基于统计肤色模型的敏感图像检测

针对敏感图像的特征,提出了一种基于肤色分布统计特征的敏感图像检测算法。首先,扫描由小波变换系数构造的零树得到图像的显著点,选择显著强的点作为初始检测集,根据检测集的邻接区颜色梯度特征直方图,采用最大熵模型检测显著性点邻接区肤色信息,利用置信传播算法估计模型参数检测肤色值。其次,由视觉感知的封闭轮廓获得肤色区域解决肤色特征光照敏感性问题。最后,采用多超球一类支持向量机进行分类。实验表明：算法分类准确率达96．32％,同时具有较快的分类速度,平均每秒处理7幅图像。     

基于PLBP的手背静脉识别

本文提出了一种基于PLBP的手背静脉识别算法。手背图片的获取采用的是低成本的红外成像设备。经过处理,图片被分为若干子图像。通过提取每一幅子图像的统一模式特征,构成了整幅图像的纹理特征向量。通过计算目标图像和测试图像的距离来表达两幅图片之间的相似度。本文所进行的实验是在通过定制的图像采集设备所采集的102个受测者的2040（每人20幅）幅图片上进行。实验结果表明,该方法优于其他的方法。     

粉状炸药自动包装工艺的改进研究

目的 针对印刷品表面缺陷检测中计算实时性差、缺陷类型识别率不高等问题,提出一种改进灰度共生矩阵(GLCM)的印刷品表面缺陷检测方法。方法 首先对主流的缺陷检测流程进行优化设计,通过对图像进行预处理和差分操作,判断待测印刷品表面是否存在形状缺陷;然后针对传统灰度共生矩阵的特征提取维度高、信息易丢失、旋转不变性差等问题,设计一种综合考虑效率和实时性的缺陷区域特征参数提取算法;最后结合得到的特征参量,通过基于支持向量机的分类器完成不同形状缺陷的分类识别。结果 实验结果表明,文中所设计的改进算法所提取的特征参量更能精确表征缺陷区域的特征,同时,特征参数的提取时间和缺陷分类识别率等指标均比传统检测方法更有优势。结论 在保证计算实时性的前提下,文中所设计的检测方法能有效完成印刷品表面缺陷区域的纹理特征识别能力,具有较高的分类识别率。     

多特征模糊推理的虹膜识别算法

为了更加完备地描述虹膜的纹理特征,利用虹膜图像灰度信息提取局部特征点、局部纹理方向和局部纹理的亮暗变化三种特征共同构成纹理的特征空间,克服了之前的多数虹膜识别算法提取单一特征易受干扰影响的局限性.然后,通过设计的模糊推理规则进行模式的分类,这种分段线性分类器的设计提高了算法线性分类的能力.分别在两个图库上进行了实验,识别率分别达到99.41%和99.67%,实验数据表明:结合多种特征能较好的反映虹膜的纹理变化特征,提高了虹膜识别的正确率,使算法具有非常优越的识别性能.     

基于直方图统计特征的虹膜粗分类

为改善大规模虹膜数据库中虹膜匹配算法的性能,本文提出了一种基于灰度统计特征的虹膜类型识别算法.该算法首先将虹膜纹理区域划分为8个子块,然后利用直方图比率提取虹膜类型的直方图统计特征.最后,根据对应子块特征的相似度将虹膜图像识别为五种类型.所提算法在CASIA虹膜库中挑选的500幅虹膜图像样本上进行了测试.仿真实验结果表...     

复杂底纹柔性卫生用品表面缺陷检测算法研究

目的 柔性卫生用品由于其材料表面不平整、产品容易发生变形等特性,给机器视觉中图像关键区域的定位和分割带来了很大问题,从而影响检测结果。为了解决复杂底纹柔性卫生用品表面脏点的检测问题,提出一种新型检测算法,以实现对复杂底纹柔性卫生用品快速又精准的检测。方法 在该检测算法中首先对采集的模板图像和样本图像进行预处理,在模板图像中创建一个合适特征模板,根据特征模板进行样本图像的匹配和定位;然后把经过精准定位后的样本图像进行关键区域分割;最后运用差影法把样本关键区域中的缺陷找出。该算法采用德国MVtec公司的halcon算子加以编程实现。结果 实验结果显示,该算法能够识别棉芯表面大于0.04 mm2的脏点,检测1幅图片的平均时间平均在100 ms,检测准确率为100%。结论 文中方法与传统方法相比,鲁棒性强,速度快,能够满足工业高速生产的需求。     

一种用于虹膜识别的眼睫毛遮挡检测算法

为了降低眼睫毛噪声对虹膜识别的影响,提高虹膜的识别率,本文提出了一种用于虹膜识别的眼睫毛遮挡检测算法.该方法根据Canny算法检测图像中的眼睫毛,对Canny算法中的参数进行研究,利用虹膜的外圆,提出了用一个扇形区域来表示眼睫毛遮挡区域.除去眼睫毛遮挡区域与瞳孔,虹膜外圆余下的部分即为虹膜的有效区域.实验结果表明,该方法可以有效地检测眼睫毛,并且所得到的扇形区域包含外圆内部所有的眼睫毛像素,可以有效提高模式的可分性和虹膜的识别率.     

多类型特征量的油水两相流流型识别算法

本文以收敛型微通道中油水两相流的流型识别为对象,将高速摄像法与神经网络算法相结合,提出了高效的、可视化的、智能化的两相流流型识别方法。该方法采用了包含图像纹理参数和流型无量纲参数的多类型特征量,更精准区分6种流型的不同特点,流型识别的收敛速度和准确率更高。BP神经网络的识别率为92. 5%,Elman神经网络的识别率为93. 7%,Elman神经网络在收敛速度与准确率方面优于BP神经网络。     

基于提升小波变换和分形维数的声纳图像识别

分形理论在图像的纹理识别中得到了广泛应用,由于分形维数不能反映图像的空间信息,容易造成误识别。针对该问题并结合声纳图像的特点,通过提升结构构造了Haar小波,并将提升小波变换同分形理论相结合,利用小波分解的多分辨率特点和分形维数的多尺度特性,提高图像的识别率。采用Levenberg-Marquardt(L-M)算法优化的BP神经网络对不同信噪比的声纳图像进行分类识别。实验结果表明,文中方法不论在识别率还是识别时间上均优于传统纹理识别方法。     

利用成像匹配与成分分离的航空图像云检测

研究了遥感图像云检测,提出了基于几何成像匹配与成分分离的航空图像云检测算法。由于传统的云检测算法未考虑云具有半透明性的特点,直接提取的云纹理包含有多余的下垫面纹理。该算法依据线性光谱混合模型,将一幅遥感图像看作是由下垫面与云的光谱线性地构成的,考虑像素间的局部平滑,进行云成分分离,采用局部二值模型(LBP)特征描述云的纹理,进而通过构建支持向量机(SVM)分类器进行云检测。对比实验结果表明,本文方法对于航空图像云检测具有一定的效果,对于薄云区域以及边缘区域也能有效检测。     

基于卷积神经网络的印刷品颜色缺陷检测

目的 提取样本图像颜色直方图特征对卷积神经网络进行训练,达到快速、高准确率检测图像颜色缺陷的目的.方法 将标准图像从RGB颜色空间转换至HSV颜色空间,通过改变图像H,S,V三分量值获取训练样本和测试样本;在HSV颜色空间中非均匀量化图像的颜色直方图,得到所有训练样本和测试样本的颜色直方图特征;利用样本图像颜色直方图特征训练卷积神经网络,然后对测试样本进行检测,研究检测的速度、准确率,并将该检测方法与逐像素、超像素、BP神经网络和支持向量机方法进行对比.结果 对于图片尺寸为512×512的彩色图像,卷积神经网络检测单幅图片的平均检测时间约为57.66 ms,训练样本图像为50000张时,卷积神经网络方法对10000张测试样本进行检测的准确率为99.77％.结论 卷积神经网络方法在保证高准确率的前提下大幅提高检测精度,对于印刷品色差缺陷在线检测具有良好的应用价值.     

A multimodal molecular image fusion method based on relative total variation and co-saliency detection

Image fusion can integrate complementary information from multimodal molecular images to provide an informative single result image. In order to obtain a better fusion effect, this article proposes a novel method based on relative total variation and co-saliency detection (RTVCSD). First, only the gray-scale anatomical image is decomposed into a base layer and a texture layer according to the relative total variation; then, the three-channel color functional image is transformed into the luminance and chroma (YUV) color space, and the luminance component Y is directly fused with the base layer of the anatomical image by comparing the co-saliency information; next, the fused base layer is linearly combined with the texture layer, and the obtained fused result is combined with the chroma information U and V of the functional image. Finally, the fused image is obtained by transforming back to the red–green–blue color space. The dataset consists of magnetic resonance imaging (MRI)/positron emission tomography images, MRI/single photon emission computed tomography (SPECT) images, computed tomography/SPECT images, and green fluorescent protein/phase contrast images, each category with 20 image pairs. Experimental results demonstrate that the proposed method RTVCSD outperforms the nine comparison algorithms in terms of visual effects and objective evaluation. RTVCSD well preserves the texture information of the anatomical image and the metabolism or protein distribution information of the functional image.     

基于支持向量机分类策略的多晶硅电池片色差检测

针对复杂颜色和纹理特征条件下,多晶硅电池片上的色差检测问题,提出了一种基于支持向量机分类策略的多晶硅电池片色差检测方法。首先对预处理后电池片图像进行颜色模型转换和通道分离,利用Otsu方法对单通道图像进行阈值分割处理,并计算各阈值图像的区域对比度,然后根据区域对比度情况选择合适的阈值图像,利用阈值图像所提供的信息提取图像特征;最后使用支持向量机分类器来判别电池片是否存在色差缺陷。实验结果表明提出的色差检测算法可以实现多晶硅电池片色差高效检测,色差缺陷检测的准确度、误检率和检测时间分别达到96.88%, 5％和109ms。     

Severity detection and infection level identification of tuberculosis using deep learning

Tuberculosis (TB) is a highly infectious disease and is one of the major health problems all over the world. The accurate detection of TB is a major challenge faced by most of the existing methods. This work addresses these issues and developed an effective mechanism for detecting TB using deep learning. Here, the color space transformation is applied for transforming the red green and blue image to LUV space, where L stands for luminance, U and V represent chromaticity values of color images. Then, adaptive thresholding is carried out for image segmentation and various features, like coverage, density, color histogram, area, length, and texture features, are extracted to enable effective classification. After the feature extraction, the size of the features is reduced using principal component analysis. The extracted features are subjected to fractional crow search-based deep convolutional neural network (FC-SVNN) for the classification. Then, the image level features, like bacilli count, bacilli area, scattering coefficients and skeleton features are considered to perform severity detection using proposed adaptive fractional crow (AFC)-deep CNN. Finally, the inflection level is determined using entropy, density and detection percentage. The proposed AFC-Deep CNN algorithm is designed by modifying FC algorithm using self-adaptive concept. The proposed AFC-Deep CNN shows better performance with maximum accuracy value as 0.935.     

基于卷积神经网络的光学遥感图像船只检测

目的研究无需进行复杂的图像预处理和人工特征提取,就能提高光学遥感图像的船只检测准确率和实现船只类型精细分类。方法对输入的检测图像,采用选择性搜索的方法产生船只候选区域,用已经标记好的训练样本对卷积神经网络进行监督训练,得到网络参数,然后使用经过监督训练的卷积神经网络提取抽象特征,并对候选区域进行分类,根据船只候选区域的分类概率同时确定船只的位置以及类型。结果与现有的2种检测方法进行对比,实验结果表明卷积神经网络能有效提高船只检测准确率,平均检测准确率达到了93.3%。结论该检测方法无需进行复杂的预处理,能同时对船只进行检测和分类,并能有效提高船只检测准确率。     

Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection

This proposal aims to enhance the accuracy of a dermoscopic skin cancer diagnosis with the aid of novel deep learning architecture. The proposed skin cancer detection model involves four main steps: (a) preprocessing, (b) segmentation, (c) feature extraction, and (d) classification. The dermoscopic images initially subjected to a preprocessing step that includes image enhancement and hair removal. After preprocessing, the segmentation of lesion is deployed by an optimized region growing algorithm. In the feature extraction phase, local features, color morphology features, and morphological transformation-based features are extracted. Moreover, the classification phase uses a modified deep learning algorithm by merging the optimization concept into recurrent neural network (RNN). As the main contribution, the region growing and RNN improved by the modified deer hunting optimization algorithm (DHOA) termed as Fitness Adaptive DHOA (FA-DHOA). Finally, the analysis has been performed to verify the effectiveness of the proposed method.     

图像处理与识别在果蔬成熟度监测中的研究及应用

目的 分析了果蔬成熟度自动监测对发展智慧农业的重要意义,对图像处理与识别技术在监测果蔬成熟度领域的研究与应用现状进行综述、总结与展望,以期为我国发展果蔬成熟度在线或自动检测识别技术提供参考。方法 对图像处理与识别在监测果蔬成熟度中的原理、优势进行分析,对特征提取、深度学习中的神经网络在该领域中的应用研究进展进行综述。结果 采用以图像处理和识别为核心的计算机视觉检测技术对果蔬的颜色、纹理等外部特征进行成熟度检测具有优势,结合神经网络对果蔬成熟度进行检测的识别率高,可在采摘、运输等场景对果蔬成熟度进行监测。结论 图像处理与识别技术在果蔬成熟度监测领域有望得到突破,将催生更多新的应用场景。     

A Method for Improving CNN-Based Image Recognition Using DCGAN

Image recognition has always been a hot research topic in the scientific community and industry. The emergence of convolutional neural networks(CNN) has made this technology turned into research focus on the field of computer vision, especially in image recognition. But it makes the recognition result largely dependent on the number and quality of training samples. Recently, DCGAN has become a frontier method for generating images, sounds, and videos. In this paper, DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model. We combine DCGAN with CNN for the second time. Use DCGAN to generate samples and training in image recognition model, which based by CNN. This method can enhance the classification model and effectively improve the accuracy of image recognition. In the experiment, we used the radar profile as dataset for 4 categories and achieved satisfactory classification performance. This paper applies image recognition technology to the meteorological field.     

Classification and Research of Skin Lesions Based on Machine Learning

Classification of skin lesions is a complex identification challenge. Due to the wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy. The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention. With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology. In this work, we try to classify seven kinds of lesion images by various models and methods of deep learning, common models of convolutional neural network in the field of image classification include ResNet, DenseNet and SENet, etc. We use a fine-tuning model with a multi-layer perceptron, by training the skin lesion model, in the validation set and test set we use data expansion based on multiple cropping, and use five models’ ensemble as the final results. The experimental results show that the program has good results in improving the sensitivity of skin lesion diagnosis.     

An Efficient Detection Approach of Content Aware Image Resizing

Content aware image resizing (CAIR) is an excellent technology used widely for image retarget. It can also be used to tamper with images and bring the trust crisis of image content to the public. Once an image is processed by CAIR, the correlation of local neighborhood pixels will be destructive. Although local binary patterns (LBP) can effectively describe the local texture, it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to noise. Therefore, to deal with the detection of CAIR, a novel forensic method based on improved local ternary patterns (ILTP) feature and gradient energy feature (GEF) is proposed in this paper. Firstly, the adaptive threshold of the original local ternary patterns (LTP) operator is improved, and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by CAIR. Secondly, the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery detection. Then, the ILTP features and the gradient energy features are concatenated into the combined features, and the combined features are used to train classifier. Finally support vector machine (SVM) is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or not. The candidate images are extracted from uncompressed color image database (UCID), then the training and testing sets are created. The experimental results with many test images show that the proposed method can detect CAIR tampering effectively, and that its performance is improved compared with other methods. It can achieve a better performance than the state-of-the-art approaches.