Soybean plant foliar disease detection using image retrieval approaches

Multimedia Tools and Applications - A research to solve the soybean foliar disease detection problem is proposed through the image retrieval method. We explore the suitability of image retrieval...     

An image processing-based crack detection technique for pressed panel products

Crack detection is an important step in assessing the quality of pressed panel products. This paper presents a fast and non-invasive crack detection technique which involves extracting the outline of the captured object and applying a unique edge line evaluation. This technique is robust against environmental condition changes and only require a low-cost web camera. After capturing an image immediately following the press process, a clear one-pixel edge line is extracted by applying a light control and a series of pre-image processing algorithms, including a valley-emphasis Otsu method and percolation-based shape recognition. Next, the initial detection at low resolution is applied to search for every possible crack using unique edge line and curvature evaluation. Finally, at high resolution, the windowed image of every possible crack is individually analyzed to detect existing cracks using a more specific evaluation process. All of these steps are completed within 0.5 s, thus allowing for the technique to be applied in real-time on a highly automated manufacturing line. To demonstrate the performance of the proposed technique, experiments are conducted on an aluminum plate with different patterns and the pressed panel products. The results show that the proposed technique can detect surface cracks on pressed panels with stable performance as well as high accuracy and efficiency.     

区间二型模糊集彩色遥感图像边缘检测方法

针对一型模糊集其隶属度函数是确定的,不具有柔性,很难满足图像的多方面边缘检测要求,及传统PalKing算法采用单一阈值对图像进行增强难以满足灰度变化丰富且含大量信息的彩色遥感图像处理的要求。提出了一种新的基于区间二型模糊集的彩色遥感图像边缘检测方法。实验结果表明,它能较好地检测出彩色遥感图像边缘,因此是一种实用有效的彩色遥感图像边缘检测方法。     

基于边缘提取的遥感图像自动镶嵌方法

为消除图像镶嵌中的接缝现象,提出一种基于边缘的优化镶嵌线选取方法。对两幅待镶嵌图像进行直方图匹配后,计算得到差值图像,将差值图像和方向作为约束,对基准图像进行边缘提取,对不连续过渡边缘采用插值修补,优化镶嵌线选取。采用LandSat遥感图像进行实验,比较平滑度及对比度等指标,比较结果表明,该方法能够优化镶嵌效果,具有较高的应用价值。     

Hilbert quantum image scrambling and graph signal processing-based image steganography

Steganography plays a big role in secret communication by concealing secret information in the carrier. This paper presents a graph signal processing-based robust image steganography technique for posting images over social networks. In the embedding, we first obtained a scrambled version of the secret image using quantum scrambling. Next, we applied graph wavelet transformation on both the cover image and scrambled secret image followed by α (alpha) blending on both image signals (cover image signal and scrambled image signal). Finally, inverse graph wavelet transformation of the resulting image was undertaken to obtain the stego image. In this paper, the use of graph wavelet transformation improved interpixel correlation, which resulted in the excellent visual quality of both the stego image and the extracted secret image. Our experiments show that the picture quality of both the cover image and the stego image is exactly the same.

     

一种指纹图像规格化期望值自动估计方法

图像规格化中的期望均值和方差值的选取,直接影响图像规格化后的质量和后续图像的处理。通过求取一组图像中少部分指纹图像的灰度边界值,对图像的期望均值和方差进行估计,并应用于该组中的其他指纹图像,可以使指纹图像获得较大的动态范围,而不至于使图像灰度受到限幅,从而实现对图像期望值的自动估计。实验结果表明,该方法简单可靠,能在不降低对比度情况下,保持图像的灰度层次。该方法鲁棒性好,能适应各种不同图像,是图像规格化的实用方法。采用该方法对图像的期望值进行自动估计,有利于自动指纹识别算法的改进以及采集设备无关性的相关研究。     

Multiresolution image motion detection and displacement estimation

A motion vision system is developed in which a moving object can be detected and image displacement can be estimated based on human visual characteristics and use of a multiresolution image. The system consists of four parts: (1) Temporal gradient, logic AND, and dynamic thresholding operations are used to obtain the primary mask. (2) A region growing algorithm is applied. (3) A hierarchical object detection algorithm is used to identify image patterns. (4) Displacement of the image is estimated by breaking each frame of the motion sequence into local regions (edges). A search is undertaken to discover how the image pattern within a given region appears displaced. This search takes the form of motion channels, the output of which are used to obtain the estimation of displacement. A correlative measure is proposed to match the patterns.     

遥感影像变化检测综述

变化检测作为土地利用/土地覆盖检测的关键技术,其目的是在同一区域不同时期的遥感数据中检测出变化的部分及其类型.针对传统的变化检测方法中存在繁重的人工劳动和检测结果效果差等问题,大量基于遥感影像的变化检测方法被提出.为了深入了解基于遥感影像的变化检测技术以及进一步研究变化检测方法,通过对大量有关变化检测的研究进行整理、分...     

Unit-Linking PCNN中的彩色图像边缘检测

PCNN模型具有相似群神经元同步发放脉冲的特性,适合于图像分割。对彩色图像的亮度分量进行对数变换,使其更符合人眼的视觉特性;在PCNN进行彩色图像R、G、B三分量分割的过程中,利用遗传算法进行神经元关键参数的选择,利用偏态指标进行迭代控制;在Unit-Linking PCNN模型中实现R、G、B三分量分割图的边缘检测,利用加权合并策略得到最终的边缘检测结果。仿真结果表明,该方法得到的结果体现了图像中更多的轮廓细节,具有很好的自适应性。     

Unit-Linking PCNN和图像熵的彩色图像分割与边缘检测

在RGB空间中,将最大香农熵准则和最小交叉熵准则两种评价准则与大概率合并和小概率合并两种合并策略相结合,提出基于Unit-Linking PCNN的四种彩色图像分割方案,并在各分量分割结果基础上利用Unit-Linking PCNN实施边缘检测,合并得到彩色图像的边缘检测结果。分析了各评价准则和合并策略的优劣,比较了各分割方案条件下的图像分割和边缘检测效果。与HSV空间中得到的相关结果进行分析比较,该文分割和边缘检测结果体现了图像的更多的细节,说明了在RGB空间中进行彩色图像分割和边缘检测的合理性。与相关文献结果相比,该方法的模型参数对图像分割结果的影响较不敏感。计算机仿真结果表明,该方法具有较好的彩色图像分割和边缘检测效果,具有较强适用性。     

遥感影像变化检测算法综述

目的 近年来遥感技术发展迅速,遥感影像变化检测作为最重要的关键技术之一,越来越多的应用在国土资源管理、地物变化、农林业的监测等领域。目前依据变化检测的流程的综述相对较少,大多数综述只针对变化信息提取的方法进行论述,为使国内外研究者对遥感影像变化检测理论、流程及其现存问题有一个比较全面的认识,对其进行系统综述。方法 通过对国内外大量的遥感影像变化检测算法进行总结、分类及比较,按照技术流程的各个环节分别论述,重点论述了变化信息提取环节中的图像分割、特征提取和分类的发展现状、基本思想及其趋势。结果 目前,多数变化检测算法主要是针对特定的条件具有较好的效果,还没有通用性算法,且现有算法在效率、精度、智能性等方面存在的问题,大多算法解决的问题及理论相对分散。结合现存问题及目前大数据影响下的技术发展状况,从数据类型、预处理方法、变化信息提取方法、算法效率、算法理论创新5个方面对遥感影像变化检测领域的未来发展趋势进行预测和展望。结论 遥感影像变化检测在多领域具有较高的研究价值,但针对目前变化检测存在的一些局限性还需要进行深入的研究,针对变化检测的研究需要从研究热点中挖掘创新思路、引入深度学习等发展趋势。     

Color image segmentation and parameter estimation in a markovian framework

An unsupervised color image segmentation algorithm is presented, using a Markov random field (MRF) pixel classification model. We propose a new method to estimate initial mean vectors effectively even if the histogram does not have clearly distinguishable peaks. The only parameter supplied by the user is the number of classes.     

Computer program for automatic estimation of digital image quality

The software system for identification of optoelectronic digital imaging systems and estimation of their quality is presented. The developed system allows to completely automate the estimation of noise, frequency response of the distoring system, and image resolution.     

Remote sensing image fusion based on Bayesian linear estimation

A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.     

An automatic region-based image segmentation algorithm for remote sensing applications

Object-based image analysis has proven its potentials for remote sensing applications, especially when using high-spatial resolution data. One of the first steps of object-based image analysis is to generate homogeneous regions from a pixel-based image, which is typically called the image segmentation process. This paper introduces a new automatic Region-based Image Segmentation Algorithm based on k-means clustering (RISA), specifically designed for remote sensing applications. The algorithm includes five steps: k-means clustering, segment initialization, seed generation, region growing, and region merging. RISA was evaluated using a case study focusing on land-cover classification for two sites: an agricultural area in the Republic of South Africa and a residential area in Fresno, CA. High spatial resolution SPOT 5 and QuickBird satellite imagery were used in the case study. RISA generated highly homogeneous regions based on visual inspection. The land-cover classification using the RISA-derived image segments resulted in higher accuracy than the classifications using the image segments derived from the Definiens software (eCognition) and original image pixels in combination with a minimum-distance classifier. Quantitative segmentation quality assessment using two object metrics showed RISA-derived segments successfully represented the reference objects.     

基于特征向量的遥感影像自动分类研究

为了实现高分辨率遥感影像自动分类及进一步提高非监督分类的精度和效率,提出了一种训练样本自动选取的面向对象自动分类方法。首先利用均值漂移算法对遥感影像进行分割,获取同质性分割单元;然后对分割对象进行多特征（光谱特征、纹理特征和形状特征）提取,基于特征向量的几何距离进行训练样本自动选择,进而利用支持向量机分类器得到分类结果。实验研究表明,提出的面向对象自动分类算法不但可以利用影像对象丰富的特征信息,而且较好地避免了“椒盐现象”,使自动分类的精度和效率得到较大提升。     

Color noise correlation-based splicing detection for image forensics

Multimedia Tools and Applications - Today, it has become very easy to manipulate digital images using image processing tools and software such as Adobe Photoshop (...     

遥感图像变化检测综述

主要介绍了变化检测的基本理论。首先简述和分析了遥感图像变化检测的基本概念,指出变化检测的本质是一类模式分类问题;然后全面回顾了现有变化检测方法,将其归纳为像素级、特征级和目标级三大类,详细论述了各种方法的基本原理和特点并对其适用范围和优缺点进行了总结。     

新型云区域检测算法

为了更加精确地检测出遥感图像中云区域的边界及细节信息,提出了将最小交叉熵和形态学相结合的方法来对遥感图像进行云区域检测。从遥感图像的灰度特征出发,通过最小交叉熵准则选取最优的阈值来检测图像中的云区域,再通过形态学的开运算,消除与云区域不相连或者被误判的小的光亮的地物信息,最后在彩色遥感图像上勾勒出云区域的边界。实验结果表明,该算法简单快速,能够很好地区分出云区域和下垫面,并且能够准确地对云区域边界细节信息做出判断。     

基于区域一致性测度的彩色图像边缘检测

针对在RGB空间很难有效区分颜色相似性的问题,选择HSI颜色空间进行图像处理和分析。首先计算饱和度、色度、亮度等色差分量,通过引入模糊熵,构造出一组基于模糊熵的信息测度分量来定量描述图像的边缘特征。利用训练样本获取该组分量,并组成一特征向量对BP神经网络进行训练,然后将训练的BP网络直接用于边缘检测。BP网络的结构和训练比较简单,而且不需要设定阈值检测边缘。实验表明,该方法具有较强的细节保持能力,达到了令人满意的边缘检测效果。