共查询到18条相似文献,搜索用时 109 毫秒
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鱼眼镜头摄像机具有大视场角的特点,常用于视觉监控、虚拟实景技术以及机器人导航等许多计算机视觉领域的应用中。鱼眼图像的变形给后期的图像处理带来麻烦,所以在后期处理中首先需要解决鱼眼影像的畸变问题,所以必须进行校正。本文根据球面坐标定位法,从鱼眼镜头的成像原理,鱼眼图像有效区域的提取,及鱼眼中心和半径的确定,详细地介绍了鱼眼图像的校正过程。 相似文献
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基于渐变方程的鱼眼图像校正方法 总被引:2,自引:0,他引:2
由于具有短焦距、大视场等特点,近年来鱼眼镜头被广泛应用于不同领域。但同时也存在鱼眼图像畸变较严重的问题,针对这一问题,提出了一种基于渐变方程和经纬映射模型的图像校正算法。利用经纬映射模型导出鱼眼图像中的相互关系,提取校正前后坐标的关系方程,然后引入渐变方程,实现图像校正。实验结果表明,该算法具有良好的校正效果。 相似文献
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大视场角的鱼眼镜头在成像时由于不同视场垂轴放大率不同,从而产生畸变使最终成像弯曲变形,这为识别和测量等应用带来了不便.针对传统经度校正方法的不足,将算法改进为双向经度鱼眼图像快速校正算法.通过对鱼眼图像有效区域进行划分,并对不同区域内的畸变点在横、纵两个方向上分别建立校正模型,确定畸变图像与理想图像之间坐标映射关系,求取校正坐标的位置.最后对图像进行非线性拉伸,改善图像中心与边缘放大率不同而产生的“膨胀感”,获得符合人眼视觉习惯的图像.运用Matlab对3组图像进行校正实验,结果表明,该算法能快速有效地对鱼眼图像进行畸变校正. 相似文献
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鱼眼镜头可以克服普通镜头视场小的缺点,但是鱼眼图像具有严重的桶形畸变,在利用鱼眼图像信息之前需要对鱼眼图像进行校正展开。鱼眼图像轮廓的提取是图像校正前至关重要的步骤,影响最终校正的效果。其中扫描线逼近算法计算量最小,速度最快,应用最广,但仍具有抗噪能力差的缺陷。针对此问题,本文提出了一种改进算法,首先引入扫描步长采用新的扫描方案提高算法的运行速度,然后采用新的阈值计算方法实现自动阈值的选取,最后利用局部二值化去噪思想抑制噪声,实验结果表明在不增加算法运行时间前提下改进算法能够有效改善鱼眼图像轮廓的提取效果,有很强的实用价值。 相似文献
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鱼眼图像在全景图拼接中优势明显,然而,由于鱼眼图像存在严重的透视畸变缺陷,往往导致拼接图像效果不理想,甚至导致拼接失败,针对该问题,提出基于 SIFT特征的鱼眼图像拼接算法,该算法先对鱼眼图像进行校正,采用SIFT特征算子对校正后的鱼眼图像进行特征提取和匹配,再利用RANSAC鲁棒算法对变换参数进行估计,最后使用加权平均法对图像进行拼接融合? 通过对不同场景下、不同重叠区域图像的全景图像拼接实验验证,与现有的拼接方法相比较,同等拼接质量下该算法得到的全景图像能够有效减少计算量、减少拼接次数,证明了该算法的适用性和有效性。 相似文献
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分割线扫描鱼眼图像有效区域提取算法 总被引:1,自引:0,他引:1
对于超大广角鱼眼图像的校正,关键是必须准确地提取出鱼眼图像的有效区域,并且同时确定有效区域的中心和半径.现有的鱼眼镜头校正算法针对高清鱼眼视频监控有一定的不足,因此为满足高清鱼眼视频监控的高分辨率需求,在现有算法的基础上提出一种分割线扫描算法.该算法首先划定一条特定水平分割线,由此寻找最接近边界线的范围区域,减少不必要像素点的检测,然后在此区域内精确寻找有效区域边界线.经实验表明,该算法在不降低提取精度的同时,可以显著降低鱼眼图像有效区域提取的时间复杂度和计算量,将以往算法的200万计算量降低到万量级以下. 相似文献
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目前已有的轮廓提取算法在提取可视化角膜生物力学分析仪(corneal visualization scheimpflug technology, Corvis ST)影像的角膜轮廓中,由于角膜边缘的局部图像灰度分布相近这一特点,提取出的角膜轮廓普遍不完整或者提取的角膜轮廓边缘出现细小突出。这会使得角膜轮廓的完整性遭到破坏,提取到的角膜轮廓与实际的角膜原始图像严重不符。本文针对Corvis ST采集的角膜图像的轮廓提取问题,基于最大类间方差法(OTSU)算法设计一种高效的图像处理方法。首先,将角膜图像进行除杂、灰度化以及图像降噪等处理,达到减少图像计算量和降低数字图像噪声干扰的目的;其次,基于OTSU算法对图像进行分割,并在此基础上加入数学形态学运算,达到平滑图像边界和填充细小“孔洞”的目的;最后,采用Canny边缘检测算法提取图像中的角膜轮廓,达成提取出高完整性角膜轮廓的目的。在相同的图像数据集上,与最新的纽扣轮廓瑕疵检测系统中设计的轮廓提取算法(B-OTSU algorithm)进行了对比实验。实验结果表明,从轮廓完整性以及准确性的角度,应用本文方法提取的角膜轮廓明显优于最新的纽扣轮... 相似文献
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Neural network based methods for fisheye distortion correction are effective and increasingly popular, although training network require a high amount of labeled data. In this paper, we propose an unsupervised fisheye correction network to address the aforementioned issue. During the training process, the predicted parameters are employed to correct strong distortion that exists in the fisheye image and synthesize the corresponding distortion using the original distortion-free image. Thus, the network is constrained with bidirectional loss to obtain more accurate distortion parameters. We calculate the two losses at the image level as opposed to directly minimizing the difference between the predicted and ground truth of distortion parameters. Additionally, we leverage the geometric prior that the distortion distribution depends on the geometric regions of fisheye images and the straight line should be straight in the corrected images. The network focuses more on the geometric prior regions as opposed to equally perceiving the whole image without any attention mechanisms. To generate more appealing corrected results in visual appearance, we introduce a coarse-to-fine inpainting network to fill the hole regions caused by the irreversible mapping function using distortion parameters. Each module of the proposed network is differentiable, and thus the entire framework is completely end-to-end. When compared with the previous supervised methods, our method is more flexible and shows better practical applications for distortion rectification. The experiment results demonstrate that our proposed method outperforms state-of-the-art methods on the correction performance without any labeled distortion parameters. 相似文献
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一种针对车载全景系统的图像拼接算法的仿真 总被引:1,自引:0,他引:1
传统的全景图像拼接算法,多采用Harris角点的特征提取或尺度不变特征转换?(SIFT)的特征匹配算子的方式,对存在重合部分的图像进行图像拼接处理。但对于车载全景图像拼接算法而言,车身四周采集到的4幅鱼眼畸变图像,使用特征提取算子的方法进行的拼接,运算的复杂度高,效率低,不能满足车载设备的实时性要求。针对这一问题,该文提出一种专门应用在车载系统的车载全景图像拼接算法,并对其进行Matlab仿真,最大限度提高算法的运算效率,以满足车载系统实时性的要求,真实的反应路况信息,辅助驾驶员安全驾驶。 相似文献
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为了实现对单幅红外图像的非均匀性校正并且对局部细节的校正效果进行优化,提出了一种局部自适应的非均匀校正算法。算法利用红外焦平面阵列的固定图像噪声呈单方向分布的特性,采用基于高斯权重思想的中值直方图非均匀算法实现红外图像的单参数校正;然后将图像分块,使图像的各局部都能够自适应地选择各自最合适的校正参数,达到优化细节的校正效果。实验结果及分析表明:与单参数中值直方图非均匀校正算法相比,提出的算法在均方根误差、峰值信噪比、图像平滑性等方面都得到了进一步的改善,并且保留了更多的图像细节,为非均匀校正提供了一种方法。 相似文献
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An array of existing active contour models is prone to suffering from the deficiencies of poor anti-noise ability, initialization sensitivity, and slow convergence. In order to handle these problems, a robust hybrid active contour method based on bias correction is proposed in this research paper The energy functional is formulated through incorporating the adaptive edge indicator function and level set formulation driven by bias field correction. The adaptive edge indicator function, which is formulated based on image gradient information, is utilized to detect object boundaries and accelerate the segmentation in the homogeneous region. The level set formulation is constructed based on an improved criterion function, in which bias field information is considered. Specifically, the bias field distribution is approximated through the local mean gray value algorithm as a prior. Moreover, a new regularized function is proposed so as to maintain the stability of curve evolution. The segmentation process is implemented by the optimized energy function and the novel regularized term. Compared to previous active contour models, the modified active contour method can yield more precise, stable, and efficient segmentation results on some challenging images. 相似文献
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红外焦平面阵列的非均匀性严重限制了红外成像系统对目标的灵敏度,降低了图像的成像质量。本文基于图像块统计特性的期望块对数似然概率(EPLL),提出了基于图像块先验的单帧红外图像非均匀性校正算法,可以完成单帧红外图像的非均匀性校正。该算法首先利用高斯混合模型完成对图像块训练分类,然后利用EPLL准则获取带有非均匀性噪声图像对应的最大似然概率对数期望图像来完成对图像的校正。仿真试验和真实红外图像实验结果表明该方法对单帧红外图像的非均匀性校正效果良好,校正速度快,可以避免鬼影的产生并且可以完成持续性工作。 相似文献
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This paper proposes a global mapping algorithm for multiple robots from an omnidirectional‐vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas–Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots. The multi‐robot mapping algorithm draws a global map by using map data obtained from all of the individual robots. Global mapping takes a long time to process because it exchanges map data from individual robots while searching all areas. An omnidirectional image sensor has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. The process calculations of the correction algorithm are improved over existing methods by correcting only the object's feature points. The proposed algorithm has two steps: first, a local map is created based on an omnidirectional‐vision SLAM approach for individual robots. Second, a global map is generated by merging individual maps from multiple robots. The reliability of the proposed mapping algorithm is verified through a comparison of maps based on the proposed algorithm and real maps. 相似文献
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Region-based approach to robust lip contour extraction 总被引:2,自引:0,他引:2
A lip contour extraction algorithm based on a deformable geometric model is proposed. The algorithm employs a stochastic cost function to find an optimum partition of an image into lip and nonlip regions such that the joint probability of the two regions is maximised. Experiments show that robust lip contour extraction from colour lip images is possible 相似文献