基于感兴趣区域的图像质量评价算法研究

目的降低计算量,提高计算效率,达到评价算法与人眼观察的一致性。方法首先提取感兴趣区域,利用眼动仪记录人眼在观察图像时的关注点、关注时长、眼球运动轨迹以及眼跳数据,然后提取显著区域。将感兴趣区域引入客观视觉模型中评价图像质量,通过测量图像的有效感知跃变百分数,客观评价图像质量的优劣,并且与传统算法进行比较。结果感兴趣区域包含了图像大部分的关键信息,即人眼关注的信息。基于不同增强方法的图像感兴趣区域内的有效感知跃变百分数均大于原始图像的有效感知跃变百分数(20.3%)。结论提出的算法优于传统客观评价算法,且客观质量评价的结果更符合人眼视觉特性。     

基于图像感兴趣区域的图像压缩方法

目的提出一种基于图像感兴趣区域的图像压缩方式,实现在减少图像存储空间时图像失真少的效果。方法采用眼动仪提取图像感兴趣区域,制作压缩掩码对图像进行分区压缩,非感兴趣区域采用DCT算法进行压缩,而感兴趣区域不做任何压缩处理直接保留原样。结果主客观评价实验表明:压缩后的图像失真较少,视觉观察效果好,且压缩后所占存储空间减半,方法简便,压缩效率高。结论结合眼动仪提取感兴趣区域的压缩方法优于基于Itti视觉模型的压缩方法,适合压缩多种类型的图像,具有较好的实用性。     

卫星应用中基于视觉显著性的感兴趣区域图像编码算法

为了满足遥感图像在卫星应用中有限信道下高保真传输的需求,本文提出了一种基于视觉显著性的感兴趣区域图像编码算法.首先,基于CCSDS标准的编码框架可以降低编码算法复杂度,便于实时实现,适合于卫星应用场合.其次,引入视觉注意机制,采用一种基于视觉显著性的自动提取算法提取感兴趣区域.最后,通过使用掩模来确定ROI区域,可以对任意形状的ROI区域进行编码.实验结果表明,与JPEG2000中的感兴趣区域图像编码算法相比,本文算法结构简单,便于实现;拥有优秀的主观视觉质量,符合人眼的感知,在高压缩比下能够为用户提供更多的信息.另外,本文算法可以作为CCSDS标准中的感兴趣区域编码算法.     

一种基于视觉感兴趣区域的彩色图像增强方法

目的把视觉感兴趣区域概念引入直方图的建造中,提出了一种新的基于视觉感兴趣区域的图像增强方法,使增强效果更符合人眼视觉感知。方法首先在标准观测环境下利用先进的眼动仪设备获得人眼感兴趣区域,然后计算各子区域的平均显著值,以确定各个子区域的权重系数,最后采取类似直方图均衡化的思想,优化配比灰度级的动态范围。结果通过主客观实验结果表明,增强后的图像更符合人眼视觉感知。结论结合了视觉感知特性的直方图增强方法,弥补了传统直方图与人眼视觉感知不一致的弊端,其增强效果更佳。     

大学生网页浏览的眼动行为及影响因素研究

本文研究大学生浏览网页的眼动行为及影响因素,为网页设计提供借鉴。共34名大学生参加了实验。被试随机浏览六幅静态网页,每幅浏览时间10 s,利用眼动仪记录其眼动行为,指标为注视点时间和区域分布。结果表明:被试整体的注视点时间分布稳定,注视点时间集中在60m s~400m s之间;被试在浏览网页时存在明显的区域偏好,其偏好顺序为中-左上-右上与左下-右下;男性被试的平均注视点时间比女性被试要短;不同类型网页的注视点平均时间有显著差异。     

运动摄像机下快速提取运动目标的新方法

本文研究摄像机和目标同时运动情况下的实时目标提取问题.首先运用背景差方法,检测出静止摄像机下的运动区域,为了克服连通域分析法耗时长的不足,提出重心偏移迭代法快速获得感兴趣运动目标.在改进Camshifi跟踪算法中,提出采用Bayesian概率法则在由Kalman滤波器预测的感兴趣区域(ROI)内获取颜色概率密度分布图像(CPDDI),引入即时背景(IB)以抑制背景特征.提出依据跟踪结果进行目标提取的方法,即结合CPDDI特征,并辅以适当的形态学滤波策略,从跟踪结果中提取出运动摄像机下的运动目标,解决目标被动态背景干扰的问题.实验结果表明,提出的算法能够较稳定和完整地提取出运动摄像机下的运动目标,对复杂动态背景的适应性较强,且算法完全达到了实时的运行速度.     

多任务情境下的压路机显示界面警示感知研究

目的 探究不同任务情境对感知压路机驾驶室显示界面警示信息的影响。方法 通过AdobePhotoshop构建质检、转场和作业3种典型任务情境界面及警示刺激材料,采用Tobii X3-120眼动仪分别对20名被试进行瞳孔直径、首次进入时间及目标总反应时间指标的参数采集。运用ErgoLAB人机环同步平台EyeTracking眼动追踪分析模块收集提取有效数据,以SPSS重复测量方差分析检验数据的差异性。结果 质检任务情境下被试瞳孔直径均值最高,首次进入时间和目标总反应时间均值最低,即该情境下警示刺激程度及感知效果最强烈,感知效率最高,而转场任务情境下对界面警示信息的感知程度最低。结论 任务情境会对界面警示信息的视觉感知产生影响,且不同任务情境的警示感知效果不同。从任务情境的角度进行研究,为工程车辆驾驶室显示界面警示信息的感知研究及设计优化提供一定参考。     

基于视觉底层特征的图像增强方法

目的提出基于视觉底层特征对不同类型图像权重的图像增强方法。方法提取图像的视觉底层特征,如颜色、亮度、方向、纹理和边缘特征;加权融合成计算显著图;与眼动仪测得的眼动显著图进行相关系数比较,以确定各视觉底层特征的最佳权重。再根据权重的大小为不同类型的图像选择合适的图像增强方法。结果采用基于视觉底层特征的图像增强方法,其增强后的效果更符合人眼视觉感知。结论针对不同类型的图像需要充分考虑各视觉底层特征的权重大小,使其能真实反映视觉感兴趣区域,以达到提高图像增强后的视觉感知一致性。     

基于HVS和时域感知失真的感兴趣区域搜索

视频质量评价模型对于感知视频编码有着重要意义.观察者对于视频场景中不同区域有不同的视觉兴趣性,而度量不同区域人眼感兴趣程度,对于构建高性能视频质量评价模型非常重要.我们在研究中发现,时域失真和时域波动分布是影响视觉兴趣性最重要的特征因素,因此定量度量视频序列的时域感知失真和时域失真波动,并根据这两个参量,采用自适应阈值判断的算法,标定出可能的兴趣像素点;同时对选中的可能兴趣点,进行空域连通分析.根据连通区域面积大小,确定1～5个可能的感兴趣区域.并根据聚类算法,确定最终的区域.最后结果证明了算法的可行性.     

基于椭圆特征区域与重要位平面分解的鲁棒图像水印算法

目的 为了解决当前基于特征点的水印方案难以描述图像的非纹理区域（像素强度变化较大的边缘、像素强度值较小或趋于0的均匀区域）,降低了局部特征区域的鲁棒性,使其抗几何失真能力不佳的问题,提出基于椭圆特征区域与重要位平面分解的鲁棒图像水印算法。方法 根据彩色载体的RGB分量,计算颜色不变性;基于颜色不变性的强度概率密度,推导概率密度梯度估计函数;利用概率密度梯度值,计算二阶Hessian矩阵,改进SURF方法,充分提取彩色载体中纹理与非纹理区域的鲁棒特征点;再求取Hessian矩阵的特征值与特征向量,以计算椭圆的长轴、短轴半径与方向角度,并以特征点为中心,建立局部椭圆特征区域;定义鲁棒特征区域选择规则,确定合适的水印嵌入位置;引入位平面分解技术,获取鲁棒椭圆特征区域的重要位平面图像,并提取其直方图,以此设计水印嵌入方法,将二值水印隐藏到这些直方图中,形成水印图像;根据水印检测机制,复原二值水印。结果 实验结果显示,与基于特征点的水印方案相比,所提算法具有更高的不可感知性与鲁棒性,复原水印失真度最小。结论 所提算法具有较高的视觉隐秘性和抗几何失真能力,在版权保护、信息防伪等领域具有较好的参考价值。     

一种有效的红外图像中人造目标分割方法

提出一种红外图像单阈值分割方法。为了减少计算量,结合先验信息选择包含待分割目标的感兴趣区域,利用Bezier曲线法平滑感兴趣区域直方图的噪声;对平滑后的感兴趣区域的直方图求解其曲率曲线,用曲率曲线的波峰所对应的灰度值作为初始分割阈值;基于先验信息从初始分割阈值中确定最佳分割阈值并进行初步分割。为了弥补单纯利用阈值法分割的缺陷,结合上述分割结果和目标的边缘信息得到封闭性良好的完整目标的二值图像。实验结果表明,提出的方法能快速有效地将红外目标从复杂的背景中分割出来,算法的计算复杂度为O(MN)。     

Recognition of diffuse liver cirrhosis based on image analysis

This paper presents non-invasive method for diagnosis of diffuse liver cirrhosis using ultrasound images. There are three main contributions of our detection method. The first contribution of this method is based on selection of multiple regions of interest (ROIs) with dynamic sizes in liver tissue. The use of multiple ROIs significantly achieves low computations that are required to classify the liver. The number of ROIs is variable and is optimised to achieve the best results. Then, simple but efficient features are extracted from each ROI. The second one is to apply a voting classification with use of multiple ROIs. The third one is to optimise the automatic selection of the parameters using the genetic algorithm. The proposed method has a higher performance compared to other research work and the obtained results show that the recognition accuracy is of 80.77%.     

Scale and skew‐invariant road sign recognition

A fast and robust method to detect and recognize scaled and skewed road signs is proposed in this paper. In the detection stage, the input color image is first quantized in HSV color model. Border tracing those regions with the same colors as road signs is adopted to find the regions of interest (ROI). The ROIs are then automatically adjusted to fit road sign shape models so as to facilitate detection verification even for scaled and skewed road signs in complicated scenes. Moreover, the ROI adjustment and verification are both performed only on border pixels; thus, the proposed road sign detector is fast. In the recognition stage, the detected road sign is normalized first. Histogram matching based on polar mesh is then adopted to measure the similarity between the scene and model road signs to accomplish recognition. Since histogram matching is fast and has high tolerance to distortion and deformation while contextual information can still be incorporated into it in a natural and elegant way, our method has high recognition accuracy and fast execution speed. Experiment results show that the detection rate and recognition accuracy of our method can achieve 94.2% and 91.7%, respectively. On an average, it takes only 4–50 and 10 ms for detection and recognition, respectively. Thus, the proposed method is effective, yet efficient. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 28–39, 2007     

Automatic Segmentation of Liver from Abdominal Computed Tomography Images Using Energy Feature

Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from the segmented liver, extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features. In this work, the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering (FCM) which is one of the algorithms to segment the images. The dataset used in this method has been collected from various repositories and scan centers. The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency. It gives better results when compared with other existing algorithms. Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.     

Some practical issues in anomaly detection and exploitation of regions of interest in hyperspectral images

We address method of detection of anomalies in hyperspectral images that consists in performing the detection when the spectral signatures of the targets are unknown. We show that, in real hyperspectral images, use of the full spectral resolution may not be necessary for detection but that the correlation properties of spectral fluctuations have to be taken into account in the design of the detection algorithm. Anomaly detectors are useful for detecting regions of interest (ROIs), but, as they are prone to false alarms, one must analyze the ROIs obtained further to decide whether they correspond to real targets. We propose a method of exploitation of these ROIs that consists in generating a single image in which the contrast of the ROI is optimized.     

嵌入掩膜的SPIHT任意形状ROI编码

感兴趣区域(ROI)编码可以在低码率条件下获得高质量的局部感兴趣区域,或在图像渐进传输中使感兴趣区域获得优先传输.本文在分析了当前各类ROI编码方法的基础上,基于SPIHT算法提出了一种支持多个任意形状感兴趣区域并生成可任意截断码流的ROI编码算法.该算法在SPIHT算法中嵌入了重要系数的ROI掩膜信息,使编码器同步地进行图像和ROI形状的编码,使得生成的码流具有任意可截断的特性.文中还就图像ROI编码的质量评价指标进行了讨论,并给出了一种充分考虑ROI和背景的重要性与面积比例差别的图像质量评价指标,称为重要性.面积加权峰值信噪比(WPSNR).实验结果表明,该算法支持有损到无损的多个任意形状ROI的图像编码,而且ROI优先级可调,能够生成具有嵌入式可截断性质的码流,在任意地方截断仍能保证解码器所需的图像信息和ROI掩膜信息,且计算复杂度和SPIHT相当,压缩效果高于BbB移位算法.适用于低码率应用或感兴趣优先渐进传输的应用.     

Unsupervised segmentation of RF echo into regions with different scattering characteristics

Recent experimental results verify that the probability distribution function of the diffuse component of the RF echo depends primarily on the concentration of the diffuse scatterers in the resolution cell. In this paper we apply these results to develop an unsupervised segmentation scheme that partitions an RF A-scan or B-scan image into statistically homogeneous regions that reflect the underlying scattering characteristics. The proposed segmentation scheme is based on a nonparametric homogeneity test that compares two regions of interest (ROI) for possible merging utilizing information about both the coherent and the diffuse component of the RF echo. For the coherent component, homogeneity is defined in terms of the estimated average spacing of each ROI. For the diffuse component, we use the nonparametric Kolmogorov-Smirnov (K-S) homogeneity statistical test that compares two empirical distributions associated with any two ROIs. This test can be used to obtain a segmentation into regions with different scattering characteristics regardless of the nature of the scattering conditions (e.g., Rayleigh regions with different scatterer concentration, different non-Rayleigh regions, or different coherent scattering regions). Finer segmentation can be obtained by learning the distributions associated with the various homogeneous regions obtained from the coarse segmenter. The proposed segmentation scheme is applied on simulated RF scans with different scatterer concentration per resolution cell, on phantom data which mimic tissue, and on liver scans. The results demonstrate the effectiveness of the segmentation algorithm even in cases of subtle differences in the scattering characteristics of each region (for example, diffuse component with scatterer density of 16 and 32 scatterers per resolution cell).     

Improved scatterer property estimates from ultrasound backscatter using gate-edge correction and a pseudo-Welch technique

Quantitative ultrasound (QUS) techniques have been widely used to estimate the size, shape and mechanical properties of tissue microstructure for specified regions of interest (ROIs). For conventional methods, an ROI size of 4 to 5 beamwidths laterally and 15 to 20 spatial pulse lengths axially has been suggested to estimate accuracy and precision better than 10% and 5%, respectively. A new method is developed to decrease the standard deviation of the quantitative ultrasound parameter estimate in terms of effective scatterer diameter (ESD) for small ROIs. The new method yielded estimates of the ESD within 10% of actual values at an ROI size of five spatial pulse lengths axially by two beamwidths laterally, and the estimates from all the ROIs had a standard deviation of 15% of the mean value. Such accuracy and precision cannot be achieved using conventional techniques with similar ROI sizes.     

A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19

In the current dire situation of the corona virus COVID-19, remote consultations were proposed to avoid cross-infection and regional differences in medical resources. However, the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry. To ensure the integrity and security of medical images, this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest (ROI) and integer wavelet transform (IWT). First, the medical image is divided into two different parts, regions of interest and non-interest regions. Then the integrity of ROI is verified using the hash algorithm, and the recovery data of the ROI region is calculated at the same time. Also, binary images with the basic information of patients are processed by logistic chaotic map encryption, and then the synthetic watermark is embedded in the medical carrier image using IWT transform. The performance of the proposed algorithm is tested by the simulation experiments based on the MATLAB program in CT images of the lungs. Experimental results show that the algorithm can precisely locate the distorted areas of an image and recover the original ROI on the basis of verifying image reliability. The maximum peak signal to noise ratio (PSNR) value of 51.24 has been achieved, which proves that the watermark is invisible and has strong robustness against noise, compression, and filtering attacks.     

A new algorithm for identifying abnormal glow curves in thermoluminescence personal dosimetry

In this paper an algorithm for the investigation of routine curves in thermoluminescence personal dosimetry is presented. In rare cases, the luminescence glow curves do not exhibit the expected form. They have an abnormal shape as a result of, for example, external contamination, hardware problems, and poor heat transfer. So, glow curves from a monthly exposure period are compared with regular glow curves. Each curve is divided into four regions of interest (ROIs) and the relationships between the different ROIs are analysed. There are few criteria combining all four ROIs, which are necessary to distinguish between normal and abnormal glow curves. For that, the numerical value and the channel of the curves maximum also need to be considered. In most cases an additional set of criteria permits the identification of the ROI in which the irregularity occurs.