Abstract:
Systems and methods are presented for processing and rendering image data during a single pass through the image data. A method includes loading scanlines of image data into a rolling band buffer, performing a windowing technique on the image data, and determining if a class change was experienced by any window having a portion in an output scanline of the buffer. The method further includes processing image data in the output scanline for a window portion that experienced a class change, using a blended rendering algorithm. If no class change is detected, the method includes processing image data in the output scanline for the window portion using a class-based rendering algorithm. The method optionally includes rendering processed image data for the output scanline to a rendering device. According to other features, an apparatus includes a rolling band buffer, a windowing processor, class-based rendering algorithms, and a blended rendering algorithm.
Abstract:
A process for color graphics image processing, related to detection and segmentation of sweeps, is provided. An input graphics image is transformed into a three-dimensional histogram in an appropriate color space 104 (e.g., CIELUV). Two-dimensional histograms are estimated from the three-dimensional histogram 106. The two-dimensional histograms are processed to detect and segment sweeps 108. Sweep segment information from the processing of the two-dimensional histograms is combined 110. The combined sweep segment information is used to process the input graphics image to identify and segment sweeps 112. Post-processing may be optionally and selectively used to reject false alarms (i.e., areas falsely identified as sweeps) 114.
Abstract:
A watermarked image generator includes a watermark data source that inputs watermark data to a watermark embedding device. The watermark embedding device halftones the input image to generate the output image made of 2×2 binary patterns, the 2×2 binary patterns forming the watermarks embedded in the output image. The watermark embedding device includes a tri-level error diffusion generator that performs tri-level error diffusion on the input image by halftoning the input image into black, white and 50% gray. A plurality of halftoning circuits successively replace each pixel of the halftoned input image with one of a plurality of the 2×2 binary patterns. The one of the plurality of 2×2 binary patterns corresponds to at least one respective bit of the watermark data when a pixel of the halftoned image data is 50% gray. The watermark data can be segmentation map data of the input image.
Abstract:
A method and system for image processing, in conjunction with classification of images between natural pictures and synthetic graphics, using SGLD texture (e.g., variance, bias, skewness, and fitness), color discreteness (e.g., R—L, R—U, and R—V normalized histograms), or edge features (e.g., pixels per detected edge, horizontal edges, and vertical edges) is provided. In another embodiment, a picture/graphics classifier using combinations of SGLD texture, color discreteness, and edge features is provided. In still another embodiment, a “soft” image classifier using combinations of two (2) or more SGLD texture, color discreteness, and edge features is provided. The “soft” classifier uses image features to classify areas of an input image in picture, graphics, or fuzzy classes.
Abstract translation:一种用于图像处理的方法和系统,结合使用SGLD纹理(例如,方差,偏差,偏度和适应度)的自然图像和合成图像之间的图像分类,颜色离散性(例如,R SUB 提供了一个或多个边缘特征(例如每个检测到的边缘,水平边缘和垂直边缘的像素)。 在另一个实施例中,提供了使用SGLD纹理,颜色离散性和边缘特征的组合的图片/图形分类器。 在另一个实施例中,提供了使用两(2)或更多SGLD纹理,颜色离散性和边缘特征的组合的“软”图像分类器。 “软”分类器使用图像特征来对图像,图形或模糊类中的输入图像的区域进行分类。
Abstract:
An annular window-shaped structuring element is provided for image processing to remove speckles from a scanned image. The window-shaped structuring element is composed of two differently sized squares sharing the same geometric center-point. The pixel to be analyzed with the structuring element is at the center-point. The structuring element is used in a method to remove speckles from binary, grayscale, and/or color images by first eroding the image, detecting speckles relative to other pixels in the image, and removing declared speckles. The method may additionally include a halftoning module to protect halftone images.
Abstract:
A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.
Abstract:
An adaptive image acquisition system and method that generates virtual view of a surveillance scene to a user (operator), in which, the user operates the system. Through viewing the virtual view, the user controls sensors that create the virtual view. The sensors comprise at least one first sensor having a higher resolution than at least one second sensor. Images from the second sensor are processed to create an image mosaic that is overlaid with images from the higher resolution first sensor. In one embodiment of the invention, the first sensor is moved using Saccade motion. In another embodiment of the invention, a user's intent is used to control the Saccade motion.
Abstract:
A passbook conveyance roller device comprises a conveyance roller and a pinch roller, which are arranged to be opposed to each other, to convey a passbook. The conveyance roller comprises an inner ring member connected to a drive shaft, a metallic outer ring member, and a rubber member fixed to, for example, an outer peripheral surface of the inner ring member and press fitted into an inner peripheral surface of the outer ring member to give elastic forces to the inner peripheral surface of the outer ring member in a plurality of positions, which are spaced circumferentially at predetermined intervals from one another and arranged in point symmetry. Both the inner ring member and the outer ring member are rotated through the elastic member when a load torque on the outer ring member is less than a set torque set by the elastic forces of the rubber member. Relative slip is generated between the inner peripheral surface of the outer ring member and the rubber member when a load torque on the outer ring member is equal to or larger than the set torque.
Abstract:
A method and apparatus for managing delivery of video over a digital-subscriber line is provided. The method includes and the apparatus is adapted for receiving at a multiplexer information indicative of an upstream volume of video traffic for termination to the multiplexer, and for controlling the multiplexer in response to the information. Controlling the multiplexer in response to the information may include regulating, in accordance with the upstream volume of traffic, an amount of traffic buffered by the multiplexer.
Abstract:
A method for automatically generating a strong classifier for determining whether at least one object is detected in at least one image is disclosed, comprising the steps of: (a) receiving a data set of training images having positive images; (b) randomly selecting a subset of positive images from the training images to create a set of candidate exemplars, wherein said positive images include at least one object of the same type as the object to be detected; (c) training a weak classifier based on at least one of the candidate exemplars, said training being based on at least one comparison of a plurality of heterogeneous compositional features located in the at least one image and corresponding heterogeneous compositional features in the one of set of candidate exemplars; (d) repeating steps (c) for each of the remaining candidate exemplars; and (e) combining the individual classifiers into a strong classifier, wherein the strong classifier is configured to determine the presence or absence in an image of the object to be detected.