Abstract: In an image processing apparatus, a first random number seed generation circuit generates a first random number seed corresponding to the line number. An image compression circuit generates compressed data by quantizing addition data acquired by adding up of first image data and a first pseudo random number generated based on the first random number seed and a quantization coefficient. A second random number seed generation circuit generates a second random number seed that corresponds to the line number and is equal to the first random number seed. An image decompression circuit generates decompressed data by dequantizing the compressed data using a quantization coefficient, and generates second image data by subtracting a second pseudo random number equal to the first pseudo random number, which is generated based on the second random number seed and the quantization coefficient, from the decompressed data.

Abstract: A system for generating a word sequence includes one or more processors in connection with a memory and one or more storage devices storing instructions causing operations that include receiving first and second input vectors, extracting first and second feature vectors, estimating a first set of weights and a second set of weights, calculating a first content vector from the first set of weights and the first feature vectors, and calculating a second content vector, transforming the first content vector into a first modal content vector having a predetermined dimension and transforming the second content vector into a second modal content vector having the predetermined dimension, estimating a set of modal attention weights, generating a weighted content vector having the predetermined dimension from the set of modal attention weights and the first and second modal content vectors, and generating a predicted word using the sequence generator.

Abstract: Disclosed subject matter relates to image processing that includes a method for marking content on a surface of an object using laser. A transformation identification computing device receives an input image of the object and extracts data corresponding to region of interest in the input image. The extracted data is compared with extracted template data corresponding to regions of interest present in template images of one of one or more template objects. A transformation in position of the object with respect to the position of the one of the one or more template objects is determined, based on the comparison. Finally, an inverse of the transformation is applied to content data that is to be marked at a desired location within the region of interest of the object. The present disclosure analyses the images at a sub-pixel level based on machine learning approach to determine transformation.

Abstract: Methods and systems for generating a simulated image from an input image are provided. One system includes one or more computer subsystems and one or more components executed by the one or more computer subsystems. The one or more components include a neural network that includes two or more encoder layers configured for determining features of an image for a specimen. The neural network also includes two or more decoder layers configured for generating one or more simulated images from the determined features. The neural network does not include a fully connected layer thereby eliminating constraints on size of the image input to the two or more encoder layers.

Abstract: The present disclosure relates to a high precision method, model, and apparatus for calibrating the intrinsic camera parameter values of one or more intrinsic parameters of a camera. The intrinsic parameters include focal lengths, skew, optical center, and radial distortion coefficients. The practical implementation of the solution consists of a camera capturing several images of a planar calibration pattern from different viewpoints, and a computer implemented processing pipeline. The solution does not require additional human input and is robust to misdetection of the calibration pattern in some of the images.

Abstract: An information processing device according to the present invention includes: a feature point sampling unit that generates, based on an initial value of a feature point used for registration of an object included in a target image to be processed, a feature point hypothesis that is a group of a plurality of the feature points in the target image; an image degradation unit that generates a degraded image that is a degraded image with degrading a reference image used for processing the target image, based on the feature point hypothesis and a degradation parameter for degrading an image; and a reliability calculation unit that calculates a reliability of the feature point hypothesis, based on the target image, the degraded image, and the feature point hypothesis.

Abstract: An image processing device having a CPU which functions as units including an image acquiring unit that acquires a plurality of images indicative of a sequence of actions of a subject; a selecting unit that selects, in accordance with an operation by a user, one of a partial action from among a plurality of kinds of partial actions included in the sequence of actions of the subject; an image selecting unit that selects a plurality of images corresponding to the specific kind of partial action selected by the selecting unit from among the plurality of images acquired by the image acquiring unit; and an image composition unit that generates a composite image wherein the composite image includes the plurality of images.

Abstract: An information processing apparatus includes a control unit that creates a plurality of learning information items including an input image and a teacher image as an expected value by image-processing the input image in accordance with a scenario described with a program code, and supplies the created plurality of learning information items to a machine learning module that composes an image processing algorithm by machine learning.

Abstract: Various embodiments provide a method for computing color descriptors of product images. For example, a number of fine color representatives can be determined to describe color variation in an image as a histogram by assigning a saturation value and a brightness value to a plurality of color hues. For each pixel of the image, the closest color among a defined fine color representative set is computed. In this example, each of the pixels is assigned a color ID corresponding to their closest matching fine color representative and at least one family color ID corresponding one or more pure color families. In this example, a histogram of the color representatives and a histogram for the color families are computed. A single color vector descriptor for the image is then determined by combining the family histogram with the color representative histogram.

Abstract: An example method described herein involves receiving a first data stream and a second data stream; determining that a plurality of entities are present in the image data of the first data stream; analyzing the first data stream to determine that an entity, of the plurality of entities, is unrecognizable in the image data of the first data stream; obtaining, by the device, a common knowledge graph associated with the first data stream and the second data stream, wherein the common knowledge graph includes information regarding the plurality of entities; annotating the common knowledge graph with first corresponding recognizable characteristics of the plurality of entities in the first data stream to generate a first annotated knowledge graph; annotating the common knowledge graph with second corresponding recognizable characteristics of the plurality of entities in the second data stream to generate a second annotated knowledge graph; determining whether the entity is recognizable based on the first annotated

Abstract: An intelligent scoring method and system for a text objective question, the method comprising: acquiring an answer image of a text objective question (101); segmenting the answer image to obtain one or more segmentation results of an answer string to be identified (102); determining whether any of the segmentation results has the same number of characters as the standard answer (103); if no, the answer is determined to be wrong (106); otherwise, calculating identification confidence of the segmentation result having the same number of words as the standard answer, and/or calculating the identification confidence of respective characters in the segmentation result having the same number of words as the standard answer (104); determining whether the answer is correct according to the calculated identification confidence (105). The method can automatically score text objective questions, thus reducing consumption of human resource, and improving scoring efficiency and accuracy.

Abstract: A method and device for controlling an irradiating light for photographing an iris is provided. The device includes a light source configured to generate light, an active lens configured to have an adjustable refractive index to change light passing through the active lens, an image capturer configured to capture an image of the iris by using a camera, and a controller configured to control the active lens to change the light passing through the active lens based on distance, motion and ambient light levels.

Abstract: Because we needed a new color saturation processing in tune with dynamic range transformations necessary for handling the recently introduced high dynamic range image encoding, we describe a color saturation modification apparatus (101) arranged to determine linear color differences (R-Y,G-Y,B-Y) on the basis of an input color (R,G,B) and a luminance (Y) of the input color, and to do a multiplication of the linear color differences (R-Y,G-Y,B-Y) with a gain (g), characterized in that the apparatus is arranged to determine the gain as a function of a difference value (V_in-Y) being defined as the value of the highest one of the linear color differences (R-Y,G-Y,B-Y).

Abstract: An edge detection apparatus according to the present invention, includes: a detection unit configured to detect a dispersion degree of gradation values of input image data; a determination unit configured to determine detection sensitivity, which is edge detectability, based on the dispersion degree detected by the detection unit; and an edge detection unit configured to detect an edge from an input image based on the input image data, at the detection sensitivity determined by the determination unit, wherein in a case where the dispersion degree is low, the determination unit determines a lower detection sensitivity, compared with a case where the dispersion degree is high.

Abstract: A method includes capturing a first image associated with a portion of a display screen being shared. The method further includes rendering the first image in a preview window of the display screen being shared to form a second image. The second image is captured so as to determine whether the first image is duplicated in the second image. The duplication of the first image in the second image is masked to form a third image. The third image is rendered in the preview window.

Abstract: An apparatus for visual search and retrieval using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, an extractor, and a comparator. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each segment, wherein the score is based, at least partially, on a classification probability of each frame. The extractor is to extract deep features from a highest ranked segment, and the comparator is to determine the top-K neighbors based on the deep features.

Abstract: There is disclosed a method of generating an index of images, the index of images for enabling comparison of the image against other images, the method executable at a server. The method comprises: determining at least one key for the index, the at least one key including at least a portion of a visual features composite parameter associated with an image to be indexed, the visual features composite parameter having been determined by executing steps of; identifying a first local region of the image and a second local region of the image.

Abstract: A method for facial recognition encode analysis comprises providing a training set of Gabor encoded arrays of face images from a database; and, for each encode array in the training set, evaluating the Gabor data to determine the accuracy of the fiducial points on which the encode array is based. The method also comprises training an outlier detection algorithm based on the evaluation of the encode arrays to obtain a decision function for a strength of accuracy of fiducial points in the encode arrays; and outputting the decision function for application to an encode array to be tested.

Abstract: A display device performs efficient driving by selectively scanning a peripheral area processed to have low resolution. The display device includes a gate driver that scans a first subset of pixel rows in a first scan block including only the peripheral area during a first frame and a second frame, and respectively scan during the first frame and the second frame a second subset of pixel rows and a third subset of pixel rows in a second scan block including the medial area and the peripheral area excluding the foveal area, and scan pixel rows of pixels in a third scan block including the foveal area, the medial area, and the peripheral area during the first frame and the second frame.

Abstract: The present disclosure is directed towards systems and methods for modifying a digital image. For example, systems and methods described herein involve identifying a target portion of a digital image to remove from the digital image. The systems and methods further involve identifying geometric features intersecting or surrounding the identified target portion. The systems and methods further involve analyzing and identifying a source portion having geometric properties that correspond to the identified geometric features intersecting the target portion. Further, the systems and methods involve removing the target portion and replacing the target portion with the source portion by aligning the geometric properties to blend together with the background surrounding the removed target portion. In this way, the output image includes a replacement portion in place of the removed target portion that blends together with the background of the output image.