一种改进的LPT图像配准方法

研究图像配准的优化问题。图像配准是全景图生成的关键技术和图像处理研究的重要问题。传统的利用对数-极坐标变换(LPT)的配准方法存在着采样不均匀的特点,易导致计算量大和对图像中心区域变化敏感的缺点。为克服上述缺点,提出一种改进的LPT配准方法。上述方法主要分为两个步骤:第一步是采样点数随角向圆周半径调整,减少总采样点数;第二是对采样图像分别做径向和角向线积分投影,通过计算相对位移和修正步骤,得到图像的缩放比例和旋转角度,达到配准的目的。实验结果表明,改进配准方法既能够保持LPT的旋转和尺度不变性,又减少了计算量和减轻了图像中心区域变化的干扰,相对典型的LPT方法傅里叶-梅林算法,具有更高的配准精度和更好的鲁棒性。

对数极坐标变换识别算法在成像制导中的应用

图像匹配是成像制导系统中常用的目标识别算法,但实时目标图与基准目标图存在的尺度差异以及旋转不可避免,这也导致失配率提高。针对这个问题,引入对数极坐标变换运算,根据对数极坐标图像的尺度、旋转不变性提出了新的目标匹配识别算法,一方面保证了在尺度变化及旋转下的高匹配率;另外新算法结合了变换图目标区域的轴向投影统计策略,保证了匹配运算量的降低;文中仿真实例验证了算法的优势与有效性。

惯性组合导航系统的实时多级景象匹配算法

针对景象匹配辅助惯性组合导航系统需要快速准确获取飞行器位置、航向偏差的要求,提出一种实时多级景象匹配算法。算法分为两级,第一级粗匹配中提出了中心点4-邻域的抗变形算法,能抗旋转和小尺度变化的影响,使定位精度达到像素级;第二级精匹配中提出了基于分支特征点,应用最小二乘法原理精确匹配定位出两幅图像间的最优相似变换参数,即飞行器的精确位置和航向偏差的算法。仿真分析表明,提出的实时多级算法能满足景象匹配辅助惯性组合导航系统实时性、精确性和鲁棒性的要求。

矩形非均匀采样算法和对数级坐标变换算法的比较分析

针对对数极坐标变换引起的图像周边模糊和图像细节丢失等现象,提出了矩形非均匀采样方法。图像中心采样区的大小可根据实际需要进行选取,对其进行均匀采样,对周边区进行分层均匀采样,随距采样中心距离的增大,采样率逐渐降低。对矩形非均匀采样、经典LPT和变参数LPT三者的运算量进行了比较,总体上说,矩形方法具有最少的运算量,而且可以克服对数极坐标变换图像的扭曲,并便于硬件实现。

图像插值算法在对数极坐标变换中的应用

传统对数极坐标变换算法对图像具有平滑作用,易造成图像细节信息丢失。该文提出将一种基于小波变换的插值算法用于图像对数极坐标映射。该算法对图像作小波变换得到4个子图,并对各子图作对数极坐标变换,其中最优对应像素的确定根据不同子图的特点采用相应的插值算法,对子图映射图作逆小波变换。实验表明该算法是有效的。

傅里叶-梅林变换(FMT)在畸变-不变图像识别中的应用

针对尺度缩放和角度旋转变化目标相关识别率低的问题，在联合相关识别中加入傅里叶-梅林变换方法。采用傅里叶-梅林变换（FMT）中的对数极坐标变换、梅林变换、傅里叶变换具有的旋转、尺度、平移（RST）不变性，可以提高JTC图像识别的性能，实现畸变-不变图像的识别。利用联合变换相关器对角度旋转0°～40°、尺度变化0～20%的目标进行计算机仿真实验。实验结果表明：在JTC中采用FMT可以实现畸变-不变图像的识别。

基于子像素的对数极坐标反向变换研究

对数极坐标变换算法不仅是对人眼视觉系统中视网膜视皮层映射的数学描述,而且也是空间变分辨率理论的重要算法。映射变换中距离上的对数运算与角度上的反正切运算导致变换阵坐标存在小数,并且值域区间过于狭窄。针对这个问题,文章提出了子像素反向变换算法,新算法下的变换阵图像具有良好的连续性,同时减弱了马赛克现象。

人眼启发的图像非均匀映射变换模型构造算法(英文)

人眼的视觉信息采集是非均匀的。这种特征和注意力机制确保了人类视觉系统在信息处理方面的优先性。首先介绍了对数极坐标映射,然后基于人类视觉系统的构造,提出了非均匀映射模型的构造规则。基于此规则,以线性模型为例进行了应用性研究。扩展了非均匀映射模型的范围,促进了空间变分辨率视觉理论的进一步发展。

融合对数极坐标和改进窄带法的图像分割方法

基于曲线演化的水平集算法近来已被广泛应用于图像分割中,针对其计算速度慢的问题,提出一种新的基于改进窄带法的图像分割方法INBM(Improved Narrow Band Method)。INBM首先将均匀采样的图像映射到对数极坐标系中,由视网膜空间分辨率机制可知,注视点都在图像兴趣区,由此形成初始轮廓,然后用改进的窄带水平集(Level Set)方法演化曲线得到最终分割结果。改进窄带法是通过降低窄带区域内的水平集函数求解个数,来减少计算时间。实验结果表明,该方法大大提高了图像分割的速度。

利用对数极坐标的快速水平集算法

针对传统水平集算法计算效率低的缺点,提出了基于对数极坐标的快速水平集算法。该算法首先将均匀采样的图像映射到对数极坐标系下,再进行水平集演化运算,从而实现减少数据量、提高算法执行效率的目的,并对直接法、窄带法以及本方法的计算速度作了对比,实验结果表明,在同样获得理想分割结果的情况下,本方法分割尺寸为128×128,256×256,512×512的图像所用时间分别为0.751min,1.549min和6.113min,其计算速度要远快于另外两种方法。

一种融合对数极坐标映射和归一化割的图像分割算法

为了缩减归一化割算法中权值矩阵的规模,提出了一种融合对数极坐标和归一化割的图像分割算法。首先将均匀采样的图像映射到对数极坐标系下,然后进行归一化割运算,达到减少时间复杂度、提高算法执行效率的目的。实验结果表明该方法能够取得理想的分割效果。

对数极坐标映射的算法实现及扩展研究

对空间分辨率可变视觉系统中的基础理论:对数极坐标映射模型进行深人研究与探讨,提出实现映射算法和还原算法的详细步骤并加以分析。对于映射算法,指出非均匀映射变换的距离轴离散化与角度轴离散化具有统一的"一一映射环";对于还原算法,提出了基于子像素的最优像素构造算法和简单反向算法两种不同的算法,以适应不同环境下的要求.

Improved autonomous star identification algorithm

The log-polar transform （LPT） is introduced into the star identification because of its rotation invariance. An improved autonomous star identification algorithm is proposed in this paper to avoid the circular shift of the feature vector and to reduce the time consumed in the star identification algorithm using LPT. In the proposed algorithm, the star pattern of the same navigation star remains unchanged when the stellar image is rotated, which makes it able to reduce the star identification time. The logarithmic values of the plane distances between the navigation and its neighbor stars are adopted to structure the feature vector of the navigation star, which enhances the robustness of star identification. In addition, some efforts are made to make it able to find the identification result with fewer comparisons, instead of searching the whole feature database. The simulation results demonstrate that the proposed algorithm can effectively acceldrate the star identification. Moreover, the recognition rate and robustness by the proposed algorithm are better than those by the LPT algorithm and the modified grid algorithm.

Mutual Information Optimization Based Dynamic Log-Polar Image Registration

Log-polar transformation(LPT)is widely used in image registration due to its scale and rotation invariant properties.Through LPT,rotation and scale transformation can be made into translation displacement in log-polar coordinates,and phase correlation technique can be used to get the displacement.In LPT based image registration,constant samples in digitalization processing produce less precise and effective results.Thus,dynamic log-polar transformation(DLPT)is used in this paper.DLPT is a method that generates several sample sets in axes to produce several results and only the effective results are used to get the final results by using statistical approach.Therefore,DLPT can get more precise and effective transformation results than the conventional LPT.Mutual information(MI)is a similarity measure to align two images and has been used in image registration for a long time.An optimal transform for image registration can be obtained by maximizing MI between the two images.Image registration based on MI is robust in noisy,occlusion and illumination changing circumstance.In this paper,we study image registration using MI and DLPT.Experiments with digitalizing images and with real image datasets are performed,and the experimental results show that the combination of MI with DLPT is an effective and precise method for image registration.

Biological object recognition approach using space variant resolution and pigeon-inspired optimization for UAV

Recognizing the target from a rotated and scaled image is an important and difficult task for computer vision. Visual system of humans has a unique space variant resolution mechanism(SVR) and log-polar transformations(LPT) is a mapping method that is invariant to rotation and scale. Motivated by biological vision, we propose a novel global LPT based template-matching algorithm(GLPT-TM) which is invariant to rotational and scale changes; and with pigeon-inspired optimization(PIO) used to optimize search strategy, a hybrid model of SVR and pigeon-inspired optimization(SVRPIO) is proposed to accomplish object recognition for unmanned aerial vehicles(UAV) with rotational and scale changes of the target. To demonstrate the efficiency, effectiveness and reliability of the proposed method, a series of experiments are carried out. By rotating and scaling the sample image randomly and recognizing the target with the method, the experimental results demonstrate that our proposed method is not only efficient due to the optimization, but effective and accurate in recognizing the target for UAV.