一种多视点视频自动颜色校正系统

针对多视点视频系统中视点间图像颜色不一致的问题,提出了一种多视点视频自动颜色校正系统。通过求取目标图像和源图像间的颜色校正矩阵,判断其是否满足全局校正的要求;对不满足要求的图像,通过图像分割和K-L变换(Karhunen-Loeve transform),建立起目标图像和源图像中各分割区域间的局部映射关系,并通过感兴趣区域匹配,来实现对源图像的校正,最后通过视频跟踪技术实现对视频图像的校正。以标准的多视点测试图像集为例,通过将新方法与直方图匹配、全局一维线性校正算法等进行比较,表明新方法能消除匹配失真的影响,且具有较好的颜色校正效果。研究结果表明该系统可以很好地揭示图像间的颜色变化关系,并且具有很好的内容自适应性,是一种有效的多视点视频图像系统颜色校正方法。     

基于偏振测量的雾天图像场景分割

现有场景分割方法主要依赖于图像亮度、颜色和纹理等特征,然而在雾天图像中提取这些特征将变得困难且不稳定.基于此本文提出了适用于雾天图像场景分割的特征矢量,以及相应的特征提取算法.特征矢量由目标偏振度、深度和颜色三部分组成.特征提取算法分别为:用去相关的方法从图像偏振度分离出大气偏振度和目标偏振度;根据雾天退化模型和雾天图像偏振表示形式推导出场景深度信息;利用两幅偏振图像求出非偏振彩色图像,从而得到场景的颜色信息.将这些特征构成的特征矢量用于基于图的分割算法中,并从两个方面比较了仅使用颜色特征和使用本文特征矢量的分割结果.最后得出结论:对于雾天图像而言,这些特征比通常的颜色特征更加有效和鲁棒.     

Edge linking and image segmentation by combining optical and digital methods

We present a hybrid method for segmentation of intensity images, which combines an optical contouring technique and digital algorithms for linking edge points or image segmentation. In a first stage, the digital image to be processed is displayed in a twisted-nematic liquid-crystal display (LCD), which is placed between a polarizer–analyzer pair at 45 deg (instead of 90 deg as occurs in standard LCDs). It is not difficult to demonstrate that the proposed setup produces a resultant image with very pronounced dark contours at middle intensity. After the optical preprocessing, two different digital algorithms are applied: an edge linking algorithm (modified chain code) and a simple thresholding technique for image segmentation. The proposed procedure works well with monochromatic and color images. The method could be useful as a robust technique for segmentation of large images in real-time, which presents potential applications in medical and biological imaging.     

CAD系统图像颜色分割的新方法

通过图像扫描仪获得的彩色图像常包含有一些样稿颜色以外的杂色或噪声颜色,当用基于一维直方图的传统的颜色统计法进行颜色分割时,会出现错分现象。对此,本文提出了基于二维直方图的颜色分割新方法,实现了一些ＣＡＤ系统图像颜色的准确分割和归并。实验结果和实际应用都证明了新方法的有效性,该方法也可应用于全真彩色图像的量化。     

基于图像分割和局部亮度调整的微光图像颜色传递算法

为提高微光夜视图像质量,提出一种基于图像分割和局部亮度调整的颜色传递算法。用简单线性迭代聚类结合K均值聚类对微光图像进行分割,在YCbCr颜色空间中利用子区域与参考图像每一个像素点上亮度的一致性,将匹配参考图像的颜色分量传递到目标图像的子区域,以目标图像纹理特征中对比度的值作为系数,调整目标图像子区域的亮度值,进行颜色空间转换并显示颜色传递结果。搭建了微光图像成像系统,进行了微光图像分割及完成了微光图像的颜色传递。结果表明,改进的分割算法将图像中不同的景物分割出来,得到的彩色微光图像的峰值信噪均值达到12.048 dB,比Welsh算法平均提高2.63 dB。     

An energy minimization method for MS lesion segmentation from T1-w and FLAIR images

In this paper, we extend the multiplicative intrinsic component optimization (MICO) algorithm to multichannel MR image segmentation, with focus on segmentation of multiple sclerosis (MS) lesions. The MICO algorithm was originally proposed by Li et al. in Ref. [1] for normal brain tissue segmentation and intensity inhomogeneity correction of a single channel MR image, which exhibits desirable advantages over other methods for MR image segmentation and intensity inhomogeneity correction in terms of segmentation accuracy and robustness. In this paper, we extend the MICO algorithm to multi-channel MR image segmentation and enable the segmentation of MS lesions. We assign different weights for different channels to control the impact of each channel. The weighted channels allow the enhancement of the impact of the FLAIR image on the segmentation of MS lesions by assigning a larger weight to the FLAIR image channel than the other channels. With the inherent mechanism of estimation of the bias field, our method is able to deal with the intensity inhomogeneity in the input multi-channel MR images. In the application of our method, we only use T1-w and FLAIR images as the input two channel MR images. Experimental results show promising result of our method.     

A false color image fusion method based on multi-resolution color transfer in normalization YCBCR space

In this paper, a false color image fusion method for merging visible and infrared images is proposed. Firstly, the source images and reference image are decomposed respectively by Laplacian pyramid transform. Then the grayscale fused image and the difference images between the normalized source images are assigned to construct YC_BC_R components. In the color transfer step, all the three channels of the color space in each decomposition level are processed with the statistic color mapping according to the color characteristics of the corresponding sub-images of the reference image. Color transfer is designed based on the multi-resolution scheme in order to significantly improve the detailed information of the final image, and to reduce excessive saturation phenomenon to have a comparatively natural color appearance compared with the classical global-coloring algorithm. Moreover, the differencing operation between the normalized source images not only provides inter-sensor contrast to make popping the potential targets but also weakens the influence of the ambient illumination variety to a certain degree. Finally, the fused results and several metrics for evaluation of fused images subjectively and objectively illustrate that the proposed color image fusion algorithm can yield a more complete mental representation of the perceived scene, resulting in better situational awareness.     

A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.     

Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies

Accurate segmentation of knee cartilage is required to obtain quantitative cartilage measurements, which is crucial for the assessment of knee pathology caused by musculoskeletal diseases or sudden injuries. This paper presents an automatic knee cartilage segmentation technique which exploits a rich set of image features from multi-contrast magnetic resonance (MR) images and the spatial dependencies between neighbouring voxels. The image features and the spatial dependencies are modelled into a support vector machine (SVM)-based association potential and a discriminative random field (DRF)-based interaction potential. Subsequently, both potentials are incorporated into an inference graphical model such that the knee cartilage segmentation is cast into an optimal labelling problem which can be efficiently solved by loopy belief propagation. The effectiveness of the proposed technique is validated on a database of multi-contrast MR images. The experimental results show that using diverse forms of image and anatomical structure information as the features are helpful in improving the segmentation, and the joint SVM-DRF model is superior to the classification models based solely on DRF or SVM in terms of accuracy when the same features are used. The developed segmentation technique achieves good performance compared with gold standard segmentations and obtained higher average DSC values than the state-of-the-art automatic cartilage segmentation studies.     

Fast color contrast enhancement method for color night vision

The increasing availability and deployment of imaging sensors operating in multiple spectral bands has led to a large research effort in color image fusion, resulting in a plethora of pixel-level image fusion algorithms. In this study a simple and fast fusion approach for color night vision is presented. The contrast of infrared and visible images is adjusted by local histogram equalization. Then the two enhanced images are fused into the three components of a Lab image in terms of a simple linear fusion strategy. To obtain false color images possessing a natural day-time color appearance, this paper adopts an approach which transfers color from the reference to the fused images in a simplified Lab space. To enhance the contrast between the target and the background, a stretch factor is introduced into the transferring equation in the b channel. Experimental results based on three different data sets show that the hot targets are popped out with intense colors while the background details present natural color appearance. Target detection experiments also show that the presented method has a better performance than the former methods, owing to the target recognition area, detection rate, color distance and running time.     

一种基于H直方图变换的白细胞图像分割方法

对检出的大量样本细胞图像进行统计分析,发现白细胞在Wright染色后着色效果不一致时色彩分量差异的规律。通过建立数学表达模型,提出了一种基于H直方图变换的白细胞图像自动分割算法。对细胞图像的色度分量直方图进行适当变换,有效削弱了血液染色的不一致性对白细胞分割的影响,实现了白细胞胞浆区域的良好分割,实验结果验证了所提分割方法的可行性和实用性。     

基于最大隶属度原则的彩色图像分割方法

提出一种基于模糊最大隶属度原则的彩色图像分割方法.从彩色图像的彩色直方图中获取目标色和背景色，建立一组色彩模糊集，计算该图像中的色彩在各模糊集中的隶属度，并基于最大隶属度原则确定色彩的归属.基于上述最大隶属度的模糊模式识别原则建立了最大隶属度-径向基函数神经网络，实现了快速有效的彩色图像分割. 关键词： 最大隶属度原则 彩色图像分割 彩色直方图 神经网络     

Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme

The application of a raw data-based, operator-independent MR segmentation technique to differentiate boundaries of tumor from edema or hemorrhage is demonstrated. A case of a glioblastoma multiforme with gross and histopathologic correlation is presented. The MR image data set was segmented into tissue classes based on three different MR weighted image parameters (T₁-, proton density-, and T₂-weighted) using unsupervised fuzzy c-means (FCM) clustering algorithm technique for pattern recognition. A radiological examination of the MR images and correlation with fuzzy clustering segmentations was performed. Results were confirmed by gross and histopathology which, to the best of our knowledge, reports the first application of this demanding approach. Based on the results of neuropathologic correlation, the application of FCM MR image segmentation to several MR images of a glioblastoma multiforme represents a viable technique for displaying diagnostically relevant tissue contrast information used in 3D volume reconstruction. With this technique, it is possible to generate segmentation images that display clinically important neuroanatomic and neuropathologic tissue contrast information from raw MR image data.     

Gibbs distribution-based Bayesian segmentation of electron microscopy nanostructure images

The use of Gibbs distribution-based Bayesian segmentation of electron microscopy images for visualizing nanostructures is investigated. Bayesian segmentation involves dividing an image into nonoverlapping areas that correspond as closely as possible to the observed image. A quantitative characteristic of this correspondence is the a posteriori probability of one variant of division or another. The most likely version is always the division with the greatest a posteriori probability. The Metropolis algorithm for stochastic relaxation is used to obtain Bayesian estimates of the a posteriori probability of a division. Our study of Bayesian segmentation requires visualization of nanostructures on an electron microscopy image of a film made of NiW nanocrystalline alloy.     

Multiple color-image fusion and watermarking based on optical interference and wavelet transform

A novel multiple color-image fusion and watermarking using optical interference and wavelet transform is proposed. In this method, each secret color image is encoded into three phase-only masks (POMs). One POM is constructed as user identity key and the other two POMs are generated as user identity key modulated by corresponding secret color image in gyrator transform domain without using any time-consuming iterative computations or post-processing of the POMs to remove inherent silhouette problem. The R, G, and B channels of different user identity keys POM are then individually multiplied to get three multiplex POMs, which are exploited as encrypted images. Similarly the R, G, and B channels of other two POMs are independently multiplied to obtain two sets of three multiplex POMs. The encrypted images are fused with gray-level cover image to produce the final encrypted image as watermarked image. The secret color images are shielded by encrypted images (which have no information about secret images) as well as cover image (which reveals no information about encrypted images). These two remarkable features of the proposed system drastically reduce the probability of the encrypted images to be searched and attacked. Each individual user has an identity key and two phase-only keys as three decryption keys besides transformation angles regarded as additional keys. Theoretical analysis and numerical simulation results validate the feasibility of the proposed method.     

基于颜色迁移和聚类分割的偏振图像融合方法

为克服当前偏振图像融合方法存在的不足,提出了一种基于颜色迁移和聚类分割的偏振图像近自然彩色融合方法.该方法首先将偏振参量图像映射到HSI颜色空间,再得到初步的融合图像后将它变换到YIQ颜色空间,并采用颜色迁移技术对其进行颜色修正.通过将修正后的图像变换到HSI颜色空间,并利用对线偏振度图像进行模糊C-均值聚类分割的结果...     

基于二维直方图和模糊熵准则的阈值化方法

图像分割是图像分析、识别和理解的基础。图像分割主要是指将图像分成各具特性的区域并提取出感兴趣目标的技术,其研究多年来一直受到人们的高度重视;阈值化法是图像分割的一种重要方法,在图像处理与识别中广为应用;针对图像分割中细节往往被忽略导致后续处理困难的问题,基于模糊关系和最大模糊熵原理提出了一种阈值化方法,对二维直方图进行模糊分割;为了获得图像分割中的细节,提出的方法根据最大熵原则自动确定模糊区域和门限,进而获得二维模糊熵和遗传算法最优解,最后获得图像细节;通过对不同灰度水平和颜色类型图像进行实验比较,实验结果表明提出的方法优于二维非模糊方法和一维模糊熵分割法,得到该方法在图像分割中获得细节的结论。     

A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm

Considering the difficulties in image segmentation caused by the complexity of diverse ecological environments and various artificial targets in high resolution remote sensing images, especially in city scene, and in order to overcome the limitations existing in the traditional segmentation algorithm, JSEG (J-Segmentation), for high resolution remote sensing image segmentation and to further improve the segmentation accuracy, WJSEG (Wavelet-JSEG), a novel multi-scale segmentation algorithm based on wavelet transform, is proposed, which is an improved JSEG algorithm. WJSEG is an improved form of JSEG in relation to three aspects, including color quantization, multi-scale segmentation and region merging by introducing the multi-scale analysis tool based on wavelet transform. Experiments have been conducted on high resolution SPOT 5 pan-sharpened multispectral image and IKONOS panchromatic image. These experimental results were compared with those gained by the traditional JSEG algorithm and the famous commercial software named eCognition, which validated the effectiveness and reliability of the proposed WJSEG algorithm.     

基于改进U-Net的量子点STM图像分割

为提升量子点图像分割精度,降低特征识别误差,提出一种基于改进U-Net的量子点图像分割方法.首先,在预处理阶段,设计了以色彩通道为权值的灰度化算法,以提升后续分割效果.其次,在STM图像分割部分,在原始U-Net结构上引入中间过渡层以均衡网络各层特征.而后,建立数据集,并通过实验对比不同分割算法的精确度、召回率、F-measure.最后,将分割算法应用于量子点的特征识别,并测试了不同分割方式对应用的影响.实验结果显示,改进灰度化方法保留细节信息丰富,明显提升了量子点分割精度;改进U-Net的平均精确率、召回率、F-measure相较原始网络分别提升了13.83%、2.16%、8.13%.同时,实验数据表明由于分割精度的提升,量子点数量、纵横比等特征参数的识别更加精确.     

Neuroradiologic MR applications with multiparametric color composite display

The purpose of this article is to demonstrate the application of a PC-based multiparameter full color composite display technique of MR images of 14 selected patients with neuropathology while assessing the ability of this technique to display clinically important neuroanatomic and neuropathologic tissues. Using a PC with a 386 microprocessor and full color 24-bit graphics display capabilities, custom and commercially available image-processing softwares were applied to spatially aligned multiparameter proton density, T₁-weighted (with and/or without gadolinium-DTPA) and T₂-weighted MR image sets obtained from 14 patients with known neuropathology to generate intensity-based color composites. Quantitative color channel applications were used to assess the ability of this technique to differentiate anatomically and pathologically confirmed tissue types into unique color regions within the full color spectrum display in each patient case. Based on the results of pathologic correlation and quantitative color imaging analysis, the application of full color composite generation techniques to multiple MR images of selected neuropathology cases represents a viable technique for displaying diagnostically relevant tissue contrast information in one color image. With this technique, it is possible to generate composites that simultaneously display uniquely color-coded neuroanatomic and neuropathologic tissue information within the context of partially natural-appearing images.