Motion detection using the randomised Hough transform: exploitinggradient information and detecting multiple moving objects

An algorithm to detect one moving object using the randomised Hough transform (RHT) has previously been proposed. This new nonmodel-based method, called motion detection using randomised Hough transform (MDRHT), was shown be to applicable to translational and rotational motion detection of one moving object. The basic, earlier version of the MDRHT utilises edge points only as its features. The MDRHT is extended to use both edge pixels and the intensity-gradient vector at edge pixels. Moreover, the MDRHT method is generalised to detect also multiple moving objects. The translational motion experiments with the variant of the technique using gradient information and coping with several moving objects give promising results in two-dimensional-motion detection and estimation, compared with the earlier version of the MDRHT     

基于SVM的静态手写签名识别方法研究

研究离线（静态）签名文字识别的问题.针对静态手写签名只有空间信息,没有时序信息的问题,提出了一种基于支持向量机的签名文字识别算法.算法首先通过图像预处理提取签名图像,然后利用防射变化对初始签名图像进行配准和矫正,并在此基础上利用滑动窗口在不同方向上提取特征.最终的识别器训练利用支持向量机完成.通过仿真实验证明,提出的新的方法较其他方法具有更高的识别准确率,可以有效的实现离线签名字体的识别,该技术可应用在个人身份认证和识别系统中.     

基于trace特征的ISAR像空间目标识别

论文提出了一种基于trace特征的逆合成孔径雷达（Inverse Synthetic Aperture Radar,ISAR）像空间目标识别算法.首先将ISAR像进行分割与归一化处理,利用Canny边缘检测、Hough变换方法提取空间目标ISAR像最长轴,确保所提特征具有旋转不变性;然后仅对最长轴所在局部区域进行Trace变换生成空间目标ISAR像的局部trace矩阵,使得所提trace特征满足低维要求;再将trace矩阵每一列向量进行移位对准操作以消除ISAR像平移对识别带来的影响并将其作为空间目标识别的特征向量;最后在特征空间内以最小欧氏距离作为不相似度,采用集成分类器AdaBoost.M2-KNN完成了5类空间目标的分类识别.通过5类空间目标的ISAR数据对该方法进行目标识别验证,并与现有的几种ISAR像特征提取方法进行了对比.结果表明论文所提算法可行有效,可以明显地提高识别率.     

基于尺度不变特征变换的图像自动配准方法

运用尺度不变特征变换方法提取图像特征,采用基于圆域的特征主方向计算方法,强化了图像的特征旋转不变性能;利用k-d搜索树与简化的广义Hough变换相结合的方式进行图像特征匹配,提高了特征匹配的效率和精度。实验表明,该方法能够对不同分辨率、不同光照、不同拍摄角度的图像完成精确自动配准。     

一种遥感图像中的道路检测方法

道路是遥感图像的重要特征。提出一种从遥感图像中检测道路目标的方法。首先,进行图像预处理,提取边缘,然后进行图像分块并利用Hough变换进行道路检测。分析了Hough变换的特点,并研究了Hough变换域极值点参数的特点,改进了Hough变换在道路目标检测中的应用。该方法不仅快速地检测机场等严格的线状道路目标,而且对具有一定曲率的道路目标同样有效。     

基于PWHT的LFM-PRBC雷达信号截获与特征提取

线性调频-伪随机二相编码调相复合调制雷达信号(LFM-PRBC)融合了两者的优点,弥补了单一信号的不足,是一种性能较好的低截获概率雷达信号,给传统雷达截获接收机带来挑战。提出了将周期Wigner Hough变换(PWHT)与相关检测相结合的方法,通过在信号的周期WignerHough域搜索峰值,检测复合调制雷达信号并实现其特征提取。通过理论分析和计算机仿真验证,该方法能实现高斯白噪声背景下低信噪比复合调制信号的截获与特征提取。     

Robust photometric invariant features from the color tensor.

Luminance-based features are widely used as low-level input for computer vision applications, even when color data is available. The extension of feature detection to the color domain prevents information loss due to isoluminance and allows us to exploit the photometric information. To fully exploit the extra information in the color data, the vector nature of color data has to be taken into account and a sound framework is needed to combine feature and photometric invariance theory. In this paper, we focus on the structure tensor, or color tensor, which adequately handles the vector nature of color images. Further, we combine the features based on the color tensor with photometric invariant derivatives to arrive at photometric invariant features. We circumvent the drawback of unstable photometric invariants by deriving an uncertainty measure to accompany the photometric invariant derivatives. The uncertainty is incorporated in the color tensor, hereby allowing the computation of robust photometric invariant features. The combination of the photometric invariance theory and tensor-based features allows for detection of a variety of features such as photometric invariant edges, corners, optical flow, and curvature. The proposed features are tested for noise characteristics and robustness to photometric changes. Experiments show that the proposed features are robust to scene incidental events and that the proposed uncertainty measure improves the applicability of full invariants.     

Principal visual word discovery for automatic license plate detection

License plates detection is widely considered a solved problem, with many systems already in operation. However, the existing algorithms or systems work well only under some controlled conditions. There are still many challenges for license plate detection in an open environment, such as various observation angles, background clutter, scale changes, multiple plates, uneven illumination, and so on. In this paper, we propose a novel scheme to automatically locate license plates by principal visual word (PVW), discovery and local feature matching. Observing that characters in different license plates are duplicates of each other, we bring in the idea of using the bag-of-words (BoW) model popularly applied in partial-duplicate image search. Unlike the classic BoW model, for each plate character, we automatically discover the PVW characterized with geometric context. Given a new image, the license plates are extracted by matching local features with PVW. Besides license plate detection, our approach can also be extended to the detection of logos and trademarks. Due to the invariance virtue of scale-invariant feature transform feature, our method can adaptively deal with various changes in the license plates, such as rotation, scaling, illumination, etc. Promising results of the proposed approach are demonstrated with an experimental study in license plate detection.     

Quality-guided lane detection by deeply modeling sophisticated traffic context

Lane detection is a useful technique in modern autonomous vehicles systems, which assists vehicle to accurately localize itself according to detected road lines. Traditional methods leveraged edge detection and Hough transform based algorithms to plot lines along the detected lane. Noticeably, they did not take the informative feature road gradient into account. In addition, most previous deep learning-based algorithms consider lane detection as pixel-wise lane segmentation, where only fixed number of lanes can be detected. In order to solve these limitations, we propose a quality guided lane detection algorithm by modeling the sophisticated traffic context, where variable number of lanes can be satisfactorily handled. Specifically, we first leverage chessboard images for camera calibration to calculate correspondence between real world and image coordinate system. Subsequently, we capture image regions of interest that only contains lane information by leveraging the prior knowledge and image quality scores. Afterwards, we design an end-to-end two-stage CNN architecture for lane detection, where binary lane mask is utilized for lane matching. Comprehensive experiments have demonstrated that our proposed method can cope with variable number of lanes effectively.     

并行Hough 变换航迹起始

Hough 变换作为一种批处理航迹起始方法,混淆了传感器量测数据的时序信息,难以克服单次扫描数据的累积效应。该文通过改变Hough 变换处理结构和计数器累加方式,提出了一种并行处理结构的Hough 变换航迹起始算法。该算法利用Hough 变换将不同时刻的量测集合分别映射到参数空间,继而将空间中具有相同索引的各次累加结果构成累加向量;再根据建立的参数空间累加规则,利用滑窗法来确定累积矩阵的输出,最后通过门限检测实现航迹起始判决。对密集杂波环境下不同扫描周期下的航迹起始问题进行了仿真验证,结果表明了并行Hough变换起始算法的有效性。      

一种改进的Hough变换提取圆的方法

本文提出了一种基于方向可变滤波器的改进Hough变换方法。该方法首先利用方向可变滤波器检测出图像边缘以及边缘的方向特性,然后基于边缘点及其方向,通过改进的Hougll变换得到圆心、半径。最后,将该算法应用于球类对象分割并得到较好的结果。     

Output SNR Analysis of the LPP‐Hough Transform

Recently, a new method called the local polynomial periodogram‐Hough transform (LHT) was proposed for linear frequency modulated (LFM) signal detection. In this letter, a closed‐form expression of the output signal‐to‐noise ratio is derived for the LHT, showing that the method exhibits a threshold effect for LFM signal detection. Comparisons with the pseudo‐Wigner‐Hough transform (PWHT) show that the threshold of the LHT is lower (better) than that of the PWHT.     

基于海天/海岸线检测的舰船目标提取方法

针对成像型反舰导弹所获红外图像的特点,提出了一种将海天/海岸线检测与目标提取相结合的舰船目标提取方法.该方法先综合运用Sobel边缘检测和局部Hough变换等数字图像处理技术从红外图像中检测出海天/海岸线,然后通过对Kapur熵二值化图像进行投影变换实现舰船目标提取.实验结果表明,该方法能够有效地确定舰船目标在海天/海...     

基于特征匹配的水下目标检测与识别

针对水下特殊环境的目标检测与识别,提出一种基于霍夫变换与几何特征相结合的特征匹配算法。通过对Canny边缘检测和迭代阈值分割的有机融合,实现了对图像的后处理式联合分割,有效地减小了光照不均等噪声干扰对目标提取的影响。通过霍夫变换直线匹配和几何特征匹配算法对规则的几何目标进行识别,具有较好的抗旋转和抗缩放性能。实验结果表明了该算法的准确性和稳定性。     

基于积分投影和Hough变换圆检测的人眼精确定位方法研究

眼睛定位是人脸识别的非常重要的一个环节,后续的图像几何归一化、图像矫正和特征提取都依赖于眼睛的位置。提出一种人眼快速定位的新方法。首先用积分投影法检测到眼睛瞳孔的大致位置;然后用Sobel边缘检测算子提取图像边缘信息;再利用基于圆的Hough变换快速定位出人眼,实验证明了本方法的有效性。     

Novel detection of conics using 2-D Hough planes

The authors present a new approach to the use of the Hough transform for the detection of ellipses in a 2-D image. In the proposed algorithm, the conventional 5-D Hough voting space is replaced by four 2-D Hough planes which require only 90 kbytes of memory for a 384×256 image. One of the main differences between the proposed transform and other techniques is the way to extract feature points from the image under question. For the accumulation process in the Hough domain, an inherent property of the suggested algorithm is its capability to effect verification. Experimental results from the authors' work on real and synthetic images show that a significant improvement of the recognition is achieved as compared to other algorithms. Furthermore, the proposed algorithm is applicable to the detection of both circular and elliptical objects concurrently     

Decision-theoretic approach to visual inspection using neuralnetworks

The Bayesian real-time network (BARTIN) is applied to solving a visual-inspection problem requiring translation, rotation and scale (TRS) invariance. The system is capable of classifying n-fold symmetric engineering parts from near-axial views which may contain more than one part. It is evaluated and compared with other approaches using real visual-inspection data. A novel TRS-invariant preprocessor, the polygon transform, which is optimised for near-circular objects, provides information about the line and circle structure in two-dimensional images. An integral part of the polygon transform is a new Hough transform for circle radii used for both scale invariance and image characterisation. The BARTIN formalism is presented from the viewpoint of subjective Bayesian analysis, and this approach demonstrates how the personal probabilities and utilities of BARTIN can be used to optimise an externally provided reward function. A method is given for adjusting the global level of caution. To handle sparse training data, parameter parsimony in the observer was achieved using a structure comprising a stripped-out Parzen-windows classifier followed by a softmax perceptron trim. For real-time operation, the system is initialised by pretraining it using data extracted from design drawings     

Colour image detection and matching using modified generalisedHough transform

A new approach to 2-dimensional (2D) colour-image detection and matching using a modified version of the generalised Hough transform (GHT) is proposed. In the conventional GHT, the useful colour information existing in the input image and the relationship between each pixel and its neighbourhood are not used. Furthermore, lighting changes in the image are not usually considered. Therefore, the conventional GHT is seldom applied to colour images. In the proposed approach, lighting are removed using normalised colour values. Next, certain critical pixels of an input colour image whose neighbourhoods have larger variances of normalised colour values are extracted. For each critical pixel, a feature vector, which includes the normalised colour values of the pixel as well as those of the pixel's neighbours, is then constructed. A modified voting rule for the GHT is therefore proposed which is based on a similarity-measure function of the feature vectors. High maximum peaks in the cell array are searched finally as the result. The proposed method is robust for colour-image detection and matching in noisy, occlusive, and lighting-change environments, as demonstrated by experimental results     

基于Hough变换与目标主轴提取的SAR图像目标方位角估计方法

该文提出了一种基于Hough变换与目标主轴提取的SAR图像目标方位角估计方法。针对传统基于Hough变换估计方法的不足,在估计过程中引入目标的主轴信息指导方位角的估计。利用基于马尔可夫随机场(MRF)的图像分割提取准确的目标成像轮廓。在此基础上,基于Hough变换检测目标轮廓的直边,基于轮廓特征点检测获得目标主轴的大致方向,结合Hough变换检测结果与目标主轴信息获得最终的方位角估计。实验结果验证了该文方法的有效性。