Target acquisition performance in the presence of JPEG image compression

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.

The Mythological Battles Around the World The Images and Meanings of International Dragon-Slayers

Dragons and dragon slayers belong to a system ofsemiology.They are images with messages.Differentcultures in the world have assigned to these imageshistorical limits,conditions of use,and introducedmultiple meanings into them.Dragons do not exist,therefore dragon slayers do not either.However,they have become an artificial existence in most cul-tures,and this existence have passed from a closed,silent existence to an oral state,open to appropria-tion by society,for there is no regulation,either nat-ural or not,which forbids imagination.Few symbols saturate human civilization sobroadly and thoroughly as those of the dragon:proudly flicking its tail across the tapestries

基于改进K-means与机器视觉的档案数据分析技术

为了提升医疗信息系统对健康档案数据的分析效率,文中采用图像采集、降噪、配准与差分等技术提取医疗图像信息,进而有效提升信息系统的数据采集效率。同时还对传统的K-means算法加以改进,并提出了一种基于类间、类内距离的聚类初始化评价指标体系(BWP),将其应用于采集到的档案数据中,以实现快速的聚类分析。将所提算法在CUDA计算平台上进行了实现,测试结果表明,该方法的聚类精度和运行效率较现有算法均有显著提升。此外,改进后K-means算法的正确聚类样本数量占比提升了4.88%,高于现有的主流指标体系,且当聚类数k的取值为16或32时,运行时间大幅降低。

结合Lee滤波的NL-Means声呐图像滤波方法

声呐图像中存在的散斑噪声不仅极大地影响了图像质量,还对图像的后续分割、增强、边缘检测等增加了很多负面影响.针对这一问题,提出了结合Lee滤波的NL-Means算法:先用Lee滤波算法对声呐图像进行一次滤波,再用滤波后的图像计算NL-Means算法中的权重部分的权值,最后用NL-Means算法结合该权重进行滤波.改进的滤波方法避免了权重计算过程中由于噪声的影响而使导致权重偏小的问题,精确了NL-Means算法中的权重分配.实验证明,改进方法对声呐图像的散斑噪声不仅有更好的抑制效果,还提升了图像的质量.

Segmentation of High Spatial Resolution Remote Sensing Images of Mountainous Areas Based on the Improved Mean Shift Algorithm

Using conventional Mean Shift Algorithm to segment high spatial resolution Remote sensing images of mountainous areas usually leads to an unsatisfactory result, due to its rich texture information. In this paper, we propose an improved Mean Shift Algorithm in consideration of the characteristics of these images. First, images were classified into several homogeneous color regions and texture regions by conducting variance detection on the color space. Next, each homogeneous color region was directly segmented to generate the preliminary results by applying the Mean Shift Algorithm. For each texture region, we conduct a high-dimensional feature space by extracting information such as color, texture and shape comprehensively, and work out a proper bandwidth according to the normalized distribution density. Then the bandwidth variable Mean Shift Algorithm was applied to obtain segmentation results by conducting the pattern classification in feature space. Last, the final results were obtained by merging these regions by means of the constructed cost functions and removing the oversegmented regions from the merged regions. It has been experimentally segmented on the high spatial resolution remote sensing images collected by Quickbird and Unmanned Aerial Vehicle(UAV). We put forward an approach to evaluate the segmentation results by using the segmentation matching index(SMI). This takes into consideration both the area and the spectrum. The experimental results suggest that the improved Mean Shift Algorithm outperforms the conventional one in terms of accuracy of segmentation.

Improved Non-Local Means Algorithm for Image Denoising

Image denoising technology is one of the forelands in the field of computer graphic and computer vision. Non-local means method is one of the great performing methods which arouse tremendous research. In this paper, an improved weighted non-local means algorithm for image denoising is proposed. The non-local means denoising method replaces each pixel by the weighted average of pixels with the surrounding neighborhoods. The proposed method evaluates on testing images with various levels noise. Experimental results show that the algorithm improves the denoising performance.

Improved Weight Function for Nonlocal Means Image Denoising

The nonlocal means( NLM) has been widely used in image processing. In this paper,we introduce a modified weight function for NLM denoising, which will compute the nonlocal similarities among the pre-processing pixel patches instead of the commonly used similarity measure based on noisy observations. By the law of large number,the norm for the pre-processing pixel patches is closer to the norm of the original clean pixel patches,so the proposed weight functions are more optimized and the selected similar patches are more accurate. Experimental results indicate the proposed algorithm achieves better restored results compared to the classical NLM's method.

Color image segmentation using mean shift and improved ant clustering

To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.

Mean shift based log-Gabor wavelet image coding

In this paper, we propose a sparse overcomplete image approximation method based on the ideas of overcomplete log-Gabor wavelet, mean shift and energy concentration. The proposed approximation method selects the necessary wavelet coefficients with a mean shift based algorithm, and concentrates energy on the selected coefficients. It can sparsely approximate the original image, and converges faster than the existing local competition based method. Then, we propose a new compression scheme based on the above approximation method. The scheme has compression performance similar to JPEG 2000. The images decoded with the proposed compression scheme appear more pleasant to the human eyes than those with JPEG 2000.

FAST IMAGE ENCODING ALGORITHM BASED ON MEAN-MATCH CORRELATION VECTOR QUANTIZATION

A mean-match correlation vector quantizer (MMCVQ) was presented for fast image encoding. In this algorithm, a sorted codebook is generated regarding the mean values of all codewords. During the encoding stage, high correlation of the adjacent image blocks is utilized, and a searching range is obtained in the sorted codebook according to the mean value of the current processing vector. In order to gain good performance, proper THd and NS are predefined on the basis of experimental experiences and additional distortion limitation. The expermental results show that the MMCVQ algorithm is much faster than the full-search VQ algorithm, and the encoding quality degradation of the proposed algorithm is only 0.3～0.4 dB compared to the full-search VQ.

Local edge direction based non-local means for image denoising

Classic non-local means (CNLM) algorithm uses the inherent self-similarity in images for noise removal. The denoised pixel value is estimated through the weighted average of all the pixels in its non-local neighborhood. In the CNLM algorithm, the differences between the pixel value and the distance of the pixel to the center are both taken into consideration to calculate the weighting coefficients. However, the Gaussian kernel cannot reflect the information of edge and structure due to its isotropy, and it has poor performance in flat regions. In this paper, an improved non-local means algorithm based on local edge direction is presented for image denoising. In edge and structure regions, the steering kernel regression (SKR) coefficients are used to calculate the weights, and in flat regions the average kernel is used. Experiments show that the proposed algorithm can effectively protect edge and structure while removing noises better when compared with the CNLM algorithm.

基于K-means聚类和图像分割的紫色土发生层边界识别

土壤学始于对土壤剖面及其形态特征的观察,剖面发生层的划分与发生层边界特征的描述是土壤调查的基础。实地划分发生层需要丰富的土壤学实践经验,存在主观和缺乏统一划分标准的问题。以紫色土剖面图像为研究对象,采用K-means聚类和图像分割技术,结合图像的颜色特征(CIELab色彩空间)和纹理特征(Entropy)识别紫色土剖面发生层边界,并与实地划分的结果进行比较。结果表明:(1)CIELab色彩空间的a、b通道和Entropy纹理特征,可以划分出供试剖面的主要发生层(A、B、C)和基岩(R);(2)聚类识别的发生层数量和发生层深度与实地识别的结果基本一致;除Z2剖面的C层和Z6剖面的Ap层聚类识别与实地识别的发生层下边界深度相差较大(分别为13cm和8cm)外,其余发生层下边界深度相差均在3 cm以内;(3)聚类识别的发生层边界形状更为不规则,明显度更为模糊。K-means聚类和图像分割技术实现了紫色土剖面发生层边界的客观识别,可为土壤剖面智能辨识系统的开发提供科学参考。

Fuzzy c-means clustering based on spatial neighborhood information for image segmentation

Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.

Soil pore identification with the adaptive fuzzy C-means method based on computed tomography images

The complex geometry and topology of soil is widely recognised as the key driver in many ecological processes. X-ray computed tomography (CT) provides insight into the internal structure of soil pores automatically and accurately. Until recently, there have not been methods to identify soil pore structures. This has restricted the development of soil science, particularly regarding pore geometry and spatial distribution. Through the adoption of the fuzzy clustering theory and the establishment of pore identification rules, a novel pore identification method is described to extract pore structures from CT soil images. The robustness of the adaptive fuzzy C-means method (AFCM), the adaptive threshold method, and Image-Pro Plus tools were compared on soil specimens under different conditions, such as frozen, saturated, and dry situations. The results demonstrate that the AFCM method is suitable for identifying pore clusters, especially tiny pores, under various soil conditions. The method would provide an optional technique for the study of soil micromorphology.

GAUSSIAN PRINCIPLE COMPONENTS FOR NONLOCAL MEANS IMAGE DENOISING

NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of NLM.Nevertheless,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of noise.In this paper,an improved scheme for image denoising is proposed.This scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of noise.PCA is then used to project those filtered image neighborhood vectors onto a lower-dimensional space.With the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual fidelity.The experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.

Local Orientation Field Based Nonlocal Means Method for Fingerprint Image De-Noising

The de-noising of the fingerprint image is one of the key tasks before the extraction of the minutiae in automatic fingerprint matching. When used for de-noising the fingerprint image, the nonlocal means method can not preserve the local minutiae in the fingerprint image very well. To address this problem, we propose a local orientation field based nonlocal means (NLM-LOF) method in this paper. Experimental results on the simulated and real images show that the proposed method can suppress noise effectively while preserving edges and details in the fingerprint image and it outperforms the state-of-art nonlocal means method in terms of qualitative metrics and visual comparisons.

基于改进K-means算法的图像分割

图像分割在图像分析的整个流程中占据关键地位,是图像理解中的重要步骤,同时,它也被看作是图像处理领域最有挑战性的难题之一。因此该研究提出一个基于改进K-means算法的图像分割方法。对图片进行等切选取初始簇心,设定阈值合并多余的簇,给定平均直径优化簇心数量及分类效果。通过实验,验证了该方法的有效性。

Image Feature Computation in Encrypted Domain Based on Mean Value

In smart environments,more and more teaching data sources are uploaded to remote cloud centers which promote the development of the smart campus.The outsourcing of massive teaching data can reduce storage burden and computational cost,but causes some privacy concerns because those teaching data(especially personal image data)may contain personal private information.In this paper,a privacy-preserving image feature extraction algorithm is proposed by using mean value features.Clients use block scrambling and chaotic map to encrypt original images before uploading to the remote servers.Cloud servers can directly extract image mean value features from encrypted images.Experiments show the effectiveness and security of our algorithm.It can achieve information search over the encrypted images on the smart campus.

基于K-means聚类算法的烘烤烟叶图像分割研究

目前我国烟叶烘烤过程主要依赖人工监测,存在主观性、模糊性和高成本等问题,使用机器视觉方法对烘烤过程烟叶质量变化进行实时监测与判断的研究逐渐增多,实时监测需建立在高效且准确的烘烤烟叶图像分割之上,因此烘烤烟叶图像分割的研究变得尤其重要。提出了基于K-means聚类算法的烘烤烟叶图像分割方法,首先读取图像并将RGB转换为CYMK颜色空间,然后提取CYMK颜色空间下的K通道灰度化图像,再对此单通道图像进行聚类,根据聚类中心确定图像分割阈值,最后利用图像处理方法对图像进行分割。研究比较了K-means、模糊C均值聚类(FCM)和高斯混合聚类(GMM)3种聚类方法,结果表明K-means算法的像素准确率为97.8%、交并比为96.43%、Dice系数为98.2%,均优于其他2种方法。K-means算法能够更好地提取烤烟的烟叶轮廓,去除冗余信息,使得分割结果更清晰。