共查询到17条相似文献,搜索用时 203 毫秒
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针对高分辨率合成孔径雷达图像设计了一种舰船目标几何特征提取算法.通过视觉注意机制检测目标区域的算法,通过频谱残差视觉显著计算模型求取显著图,完成显著区域的检测以实现舰船目标的初步定位,基于获得的视觉显著图采用最大熵算法完成阈值分割筛选出舰船区域.在提取的舰船切片的基础上,采用针对几何特征的提取算法,经图像预处理、方位角估计、旋转获取最佳表征舰船目标几何轮廓的外接矩形,相对有效准确地提取几何特征;最后,采用典型的TerraSAR-X数据进行仿真实验.结果表明,与传统方法相比,本文提出的频谱残差视觉模型完成合成孔径雷达图像舰船切片的区域分割能够有效降低虚警率,舰船目标的检测速度提高了25%~50%.该方法能够快速稳定地提取舰船目标的几何特征,也更加符合实际高分辨率图像舰船目标检测的应用需求. 相似文献
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基于视觉注意的彩色图像检索方法 总被引:1,自引:1,他引:0
基于视觉注意模型提取的特征能够反映图像高层语义的新特征,将视觉注意机制引入到图像分析领域能有效地减小语义鸿沟,获得高效的图像检索性能.根据视觉感知的特点,对Itti视觉注意模型进行了改进.采用主分量图表示亮度图,将纹理粗糙度信息融入视觉注意模型,进而提出了一种基于视觉注意空间分布特征的图像检索算法.首先由改进视觉注意模型将图像分解得到38个视觉特征图,然后采用网格平分法提取视觉特征图的空间分布信息,组成特征矢量来多层次地对图像特征进行描述,用于图像检索.实验结果表明,该算法利用基于改进注意力模型方法来提取图像空间分布特征进行图像检索,能获得较高的检索率. 相似文献
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基于视觉注意模型提取的特征能够反映图像高层语义的新特征,将视觉注意机制引入到图像分析领域能有效地减小语义鸿沟,获得高效的图像检索性能.根据视觉感知的特点,对Itti视觉注意模型进行了改进.采用主分量图表示亮度图,将纹理粗糙度信息融入视觉注意模型,进而提出了一种基于视觉注意空间分布特征的图像检索算法.首先由改进视觉注意模型将图像分解得到38个视觉特征图,然后采用网格平分法提取视觉特征图的空间分布信息,组成特征矢量来多层次地对图像特征进行描述,用于图像检索.实验结果表明,该算法利用基于改进注意力模型方法来提取图像空间分布特征进行图像检索,能获得较高的检索率. 相似文献
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提出了一种空域和时域相结合的视频显著性检测算法.对单帧图像,受视觉皮层层次化感知特性和Gestalt视觉心理学的启发,提出了一种层次化的静态显著图检测方法.在底层,通过符合生物视觉特性的特征图像(双对立颜色特征及亮度特征图像)的非线性简化模型来合成特征图像,形成多个候选显著区域;在中层,根据矩阵的最小Frobenius-范数(F-范数)性质选取竞争力最强的候选显著区域作为局部显著区域;在高层,利用Gestalt视觉心理学的核心理论,对在中层得到的局部显著区域进行整合,得到具有整体感知的空域显著图.对序列帧图像,基于运动目标在位置、运动幅度和运动方向一致性的假设,对Lucas-Kanade算法检测出的光流点进行二分类,排除噪声点的干扰,并利用光流点的运动幅度来衡量运动目标运动显著性.最后,基于人类视觉对动态信息与静态信息敏感度的差异提出了一种空域和时域显著图融合的通用模型.实验结果表明,该方法能够抑制视频背景中的噪声并且解决了运动目标稀疏等问题,能够较好地从复杂场景中检测出视频中的显著区域. 相似文献
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针对传统视觉显著性模型在自顶向下的任务指导和动态信息处理方面的不足,设计并实现了融入运动特征的视觉显著性模型。利用该模型提取了图像的静态特征和动态特征,静态特征的提取在图像的亮度、颜色和方向通道进行,运动特征的提取采用基于多尺度差分的特征提取方法实现,然后各通道分别通过滤波、差分得到显著图,在生成全局显著图时,提出多通道参数估计方法,计算图像感兴趣区域与眼动感兴趣区域的相似度,从而可在图像上准确定位目标位置。针对20组视频图像序列(每组50帧)进行了实验,结果表明:本文算法提取注意焦点即目标区域的平均相似度为0.87,使用本文算法能够根据不同任务情境,选择各特征通道的权重参数,从而可有效提高目标搜索的效率。 相似文献
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基于视觉熵的视觉注意计算模型 总被引:2,自引:1,他引:1
提出了一种基于视觉熵的模拟生物视觉信息处理的视觉沣意计算模型,在使用图像的低层特征的基础上,充分考虑了视觉熵中人眼对图像信息的主观量度.该模型首先将图像分成4×4的图像块,分别计算每个图像块的均值、方差、幅度和亮度变化率;采用中央邻域差算子得到各图像的特征图,合并成一个显著图;计算该图中每个显著区域的视觉熵,将最显著Ⅸ域作为视觉注意的焦点,利用人类视觉注意的抑制返回机制,实现视觉注意焦点的转移,从而完成模拟人类视觉注意的整个过程.实验结果表明,该模型基本上能够模拟视觉注意的过程,而且复杂度较低,具有较高的实用价值. 相似文献
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提出了一种鲁棒性基准孔检测方法。通过显著性检测对图像进行预处理,获得显著图,利用Hough圆检测算法提取显著图中基准孔感兴趣区域;基于双阈值进行轮廓分割,并利用主成分分析提取基准孔像素级轮廓,然后使用Bazen方法提取基准孔亚像素轮廓;基于随机采样一致性(RANSAC)原理实现基准孔高精度定位。实验结果表明,该算法的拟合精度高,对真实基准孔的定位误差为0.027 mm,检测性能优良,对于反光、刀具痕迹、光照不均匀、带倒角螺纹孔、内嵌轮廓、遮挡等情况下的基准孔检测仍具有很好的鲁棒性。 相似文献
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Most of the visual attention models are based on the concept of a two-dimensional saliency map, which encodes the conspicuity of the object in the visual scene. The visual attention model proposed by Laurent Itti is used in this work. In Itti's model, the saliency map is calculated via combining the information across several modalities, including color, intensity, and orientation. In this work, we propose a pre-training process to select the weightings used in the combining of feature maps to make the target more conspicuity in the saliency map. Harmony search (HS) algorithm is used in the pre-training process to obtain the weightings. HS is a new heuristic algorithm, which mimics the improvisation of music players. Its performance has been verified by many benchmark problems. We modify the pitch adjustment process of the original HS to improve the optimization performance and accelerate the convergence rate. The modified algorithm is named Gaussian harmony search (GHS). 相似文献
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A biologically inspired spatiotemporal saliency attention model based on entropy value is proposed in this paper. This model includes a dynamic attention phase and a static attention phase. In the dynamic attention phase, low-level visual features are extracted from current and some previous frames. Every feature map is resized into some different sizes. The feature maps in same size and same feature for all the frames are used to calculate the entropy value map. All the entropy maps are normalized and are fused into a dynamic saliency map. In the static attention phase, same features are extracted and form multi-scale feature maps by center-surround differences in current frame, and then those feature maps are transformed into conspicuity maps, which are linearly combined into a static saliency map. Our model decides salient regions based on a spatiotemporal saliency map which is generated by integration of the dynamic and the static saliency map. Experimental results indicate that: when there is noise among the frames or there is change of illumination among the frames, our model is excellent to Shi's model and Marat's model; when the moving objects do not belong to the static salient regions, our model is better than Ban's model. 相似文献
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Image fusion techniques aim at transferring useful information from the input source images to the fused image. The common assumption for most fusion approaches is that the useful information is defined by local features such as contrast, variance, and gradient. However, there is no consideration of global visual attention of the whole source images which indicates the “interesting” information of the source images. In this paper, we firstly review the patch-based image fusion methods which attract the attention and interest of many researchers. Then, a visual attention guided patch-based image fusion method is proposed. The visual attention maps of the source images are calculated from the sparse represent coefficients of the source images. Then, the sparse coefficients are fused with the guidance of visual attention maps in order to emphasize the global “interesting” objects in the source images. Finally, the fused image is reconstructed from the fused sparse coefficients. The new fusion strategy ensures that the objects being “interesting” for our visual system are preserved in the fused image. The proposed approach is tested on infrared and visual, medical, and multi-focus images. The results compared with those of traditional methods show obvious improvement in objective and subjective quality measurements. 相似文献
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针对传统显著性模型在自然图像的显著性物体检测中存在的缺陷,提出了一种利用背景原型(background prototypes)进行对比的视觉关注模型,以实现显著性物体的检测与提取;传统显著性模型主要通过计算区域中心与四周区域差异性实现显著性检测,而自然场景中显著性区域和背景区域往往都存在较大差异,导致在复杂图像中难以获得理想检测效果;基于背景原型对比度的显著性物体检测方法在图像分割生成的超像素图基础上,选择距离图像中心较远的图像区域作为背景原型区域,通过计算图像中任意区域与这些背景原型区域的颜色对比度准确检测和提取图像中的显著性物体;实验结果表明,基于背景原型对比度的显著性模型可以更好地滤除杂乱背景,产生更稳定、准确的显著图,在准确率、召回率和F-measure等关键性能和直观视觉效果上均优于目前最先进的显著性模型,计算复杂度低,利于应用推广。 相似文献
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Infrared and visible image fusion is a key problem in the field of multi-sensor image fusion. To better preserve the significant information of the infrared and visible images in the final fused image, the saliency maps of the source images is introduced into the fusion procedure. Firstly, under the framework of the joint sparse representation (JSR) model, the global and local saliency maps of the source images are obtained based on sparse coefficients. Then, a saliency detection model is proposed, which combines the global and local saliency maps to generate an integrated saliency map. Finally, a weighted fusion algorithm based on the integrated saliency map is developed to achieve the fusion progress. The experimental results show that our method is superior to the state-of-the-art methods in terms of several universal quality evaluation indexes, as well as in the visual quality. 相似文献
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传统的基于形状信息目标定位的算法,对目标观测角度发生形变情况下的定位存在不少困难,针对该问题,提出了一种基于稀疏活动轮廓模型的感兴趣目标(OOI)检测算法.首先通过共同勾画算法学习到感兴趣目标的稀疏活动轮廓模型,它能够清晰地定义感兴趣目标模式;同时构成该模型的Gabor轮廓基元可以通过扰动进行局部的调整以适配图像,在一... 相似文献