一种用于图像拼接的图像序列自动排序算法

为实现全景图拼接中图像序列的自动排序,提出了一种利用相位相关法判断两幅图像是否重叠以及两幅重叠图像的位置关系的完全自动的图像序列排序算法。该算法避免了图像序列排序中的人工干预。实验结果表明,该算法能有效地实现顺序混乱的图像序列的自动排序,并能很好地应用于全景图拼接。     

基于Hu向量相似度的图像序列自动排序算法

为了实现全景图拼接中图像序列的自动排序,提出了一种利用图像Hu矩向量相似度的图像序列全自动排序算法。首先利用图像的七个Hu矩构造一个矩向量,然后计算两个矩向量的欧氏距离,用以表示两幅图像的相似度,最后根据图像序列中各图像之间的相似度实现全自动排序。实验结果表明,该算法能有效地实现顺序混乱的图像序列的自动排序;同时,对图像序列中存在旋转及尺寸大小不同的特殊情况也能很好地处理。     

一种基于SIFT特征的航拍图像序列自动拼接方法

针对航拍图像序列拼接问题,提出一种基于尺度不变特征变换（SIFT）特征的自动拼接方法,该方法主要包括图像配准和镶嵌两个步骤。由于航拍序列各帧图像之间存在较大差异,因而常用的基于特征的配准方法适用性较差,对此,提出利用SIFT特征来实现准确、稳健的航拍图像配准。进一步的,提出一种基于视觉特征的色彩融合方法以取得平滑的镶嵌效果。最后,通过对真实航拍序列进行拼接实验验证了所提方法的有效性。     

基于改进ORB的无人机航拍图像拼接算法

为满足无人机航拍对图像拼接实时性和鲁棒性的要求,提出一种基于改进ORB的无人机航拍图像拼接算法。首先,特征检测引入SURF算法中的海森检测算子,解决尺度不变性问题;其次,用ORB算法进行特征描述,使其具备旋转不变性;接着采用汉明距离进行准确高效的图像特征粗匹配;然后,用KNN算法去除误差较大的匹配,缩小搜索范围,用RANSAC算法再次提纯;最后,使用泊松融合进行图像无缝融合拼接。实验表明,改进ORB算法实时性好、鲁棒性强,处理速度是SURF算法的5倍,具有抗噪性、旋转不变性和尺度不变性。     

一种改进的计数排序算法

提出了一种新的计数排序算法,在保持排序稳定性的前提下,相比原始计数排序适用范围更广、拥有更好的时间和空间效率。此外,大量实验数据表明,算法在给定的任何数据规模下,时间效率均优于原始计数排序。当输入规模达到元素取值上限的1/10时,相比原始计数排序时间效率提高了51%,相比快速排序提高了39%;当输入规模与元素取值上限相等时,相比原始计数排序时间效率提高了67%,相比快速排序提高了71%。随着数据规模的增长,效率提高的越明显。     

改进的航拍图像序列背景运动补偿技术

针对航拍图像序列提出用于提取图像特征点的最小亮度变化算法。利用特征点进行运动矢量估计,使用自适应十字模板搜索算法找到各个特征点的匹配点,结合随机样本一致算法估计背景运动模型的仿射变换参数,实现一种改进的背景运动补偿技术。该技术保障了航拍图像中运动目标检测的有效性。     

基于改进KAZE的无人机航拍图像拼接算法

为了更好地解决航拍图像易受光照、旋转变化、尺度变化等影响,KAZE算法实时性较差以及基于K近邻的特征匹配算法耗时较长等问题,该文提出了一种基于改进KAZE的无人机航拍图像拼接算法.该方法首先利用加速的KAZE算法提取图像的特征点,采用二进制特征描述子FREAK（Fast retina keypoint）进行特征点描述,然后使用Grid-KNN算法进行特征点粗匹配,利用随机一致性算法对匹配的特征点进一步提纯并计算几何变换模型,最后采用加权平均算法对图像进行融合.实验结果表明,该文所提算法使图像在光照变化、旋转变化及尺度变化下具有较好的性能,且处理速度较KAZE算法与K近邻特征匹配算法有较大提升,是一种稳定、精确度高、拼接效果良好的无人机航拍图像拼接方法.     

基于全局拼接的航拍图像拼接算法研究

研究航拍图像的拼接问题,提高图像拼接的准确度。由于当通过航拍获取的图像中部分图像相互存在重叠区域的比例不大时,造成拼接不准确。传统的区域的图像拼接算法无法将具有较小重叠区域的图像准确拼接。为了提高航拍图像拼接的准确率,提出一种全局的图像拼接算法,通过使用SIFT算法提取图像的SIFT特征点,根据位置误差最小的原则完成两幅图像的SIFT特征点匹配,最后利用整体最优化方法对拼接结果进行优化,采用全局特征点的拼接方法,可避免传统方法只利用重叠区域灰度特征而不能准确拼接低重叠度图像的问题。实验证明,改进方法利用图像的全局信息,准确地实现图像的拼接,取得了满意的结果。     

一种基于改进AOD-Net的航拍图像去雾算法

针对航拍图像易受雾气影响, AOD-Net (All in one dehazing network)算法对图像去雾后容易出现细节模糊、对比度过高和图像偏暗等问题, 本文提出了一种基于改进AOD-Net的航拍图像去雾算法. 本文主要从网络结构、损失函数、训练方式三个方面对AOD-Net进行改良. 首先在AOD-Net的第二个特征融合层上添加了第一层的特征图, 用全逐点卷积替换了传统卷积方式, 并用多尺度结构提升了网络对细节的处理能力. 然后用包含有图像重构损失函数、SSIM (Structural similarity)损失函数以及TV (Total variation)损失函数的复合损失函数优化去雾图的对比度、亮度以及色彩饱和度. 最后采用分段式的训练方式进一步提升了去雾图的质量. 实验结果表明, 经该算法去雾后的图像拥有令人满意的去雾结果, 图像的饱和度和对比度相较于AOD-Net更自然. 与其他对比算法相比, 该算法在合成图像实验、真实航拍图像实验以及算法耗时测试的综合表现上更好, 更适用于航拍图像实时去雾.     

一种改进的非支配排序遗传算法

提出了一种改进的非支配排序遗传算法。通过扩大第一代种群规模,在初期加速种群的进化;对选择算子引入概率操作来提高种群的多样性;同时引入混合交叉算子,动态调节算法的搜索空间。最后以收敛性和分布性作为性能指标,使用公开的多目标测试函数对其进行测试,并与基本的非支配排序遗传算法和改进的多目标粒子群算法进行比较。实验结果表明,改进后的非支配排序遗传算法在收敛性和分布性两方面均有提升。     

一种图像置乱的改进算法

提出了一种改进的基于混沌的图像置乱算法.该算法在对图像运用抽样技术预处理的基础上,利用Logistic映射产生的伪随机序列,对图像进行灰度值的变换和全局的块置乱.实验仿真表明,该加密算法具有良好的加密效果,能有效地抵御统计攻击和差分攻击.     

An improved image quality algorithm for exemplar-based image inpainting

Image inpainting is a common technique for repairing image regions that are scratched or damaged. This process involves reconstructing damaged parts and filling-in regions in which data/colour information is missing. There are many potential applications for image inpainting, such as repairing old images, repairing scratched images, removing unwanted objects, and filling-in missing areas. This paper develops an exemplar-based algorithm, one of the most important and popular image inpainting techniques, to fill-in missing regions caused by removing unwanted objects, image compression, scratches, or image transformation via the Internet. The proposed algorithm includes two phases of searching to select the best-matching information. In the first phase, the searching mechanism uses the entire image to find and select the most similar patches using the Euclidean distance. The second phase measures the distance between the location of the selected patches and the location of the patch to be filled. The performance of the proposed approach is evaluated through comprehensive experiments on several well-known images used in this area of research. The experimental results demonstrate the superior performance of the proposed approach over some state-of-the-art approaches in terms of quality in terms of both objective (using the peak signal-to-noise ratio (PSNR) as well as the structural similarity index method (SSIM)) and subjective (i.e., visual) measures.

     

An automatic fuzzy c-means algorithm for image segmentation

Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm must be estimated by expertise users to determine the cluster number. So, we propose an automatic fuzzy clustering algorithm (AFCM) for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known beforehand. In order to get better segmentation quality, this paper presents an algorithm based on AFCM algorithm, called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM). AMFCM algorithm incorporates spatial information into the membership function for clustering. The spatial function is the weighted summation of the membership function in the neighborhood of each pixel under consideration. Experimental results show that AMFCM algorithm not only can spontaneously estimate the appropriate number of clusters but also can get better segmentation quality.     

An image segmentation method using automatic threshold based on improved genetic selecting algorithm

In this paper, an image segmentation method using automatic threshold based on improved genetic selecting algorithm is presented. Optimal threshold for image segmentation is converted into an optimization problem in this new method. In order to achieve good effects for image segmentation, the optimal threshold is solved by using optimizing efficiency of improved genetic selecting algorithm that can achieve a global optimum. The genetic selecting algorithm is optimized by using simulated annealing temperature parameters to achieve appropriate selective pressures. Encoding, crossover, mutation operator and other parameters of genetic selecting algorithm are improved moderately in this method. It can overcome the shortcomings of the existing image segmentation methods, which only consider pixel gray value without considering spatial features and large computational complexity of these algorithms. Experiment results show that the new algorithm greatly reduces the optimization time, enhances the anti-noise performance of image segmentation, and improves the efficiency of image segmentation. Experimental results also show that the new algorithm can get better segmentation effect than that of Otsu’s method when the gray-level distribution of the background follows normal distribution approximately, and the target region is less than the background region. Therefore, the new method can facilitate subsequent processing for computer vision, and can be applied to realtime image segmentation.     

一种改进的桥梁图像分水岭分割算法

针对传统分水岭分割方法存在的过分割问题,提出了一种改进的桥梁图像分水岭分割算法。该算法首先对桥梁裂缝图像进行高低帽形态学滤波,并运用多尺度梯度算子提取梯度图像,在分水岭变换之前使用自适应的标记提取方法对区域极小值进行标定,然后对初步分水岭分割的过分割区域使用改进fisher距离的区域合并算法进行合并,取散度作为停止度量。实验表明,该算法减少了分水岭算法的过分割现象,提高了桥梁图像分割的精确性,具有很好的鲁棒性和适应性。     

An improved clustering analyzing algorithm for image index

This paper provides a survey of current clustering analysis in image indexing at first, then the problems are solved and the new directions in the future in this domain are pointed out. In the end, it proposes a new clustering algorithm with the combination of k-means clustering algorithm, genetic algorithm and chaotic optimization, a new idea of dimensionality reduction.     

An improved SPIHT algorithm for lossless image coding

In this paper, we propose a new wavelet-based lossless image coder that is based on a state-of-the-art algorithm, namely SPIHT (set partitioning in hierarchical trees). An algorithmic modification is introduced in order to increase its efficiency. This consists of adding a new test on direct descendants in the sets of type A to process the parent coefficients that are significant due to their nondirect descendants. Also, new sets of type C are defined to perform a separate sorting of the sets that have insignificant children. The idea behind the second proposition is to remove all tests over the entries (A, B and C) since the number of significant sets is much higher than that of insignificant sets. A number of experiments, carried out on various test images, demonstrates significant improvement over the conventional SPIHT for both greyscale and colour images.     

一种改进的医学图像压缩编码算法

提出了一种基于四叉树分割的分形图像编码的改进算法,通过调整父块与子块的误差来控制编码速度和解码质量。仿真实验表明,该算法有效地提高了图像编码的效率,具有可行性。     

An auditory onset detection algorithm for improved automatic source localization

An algorithm is described which detects auditory onsets quickly in arbitrary binaural audio streams. Aspects of the precedence effect are implemented to speed up computation, and to increase the usability of the output. The onset detector is tested with a number of binaural signals. Onsets that are suitable for spatial auditory processing are found reliably. This will allow spatial feature extraction to be performed.