快速鲁棒特征算法的CUDA加速优化

提出一种基于统一计算设备架构(Compute Unified Device Architecture,CUDA)的快速鲁棒特征(Speed-up Robust Feature,SURF)图像匹配算法。分析了SURF算法的并行性,在图像处理单元(Graphics Processing Unit,GPU)的线程映射和内存模型方面对算法的构建尺度空间、特征点提取、特征点主方向的确定、特征描述子的生成及特征匹配5个步骤进行CUDA加速优化。实验表明,相比适用于CPU的SURF算法,文中提出的适用于GPU的SURF算法在处理30MB的图片时性能提高了33倍。适用于GPU的SURF算法拓展了SURF算法在遥感等领域的快速应用,尤其是大影像的快速配准。     

一种基于图像匹配的地面交通标志实时识别方法

地面交通标志检测识别是智能驾驶领域的一个研究方向,实时性、准确率是该研究的重点。图像匹配的方法是常用的模式识别方法。文中介绍了一种结合先验知识和图像匹配的地面交通标志检测识别方法。算法包括两部分:预处理和检测识别。预处理阶段包括图像压缩、感兴趣区域提取、形态学处理、中值滤波和逆透视等步骤,实现图像降噪和正畸,为检测识别做准备。检测识别阶段包括轮廓提取、面积过滤、图像匹配等步骤,目的是判断待测图像是否含有地面交通标志及其种类。实验证明,该算法实时性好、鲁棒性强、准确率高。     

一种二次阈值调整SIFT算法

SIFT算法因其良好的特征提取和匹配效果得到了广泛的应用,但在光照不足和模糊条件下其效果不能令人满意,为此提出了一种基于全局信息和局部信息的自适应SIFT算法。利用图像的对比度信息得到初始阈值,使该阈值适应光照不足和模糊图像,根据周围特征点分布情况来对阈值进行二次调整以控制特征点数目及分布,并改进了误匹配剔除方法。实验结果表明,改进后的SIFT算法不仅能很好地适应低光照和模糊图像,而且可以调节特征点数目,降低簇效应。     

图像增强技术在安防监控系统中的应用

数字化的图像处理技术里面对于各个综合知识的跨度是相当大的,对于各个知识面的要求也是十分高的。那么图像处理技术在实际的生活应用中也是被广泛运用。随着科技时代的到来,人们对于图像的画质要求更高,所以对于图像处理技术的研发已经是整个行业必须解决的问题。那么在这样一个要求越来越高的情形下,在视频监控行业里面图像处理技术起到了那些作用？是怎么样去应用到安防监控行业里面的？本文针对这些问题作出了一些简单的讨论和研究。     

A new approach for inner-knuckle-print recognition

ObjectiveThis paper proposed a new approach for inner-knuckle-print (IKP) recognition. In traditional IKP recognition systems, the region of interest (ROI) is extracted from the image of the whole hand and the directions of the fingers being imaged are not restricted. The result maybe incorrect because that the shape and surface of the fingers may vary greatly. Moreover, if the direction of the finger being imaged is not restricted, there may be severe rotation transform between intra-class IKPs. To overcome these drawbacks, we develop a new data acquisition scheme as well as an efficient personal authentication algorithm.MethodsThe new scheme is designed to capture the image of the inner surface of the middle knuckles of the middle and ring fingers. The fingers being imaged are kept horizontal with two pegs, so that the rotation angle between different images obtained from the same hand can be minimized. The new personal authentication algorithm consists of the next four steps. Firstly, two regions of interest (ROI), each of which contains the inner surface of a knuckle, are cropped from the original image. Secondly, line features are extracted from the ROIs based on the combination of Gabor filtering and derivative line detection method. Then, binary line images are matched by using a cross-correlation-based method. Finally, the input data is classified through score level fusion.ResultsTo evaluate the proposed IKP recognition system, a finger image database which includes 2000 images from 100 volunteers is established. The images are captured on two separate occasions, at an interval of around two months. Most of the volunteers are not familiar with the image acquisition process. The experimental results show that the proposed system achieves high recognition rate and it works in real time. Moreover, the proposed line feature extraction method outperforms traditional Gabor filter based line detection method and derivative line detection method in accuracy.ConclusionThe proposed IKP system is robust and accurate. It may promote the application and popularization of IKP recognition.     

A new visual cryptography with multi-level encoding

Visual secret sharing (VSS) is a visual cryptography scheme which decodes secret messages into several enlarged shares, and distributes them to different participants. The participants can recover the secret messages by stacking their shares, and then secret message can be revealed by human visual sensitivity. Afterward some researchers start to research size invariant scheme, and apply to encode grayscale images such as scenic photos or pictures, not only binary messages. Owing to the gray values distribution of pictures are different, extreme distribution may cause blurred revealed image. In this paper, we proposed a size invariant VSS scheme which is suitable for different distribution of image's gray values. Experiment results show that the reconstructed images of our method, for brighter, darker, and normal images, have clearer and higher contrast, and without apparent artifact and unexpected contour.     

基于Snake的图像分割算法研究

图像分割算法是指从待割图像中提取出感兴趣的目标,以便进行图像分析与图像理解。Snake算法不同于传统的图像分割方法。文中详细介绍了Snake模型的数学机理及离散化方法,最后利用贪婪法实现了Snake算法,并应用与实际图像的分割。     

人体数字图像中各组织影像的边缘提取算法研究

针对人体数字医学图像中各组织影像界限不明显、图像总体模糊、单个组织影像不均匀等特点,提出一种能够有效提取影像边缘以区分和观察各组织影像的方法。利用计算机图像处理技术,对组织影像进行灰度处理,进而根据不同组织影像的特征,选定相应的阈值,用算法程序对组织影像边缘进行有效提取,得到一系列效果图,通过观察分析得出实验结果。结果表明,该方法能够有效地提取出人体不同组织影像的边缘,有助于医生的临床诊断与分析。     

互信息在图像检索中的应用

介绍了一种用互信息来衡量相似性图像检索方法.该方法首先生成一种在统计上有代表性的视觉模式,使用这种模式的分布作为图像内容的描述符;基于该内容描述,设计了其互信息的计算方法以衡量图像的相似性.实验结果表明,在图像检索中,相对于其它如KL散度和L2规范等方法,互信息是一种更为有效的衡量相似性的方法.