Abstract: A hand detection method, a hand segmentation method, an image detection method and system, a storage medium, and a device are provided. The image detection method includes: determining a first starting point in a connected domain of an image to be detected; determining n farthest extremum points different from the first starting point, wherein an Nth farthest extremum point is a pixel point in the connected domain having a maximum geodesic distance to an Nth starting point, an (N+1)th starting point is the Nth farthest extremum point, and n and N are both positive integers; performing region growing with the n farthest extremum points as initial points respectively, to acquire n regions in the connected domain; judging whether a relationship between a preset feature of each region and a preset feature of the connected domain satisfies a selection condition, to determine an available region satisfying the selection condition.

Abstract: A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.

Abstract: A mobile terminal includes a memory, and a processor coupled to the memory, wherein the processor is configured to execute first acquiring a frame obtained through photographing, second acquiring document image data of a document from the frame, first determining whether a form partial feature in a registered form and a document partial feature at a position corresponding to a position of the partial feature match, the document partial feature being in the document, and third acquiring a frame obtained through re-photographing when it is determined that the form partial feature and the document partial feature do not match.

Abstract: Systems and methods are disclosed for estimating aesthetic quality of digital images using deep learning. In particular, the disclosed systems and methods describe training a neural network to generate an aesthetic quality score digital images. In particular, the neural network includes a training structure that compares relative rankings of pairs of training images to accurately predict a relative ranking of a digital image. Additionally, in training the neural network, an image rating system can utilize content-aware and user-aware sampling techniques to identify pairs of training images that have similar content and/or that have been rated by the same or different users. Using content-aware and user-aware sampling techniques, the neural network can be trained to accurately predict aesthetic quality ratings that reflect subjective opinions of most users as well as provide aesthetic scores for digital images that represent the wide spectrum of aesthetic preferences of various users.

Abstract: Systems and methods for automatic processing of receipts to capture data from the receipts are presented. Upon receiving an image of a receipt, an optical character recognition (OCR) of the receipt content embodied in the image is executed. The OCR of the receipt content results in machine-encoded text content of the receipt content embodied in the image. Tokens are generated from the machine-encoded text content and data groups are constructed according to horizontal lines of generated tokens. Potential product items are identified for at least some of the constructed data groups and the potential product items are evaluated for the at least some constructed data groups. The evaluation of the potential product items for the at least some constructed data groups identifies receipt data, such as product items and vendor information, associated with the least some constructed data groups. The identified receipt data is captured and stored with the image of the receipt in a data store.

Abstract: An image processing system includes: an analysis unit configured to obtain information indicating a degree of curvature of a retina from a tomographic image of an eye to be examined; and an obtaining unit configured to obtain a category of the eye to be examined based on an analysis result.

Abstract: Systems and methods for intelligent traffic stop classifier loading are provided. A processor may receive a plurality of inputs related to a current context of a law enforcement officer. Based on the plurality of inputs, it may be determined that the current context of the law enforcement officer is a vehicle traffic stop. An image classifier may be loaded onto an image capture device associated with the law enforcement officer based on the vehicle traffic stop determination. An object type associated with the image classifier may be scanned for using the image classifier loaded onto the image capture device.

Abstract: Systems and methods are provided for improving filtering performance and Bjøntegaard-Delta (BD) rate savings for video processing. In addition to computing the artifacts between a given compressed image and a restored clean image after filtering using Deep Residual Learning (DRL) for recovering the residual between input and output, filtering strength of a loop filter may be controlled by the content of the region of the image, such that, in more important areas, such as the face and edges, the filtering strength may be increased while in less important areas, such as textures and backgrounds, the filtering strength may be decreased.

Abstract: In a method for obtaining information from a coding body (1, 15), information is assigned to a three-dimensional form of at least one portion of the coding body (1, 15), at least one two-dimensional image of at least the portion of the coding body (1, 15) is generated, the form of the portion is identified on the basis of the image and the information that has been assigned to the identified form is accessed. A corresponding system comprises: at least one coding body (1, 15) having at least one portion with a three-dimensional form that has information assigned; at least one image-capture means (8, 19, 24) for generating two-dimensional images; and at least one processor (9, 21, 25) configured to identify the form of the portion on the basis of at least one two-dimensional image of at least the portion of the coding body (1, 15) and to access the information assigned to the identified form.

Abstract: A method and an apparatus are provided for sharpening an image, by an image processor of an electronic device. An input image is received. Low pass filtering is applied to the input image to generate a first image and a second image. A kernel size of first image and the second image are different. Edge preserving filtering is applied to the input image to generate a third image and a fourth image. A kernel size of the third image and the fourth image are different. The first image is subtracted from the third image to obtain a first resulting image. The first image has a larger kernel size than the third image. The second image from the fourth image to obtain a second resulting image. The second image has a larger kernel size than the fourth image. The first resultant image, the second resultant image, and the input image are summed to generate a sharpened image.

Abstract: An image processing method includes: assigning a predetermined new label to the pixels determined to be assigned the label; and setting a position of the pixel assigned the label as a target position and assigning the label with the same value to peripheral pixels located around the pixel at the target position. A label update step repeats a peripheral pixel determination step, a label assignment step of assigning the label to the peripheral pixels, a target position movement step of moving a new target position that is a position of any one of the peripheral pixels assigned the label, and a return position storage step of, when there are a plurality of peripheral pixels assigned the label, storing positions of the peripheral pixels, other than the peripheral pixel at the new target position moved in the target position movement step, until there is no return position.

Abstract: The present disclosure is directed to systems and methods for generating investigations of user behavior. In an example embodiment, the system includes a video camera configured to capture video of user activity, a video analytic module to perform real-time video processing of the captured video to generate non-video data from video, and a computer configured to receive the video and the non-video data from the video camera. The computer includes a video analytics module configured to analyze one of video and non-video data to identify occurrences of particular user behavior, and an investigation generation module configured to generate an investigation containing at least one video sequence of the particular user behavior. In some embodiments, the investigation is generated in near real time. The particular user behavior may be defined as an action, an inaction, a movement, a plurality of event occurrences, a temporal event and/or an externally-generated event.

Abstract: A method of operating a dimensioning system to determine dimensional information for objects is disclosed. A number of images are acquired. Objects in at least one of the acquired images are computationally identified. One object represented in the at least one of the acquired images is computationally initially selected as a candidate for processing. An indication of the initially selected object is provided to a user. At least one user input indicative of an object selected for processing is received. Dimensional data for the object indicated by the received user input is computationally determined.

Abstract: A vehicle control device (100) comprising: a recognizer (130) recognizing surrounding situations of a vehicle; a setting unit (154) setting a virtual lane in an area inside a crossing that is present in an advancement direction of the vehicle on the basis of a recognition result acquired by the recognizer; and a driving controller (150, 160) controlling one or both of steering and acceleration/deceleration of the vehicle and causing the vehicle to run within the virtual lane and pass through the crossing in a case in which an object recognized by the recognizer is not present on the virtual lane, wherein, in a case in which an area inside the crossing has a width for which only one virtual lane is settable, the driving controller handles an oncoming vehicle recognized by the recognizer as an object present on the virtual lane.

Abstract: Virtually every cancer patient is imaged with CT, PET or MRI. Importantly, such imaging reveals that tumors are complex and heterogeneous, often containing multiple habitats within them. Disclosed herein are methods for analyzing these images to infer cellular and molecular structure in each of these habitats. The methods can involve spatially superimposing two or more radiological images of the tumor sufficient to define regional habitat variations in two or more ecological dynamics in the tumor, and comparing the habitat variations to one or more controls to predict the severity of the tumor.

Abstract: A computer implemented method using artificial intelligence for matching images with locations and directions by acquiring a plurality of panoramic images, detecting objects and their locations in each of the panoramic images, acquiring a floorplan image, detecting objects and their locations in the floorplan image, comparing the objects and locations detect in each of the panoramic image to the objects and locations detected in the floorplan image, and determining a location in the floorplan image where each panoramic image was taken.

Abstract: Systems and methods for predictive/reconstructive visual object tracking are disclosed. The visual object tracking has advanced abilities to track objects in scenes, which can have a variety of applications as discussed in this disclosure. In some exemplary implementations, a visual system can comprise a plurality of associative memory units, wherein each associative memory unit has a plurality of layers. The associative memory units can be communicatively coupled to each other in a hierarchical structure, wherein data in associative memory units in higher levels of the hierarchical structure are more abstract than lower associative memory units. The associative memory units can communicate to one another supplying contextual data.

Abstract: An illustrative example embodiment of a detector device, which may be useful on an automated vehicle, includes an array of detectors arranged in one dimension. The array includes a plurality of first detectors and a plurality of second detectors. The first detectors respectively have one of the second detectors between the first detector and an adjacent one of the first detectors. The first detectors respectively are spaced from the one of the second detectors by a first distance. The one of the second detectors are respectively spaced from the adjacent one of the first detectors by a second distance that is larger than the first distance. The first detectors are spaced from each other by a third distance that is a sum of the first and second distance. The second detectors are also spaced from each other by the third distance.

Abstract: A method for determining if a papillary print is comprised of living human tissue or not, using a papillary print sensor comprising in superposition, a contact surface, an array optical sensor, and a plurality of illuminating devices parallel between them. The method comprises illumination of the papillary print by the illuminating devices forming together, on the contact surface, a light pattern which is uniform along an axis that extends from one side to the other of a detecting surface of the array optical sensor, and acquisition of an image by the array optical sensor, with these steps being implemented at least once; in each image, selection of the pixels corresponding to the valleys or ridges of the print; and using the pixels selected, extraction of an optical characteristic defining the response to illumination, of the material comprising the papillary print.

Abstract: The disclosed technology includes systems and methods for enhancing machine vision object recognition based on a plurality of captured images and an accumulation of corresponding classification analysis scores. A method is provided for capturing, with a camera of a mobile computing device, a plurality of images, each image of the plurality of images comprising a first object. The method includes processing, with a classification module comprising a trained neural network processing engine, at least a portion of the plurality of images. The method includes generating, with the classification module and based on the processing, one or more object classification scores associated with the first object. The method includes accumulating, with an accumulating module, the one or more object classification scores. And responsive to a timeout or an accumulated score exceeding a predetermined threshold, the method includes outputting classification information of the first object.