一种基于进化策略的图像分割方法

文中提出一种基于进化策略求解分割阈值的方法,并在方法中引入了部分个体的交叉和个体的年龄参数,以进一步模拟自然界的进化过程,从而改善了整个方法的计算效率.对使用最大类间的方差准则和最大熵准则的实验结果表明,这种方法能够找到较优的分割阈值,可以方便地实现对图像的分割.     

一种新的双重标记免疫组化图像自动分割方法

本文描述一种自动分割双重标记免疫组化图像的方法,此方法直接从双重标记的两种阳性组织细胞着色特征对其进行分离。与常用的彩色反卷积方法相比,此方法提高了分割准确度且需要样品切片的数量从3张减少到1张。通过细胞角蛋白和S-100蛋白双重标记,分别经二氨基联苯胺(DAB)与3-氨基-9-乙基咔唑(AEC)显色的舌鳞癌样品图像的实验证明,此方法可以有效地将鳞癌与神经组织细胞两种不同的染色区域分割出来,与手动分割的结果作比较其准确度和精确度都有较大的提高。此方法也适用于分割其他用DAB和AEC双重染色的免疫组化图像。     

一种基于混沌映射的快速图像加密算法优化

为了解决现有图像加密算法存在随图像尺寸变大导致加密时间迅速增加的问题,采用基于logistic和Arnold映射的改进加密算法实现了快速图像加密算法的优化。该算法基于两种混沌映射对原文图像进行像素置乱和灰度值替代,像素置乱是按图像大小选择以H个相邻像素为单位进行,通过适当调整H的取值实现加密时间优化;灰度值替代是利用Arnold映射产生混沌序列对置乱图像进行操作而得到密文图像。结果表明,对于256×256的Lena标准图像,加密时间降低到0.0817s。该算法具有密钥空间大和加密速度快等优点,能有效抵抗穷举、统计和差分等方式的攻击。     

基于Kohonen神经网络的深度图像分割方法

提出一种基于Ｋｏｈｏｎｅｎ神经网络无监督的深度图像分割方法。首先计算了深度图像各点的导数,进而得到各点的法向,以法向和深度值作为每一点的特征矢量,引入自组织神经网络进行初始的聚类;为消除初始聚类产生的过分割现象,采取相邻表面片法向分析的方法进行再分割,得到最终的分割结果。本方法避免了通常的区域分割方法初始种子不易选取的弱点,聚类所用样本少,速度快。实验结果表现了算法良好的性能。     

一种全局最小化的图像分割方法

曲线活动模型是图像分割中应用广泛且成功的一类模型,但由于能量泛函的非凸构造,使得其分割结果往往陷入局部解的困境。为了克服这一点,该文在已有的曲线活动模型之一背景去除模型之上,从Heaviside函数的近似入手,提出了凸的能量泛函,并对其最小化,得到了相应的全局最小解求解方程。实验表明,该方法分割结果准确,分割速度快,具有一定的抗噪性,且对初始曲线的位置选取无特殊要求。     

一种基于SOFM网络图像分割的彩色化方法

本文采用了SOFM神经元网络对图像进行分割,通过分割区域的外轮廓的提取,保证了种子点在扩散时的终止条件,采用局部线性映射的方法进行彩色化处理,能够完成快速、准确的彩色化处理.     

一种基于区域显著性的红外图像目标分割方法

提出了一种基于区域显著性的红外图像目标分割方法,即首先在方差空间中提取显著性区域,然后根据图像复杂度对显著性区域进行筛选,最后采用阈值分割方法分割显著性区域,获取目标.算法具有较强的适用性和工程实用性.     

一种基于可变形模型的图像分割算法

提出了一种新的基于可变形模型的图像分割算法。该算法在模拟气球膨胀法的基础上，对内外部力场进行了改进。并通过控制力场的方向，使Snakes不断向图像内部收缩分裂，最终完成对图像的分割。对合成图像和实际图像的实验表明，这一方法是行之有效的。     

一种基于彩色图像的运动人体分割方法

图像分割是计算机早期视觉不可缺少的一步。彩色图像由于具有比灰度图像更多的视觉信忠．受到了越来越多的重视。该文运用改进的背景差分方法，结合直方图双阈值分割和数学形态学的算法。在彩色图像序列中获得运动人体。实验结果表明上述算法对噪声抑制和人体图像断裂处填充都是有效的，能够实时从彩色图像序列中分割出运动人体。     

一种基于综合集成和数学形态学的图像分割方法

图像分割是图像处理的重要步骤,是计算机视觉的基础,是模式识别与图像理解的重要组成部分。由于光照不均匀而形成的灰度图像,采取单一的分割方法不能获得良好的分割结果,为此,采用综合集成的方法对此类图像进行分割,并用数学形态学的运算对分割结果进行处理,改善了分割效果。试验结果表明,基于综合集成和数学形态学的分割方法能有效地分割这一类图像,获得良好的分割结果。     

AN EXPANSION AND CORRECTION ALGORITHM FOR PHOTOELECTRIC IMAGE WITH GEOMETRIC DISTORTION

A creepy hotoelectric endoscopy system with good performance is studied,and an expansion and correction algorithm for a compressed photoelectric image with serious geometric distortion is presented.The algorithm can not only correct the geometric geometric distortion,but also restore the gray-level distribution by means of ternary convolution algorithm.The details and the outline in the image are very clear.It is proved to be of high performance in practice.     

Multiple transformation function estimation for image enhancement

Most deep learning-based image enhancement algorithms have been developed based on the image-to-image translation approach, in which enhancement processes are difficult to interpret. In this paper, we propose a novel interpretable image enhancement algorithm that estimates multiple transformation functions to describe complex color mapping. First, we develop a histogram-based multiple transformation function estimation network (HMTF-Net) to estimate multiple transformation functions by exploiting both the spatial and statistical information of the input images. Second, we estimate pixel-wise weight maps, which indicate the contribution of each transformation function at each pixel, based on the local structures of the input image and the transformed images obtained by each transformation function. Finally, we obtain the enhanced image as the weighted sum of the transformed images using the estimated weight maps. Extensive experiments confirm the effectiveness of the proposed approach and demonstrate that the proposed algorithm outperforms state-of-the-art image enhancement algorithms for different image enhancement tasks.     

一种基于目标特征的多门限图像分割方法

本文提出了一种基于目标特征的多门限图像分割方法。它通过对特征量匹配的方式将属于景物的先验空间知识引入分割过程,使图像分割由单纯的灰度聚类过程变成灰度－几何空间聚类过程。实验结果表明,本文提出的方法可以完整地分割出复杂背景图像中本身具有多个灰度层次的景物。     

彩色磨粒图像自动分割技术

本文针对彩色显微磨粒图像特征,选择了最大类间方差分割法,并对其进行了推广以应用于彩色图像分割.在详细分析各种彩色特征的前提下,通过选择合适的正交彩色特征量I1、I2和I3,成功地进行了两类彩色磨粒图像分割和目标提取.同时,本文提出了基于彩色特征量直方图三点循环平滑处理的特征量自动选取技术,实现彩色特征量的自动选取,避免了人的主观判断和决定,最终实现了彩色磨粒图像的自动分割.算例表明了本文方法的简洁有效性.     

Unsupervised fisheye image correction through bidirectional loss with geometric prior

Neural network based methods for fisheye distortion correction are effective and increasingly popular, although training network require a high amount of labeled data. In this paper, we propose an unsupervised fisheye correction network to address the aforementioned issue. During the training process, the predicted parameters are employed to correct strong distortion that exists in the fisheye image and synthesize the corresponding distortion using the original distortion-free image. Thus, the network is constrained with bidirectional loss to obtain more accurate distortion parameters. We calculate the two losses at the image level as opposed to directly minimizing the difference between the predicted and ground truth of distortion parameters. Additionally, we leverage the geometric prior that the distortion distribution depends on the geometric regions of fisheye images and the straight line should be straight in the corrected images. The network focuses more on the geometric prior regions as opposed to equally perceiving the whole image without any attention mechanisms. To generate more appealing corrected results in visual appearance, we introduce a coarse-to-fine inpainting network to fill the hole regions caused by the irreversible mapping function using distortion parameters. Each module of the proposed network is differentiable, and thus the entire framework is completely end-to-end. When compared with the previous supervised methods, our method is more flexible and shows better practical applications for distortion rectification. The experiment results demonstrate that our proposed method outperforms state-of-the-art methods on the correction performance without any labeled distortion parameters.     

Color image enhancement with high saturation using piecewise linear gamut mapping

Most of the color image enhancement algorithms are implemented in two stages: gray scale image enhancement, which finds the target intensity, and then gamut mapping of the original color coordinates to the target. Therefore, hue preserving gamut mapping is an essential and crucial step, which influences colorfulness. In conventional color mapping methods, color saturation is reduced after intensity modification, which deteriorates subjective image quality. In this paper, a new color enhancement algorithm resulting in high color saturation is proposed. The proposed method employs multiplicative and additive color mapping to improve color saturation without clipping of a color component for increased target intensity as well as decreased cases. This new scheme is fast and effective, therefore, it can be employed to real time applications such as video signal processing.     

Image synthesis via adversarial geometric consistency pursuit

Realistic image synthesis is a class of vision problems where the goal is to synthesize new images by fusing a source image and a target image. It is a challenging problem due to the visual gap between the two images, including geometry and appearance. To synthetically address the geometric distortion and appearance realism, we propose a novel method, the adversarial geometric consistency pursuit model (AGCP), which explicitly allows seamless image synthesis. The proposed method takes geometric correction and appearance harmonization into account, handling the relative scaling, spatial layout, color, viewpoint, and distortion transformation to generate a realistic composite image. Moreover, we also propose a joint geometric consistency pursuit loss that handles the geometric consistency and enhances the network to generalize better for different scales of source images. Our comparative evaluation demonstrates the effectiveness of the proposed method in the aforementioned challenging cases.     

Motion compensation for very low bit-rate video

A combination of spatial transformation and image segmentation is used to compensate for non-uniform intensity changes in moving scenes. The method efficiently tracks movements such that the motion vectors alone can be employed to represent a moving object with complex motion. Using fast transformation and interpolation algorithms, it is shown that while the compression efficiency of the presented method is far superior to that of the conventional full search block-matching motion estimation, its computational complexity is still affordable.     

图像几何校正控制点的自动确定方法

依据图像几何校正的一般思路，提出了一种有效控制点（CP）自动确定方法，首先定义了基准控制点（RCP）及畸变控制点（DCP）的概念，设计了一种基于图像方差的检测模板，提出了相应的检测准则及搜索方案，然后在畸变图像中利用归一化积相关图像匹配算法实现了基准控制点相对应的畸变控制点的精确定位，并给出了方法的算法流程，最后，以几何旋转畸变图像为例进行仿真实验，准确辨识出了畸变模型的参数，验证了方法的可行性有效性。