Abstract: Background-oriented schlieren is a method of visualizing refractive disturbances by comparing digital images with and without a refractive disturbance distorting a background pattern. Traditionally, backgrounds consist of random distributions of high-contrast color transitions or speckle patterns. To image a refractive disturbance, a digital image correlation algorithm is used to identify the location and magnitude of apparent pixel shifts in the background pattern between the two images. Here, a novel method of using color gradient backgrounds is explored as an alternative that eliminates the need to perform a complex image correlation between the digital images. A simple image subtraction can be used instead to identify the location, magnitude, and direction of the image distortions. Gradient backgrounds are demonstrated to provide quantitative data only limited by the camera’s pixel resolution, whereas speckle backgrounds limit resolution to the size of the random pattern features and image correlation window size. Quantitative measurement of density in a thermal boundary layer is presented. Two-dimensional gradient backgrounds using multiple colors are demonstrated to allow measurement of two-dimensional refractions. A computer screen is used as the background, which allows for rapid modification of the gradient to tune sensitivity for a particular application.

Abstract: We exploit human color metamers to send light-modulated messages less visible to the human eye, but recoverable by cameras. These messages are a key component to camera-display messaging, such as handheld smartphones capturing information from electronic signage. Each color pixel in the display image is modified by a particular color gradient vector. The challenge is to find the color gradient that maximizes camera response, while minimizing human response. The mismatch in human spectral and camera sensitivity curves creates an opportunity for hidden messaging. Our approach does not require knowledge of these sensitivity curves, instead we employ a data-driven method. We learn an ellipsoidal partitioning of the six-dimensional space of colors and color gradients. This partitioning creates metamer sets defined by the base color at the display pixel and the color gradient direction for message encoding. We sample from the resulting metamer sets to find color steps for each base color to embed a binary message into an arbitrary image with reduced visible artifacts. Unlike previous methods that rely on visually obtrusive intensity modulation, we embed with color so that the message is more hidden. Ordinary displays and cameras are used without the need for expensive LEDs or high speed devices. The primary contribution of this work is a framework to map the pixels in an arbitrary image to a metamer pair for steganographic photo messaging.

Abstract: Person re-identification refers to match the same pedestrian across disjoint views in non-overlapping camera networks. Lots of local and global features in the literature are put forward to solve the matching problem, where color feature is robust to viewpoint variance and gradient feature provides a rich representation robust to illumination change. However, how to effectively combine the color and gradient features is an open problem. In this paper, to effectively leverage the color-gradient property in multiple color spaces, we propose a novel Second Order Histogram feature (SOH) for person reidentification in large surveillance dataset. Firstly, we utilize discrete encoding to transform commonly used color space into Encoding Color Space (ECS), and calculate the statistical gradient features on each color channel. Then, a second order statistical distribution is calculated on each cell map with a spatial partition. In this way, the proposed SOH feature effectively leverages the statistical property of gradient and color as well as reduces the redundant information. Finally, a metric learned by KISSME [1] with Mahalanobis distance is used for person matching. Experimental results on three public datasets, VIPeR, CAVIAR and CUHK01, show the promise of the proposed approach.

Abstract: Clothing retrieval system has become commercially great challenge to retrieve the particular cloth from the large database. As this system deals with the varieties in garments appearance, layering, style, and body shape and posture. For a particular inquiry picture, substantial database of labeled design pictures are analyzed to get particular image of cloth from large database. This system works same as the content retrieval system. This paper deals with the review of the clothing retrieval system and various techniques that are being used in the retrieval. The proposed work compares between simple segmentation techniques for color histogram, Gabor filtering and wavelet filtering with proposed hybrid filtering using shadow shading and color gradient based Gabor filtering, wavelets derivative for color based edge derivative filtering. The results were conducted using clothing parsing dataset with different styles and the evaluations have shown the increase in precision and accuracy of the query based retrieval system.

Abstract: A method of driving a display uses first and second illumination cycles of the display. In each cycle, a first set of pixels is illuminated with a first color and a second set of pixels is illuminated with a second color. The first and second colors of the two cycles together include at least three colors for forming an image. This method provides a sequential drive scheme, in that at least two cycles are used with different color properties. However, each cycle uses at least two different colors, so that each cycle is not a single color across the whole display area. In this way, the color sequence is alternated spatially as well as temporally.

Abstract: The invention discloses a method for designing a bandhnu pattern by dedicated fractal software UltraFractal on the basis of a fractal principle. The method comprises the following four steps: (1) in the UltraFractal, according to bandhnu pattern geometry, selecting a fractal complex iterated function, and setting the parameters of the complex iterated function to draw a fractal graph; (2) on the basis of the drawn fractal graph, selecting a mapping function again according to the design requirements of the bandhnu pattern to change the geometry appearance of the graph; (3) according to the color requirements of the bandhnu pattern, selecting and designing an internal color function and an external color function; and (4) according to the color change characteristics of the bandhnu pattern, regulating an UltraFractal color gradient editor to simulate a color halo effect of the bandhnu pattern. The design of the bandhnu pattern is realized through the four main steps.

Abstract: According to exemplary methods, a print job including an electronic document having a color image is received into a computerized device having a marking device including a print engine. A sheet of the print job is analyzed by the computerized device. In the analysis, a contone of the color image is converted to multi-bit output using multi-level vector halftoning. Pairs of complementary colors are selected, and substitute color channels for drop sizes are determined for each pair of complementary colors. Binary vector halftoning is applied using the substitute color channels for each pair of complementary colors. It is determined if ink is to be printed for the complementary colors. Multi-level processing is applied to determine an amount of ink for one color of each of the pairs of complementary colors. Pixels of the color image are rendered using the ink amount for the color of the pair of complementary colors.

Abstract: Photographic volumes keep the original color in each voxel, and play an important role in medical and biological researches. The gradient is one of the most widely used attributes in volume visualization. However, it is more difficult to accurately estimate gradients for photographic volumes than scalar volumes. Current gradient estimators for photographic volumes do not work well for all cases, especially when the data is noisy. In this paper, we propose a new method to estimate gradients accurately and robustly for photographic volumes. Colors are directly used for gradient estimation instead of being converted to grayscale values, to ensure the accuracy of the gradient direction. For each of three gradient components in x, y and z directions, different filters are combined to reduce the negative effect of noises and generate an accurate result. Experiment results show that the proposed method can estimate gradients robustly in the presence of noise and outperforms other gradient estimators in photographic volume visualization.

Abstract: A method for calculating substitution colors for spot colors by using a computer for computer-aided color control of a four-color printing process in a printing machine includes creating a set of characterization data, which describe the relationship between tonal values of the process colors CMYK used and resultant printed color values, by using the computer, adapting to the printing process and interpolating the set of characterization data by using the computer and calculating the substitution colors including or formed of two chromatic and an achromatic color from the adapted and interpolated characterization data with a basic condition by using the computer. The calculated substitution colors are used in the computer-aided color control of the four-color printing process to process a current print job.

Abstract: Continuing a discussion by Kuehni, this note examines the problem of fitting as many as possible colors in a 1-JND radius sphere such that each pair of colors is separated by at least 1 JND. Kuehni announced nine. A first estimate yields a maximum of 13, but this is too many because colors populating adjacent spheres will be too close to each other. Accordingly, I derive the maximum number, , of discriminable colors per unit volume of color space, and then formally compute from this number packing density a number of colors inside the unit sphere. That estimate, nearly 6, will undoubtedly erode when discrete color points are chosen within the unit sphere. Kuehni's estimate of 9 is too high. © 2016 Wiley Periodicals, Inc. Col Res Appl, 2016

Abstract: Most existing methods for image copy-move forgery detection(CMFD)operate on grayscale images. Although the keypoint-based methods have the advantages of strong robustness and low computational cost, they cannot identify the flat duplicated regions without reliable extracted features. In this paper, we propose a new CMFD method by using speeded-up robust feature(SURF)in the opponent color space. Our method starts by converting the inspected image from RGB to the opponent color space. The color gradient per pixel is calculated and taken as the work space for SURF to extract the keypoints. The matched keypoints are clustered and their geometric transformations are estimated. Finally, the false matches are removed. Experimental results show that the proposed technique can effectively expose the duplicated regions with various transformations, even when the duplication regions are flat.

Abstract: Target tracking using color based appearance models is very popular in visual tracking. However, trackers based only on color are fragile and often drift to the background when it has similar appearances. In this paper, we propose an efficient way to use distinctive target colors to track the target and eliminate the drift problem. Colors are sampled from the target and its immediate surrounding region. And color samples coming from target result in more distinctive target color. In our approach, we use a short and a long time color histogram to represent the target color. The short time color histogram is used to calculate the distinctiveness of colors while the long time color histogram is used to keep the target color that is consistent over time. In our approach, the target is not marked as a rectangle or other geometric primitives, instead, we track it with its own silhouette. Using silhouette to mark target significantly reduces the false positive information during online learning. Also, the color models are updated with a dynamic learning factor which is based on the tracking result. After testing with many tracking sequences and comparison with other state-of-art trackers, the proposed tracking algorithm shows comparably better performance with very high tracking rate.

Abstract: Visual tracking is a challenging problem in computer vision. Many visual trackers either rely on luminance information or other simple color representations for image description. This paper introduces a tracking algorithm using unit-linking PCNN (Pulse Coupled Neural Network) image icon and particle filter. This approach has the translation, rotation, and scale invariance for using unit-linking PCNN image icon as the features. The experimental results show the proposed approach is with 16.43 % higher median distance precision than the color gradient-based tracker. This unit-linking PCNN image icon-based particle filter tracker can better solve the problems caused by partial occlusions, or out-of-plane rotation, or scale variation, or non-rigid object deformation, or fast motion.