Automatic watershed segmentation of randomly textured color images

A new method is proposed for processing randomly textured color images. The method is based on a bottom-up segmentation algorithm that takes into consideration both color and texture properties of the image. An LUV gradient is introduced, which provides both a color similarity measure and a basis for applying the watershed transform. The patches of watershed mosaic are merged according to their color contrast until a termination criterion is met. This criterion is based on the topology of the typical processed image. The resulting algorithm does not require any additional information, be it various thresholds, marker extraction rules, and suchlike, thus being suitable for automatic processing of color images. The algorithm is demonstrated within the framework of the problem of automatic granite inspection. The segmentation procedure has been found to be very robust, producing good results not only on granite images, but on the wide range of other noisy color images as well, subject to the termination criterion.     

Quality image metrics for synthetic images based on perceptual color differences

Due to the improvement of image rendering processes, and the increasing importance of quantitative comparisons among synthetic color images, it is essential to define perceptually based metrics which enable to objectively assess the visual quality of digital simulations. In response to this need, this paper proposes a new methodology for the determination of an objective image quality metric, and gives an answer to this problem through three metrics. This methodology is based on the LLAB color space for perception of color in complex images, a modification of the CIELab1976 color space. The first metric proposed is a pixel by pixel metric which introduces a local distance map between two images. The second metric associates, to a pair of images, a global value. Finally, the third metric uses a recursive subdivision of the images to obtain an adaptative distance map, rougher but less expensive to compute than the first method.     

Generating segmented meshes from textured color images

This paper presents a new framework for generating triangular meshes from textured color images. The proposed framework combines a texture classification technique, called W-operator, with Imesh, a method originally conceived to generate simplicial meshes from gray scale images. An extension of W-operators to handle textured color images is proposed, which employs a combination of RGB and HSV channels and Sequential Floating Forward Search guided by mean conditional entropy criterion to extract features from the training data. The W-operator is built into the local error estimation used by Imesh to choose the mesh vertices. Furthermore, the W-operator also enables to assign a label to the triangles during the mesh construction, thus allowing to obtain a segmented mesh at the end of the process. The presented results show that the combination of W-operators with Imesh gives rise to a texture classification-based triangle mesh generation framework that outperforms pixel based methods.     

Local pixel statistics based impulse detection and hybrid color filtering for restoration of digital color images

This paper presents a two stage filtering system to remove random valued impulse noise from color images based on local statistics of the filtering window under consideration. In the first stage, to detect the noisy pixel, the locally adaptive threshold is derived from the pixels of the filtering window. In the second stage, the restoration of the noisy pixel is done on the basis of brightness and chromaticity information obtained from the neighbouring pixels in the filtering window. Simulation results show that the proposed scheme yields much superior performance in comparison with other color image filtering methods.     

基于颜色梯度特性的彩色图像隐写分析

基于彩色图像任意2个颜色通道间梯度方向随机序列及颜色梯度和随机序列的振荡特性在隐写前后均有明显变化的特征,提出了一种基于颜色梯度特性的彩色图像盲隐写分析算法。算法应用HHT(Hilbert-Huang transform)提取4个序列在隐写前后的内在振荡特征差异,构建基于Hilbert谱的特征向量,采用SVM分类器进行分类判别。实验表明,该算法误检率低,检测性能优于现有彩色图像隐写分析算法,可以实现彩色图像隐写的可靠检测。     

Robust color image hiding method in DCT domain

Although muchresearch has been devotedto embed agray i mage and binary watermark information into agray cover-i mage or a color cover-i mage[1 ,2],little re-search has been done on inserting a color i mage into acolor cover-i mage.Color i mage is characte…     

True color 1024×768 microdisplay with analog in-pixelpulsewidth modulation and retinal averaging offset correction

A liquid-crystal-on-silicon microdisplay based on a 1024×768 two-dimensional pixel array fabricated in a digital 0.35-μm CMOS process displays images with a color depth of 8 bits per color. The pixel pitch is 12 μm and the total chip area is 214 mm². Pixel brightness is controlled by modulating the pulsewidth of the pixel voltage drive signal with an in-pixel analog pulsewidth modulation (PWM) circuit which utilizes human optic nerve spatio-temporal averaging to eliminate comparator offset. The 16 million transistor chip displays images at a maximum rate of 85 Hz and has a power dissipation of 200 mW from a single 3.3-V supply     

基于邻域像素差分矩阵的彩色空域图像隐写分析特征

通过分析灰度空域图像隐写直接扩展至彩色空域图像潜在的安全问题,提出了针对彩色空域图像隐写的隐写分析特征。首先,计算颜色通道内邻域像素差分矩阵的Markov转移概率矩阵,提取颜色通道内特征并进行融合;然后,计算颜色通道间邻域像素差分矩阵的Markov转移概率矩阵,并将水平和垂直共4个方向与对角和反对角共4个方向的转移概率矩阵分别进行融合,作为颜色通道间特征;最后,颜色通道内特征与颜色通道间特征构成最终的隐写分析特征,并使用集成分类器作为特征训练和测试工具。实验结果表明,该隐写分析特征可有效检测彩色空域图像隐写,并且颜色通道间特征能有效捕获内容自适应隐写对彩色图像的颜色通道间相关性的影响。     

彩色分焦平面偏振图像配准方法北大核心CSCD

彩色分焦平面偏振相机是一种新型偏振成像装置,能够同时获取目标0°,45°,90°和135°四个偏振方向的彩色三通道(RGB)强度信息,但需要解决分辨率损失和像元错位的问题。本文提出一种适用于分焦平面相机的彩色偏振图像配准方法。通过实验对所提配准方法进行验证,结果表明:所提配准方法能够降低像元错位问题对后续彩色偏振图像解析精度的影响。     

Novel color demosaicking for noisy color filter array data

Single sensor digital color still/video cameras use color demosaicking to reproduce full color images from color filter array (CFA) data. The quality of interpolated image will be degraded due to the sensor noise introduced during the image capture process. Many conventional demosaicking-denoising solutions adopt the channel-dependent noise model, which may fit the CMOS/CCD image sensor less than signal-dependent noise model. In this paper, the wavelet sub-band decomposition and synthesis are applied to interpolate the CFA data with signal-dependent noise model. The major contributions of this work include: (1) The combination of LMMSE and statistical calculation in wavelet domain are utilized to suppress the signal-dependent noise, which is separated into additive noise and multiplicative noise. (2) In CFA data, it has been verified that the quantitative relationship between the current pixel and the adjacent pixel, which locate in the same edge. Both simulated and real CFA images are employed to compare the proposed algorithm with the state-of-the-art techniques reported in the literature. The experimental results confirm that our method outperforms them both on demosaicking performance and on computational cost, when they process the noisy color filter array data.     

基于最小生成树和局部阈值的彩色图像分割方法

针对分水岭图像分割算法对于彩色图像的过度分割问题,本文提出一种基于最小生成树和局部阈值的解决方法。该方法主要利用图论中的最小生成树,对分水岭算法产生的过度分割区域进行合并。与其它的基于最小生成树的方法不同,该方法只有当构造出一棵完整的最小生成树时,才能计算出一个局部阈值,该局部阈值可确定原构造最小生成树过程的终止条件,进而可分割出彩色图像中的两个区域。重复上述过程,可分割出原彩色图像中的所有区域。实验证明,本文方法能够对彩色图像进行准确的分割,并且分割结果能很好地符合人眼的感知。     

A novel block truncation coding of color images using aquaternion-moment-preserving principle

Block truncation coding (BTC) is an efficient tool for image compression. To compress color-pixel blocks, a novel color BTC algorithm, called quaternion-moment block truncation coding (QMBTC), is presented. Analytical formulas for QMBTC, whose computation time is on the order of pixel block size, are derived by using quaternion arithmetic and the moment-preserving principle. The proposed color BTC algorithm can adaptively truncate a pixel block into one or two output classes according to the distribution of the color values inside the blocks. The experimental results show that the compression ratio is increased as compared with existing color BTC algorithms, and the picture quality of the reconstructed images is satisfactory. In addition, a post-BTC data compression scheme is proposed to further compress the subimage constructed by reproduction colors of truncated pixel blocks. Using a lookup table to display decoded data, this postprocessing scheme can output images acceptable to human eyes     

An adaptive color plane interpolation method based on edge detection

In this letter, an adaptive interpolation algorithm based on edge detection is proposed. With this algorithm, all the missing green values can be reconstructed in Bayer pattern image by using edge detection interpolation method. Reconstructed images composed of green pixels are classified according to the high frequency components in image, and the threshold T needed for all kinds of green images in the edge detection is determined through experiments. The edge detection is carried out based on the one Dimensional （1D） gradient operator. If the gradient value is greater than T, this pixel is located on the edge; otherwise the pixel is in the smooth area of the image. Finally, the simple bilinear interpolation is used for the smooth area while the Laplacian interpolation with the second-order correction term is adopted to reconstruct the other red/blue values on the edge. This algorithm resolves effectively the conflicts between reconstructing high quality color image and reducing computational complexity, and thus largely enhances the processing speed for the reconstructed color image.     

基于单向差值扩展的彩色图像可逆数据隐藏

针对Tian差值扩展算法存在过分修改像素值、须嵌入定位图等缺点,提出一种基于单向差值扩展的彩色图像可逆数据隐藏算法。利用色彩分量间的相关性减小差值,并用较小的差值扩展量扩展单个色彩分量的像素值,扩展方向由上溢和下溢像素的数量决定;采用预先调整像素值的方法避免像素溢出,少量的调整信息即可代替定位图,嵌入容量大幅提高;提取端根据临界像素值的顺序定位调整像素的位置,用调整信息恢复调整像素,因此不需要嵌入定位图,在提取信息的同时可无损地恢复原始图像。实验结果表明,本文算法在保证图像质量的同时,嵌入容量有很大的提高。     

Histogram-based segmentation in a perceptually uniform color space

In this work, we present a segmentation algorithm for color images that uses the watershed algorithm to segment either the two-dimensional (2-D) or the three-dimensional (3-D) color histogram of an image. For compliance with the way humans perceive color, this segmentation has to take place in a perceptually uniform color space like the Luv space. To avoid oversegmentation, the watershed algorithm has to be applied to a smoothed histogram.     

Adaptive progressive filter to remove impulse noise in highly corrupted color images

In this paper, an adaptive progressive filtering (APF) technique with low computational complexity is proposed for removing impulse noise in highly corrupted color images. Color images that are corrupted with impulse noise are generally filtered by applying a vector-based approach. Vector-based methods tend to cluster the noise and receive a lower noise reduction performance when the noise ratio is high. To improve the performance, in the proposed technique, a new reliable estimation of impulse noise intensity and noise type is made initially, and then a progressive restoration mechanism is devised, using multi-pass non-linear operations with selected processing windows adapted to the estimation. The effect of impulse detection based on geometric characteristics and features of the corrupt pixel/pixel regions and the exact estimation of impulse noise intensity and type are used in the APF to efficiently support the progressive filtering mechanism. Through experiments conducted using a range of color images, the proposed filtering technique has demonstrated superior performance to that of well-known benchmark techniques, in terms of standard objective measurements, visual image quality, and the computational complexity.     

Color image sharpening based on local color statistics

This paper presents an effective color image sharpening method, which is based on local color statistics. First, the variance of a set of color samples is measured by a scalar that is computed based on the sum of distances of color vectors, whereas other studies usually treat a color variance as a 3D vector. This is because what a variance expresses is the degree of the deviation of the image (vector) signal from its mean, indicating that describing this degree of deviation by a scalar is reasonable. Then, the local scalar variance and mean vector are combined together to measure the change of color image signal from a pixel to its neighboring ones, and the polarity of the change is determined by the change of luminance. Finally, based on the measure of the change, an effective sharpening operator is developed. Experimental results show that the proposed method excellently sharpens different kinds of color images and at the same time preserves image chromaticity well, and outperforms other typical sharpening techniques in both objective assessment and visual evaluation.

     

Automatic detection of red lesions in digital color fundus photographs

The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy. In this paper, a novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al. (1998) with two important new contributions. The first contribution is a new red lesion candidate detection system based on pixel classification. Using this technique, vasculature and red lesions are separated from the background of the image. After removal of the connected vasculature the remaining objects are considered possible red lesions. Second, an extensive number of new features are added to those proposed by Spencer-Frame. The detected candidate objects are classified using all features and a k-nearest neighbor classifier. An extensive evaluation was performed on a test set composed of images representative of those normally found in a screening set. When determining whether an image contains red lesions the system achieves a sensitivity of 100% at a specificity of 87%. The method is compared with several different automatic systems and is shown to outperform them all. Performance is close to that of a human expert examining the images for the presence of red lesions.     

基于双直方图平移的彩色图像可逆数据隐藏

提出一种基于双直方图平移的彩色图像可逆数据隐藏算法.利用色彩分量间的相关性减小差值,使差值直方图更加紧凑、直方图的峰值更大.采用直接调整像素值的方法在差值中嵌人数据,由像素值的二次可调整性定位不可调整像素,并嵌入少量的标志信息代替定位图,从而提高嵌入容量.实验结果表明,本文算法在保证图像质量的同时大幅提高了嵌入容量,与...     

LS-SVM based image segmentation using color and texture information

Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we present an effective color image segmentation approach based on pixel classification with least squares support vector machine (LS-SVM). Firstly, the pixel-level color feature, Homogeneity, is extracted in consideration of local human visual sensitivity for color pattern variation in HSV color space. Secondly, the image pixel’s texture features, Maximum local energy, Maximum gradient, and Maximum second moment matrix, are represented via Gabor filter. Then, both the pixel-level color feature and texture feature are used as input of LS-SVM model (classifier), and the LS-SVM model (classifier) is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained LS-SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of LS-SVM classifier. Experimental evidence shows that the proposed method has very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.