一种图象纹理特征提取与分割的新方法

本文介绍了一种用于提取图象纹理特征的新的二维最佳数字滤波器（ＯＤＦ），该滤波器是基于有限长球序列（ＦＰＳＳ）和离散付里叶变换（ＤＦＴ）设计的，它具有能量损失最小的最佳频率响应特性。用若干具有不同中心频率和带宽的这种滤波器覆盖频率平面，可以抽取图象的纹理转征，进而利用一个非参数分类器，可对纹理图象实行有效的分类和分割。文中给出了一个人工纹理图象和一个自然纹理图象分割的实例。     

基于分形的混合图象压缩方法

分形图象压缩方法是近年来迅速兴起的一种高倍率图象压缩方法，它依据分形原理，利用迭代函数系统（IFS）来抽取自然图象中的自相似性，达到压缩图象的目的；解码时利用拼帖定理来快速恢复图象。然而它最大的缺点是速度太慢。为此，本文将分形图象压缩同传统的块截取变换方法（BlockTruncatingCoding）结合，在压缩速度、压缩倍数和压缩失真方面有个折衷，达到较好的性能。     

用DSP实现图象的直接捕获与显示

用ＤＳＰ实现图象的直接捕获与显示国澄明，李铁盘（天津大学电子工程系，天津３０００７２）（河南教育学院，郑州４５０００３）ＤｉｒｅｃｔＶｉｄｅｏＡｃｑｕｉｓｉｔｉｏｎａｎｄＤｉｓｐｌａｙｉｎｇｂｙＤＳＰ￥／／大多数视频捕获系统都要有专门的控制电路，用来...     

基于低速信道的远程图象监控系统

提出了一种以计算机通信为核心采用图象压缩技术在低速信道（公用电话网，ＤＤＮ）上实现远程图象监控的方案，并进行了系统研制。该系统特别适合于图象的存储与管理，已成功地应用于电话局程控机房的无人值守系统。     

基于彩色空间3D广义共发矩阵的视觉信息检索

本文针对基于内容的图象检索（CBIR）提出一组基于HSV空间的3D广义共发矩阵的新颖的图象纹理特征，通过在CBIR检索系统iPhoto中（25，000张图象）上测试，利用本文特征地传统灰度共发矩阵。     

一种改进的马尔可夫随机场图象恢复与分割模型

在现有的马尔可夫随机场图象恢复与分割模型中，图象场能量最低组态被看成是原始景物的一种最优估计，但在图象灰度值发生变化的边界上，能量最低组态不对应于原始景物，从而造成恢复（或分割）误差，本文对这类模型作了改进，利用改进的模型给出了一种引入边界信息的松弛算法，并给出了应用该算法对低信噪比图象进行恢复处理的计算机模拟结果。     

小波变换在红外图象处理中的应用

小波变换近年来在图象处理中的应用日益受到人们的关注。小波变换可能同时在时间（或空间）和频率域对信号进行分析，是多分辨率分析的有力工具。本文介绍了小波变换在红外图象处理中的应用，例如红外／可见光图象的配准与融合，红外图象增强，红外图像中的微弱目标探测等。     

专家系统在遥感图象分类中的应用

本文简单回顾了人工智能、专家系统的思想和技术在遥感图象分类中的应用,讨论了在遥感图象分类中人工智能、专家系统的作用。研究了在遥感图象分类专家系统中知识获取、知识表达、推理控制等问题。     

一种单镜头运动测距方法

本文利用立体角的概念,研究了静止物体平面由于镜头摇摆产生的成象区域的变化,从而可由两幅图象求出镜头到物体平面的距离。该法优点是无需对运动前后两幅图象进行特征点选择和匹配,适用于物体在镜头逐渐接近时图象上出现较大透视变形的情况。     

用全息法拍摄金属／气体混合物图象

用全息法拍摄金属／气体混合物图象加州Ｓｔｒａｔｏｎｉｃｓ公司与美国标准技术研究院一起正在研制一种能获取和重建高分辨工业图象的全息仪，希望能观测和研究超声惰性气体金属雾化（ＳＩＧＭＡ）室内的相互作用。当气体与金属在注入器出口处相遇时便形成原子雾化羽，然...     

Identification of inpainted images and natural images for digital forensics

Image forensics is a form of image analysis for finding out the condition of an image in the complete absence of any digital watermark or signature.It can be used to authenticate digital images and identify their sources.While the technology of exemplar-based inpainting provides an approach to remove objects from an image and play visual tricks.In this paper,as a first attempt,a method based on zero-connectivity feature and fuzzy membership is proposed to discriminate natural images from inpainted images.Firstly,zero-connectivity labeling is applied on block pairs to yield matching degree feature of all blocks in the region of suspicious,then the fuzzy memberships are computed and the tampered regions are identified by a cut set.Experimental results demonstrate the effectiveness of our method in detecting inpainted images.     

Pair comparison based progressive subjective quality ranking for underwater images

Underwater images contain an interacting mixture of distortions due to the physicochemical properties of the water, suspended organic matter and floating particles in water. Unlike images in traditional natural image quality databases, underwater images are often difficult to acquire with reference images and sets of images with gradient distortion. Therefore, it is even more difficult for the viewers to assign an absolute psychophysical scale to the quality of underwater images. In this paper, we propose a pairwise subjective comparison procedure for underwater images quality ranking inspired by the intuitive suppression and competence mechanisms in visual perception. In the proposed method, we construct a preselection based initial image quality dataset by full pairwise comparison, which also enables online adaptive new image updating. The proposed method is not constrained by the lack of reference images, and is reliable and sensitive to images with discriminable distortion level and various image contents. The proposed pairwise comparison further allows an uncertain choice, which does not require a reinforce human opinion. To the best of our knowledge, this is the first implementation for underwater image subjective quality ranking, and a new approach to the image quality ranking for different image contents with unknown distortion level. We demonstrate that the obtained subjective image ranking correlates well with the human perception of quality difference among the underwater images than that of the single stimuli image quality assessment with finite labor burden. Moreover, our proposed method accurately characterize the gradual degradation in the underwater image sequence taken in controlled conditions. The proposed progressive learning ranking is also an alternative way to realize adaptive extension of the existing image quality databases.     

Single underwater image haze removal with a learning-based approach to blurriness estimation

Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks.     

A Multi-Directional Search technique for image annotation propagation

Image annotation has attracted lots of attention due to its importance in image understanding and search areas. In this paper, we propose a novel Multi-Directional Search framework for semi-automatic annotation propagation. In this system, the user interacts with the system to provide example images and the corresponding annotations during the annotation propagation process. In each iteration, the example images are clustered and the corresponding annotations are propagated separately to each cluster: images in the local neighborhood are annotated. Furthermore, some of those images are returned to the user for further annotation. As the user marks more images, the annotation process goes into multiple directions in the feature space. The query movements can be treated as multiple path navigation. Each path could be further split based on the user’s input. In this manner, the system provides accurate annotation assistance to the user - images with the same semantic meaning but different visual characteristics can be handled effectively. From comprehensive experiments on Corel and U. of Washington image databases, the proposed technique shows accuracy and efficiency on annotating image databases.     

Color image quality assessment based on sparse representation and reconstruction residual

Image quality assessment (IQA) is a fundamental problem in image processing. While in practice almost all images are represented in the color format, most of the current IQA metrics are designed in gray-scale domain. Color influences the perception of image quality, especially in the case where images are subject to color distortions. With this consideration, this paper presents a novel color image quality index based on Sparse Representation and Reconstruction Residual (SRRR). An overcomplete color dictionary is first trained using natural color images. Then both reference and distorted images are represented using the color dictionary, based on which two feature maps are constructed to measure structure and color distortions in a holistic manner. With the consideration that the feature maps are insensitive to image contrast change, the reconstruction residuals are computed and used as a complementary feature. Additionally, luminance similarity is also incorporated to produce the overall quality score for color images. Experiments on public databases demonstrate that the proposed method achieves promising performance in evaluating traditional distortions, and it outperforms the existing metrics when used for quality evaluation of color-distorted images.     

MRI and CT image indexing and retrieval using local mesh peak valley edge patterns

In this paper, a new pattern based feature, local mesh peak valley edge pattern (LMePVEP) is proposed for biomedical image indexing and retrieval. The standard LBP extracts the gray scale relationship between the center pixel and its surrounding neighbors in an image. Whereas the proposed method extracts the gray scale relationship among the neighbors for a given center pixel in an image. The relations among the neighbors are peak/valley edges which are obtained by performing the first-order derivative. The performance of the proposed method (LMePVEP) is tested by conducting two experiments on two benchmark biomedical databases. Further, it is mentioned that the databases used for experiments are OASIS−MRI database which is the magnetic resonance imaging (MRI) database and VIA/I–ELCAP-CT database which includes region of interest computer tomography (CT) images. The results after being investigated show a significant improvement in terms average retrieval precision (ARP) and average retrieval rate (ARR) as compared to LBP and LBP variant features.     

Stereoscopic image quality assessment considering visual mechanism and multi-loss constraints

In this paper, a convolutional neural network (CNN) with multi-loss constraints is designed for stereoscopic image quality assessment (SIQA). A stereoscopic image not only contains monocular information, but also provides binocular information which is as identically crucial as the former. So we take the image patches of left-view images, right-view images and the difference images as the inputs of the network to utilize monocular information and binocular information. Moreover, we propose a method to obtain proxy label of each image patch. It preserves the quality difference between different regions and views. In addition, the multiple loss functions with adaptive loss weights are introduced in the network, which consider both local features and global features and constrain the feature learning from multiple perspectives. And the adaptive loss weights also make the multi-loss CNN more flexible. The experimental results on four public SIQA databases show that the proposed method is superior to other existing SIQA methods with state-of-the-art performance.     

An efficient reversible authentication scheme for demosaiced images with improved detectability

The existing probability based reversible authentication schemes for demosaiced images embed authentication codes into rebuilt components of image pixels. The original demosaiced image can be totally recovered if the marked image is unaltered. Although these schemes offer the goal of pixel-wise tamper detection, the generated authentication codes are irrelevant to the image pixels, causing some undetectable intentional alterations. The proposed method pre-processes the rebuilt components of demosaiced images and hashes them to generate authentication codes. With the guide of a randomly-generated reference table, authentication codes are embedded into the rebuilt components of demosaiced images. Since the distortions of image pixels are sensitive to the embedded authentication codes, the proposed method further alters the pre-processed pixels to generate a set of authentication codes. One of the authentication codes that minimizes the distortion is embedded to generate marked demosaiced images. The results show that the proposed method offers a better image quality than prior state-of-the-art works, and is capable of detecting a variety of tampering.