引入形态学处理的去隔行自适应算法

分析了现有的各种去隔行算法,在此基础上,将形态学处理引入基于运动检测的自适应去隔行技术中,对隔行图像进行精确的场内插值,通过运动检测将图像中的像素进行分类,针对不同类型的像素点自适应采取不同的插值算法.     

于Ohta颜色空间的火焰检测

为了提高火焰检测算法的准确率和满足实时性的要求,在分析火焰颜色特征的基础上,提出一种基于Ohta颜色空间并利用最大熵阈值分割法改进的火焰颜色特征模型.该模型可有效提取疑似火焰区域,然后通过光流法分析火焰运动方向特征,进一步判断是否有火灾的发生.实验结果表明,该算法具有较好的实时性,能够有效地提高火灾识别的准确率,降低误检率,在日常消防系统中具有重要的应用价值.     

Monochromatic-Based Method for Impulse Noise Detection and Suppression in Color Images

A new method to detect and reduce the impulse noise in color images is presented in this paper. The method consists of two stages: detection and filtering. Since each of the individual channels (components) of the color image can be considered as a monochrome image, both stages are applied to each channel separately, and then the individual results are combined into one output image. The corrupted pixels are detected in the first stage based on a proposed innovative switching technique. The noise-free pixels are copied to their corresponding locations in the output image. In the second stage, average filtering is applied only to those pixels which are determined to be noisy in the first stage, and only noise-free pixel values are involved in calculating this average. The size of the sliding window depends on the estimated noise density and is very small even for high noise densities. The proposed method is effective in noise reduction while preserving edge details and color chromaticity. Simulation results show that the proposed method outperforms all the tested existing state-of-the-art methods used in digital color image restoration in both standard objective measurements and perceived image quality.     

A Color CMOS Imager With 4 $times$ 4 White-RGB Color Filter Array for Increased Low-Illumination Signal-to-Noise Ratio

A color CMOS image sensor with the 4 times 4 White-RGB color filter array (CFA) including 50% white pixels has been developed. A transparent layer has been fabricated on the white pixel to realize over 95% transmission for visible light with wavelengths of 400-700 nm. Pixel pitch and number of the pixels were 3.3 mum and 2 million, respectively. With the simple and low-noise color separation process, low-illumination signal-to-noise ratios of luminance signal have been increased by 6 dB, compared with those of the Bayer pattern. Moreover, by locating the pixels so that every color components can be detected in every column and line, color artifacts at the edge were suppressed. The edge detection process became unnecessary and the process time was reduced by 70%. The new CFA has the potential to significantly increase the sensitivity of CMOS/CCD image sensors.     

Fast and Efficient Method for Fire Detection Using Image Processing

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision‐based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand‐alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE L*a*b* color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state‐of‐the‐art fire detection method.     

Impulsive noise filtering based on noise detection in corrupted digital color images

This paper is an enhancement to our earlier research with grey-scale images. In this paper, we propose two new detection-estimation based image filtering algorithms that effectively remove corrupted pixels with impulsive noise in digital color images. The existing methods for enhancing corrupted color images typically possess inherent problems in computation time and smoothing out edges because all pixels are filtered. Our proposed algorithms first classify corrupted pixels in each channel or in each pixel. Because marginal or vector median filtering is only performed for the classified pixels, the process is computationally efficient, and edges are preserved well. In addition, because there is no appropriate criterion to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor for comparing the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than existing methods, in both objective and subjective evaluations.This work was supported by the Korea Science & Engineering Foundation (KOSEF) under grant no. 981-0912-057-2.     

一个基于视频图像检测火灾的算法

针对传统火灾检测技术在面对大空间结构建筑及复杂环境中的不足,提出一种基于视频图像检测早期火灾的算法。该算法首先对视频图像进行阈值分割,然后根据火焰的颜色特征获取其中的疑似火焰区域。在此基础上计算疑似火焰区域的圆形度,并将圆形度与其整体特性相结合进行火灾的早期探测和预报。     

一种改进的火灾颜色模型检测算法

从火焰的颜色特征出发,综合考虑已有颜色模型的优缺点,提出一种改进的基于颜色模型的火灾检测方法.首先结合HSI颜色模型和YCbCr颜色模型对火灾图像进行预处理提取出可能火焰区域,然后在HSI空间上采用颜色空间距离法去除噪声.实验结果表明,该方法提高了火灾图像识别的准确度,并能在一定程度上消除干扰,具有较好的适用性.     

彩色图像的量子置乱算法

量子图像置乱是量子图像处理的典型问题,目前国内外文献绝大多数都是针对像素位置实施置乱,这种置乱方法通过改变像素的空间位置,可使图像不再包含任何可视化信息,然而并未改变像素灰度值的统计特性,从而降低了置乱方案的安全性。针对该问题,首先基于改进的FRQI描述,提出了一种针对像素位置的置乱方法,然后基于改进的NEQR描述,提出了一种针对像素颜色的置乱方法。这些方法或者显著加强了位置置乱效果,或者显著改变了像素灰度值的统计特性。经典计算机上的仿真结果验证了提出方法的有效性。      

结合FCM聚类与SVM的火焰检测算法

针对传统视频型火焰检测算法误报率高、局限性强等问题,提出一种四步火焰检测算法。首先利用一种自适应混合高斯模型（GMM）检测视频序列中的运动目标;然后采用模糊C 均值（FCM）聚类算法分割疑似火焰区域与非火区域;再提取疑似火焰区域的面积变化、表面不均度等时空特征参数;最后将这些特征参数输入训练好的支持向量机（SVM）分类器以识别火焰区域。实验结果表明,算法不但在提高了检测率的同时降低了误检率,而且适用范围广,是一种有效的火焰检测算法。     

一种基于量子机制的图像显著性检测方案

提出了一种与传统方法相比效率更高的量子图像显著性检测方案.为了在量子计算机中表示和存储RGB图像,并计算不同像素间的反差,此方案采用3量子位描述颜色信息,把2轨×2图像矩阵编码为量子叠加态;结合Hadamard门和受控旋转算子,计算基态概率幅可反映像素在RGB三通道上的全局颜色反差;通过有限次数的投影测量可得到像素的归一化颜色反差及位置信息,并构建显著图.给出了相关量子电路的实现和复杂度分析.与多种传统显著性检测算法进行了对比实验,结果表明提出的方案具有良好的检测效果和更高的检测效率.     

基于视频序列的火灾烟雾颜色检测算法

为了提高视频烟雾检测的准确性,克服图像型火灾烟雾检测对复杂环境的低适应性,实现对火灾烟雾的实时检测,提出了一种基于视频序列的火灾烟雾颜色检测算法.该方法首先采用基于Kalman滤波的背景重建法提取出烟雾区域的像素,然后再将其归一化到RGB空间模型,分析各颜色分量的数据,并将这些表征烟雾颜色信息的数据经Matlab进行曲线拟合分析,最终确定出烟雾颜色的决策条件,并对来自网络的火灾视频和其他运动视频进行测试.实验结果表明:基于视频序列的火灾烟雾颜色检测能够很好地将火灾烟雾和其他干扰物区分开,达到早期预警的目的.     

Switching impulse noise filter based on Laplacian convolution and pixels grouping for color images

This paper presents a new switching filter consisting of three steps to restore color images corrupted by impulse noise. Firstly, Laplacian convolution is performed on pixels in four directions to mark the pixels which are radically different in value from neighboring pixels as noise candidates. Secondly, those missed neighboring pixels involved in the step of pixels grouping decrease the occurrence of false detection. Pixels in the observation window are separated into noisy pixels and normal pixels with a dividing threshold, whose value is assigned according to a noise density estimator. Finally, a modified arithmetic mean filter is applied to restore the polluted image. Extensive experiments show that the proposed method achieves better performance than comparative methods in terms of peak-signal-to-noise ratio and structural similarity. The proposed method can effectively remove impulse noise in which noise density is varying from 10 to 80%.     

利用信号相关对BAYER格式图像色彩复原

目前将单CCD采集的BAYER CFA格式图像进行色彩复原有两种通用方法:双线性插值和边缘检测.为了降低这两种方法在图像边缘引进错误颜色的数量,根据RGB彩色图像中R、G、B三个色彩通道的高度相关的特性:即在5×5像素的小区域内认为绿色减红色以及绿色减兰色为常数,提出了利用信号相关对BAYER格式图像彩色复原的方法,并通过SNR(信噪比)和NCD(归一化彩色差异规范)两种评估方法进行检测.从实验结果可以看出文中提出的方法相对于通用方法提高了图像的信噪比,锐化了图像的边缘,减少了图像高频信息的错误像素的数量,提高视觉质量.     

基于色彩制约耦合距离惩罚的图像篡改检测

当前较多图像篡改检测方法主要通过对图像特征间的距离进行测量来完成特征匹配,忽略了图像的色彩信息,导致检测结果中存在较多的误检测和漏检测现象。对此,本文将色彩信息引入到图像特征匹配过程中,设计了一种采用色彩制约模型的篡改检测算法。利用Laplacian算子与Harris算子提取图像特征,并利用像素点的红(R)、绿(G)、蓝(B)三原色信息,结合特征描述符建立色彩制约模型,对特征点间的色彩信息进行度量,再借助该度量值与特征点间的距离测量值共同完成图像特征匹配,充分剔除误匹配现象,有效提高匹配准确度。该算法还根据特征点间距离方差构造距离惩罚模型,对匹配后的图像特征进行聚类,准确识别篡改内容。通过实验结果发现,与其他篡改检测算法相比,本文算法不仅对伪造内容具备更高的检测准确度,而且对模糊及旋转等内容操作也具有更好的适应性。     

A new fuzzy color correlated impulse noise reduction method.

A new impulse noise reduction method for color images is presented. Color images that are corrupted with impulse noise are generally filtered by applying a grayscale algorithm on each color component separately or using a vector-based approach where each pixel is considered as a single vector. The first approach causes artefacts especially on edge and texture pixels. Vector-based methods were successfully introduced to overcome this problem. Nevertheless, they tend to cluster the noise and to receive a lower noise reduction performance. In this paper, we discuss an alternative technique which gives a good noise reduction performance while much less artefacts are introduced. The main difference between the proposed method and other classical noise reduction methods is that the color information is taken into account to develop (1) a better impulse noise detection method and (2) a noise reduction method that filters only the corrupted pixels while preserving the color and the edge sharpness. Experimental results show that the proposed method provides a significant improvement on other existing filters.     

A comparative study of irregular pyramid matching in bag-of-bags of words model for image retrieval

In this paper, we assess three standard approaches to build irregular pyramid partitions for image retrieval in the bag-of-bags of words model that we recently proposed. These three approaches are: kernel \(k\)-means to optimize multilevel weighted graph cuts, normalized cuts and graph cuts, respectively. The bag-of-bags of words (BBoW) model is an approach based on irregular pyramid partitions over the image. An image is first represented as a connected graph of local features on a regular grid of pixels. Irregular partitions (subgraphs) of the image are further built by using graph partitioning methods. Each subgraph in the partition is then represented by its own signature. The BBoW model with the aid of graph extends the classical bag-of-words model, by embedding color homogeneity and limited spatial information through irregular partitions of an image. Compared with existing methods for image retrieval, such as spatial pyramid matching, the BBoW model does not assume that similar parts of a scene always appear at the same location in images of the same category. The extension of the proposed model to pyramid gives rise to a method we name irregular pyramid matching. The experiments on Caltech-101 benchmark demonstrate that applying kernel \(k\)-means to graph clustering process produces better retrieval results, as compared with other graph partitioning methods such as graph cuts and normalized cuts for BBoW. Moreover, this proposed method achieves comparable results and outperforms SPM in 19 object categories on the whole Caltech-101 dataset.     

Similarity relation matrix-based color edge detection

A novel edge detection algorithm for color images was described in this paper. In the proposed method, smoothness of each pixel in color image is firstly calculated by means of similarity relation matrix and is normalized to maximum gray level. In other words, color image in three-dimensional color spaces is mapped into one dimension. Accordingly the edges are performed in such a way that pixels lower than thresholds are assigned to be edge. Thus with proposed method, edge pixels in a color image are detected simultaneously without any complex calculations such as gradient, Laplace and statistical calculations.     

Similar neighbor criterion for impulse noise removal in images

In this paper, a new method is proposed for removing and restoring random-valued impulse noise in images. This approach is based on a similar neighbor criterion, in which any pixel to be considered as an original pixel it should have sufficient numbers of similar neighboring pixels in a set of filtering windows. Compared with other well known methods in the literature, this technique achieves superior performance in restoring heavily corrupted noisy images. Furthermore, it has low computational complexity, and equally effective in restoring corrupted color and gray-level images.     

Vehicle detection using normalized color and edge map.

This paper presents a novel vehicle detection approach for detecting vehicles from static images using color and edges. Different from traditional methods, which use motion features to detect vehicles, this method introduces a new color transform model to find important "vehicle color" for quickly locating possible vehicle candidates. Since vehicles have various colors under different weather and lighting conditions, seldom works were proposed for the detection of vehicles using colors. The proposed new color transform model has excellent capabilities to identify vehicle pixels from background, even though the pixels are lighted under varying illuminations. After finding possible vehicle candidates, three important features, including corners, edge maps, and coefficients of wavelet transforms, are used for constructing a cascade multichannel classifier. According to this classifier, an effective scanning can be performed to verify all possible candidates quickly. The scanning process can be quickly achieved because most background pixels are eliminated in advance by the color feature. Experimental results show that the integration of global color features and local edge features is powerful in the detection of vehicles. The average accuracy rate of vehicle detection is 94.9%.