共查询到18条相似文献,搜索用时 187 毫秒
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提出一种基于视觉注意机制的运动目标跟踪方法。该方法借鉴人类的视觉注意机制的研究成果,建立视觉注意机制的计算模型,计算视频中各部分内容的视觉显著性。结合视觉显著性计算结果,提取视频图像中的显著性目标。利用颜色分布模型作为目标的特征表示模型,与视频中各显著目标进行特征匹配,实现目标的跟踪。在多个视频序列中进行实验,并给出相应的实验结果及分析。实验结果表明,提出的目标检测与跟踪算法是正确有效的。 相似文献
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针对基于感兴趣区域的有损视频压缩在低码率编码条件下容易产生明显的编码人工痕迹,提出一种基于注意力权重矩阵的四元傅里叶变换的视觉显著性视频编码模型。该方法引入人眼视觉注意力权重矩阵对不同区域图像四元数予以加权,该四元数由图像的亮度、色度和运动特征组成。图像视觉显著图可由其四元数特征的四元傅里叶相位谱获取。结合中心凹恰可觉察失真(FJND)模型将其应用于基于感兴趣区域视频编码,可提高视频编码质量。与五种流行的显著性检测算法在两个大型眼动跟踪数据库上进行对比实验,结果表明提出的算法显著性检测精度明显高于对比算法。此外,与最新的基于显著性视频编码方法在10段标准视频上进行编码视频的主观质量对比,该方法能提高低码率编码视频的主观视觉质量,且优于对比算法。 相似文献
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鉴于在任务相关的视觉注意中,需要建立基于任务的视觉注意显著图来引导视觉注意,为此利用与人认知过程相接近的协同感知理论来研究基于任务的视觉注意计算模型,即首先利用协同识别理论研究二义及多义模式的视觉感知,得到协同视觉感知理论;然后将协同视觉感知中的模式与从视觉注意模型中提取的底层视觉特征相对应,利用偏置矩阵的性质计算底层视觉特征间受任务影响而产生的偏置,再由此偏置和底层视觉特征生成基于任务的视觉注意显著图;最后提出了基于协同感知理论的视觉选择注意计算模型。该算法用于基于任务的视觉搜索的实验结果表明,该算法是有效的,在认知上是合理的。 相似文献
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肇事车辆的锁定是智能交通系统中一个十分重要的问题,因此针对肇事车辆的锁定,提出了一种基于多层级联视觉注意模型的肇事车辆匹配方法.在模型的每一层中,基于传统视觉注意模型的思想,通过生成显著图的方式提取车辆的一个显著性特征,如颜色、车标,并将其与肇事车辆进行匹配,过滤掉特征不相似的车辆,经过多次显著性特征提取和匹配,最终获得唯一的肇事车辆.实验结果表明,该模型可以准确地从车辆数据库中锁定肇事车辆,且对光照变化和噪声有较强的鲁棒性. 相似文献
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基于人类视觉感知理论,提出一个改进的Itti视觉注意模型用于图像检索。该改进视觉注意模型是在充分考虑纹理特征与视觉感知关系的基础上,构造一个粗糙度图,用作视觉注意模型的一个初级视觉特征。首先通过该改进视觉注意模型得到50个视觉特征图;然后分别对每个视觉特征图采用局部二值模式傅里叶直方图(LBP-HF)方法抽取其分布信息,从而获得每幅图像的高维特征;最后利用局部保持投影(LPP)方法进行维数约简,以获取具有图像间局部几何和鉴别信息的低维特征用于图像检索。实验结果表明,该算法能获得较好的检索效果。 相似文献
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Ying YuAuthor Vitae Bin WangAuthor Vitae Liming ZhangAuthor Vitae 《Neurocomputing》2011,74(11):2008-2017
This paper proposes a bottom-up attention model based on pulsed Hebbian neural networks. The salience of the visual input can be generated through the networks using a simple normalization process, which can be calculated rapidly. Moreover, visual salience in this model can be represented as binary codes that mimic neuronal pulses in the human brain. Experimental results on psychophysical patterns and eye fixation prediction for natural images prove the effectiveness and efficiency of the model. In an arduous task of detecting ships in synthetic aperture radar (SAR) images, there are large amounts of data to be processed in real time. As a fast and effective technique for saliency detection, the proposed model is applied to ship detection in SAR images and its robustness against speckles is further proved. 相似文献
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A biologically inspired object-based visual attention model is proposed in this paper. This model includes a training phase
and an attention phase. In the training phase, all training targets are fused into a target class and all training backgrounds
are fused into a background class. Weight vector is computed as the ratio of the mean target class saliency and the mean background
class saliency for each feature. In the attention phase, for an attended scene, all feature maps are combined into a top-down
salience map with the weight vector by a hierarchy method. Then, top-down and bottom-up salience map are fused into a global
salience map which guides the visual attention. At last, the size of each salient region is obtained by maximizing entropy.
The merit of our model is that it can attend a class target object which can appear in the corresponding background class.
Experimental results indicate that: when the attended target object doesn’t always appear in the background corresponding
to that in the training images, our proposed model is excellent to Navalpakkam’s model and the top-down approach of VOCUS. 相似文献
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Visual hierarchy is an important notion in urban imagery research. As the skeletons of cities, urban streets attract more attention from urban residents and street network hierarchies are important references for urban planning and urban studies. However, due to the characteristic of over-regularization, it is often difficult for humans to differentiate visual salience for grid-like street networks, resulting in the hierarchies of grid-like streets yielded by existing methods being prone to cause visual cognitive confusion. Therefore, in this study, we proposed a novel model to quantify the extent to which a street attracts human visual attention through emulating the visual attention mechanism that can capture the focus of relatively significant elements at different levels of perception. Using the natural street (also known as the stroke) as the sensor unit, the comprehensive visual salience (CVS) index combining the geometric competitive factors of natural streets at the local scale and psychological competitive factors of natural streets at the global scale is designed. Finally, the visual salience of the urban natural streets is ranked by these CVS scores and the visual hierarchy is derived by the head/tail breaks scheme. The model was applied to eight typical grid-like street networks and the results show that the performance of visual discrimination on street hierarchies is greatly improved. Our hierarchy generation method could effectively detect visually prominent streets for grid-like street networks and generate the visual hierarchies of grid-like street networks that conform to the hierarchies perceived by human eyes. These results would provide helpful suggestions in practical urban street network applications. 相似文献
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Object-based visual attention for computer vision 总被引:6,自引:0,他引:6
In this paper, a novel model of object-based visual attention extending Duncan's Integrated Competition Hypothesis [Phil. Trans. R. Soc. London B 353 (1998) 1307-1317] is presented. In contrast to the attention mechanisms used in most previous machine vision systems which drive attention based on the spatial location hypothesis, the mechanisms which direct visual attention in our system are object-driven as well as feature-driven. The competition to gain visual attention occurs not only within an object but also between objects. For this purpose, two new mechanisms in the proposed model are described and analyzed in detail. The first mechanism computes the visual salience of objects and groupings; the second one implements the hierarchical selectivity of attentional shifts. The results of the new approach on synthetic and natural images are reported. 相似文献
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Chenggang Clarence YAN Hongtao XIE Bing ZHANG Yanping MA Qiong DAI Yizhi LIU 《Frontiers of Computer Science》2015,9(5):741
Although the distance between binary codes can be computed fast in Hamming space, linear search is not practical for large scale datasets. Therefore attention has been paid to the efficiency of performing approximate nearest neighbor search, in which hierarchical clustering trees (HCT) are widely used. However, HCT select cluster centers randomly and build indexes with the entire binary code, this degrades search performance. In this paper, we first propose a new clustering algorithm, which chooses cluster centers on the basis of relative distances and uses a more homogeneous partition of the dataset than HCT has to build the hierarchical clustering trees. Then, we present an algorithm to compress binary codes by extracting distinctive bits according to the standard deviation of each bit. Consequently, a new index is proposed using compressed binary codes based on hierarchical decomposition of binary spaces. Experiments conducted on reference datasets and a dataset of one billion binary codes demonstrate the effectiveness and efficiency of our method. 相似文献
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目的 稀疏编码是当前广泛使用的一种图像表示方法,针对稀疏编码及其改进算法计算过程复杂、费时等问题,提出一种哈希编码结合空间金字塔的图像分类算法。方法 首先,提取图像的局部特征点,构成局部特征点描述集。其次,学习自编码哈希函数,将局部特征点表示为二进制哈希编码。然后,在二进制哈希编码的基础上进行K均值聚类生成二进制视觉词典。最后,结合空间金字塔模型,将图像表示为空间金字塔直方图向量,并应用于图像分类。结果 在常用的Caltech-101和Scene-15数据集上进行实验验证,并和目前与稀疏编码相关的算法进行实验对比。与稀疏编码相关的算法相比,本文算法词典学习时间缩短了50%,在线编码速度提高了1.3~12.4倍,分类正确率提高了1%~5%。结论 提出了一种哈希编码结合空间金字塔的图像分类算法,利用哈希编码代替稀疏编码对局部特征点进行编码,并结合空间金字塔模型用于图像分类。实验结果表明,本文算法词典学习时间更短、编码速度更快,适用于在线词典学习和应用。 相似文献
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Salience detection is a principle mechanism to facilitate visual attention. A good visualization guides the observer's attention to the relevant aspects of the representation. Hence, the distribution of salience over a visualization image is an essential measure of the quality of the visualization. We describe a method for computing such a metric for a visualization image in the context of a given dataset. We show how this technique can be used to analyze a visualization's salience, improve an existing visualization, and choose the best representation from a set of alternatives. The usefulness of this proposed metric is illustrated using examples from information visualization, volume visualization and flow visualization. 相似文献
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一种基于混沌加密的自适应图像水印方法 总被引:7,自引:1,他引:6
针对数字水印所要求的安全性、鲁棒性和隐蔽性等特性,提出了一种使用二维混沌加密和人类视觉模型的小波域数字图像水印技术。研究实现了对有意义水印灰度图像的二维Logistic混沌映射加密算法,并结合人类视觉模型,计算加密二值水印的分块嵌入强度,自适应地完成水印在载体图像的小波分解系数中的嵌入和提取过程。实验结果表明,所实现的加密和水印嵌入算法计算量小,能承受常规的数字图像处理,具有良好的数字水印特性。 相似文献