Abstract: A refined segmentation system, method and device of an image shadow area are provided. The system of the present invention includes: a feature extraction network, a reverse fusion network, and a weighted fusion network. The feature extraction network includes a plurality of sampling layers which are arranged sequentially, a plurality of segmentation features of the shadow areas in the input images are obtained through the sampling layers sequentially. The reverse fusion network includes a plurality of layered reverse fusion branches, each of which includes a plurality of feature fusion layers arranged in sequence, and two input features are fused in sequence through each feature fusion layer. The weighted fusion network is configured to perform weighted fusion on outputs of the plurality of reverse fusion branches to obtain a final segmentation result of the shadow area of the input image.

Abstract: Some implementations may include a method for watermarking an identification document, the method including: modulating a spatial luminance pattern associated with a first digital watermark to encode a first portion of personally identifiable information; modulating a spatial chrominance distribution associated with a second digital watermark to encode a second portion of the personally identifiable information; and applying the first digital watermark and the second digital watermark to the identification document.

Abstract: An electronic device and a method of operating the electronic device are provided. The electronic device includes a proximity detector; an iris recognition module; a memory; and a processor electrically connected to the proximity detector, the iris recognition module, and the memory, wherein the processor is configured to execute an iris recognition operation based on the iris recognition module; determine proximity of an object based on the proximity detector while the iris recognition operation is performed; and, if the proximity of the object includes within a set reference range, stop the iris recognition operation.

Abstract: A method and system for large image matting, where alpha mask ?R for a large image is built with the help of alpha mask ?R found for the reduced image. In the current context, a large image is an image for which data structures required by the closed form method do not fit available memory. To overcome memory limitation the large image is divided into a plurality of smaller size sub-images. To find an alpha mask for every sub-image a sparse linear system is solved where the pixel values sampled from reduced image alpha mask ?R are used as constraints on a sparse linear system.

Abstract: A method of classifying an image is disclosed. The method comprises: applying a computer vision procedure to the image to detect therein candidate image regions suspected as being occupied by a target; presenting to an observer each candidate image region as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of the target by the observer; and determining an existence of the target in the image is based, at least in part, on the identification of the neurophysiological event.

Abstract: An artificial intelligence system for identifying attributes in an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: extracting first features; categorizing the first images in a first group or a second group; modifying first metadata associated with each image in the first images to include a binary label; calculating a classification function; classifying a second plurality of images using the classification function; extracting second features from the second images classified in the first group; categorizing the second images in the first group by attribute; calculating an attribute identification function that identifies attributes of the second images; and identifying at least one attribute associated with a client image using the attribute identification function, the client image being received from the client device.

Abstract: A cognitive, artificially intelligent system employs, in part, visual recognition and image processing to transform electronic design information, such as a schematic diagram, into a machine-readable data structure suitable for machine-based comparison of one data structure against another data structure of like kind. A comparison method identifies and quantifies similarities or equivalencies between the any such data structures, and hence between respectively corresponding electronic designs.

Abstract: A method is described that includes loading an array of content into a two-dimensional shift register. The two-dimensional shift register is coupled to an execution lane array. The method includes repeatedly performing a first sequence including: shifting with the shift register first content residing along a particular row or column into another parallel row or column where second content resides and performing operations with a particular corresponding row or column of the execution lane array on the first and second content. The method also includes repeatedly performing a second sequence including: shifting with the shift register content from a set of first locations along a resultant row or column that is parallel with the rows or columns of the first sequence into a corresponding set of second locations along the resultant row or column. The resultant row or column has values determined from the operations of the first sequence.

Abstract: Embodiments of the present disclosure provide a method, apparatus, and computer readable storage medium for processing an image. The method described herein includes determining a to-be-processed first region and a to-be-processed second region in an input image, the first region is at least partially filled with a first color, the second region is at least partially filled with a second color, and the first region and the second region are adjacent regions sharing a common boundary region. The method further includes shrinking the first region and the second region, to enable neither of the shrunken first region and the shrunken second region to include the boundary region. The method further comprises expanding the shrunken first region and the shrunken second region, to enable the boundary region to be filled with at least one color of the first color or the second color.

Abstract: A system for computer-aided triage includes a router, a remote computing system, and a client application. A method for computer-aided triage includes receiving a data packet associated with a patient and taken at a point of care; checking for a suspected condition associated with the data packet; in an event that the suspected condition is detected, determining a recipient based on the suspected condition; and transmitting information to a device associated with the recipient.

Abstract: An automated airfield ground lighting inspection system and method is disclosed. An image acquisition means captures image streams of the airfield ground lighting system lights when moved across an airfield. A location sensor detects positional information for the image acquisition means when capturing the plurality of images comprising the image streams. An image processor coupled to the image acquisition means and the location sensor processes the image stream of a light of the airfield ground lighting system by: (a) associating characteristics of a plurality of points in an image with an item in the light to be checked, and using this association for extraction of the points; (b) verifying each extracted point; and (c) determining the state of the light of the image stream by processing the verified extracted points comprising an item to be checked.

Abstract: An image processing device includes a stitching processing unit that composites composition source images generated from captured images under a preset processing condition to generate a composite image, a screen generation unit that generates a screen and outputs the composite image to a display input device, a touch operation determination unit that determines a camera image region to be adjusted based on a detection result of a touch operation on the screen and determines an adjustment item according to an operation mode of the touch operation, and a processing condition setting unit that sets a temporary processing condition related to the camera image region to be adjusted. The stitching processing unit generates the composition source image from the captured image of the camera corresponding to the camera image region to be adjusted under the temporary processing condition and updates the composite image by compositing the composition source images.

Abstract: A scale transformation adjustment unit performs a scale transformation corresponding to a change of an image range with respect to an actual image by using a scale transformation by an optical zoom of an image pickup unit that generates the actual image, and generates a coordinate transformation matrix including the scale transformation.

Abstract: An image processing device is provided that includes: an acquisition unit that acquires a plurality of viewpoint images which are captured at a plurality of different viewpoints by an imaging unit; a detection unit that detects viewpoint-relevant information between the plurality of viewpoint images; a determination unit that determines a second imaging condition in which image change between before and after predetermined image processing is greater than that in a first imaging condition in which the plurality of viewpoint images are captured on the basis of the first imaging condition and the viewpoint-relevant information; and an output unit that outputs the second imaging condition determined by the determination unit.

Abstract: The embodiments of the present application provide a method, apparatus and system for processing an image of a logistics parcel, which can capture an image with a barcode in a proper position. The method comprises: identifying a barcode in a Nth image to acquire position of the barcode in the Nth image, and placing the position into a queue; predicting a position that the barcode would locate in a (N+1)th image to be captured; identifying a barcode in the (N+1)th image to acquire position of the barcode relative to the (N+1)th image; determining whether the position of the barcode in the (N+1)th image matches with the predicted position; if so, placing the information of the barcode in the (N+1)th image into the queue; and after the parcel leaves the field of view, selecting, according to a preset optimal position of the barcode in an image and the information of the barcode placed into the queue, an optimal image from all images captured for the parcel.

Abstract: An apparatus includes a first circuit and a second circuit. The first circuit may be configured to (i) generate a sharpened region by sharpening an original region of an image and (ii) generate edge information by detecting an edge in the original region of the image. The second circuit may be configured to (i) evaluate a current sample of the sharpened region proximate to the edge for an artifact based on the edge information and (ii) generate a final sample by adjusting the current sample in the sharpened region where the artifact is detected. The artifact may be an overshoot or an undershoot.

Abstract: Method and related apparatus for analysing a surface of a tyre, includes: acquiring one or more digital images of a surface portion having a pattern that includes a scheme that is repeated substantially equal thereto in a plurality of positions; identifying a plurality of first regions of said one or more digital images, each corresponding to a respective scheme sub-portion; identifying a respective plurality of regions homologous to each first region, wherein the respective scheme sub-portion is substantially identical to the respective scheme sub-portion of each first region; calculating a model of the respective scheme sub-portion, where each pixel is associated with a mean value of the values associated with the pixels of each first region and of the respective homologous regions having the same relative coordinates of said each pixel; and obtaining a pattern model using the calculated scheme sub-portion models.

Abstract: Systems and methods for encoding high resolution data associated with a relatively large number of bits to an encoded form having a relatively reduced number of bits. The method includes, by a processor: receiving an input image comprising one or more high resolution objects. The method further includes, for each of the one or more high resolution objects: identifying an object family for that object and determining whether a reference table exists for the object family. If a reference table exists for the object family, the method includes determining a size of that object, and identifying a tag based on the size. The method also includes encoding that object to form an encoded object having a relatively reduced number of bits, associating the identified tag with the encoded object, and saving the encoded object.

Abstract: According to an example, an apparatus may include an image capture device, a controller, and a computer readable storage medium. The computer readable storage medium may include instructions that may cause the controller to receive, from the image capture device, an image of a testing agent deposited in a testing pattern onto a layer of build materials, in which the layer of build materials is to form a section of a three-dimensional printed part. The instructions may also cause the controller to determine a condition of the deposited testing pattern in the received image, wherein the determined condition is to be used to determine a quality level of the layer of build materials based upon the determined condition.

Abstract: An apparatus includes a distribution generator configured to determine a combination of each of first regions in a first image and a corresponding one of second regions in a second image based on feature amounts of the first regions and feature amounts of the second regions, and generate first distribution information indicating distribution of the difference between a first position of a first region in the first image and a second position of a second region in the second image; a similarity generator configured to generate second distribution information indicating distribution of the difference between the first position and the second position, calculate a first similarity between the first image and the second image, and calculate a second similarity between the first image and the second image; and a processor configured to generate a comparison result by comparing the first image and the second image.