Abstract: An image processing apparatus includes a processing unit configured to execute image processing on an image signal; a filtering unit configured to execute filtering processing; and a changing unit configured to, in a case where a range of a value which a predetermined color component of the image signal can take is changed from a first range to a second range narrower than the first range by the image processing, change the value of the predetermined color component of the filtered image signal based on the first range and the second range.

Abstract: Systems and methods for a content-adaptive photo-enhancement recommender that produces multiple enhanced images based on content and color layout parameters derived from related high-quality images are disclosed. In some embodiments, a user-uploaded image is enhanced based on parameters learned from high-quality images similar in content to the user-uploaded image and obtained using content-based image retrieval techniques.

Abstract: Techniques are disclosed for automatically transferring a style of at least two reference images to an input image. The resulting transformation of the input image matches the visual styles of the reference images without changing the identity of the subject of the input image. Each image is decomposed into levels of detail with corresponding energy levels and a residual. A style transfer operation is performed at each energy level and residual using the reference image that most closely matches the input image at each energy level. The transformations of each level of detail and the residual of the input image are aggregated to generate an output image having the styles of the reference images. In some cases, the transformations are performed on the foreground of the input image, and the background can be transformed by an amount that is proportional to the aggregated transformations of the foreground.

Abstract: An apparatus and method for extracting an object of interest from an image using image matting are disclosed herein. The apparatus for extracting an object of interest from an image using image matting includes a saliency map generation unit, a trimap generation unit, and an alpha map generation unit. The saliency map generation unit generates a saliency map corresponding to an object of interest inside an input image using a color space probability distribution corresponding to the input image. The trimap generation unit generates meta-trimaps using filters, and generates a trimap by clustering the meta-trimaps. The alpha map generation unit generates an alpha map using the trimap and a matting Laplacian matrix, and extracts the object of interest based on image matting using the alpha map and the input image.

Abstract: A computer determines one or more brightness levels associated with a first set of pixels within a light source in an image. The computer determines a brightness effect on a second set of pixels in the image based on the one or more brightness levels associated with the first set of pixels within the light source. The computer receives input to alter at least one of the one or more brightness levels associated with the first set of pixels within the light source. The computer alters one or more brightness levels associated with the second set of pixels in the image based on the received input and the determined brightness effect.

Abstract: A method may include processing two or more fiducials included in a three-dimensional medical image and included in a current image to generate two or more transform coefficients of a transform, and applying the transform to the three-dimensional medical image to form a present image.

Abstract: A compressed font file including compressed data is received. The compressed data is decompressed to generate decompressed data. An attempt to validate the decompressed data is made. Within the decompressed data, a predetermined pattern of invalid data corresponding to an element of a font file is detected. The font file is reconstructed by replacing at least some of the invalid data with the element.

Abstract: It is an object to obtain a high-resolution image generation apparatus that can generate a high-quality image by sorting out good portions from faulty portions in optical flow. The present invention includes: a hard disk that stores multi-viewpoint image information and parameter information of a base image and a plurality of reference images; an optical flow computation unit that extracts a plurality of pairs of the base image and a reference image, the plurality of pairs each including a different reference image, and computes optical flow for each pair; a depth information computation unit that computes depth information which indicates depth of each pixel of the base image for each pair, based on optical flow and the parameter information; and an optical flow sorting unit that determines whether or not each piece of pixel-based correspondence information included in the optical flow of each pair is to be used for improving a resolution of the base image, based on two pieces of the depth information.

Abstract: An image processing method includes: a generating step of generating a cumulative histogram of density values of pixels included in at least each of a first image and a second image; a calculating step of calculating a distance between a density value on a first histogram generated from the first image and a density value on a second histogram generated from the second image, in a space defined to include histogram frequencies of the cumulative histograms and the density values; and a determining step of determining a correspondence relation between the density values included in the first image and the density values included in the second image based on the calculated distances between the density values, and determining a conversion function for correcting a density value between the first image and the second image based on the determined correspondence relation.

Abstract: An image processing apparatus is provided including a storage unit for storing relationships of color coordinates between a plurality of reference colors under each of a plurality of different light sources for each light source, with respect to the plurality of reference colors set in a color space and being different from one another; and a calculation unit for detecting color coordinates of the plurality of reference colors from an object image to be processed, and identifying a light source used in capturing of the object image according to a similarity between relationships of the detected color coordinates and the relationships of color coordinates stored in the storage unit for each light source.

Abstract: Aspects of the subject technology relate to automatically and selectively applying a fill light filter to an image. A process includes determining an edge-preserved, smoothed version of the image, and determining a grayscale version of the image. The process also includes comparing each pixel of the edge-preserved, smoothed version of the image to each corresponding pixel of the grayscale version of the image. The process also includes applying the fill light filter to the image based on the comparison. The fill light filter is automatically adjusted based on identifying regions in the image. Selectively applying the fill light filter can reduce artifacts and noise from forming or being amplified as a result of the fill light filter.

Abstract: A system that incorporates the subject disclosure may include, for example, partitioning the image into a group of blocks, calculating principle bilateral filtered image components for a first subset of the group of blocks where the principle bilateral filtered image components are not calculated for a second subset of the group of blocks, and applying an infinite impulse response filter to the image using the principle bilateral filtered image components. Other embodiments are disclosed.

Abstract: A system, method, and computer program product are provided for generating an image result based on availability of a network resource. In use, a request is received for one or more image operations. Additionally, an availability of a network resource is identified. Next, if the network resource is not available, a result is generated using a subset of the one or more image operations. Further, if the network resource is available, a result is generated using each of the one or more image operations. Additional systems, methods, and computer program products are also presented.

Abstract: A vertical and horizontal line detection method for document images includes generating multiple binary images from the input grayscale document image based on multiple binarization thresholds, detecting horizontal and vertical lines in each of the multiple binary images independently, and merging the detection results from the multiple binary images. The line detection process for each binary image include applying an opening operation using a vertical or horizontal line as the structuring element, and removing connected components that are not vertical or horizontal lines based on a stroke width analysis. The boundaries of the detected lines are obtained using horizontal and vertical projections.

Abstract: During a description technique (100), a local descriptor for an object (300) is generated (122) by computing a 2-dimensional histogram (600) of pairs of angles (514, 516) between pairs of line segments (510, 512) that are aligned with edge pixels associated with the object (300). The pairs of line segments (510, 512) belong to a subset of k neighboring or proximate line segments (310). Moreover, this 2D histogram (600) may represent the relative displacement and the relative orientations of the pairs of line segments (510, 512) in the subset as weights in bins or cells defined by angular quantization values, and the 2D histogram (600 may exclude lengths of the line segments. Subsequently, the generated 2D histogram (600) may be compared (210) to predefined sets of descriptors in a model library that are associated with a set of objects, and the object may be identified (212) as one of the set of objects based on a group of match scores determined in the comparisons.

Abstract: A face detection apparatus detecting a face from an image including the face of a target person in a region imaged by an imaging unit, includes: a model fitting unit generating a first model of the face based on a first image from the imaging unit; a tracking unit fitting the first model to a second image from the imaging unit; a position detection unit detecting positions of face parts based on the second image; and a comparison unit determining whether or not a deviation occurs between the positions of the face parts in the first model fitted by the tracking unit and the positions of the face parts detected by the position detection unit. When the comparison unit determines that the deviation occurs, the model fitting unit generates a second model of the face based on the second image.

Abstract: A haze image discriminating apparatus has a first calculator to calculate a difference between a color difference component of an image signal of each of a plurality of pixels in an image and a gray value, and calculate a first ratio of pixels in which the difference is lower than a first reference value, to the plurality of pixels, a second calculator to calculate a second ratio of pixels in which a luminance component of the pixel signal of each of the plurality of pixels is lower than a second reference value, to the plurality of pixels, and a haze-degree determining unit to determine that a degree of haze of the image is higher as the first ratio is higher and the second ratio is lower.

Abstract: A method for mapping a target image to a reference image includes receiving a target image; receiving a reference image that overlaps the target image; preprocessing the target image, wherein the preprocessing includes: rejecting a target image with a shadow region above a shadow threshold while keeping a target image with a shadow region below a shadow threshold; providing an uncertainty in a location of the kept target image relative to the reference image; transforming the kept target image to an atlas projection to match the reference image; partitioning the transformed kept target image into a sub-region; and determining a matching statistic for each sub-region to determine a location for each sub-region relative to the reference image.

Abstract: Apparatus 10 for processing image data comprises an input 11 arranged to receive image data representing an image. The image data comprises a luminance component 13 at a first spatial resolution and at least one chrominance component 14 at a second, lower, spatial resolution. An up-sampling unit 18 up-samples the spatial resolution of the at least one chrominance component 14 to form an up-sampled chrominance component. A guided image filter (GIF) 20 filters the up-sampled chrominance component. The guided image filter comprises a radius parameter which determines an extent of the filter around a pixel. A control unit 15 for the guided image filter 20 determines a value of the radius parameter of the guided image filter. The value of the radius parameter is variable for different pixels of the image, or for different regions of pixels of the image. The control unit 15 can analyze the image data to determine the value of the radius parameter.

Abstract: The invention relates to image block encoding and decoding. More precisely, the invention relates to block prediction using reference blocks of the image. It is proposed a method which comprises using processing means for using at least the block for determining one reference block in an already encoded part of the image, encoding and reconstructing a part of the image, using at least the block for determining one reference block in the reconstructed part and a vector referencing the one reference block, using the one reference block for determining at least one further reference block in the reconstructed part and encoding the determined vector and a residual of the block with respect to a combination of the one and the at least one further reference block. Using the block for determination of the one reference block allows for selecting it such that it resembles the block and correlation with the block is maintained.