Structure aware image denoising and noise variance estimation techniques are described. In one or more implementations, structure-aware denoising is described which may take into account a structure of patches as part of the denoising operations. This may be used to select one or more reference patches for a pixel based on a structure of the patch, may be used to compute weights for patches that are to be used to denoised a pixel based on similarity of the patches, and so on. Additionally, implementations are described to estimate noise variance in an image using a map of patches of an image to identify regions having pixels having a variance that is below a threshold. The patches from the one or more regions may then be used to estimate noise variance for the image.

A computer-implemented method is provided for classifying an input signal against a set of pre-classified signals. A computer system may calculate, for each of one or more signals of the set of pre-classified signals, a parallelism value indicating a level of the parallelism between that signal and the input signal. The computer system may calculate, for a first subset of the set of pre-classified signals, a sparse vector, wherein each element of the sparse vector serves as a coefficient for a corresponding signal of the first subset. The computer system may determine, for each of the signals in the set of pre-classified signals, a similarity value indicating a level of similarity between that signal and the input signal.

Examples disclosed herein relate to determining the shape of an object representation. In one implementation, a processor determines contours of a silhouette of an object representation above a contour degree threshold, where a contour degree is determined based on the area of the contour with respect to itself. The processor may identify a shape type of the object representation based on a comparison of the determined contours to shape definition information.

A method and device for automatically classifying document hardcopy images by using document hardcopy image descriptors are provided. The method and device include providing a document hardcopy image, the document hardcopy image having image features, extracting image descriptors by a first set of image descriptor extractors, each image descriptor of the image descriptors being descriptive of the image features of the document hardcopy image, estimating class probabilities of the document hardcopy image by multiple trained classifiers based on the image descriptors, determining a most probable class of the document hardcopy image by a trained meta-classifier based on the class probabilities estimated by the multiple trained classifiers, inputting the document hardcopy image and the most probable class of the document hardcopy image to an assigner, and assigning, by the assigner, the most probable class determined by the trained meta-classifier to the document hardcopy image to obtain a classified document hardcopy image.

A method and system detection of inherent noise present within a video source prior to digital video compression is disclosed. A noise image is extracted by subtracting a current image from its filtered version. Each pixel of the extracted noise image is normalized based on a determined principal edge image and the analog noise pixels are accumulated to generate an intermediate noise confidence value. Analog noise may be detected based on an analog noise confidence value generated based on the intermediate noise confidence value and a ringing metric, a blockiness metric, a motion vector cost of the current image, a blurriness exception weight, a flashiness exception weight, and a pan blur exception weight. The method may further comprise detection of high frequency noise based on determining a high frequency noise confidence value that may be based on a high frequency noise value and a frequency component with highest magnitude.

Vehicles and other items often have corresponding documentation, such as registration cards, that includes a significant amount of informative textual information that can be used in identifying the item. Traditional OCR may be unsuccessful when dealing with non-cooperative images. Accordingly, features such as dewarping, text alignment, and line identification and removal may aid in OCR of non-cooperative images. Dewarping involves determining curvature of a document depicted in an image and processing the image to dewarp the image of the document to make it more accurately conform to the ideal of a cooperative image. Text alignment involves determining an actual alignment of depicted text, even when the depicted text is not aligned with depicted visual cues. Line identification and removal involves identifying portions of the image that depict lines and removing those lines prior to OCR processing of the image.

A gesture control system for a device for determining which one of a plurality of devices is to be controlled by a gesture acquires images of a gesture from each of the electronic devices; establishes a three dimensional coordinate system for the gesture image; calculates an angle between a first vector from a start point of the gesture to a center point of each electronic device and a second vector from an end point of the gesture to the center point of each electronic device. Thereby, the electronic device intended as the object to be controlled by the gesture can be determined, according to whether the angle between the first vector and the second vector is less than a preset value. A gesture control method is also provided.

A mechanism is provided for modifying an existing recipe to meet a set of desired colors for a final food dish. Responsive to receiving a request to modify the existing recipe to meet the set of desired colors, at least one of the set of existing colors to be changed to meet the desired set of colors is identified. An ingredient-action-sequence triplet associated with each at least one existing color to be changed is identified and, from a corpus of ingredient-action-sequence triplets associated with other existing recipes, one or more substitution candidates that can produce the at least one target color arc identified. The one or more substitution candidates are ranked based on how each candidate pairs best with other ingredients in the existing recipe. Based on a selection of a substitution candidate from the one or more substitution candidates, the existing recipe is modified with the substitute candidate.

The image stabilization control apparatus that performs, using a shake detection signal acquired through a shake detector configured to detect a shake and a motion vector detection signal indicating a motion vector detected in a video signal produced through an image sensor, image stabilization control for reducing image blur due to the shake. The apparatus includes a predictor that produces, using the motion vector detection signal, a predicted error signal that indicates a predicted value of an error signal included in the shake detection signal, a signal producer that subtracts the predicted error signal from the shake detection signal to produce a first image stabilization signal, and a controller that performs the image stabilization control using the first image stabilization signal.

A method for image processing includes obtaining a mask of a stroke from an image and identifying a plurality of cross edges for the stroke based on the mask and a reference line. The plurality of cross edges includes a group of adjacent cross edges that intersect the reference line. The method further includes (a) calculating a first vector based on positions of at least two of the cross edges in the group, (b) expanding the group, based on the first vector, to include cross edges adjacent to the group that do not intersect the reference line, (c) calculating a second vector based on positions of at least two of the cross edges in the expanded group, and (d) expanding the expanded group, based on the second vector, to include a second group of adjacent cross edges nearby the expanded group that do not intersect the reference line.