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基于内容的图像过滤新方法 总被引:2,自引:0,他引:2
随着互联网和多媒体技术的发展,色情图像在网络上的传播越来越泛滥.为了有效地过滤色情图像,提出了一种基于内容的图像过滤新方法.该方法首先利用统计肤色模型分割出皮肤区域,再通过轮廓勾勒算法提取皮肤区域边界,最后对边界点序列进行傅立叶变换,提取用傅立叶描述符表示的肤色区域的形状特征,进而利用分类器对图像进行分类来判断图像是否为色情图像.实验结果表明,该方法具有较好的准确率和误检率. 相似文献
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随着网络和多媒体技术的发展,互联网中色情图像的传播越来越泛滥,为有效地过滤敏感图像,文章提出一种基于Otsu自适应阈值分割的人体面积比例计算方法。整个检测系统利用人体肤色模型和面部识别模型,并结合面积比例识别算法等图像特征识别技术,实现对网络敏感图像的检测。实验结果表明,该方法具有较高的准确率和较快的实时在线检测速度,具有良好的实用性和应用价值。 相似文献
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互联网技术飞速发展给人们带来便利的同时,网络上大量色情淫秽等不良信息极大地干扰了正常的网络生活。根据当今网络不良视频的特点,文中提出了一种基于MPEG-7颜色描述子与动态肤色检测技术相结合的视频过滤算法。该算法综合考虑视频的静态信息和动态信息,采用支持向量机(SVM)进行学习分类,综合两类特征得到最终结果。通过实验分析,该算法有效提高了分类准确率,在当今网络环境中有着广泛的应用前景。 相似文献
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一种基于彩色图像的运动人体分割方法 总被引:4,自引:3,他引:4
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。 相似文献
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针对有明显色彩和亮度差异的图片,在进行传统的图像拼接后,会产生肉眼可见的拼接缝,严重影响拼接效果。因此,提出一种针对存在色差的图像拼接算法。首先,计算相邻图片间的色彩校正参数和每个彩色通道的全局色彩调整参数,选择适合的颜色和亮度,对每一幅图片进行色彩校正。其次,引入SIFT和RANSAC算法,实现了图像的准确配准。最后,采取0.1加权融合算法和平均融合算法结合进行图像融合,得到最终的全景图像。实验结果表明,由于已经进行图像间的校正,减少了待拼接图像的色差,使得图像的融合更加简单。该算法对于存在色差的图片序列可以实现无缝快速的拼接,并且可根据需要调整图像的色彩和亮度。 相似文献
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Wenjin Li 《Wireless Personal Communications》2018,103(2):1153-1160
Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy. 相似文献
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Qing-Zhong Li Wen-Jin Wang 《Journal of Visual Communication and Image Representation》2010,21(7):762-769
Underwater image compression has been the key technology for transmitting massive amount of image data via underwater acoustic channel with limited bandwidth. According to the characteristics of underwater color images, an efficient underwater image compression method has been developed. The new coding scheme employs a wavelet-based preprocessing method to remove the visual redundancy, and adopts a Wavelet Tree-based Wavelet Difference Reduction (WTWDR) algorithm to remove the spatial redundancy of underwater color images. Instead of scanning whole transformed image like the WDR method, the difference reduction coding is used for each significant wavelet tree in the proposed WTWDR algorithm based on the correlation between the subbands of higher levels and lower levels of a transformed image. The experimental results show that for underwater color images the proposed method outperforms both WDR and SPIHT at very low bit rates in terms of compression ratio and reconstructed quality, while for natural images it has similar performance with WDR and SPIHT. Hence, the proposed approach is especially suitable for underwater color image compression at very low bit rates. 相似文献
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本文给出了一种基于假彩色的像素级多传感器图像融合算法,并将其用于两幅灰度图像的融合中.这种算法将现有的图像融合技术和彩色显示技术相结合,在灰度融合图像的基础上利用色差来表现原图像与灰度融合图像的细节差异.该算法分为三步:首先,用选择与平均相结合的方法得到两幅原图像的灰度融合图像;接着,求出灰度融合图像与两幅原图像的差异图像;最后,将两幅差异图像和灰度融合图像分别送至R、G、B颜色通道进行显示.比起灰度融合图像,最终得到的彩色融合图像在色彩上更丰富,包含更多的细节,直觉上更容易辨认. 相似文献
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Object detection in unconstrained images is an important image understanding problem with many potential applications. There has been little success in creating a single algorithm that can detect arbitrary objects in unconstrained images; instead, algorithms typically must be customized for each specific object. Consequently, it typically requires a large number of exemplars (for rigid objects) or a large amount of human intuition (for nonrigid objects) to develop a robust algorithm. We present a robust algorithm designed to detect a class of compound color objects given a single model image. A compound color object is defined as having a set of multiple, particular colors arranged spatially in a particular way, including flags, logos, cartoon characters, people in uniforms, etc. Our approach is based on a particular type of spatial-color joint probability function called the color edge co-occurrence histogram. In addition, our algorithm employs perceptual color naming to handle color variation, and prescreening to limit the search scope (i.e., size and location) for the object. Experimental results demonstrated that the proposed algorithm is insensitive to object rotation, scaling, partial occlusion, and folding, outperforming a closely related algorithm based on color co-occurrence histograms by a decisive margin. 相似文献
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彩色图像分割是目前图像处理和模式识别中的一个重要研究领域.彩色图像可认为是由许多不同高斯随机变量共同作用而形成的,即利用高斯混合体模型可以描述彩色图像.图像中不同的部分对应数学模型中的不同高斯随机变量.因此,利用期望最大(EM)算法来求解随机变量的特征值,并用其对图像上的点进行分类,就可在一定程度上解决彩色图像分割的问题. 相似文献