if the determination result is yes, otherwise determining, from the visual dictionary, a visual word nearest to the visual feature. The method includes calculating a residual between the visual feature and the visual word nearest to the visual feature, determining the residual as the new visual feature, and returning to clustering the visual feature, generating a visual dictionary, and adding 1 to the number of the visual dictionaries.

A location-sensitive saliency prediction neural network generates location-sensitive saliency data for an image. The location-sensitive saliency prediction neural network includes, at least, a filter module, an inception module, and a location-bias module. The filter module extracts visual features at multiple contextual levels, and generates a feature map of the image. The inception module generates a multi-scale semantic structure, based on multiple scales of semantic content depicted in the image. In some cases, the inception block performs parallel analysis of the feature map, such as by parallel multiple layers, to determine the multiple scales of semantic content. The location-bias module generates a location-sensitive saliency map of location-dependent context of the image based on the multi-scale semantic structure and on a bias map. In some cases, the bias map indicates location-specific weights for one or more regions of the image.

Various embodiments enable an image recognition system reduce the number image match candidates before running a full-fledged pair-wise match on all image match candidates. In order to accomplish this, each inventory image can be assigned to a group. For example, a title for a book sold by an electronic marketplace could be available in multiple languages, in multiple bindings, and the book could be available in print, audio book, or electronic book. Each one of these variations could be associated with its own similarly looking inventory image, each of which could be returned as a valid match to a query image for the book. Accordingly, the inventory images for these variations could be assigned to a group for the book and, instead of geometrically processing an image for each variation, the image match system can process a single image representing all of the variations.