This article presents a good robust and real-time system scheme of the mobile robot obstacle detection and navigation, which principle of work is based on the feature descriptor SURF. In this scheme, firstly, the imag...This article presents a good robust and real-time system scheme of the mobile robot obstacle detection and navigation, which principle of work is based on the feature descriptor SURF. In this scheme, firstly, the image information of the mobile robot path was captured by the binocular camera; then the feature points were extracted and corresponding matched using SURF to the binocular images as the undetected obstacles; finally fixed the position of the objective by the parallax between the matching points combining with the binocular vision calibration model. Theoretical derivation and experimental results show that this scheme is more accurate for the detection and navigation of the interest points. It has fast matching speed and high accuracy and low error. So, it has certain practical effect and popularizing value for the mobile robot real-time obstacle avoidance and navigation.展开更多
针对传统SURF算法(speeded up robust features)在拼接高分辨率无人机航拍图像时运行速度慢、特征匹配率低的特点,提出了一种基于IB-SURF(image block-SURF)技术的无人机图像拼接算法。结合无人机定位定姿系统(position and orientation...针对传统SURF算法(speeded up robust features)在拼接高分辨率无人机航拍图像时运行速度慢、特征匹配率低的特点,提出了一种基于IB-SURF(image block-SURF)技术的无人机图像拼接算法。结合无人机定位定姿系统(position and orientation system,POS)求取图像重叠区域;构造掩模在无人机图像重叠区域检测特征点,减少特征提取时间;借助图像分块(image block,IB)的思想对图像划分网格,精简筛选特征点;引入Neighborhood-KNN(neighborhood-K nearest neighbors)进行特征点匹配,提高图像匹配效率。实验结果表明,IB-SURF算法有较快的运行速度和较高的特征匹配率,平均特征匹配率达到84.3%,特征匹配正确率超过95.1%,为图像高质量拼接提供了技术基础。展开更多
Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration whe...Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature（SURF）,and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object＇s position.At last,the study makes registration experiment by scale invariant feature transform（SIFT）,SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi（KLT） 3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping（vSLAM） in a period of time.展开更多
Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kin...Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.展开更多
要实现指纹识别技术,必须使用图像特征的提取技术,尺度不变特征变换(ScaleInvariantFeature Transform,SIFT)和加速稳健特征(Speeded Up Robust Features,SURF)是目前运用比较广泛的两种图像特征提取算法。为验证哪种算法在指纹识别领...要实现指纹识别技术,必须使用图像特征的提取技术,尺度不变特征变换(ScaleInvariantFeature Transform,SIFT)和加速稳健特征(Speeded Up Robust Features,SURF)是目前运用比较广泛的两种图像特征提取算法。为验证哪种算法在指纹识别领域更适用,文章使用同一手指指纹的多张照片为图库,分别使用两种方法进行同一图自我匹配、指纹角度、范围不同图片匹配及较为模糊的图片匹配等实验。通过实验得出:虽然SIFT算法比SURF算法耗时更长,但是使用SIFT算法的运算量小于SURF算法,因此两种算法花费的时间近乎相等;在指纹对比库的创建方面,SIFT算法要优于SURF算法;在指纹匹配识别过程中,无论是同一张图片自我匹配,还是指纹角度和范围不同的图片进行匹配、指纹不清晰的图片进行匹配,SIFT算法的成功匹配点比SURF算法的成功匹配点分布更均匀,且数量相近或更多。可见,在指纹识别系统中,使用SIFT算法比SURF算法更合适。展开更多
针对传统图像匹配算法计算量大、耗时长等缺陷,提出一种基于SURF(speeded up robust features)的图像特征点快速匹配算法.首先对图像采用SURF算法提取特征点;然后通过Haar小波变换确定特征点的主方向和特征点描述子,使用优化的最近邻搜...针对传统图像匹配算法计算量大、耗时长等缺陷,提出一种基于SURF(speeded up robust features)的图像特征点快速匹配算法.首先对图像采用SURF算法提取特征点;然后通过Haar小波变换确定特征点的主方向和特征点描述子,使用优化的最近邻搜索算法(best bin first,BBF)进行特征点匹配;最后根据实际需要选取相似度最高的前n对匹配点进行对比实验.实验结果表明:该算法鲁棒性强,速度快,匹配准确性高,具有较大的应用价值.展开更多
如何构造紧凑而有效的特征描述子是机器视觉和模式识别领域重要的研究课题之一。针对SURF(Speeded Up Robust Features)算法的Haar描述子不能充分利用特征点周围信息的缺陷,该文提出了一种新的局部不变描述子——加窗灰度差直方图(Windo...如何构造紧凑而有效的特征描述子是机器视觉和模式识别领域重要的研究课题之一。针对SURF(Speeded Up Robust Features)算法的Haar描述子不能充分利用特征点周围信息的缺陷,该文提出了一种新的局部不变描述子——加窗灰度差直方图(Windowed Intensity Difference Histogram,WIDH),该描述子基于特征点周围邻域一个较小的核心区域,通过窗口模板的移动充分利用外围作用区域的灰度差信息,构造了一个维度低且辨识力很强,运算简单高效的描述矢量。实验表明,将WIDH用于改进SURF算法的Haar描述子时,可以用更低维的矢量获取与SURF相近或更好的辨识能力。在抗模糊性和抗噪性方面,WIDH明显优于SURF的Haar描述子,相同的错误率下查全率分别提高了大约35%和50%。展开更多
为解决车辆压线行驶或发生交通事故时占用多个车道而无法采集到完整的车辆图像问题,基于SURF(speed up robust features)算法和最佳缝合线的思想,提出一种车道图像序列拼接方法。首先根据车辆运动轨迹提出寻找最优对应图像算法,用于找...为解决车辆压线行驶或发生交通事故时占用多个车道而无法采集到完整的车辆图像问题,基于SURF(speed up robust features)算法和最佳缝合线的思想,提出一种车道图像序列拼接方法。首先根据车辆运动轨迹提出寻找最优对应图像算法,用于找出近似同步拍摄图像;然后用SURF检测图像中的关键点对图像进行粗配准,用RANSAC方法去除误配准点实现精配准;最后利用最佳缝合线方法沿图像的重叠区域进行分割拼接。实验表明:利用车道图像序列拼接方法能找出近似同步拍摄图像,准确计算出图像的重叠区域,避免缝合线经过运动区域,解决了拼接过程中产生的重影、裂缝等问题,合成了质量较高的多车道图像,以此确保获得完整的车辆图像。展开更多
SURF算子为了改善SIFT的计算复杂度高的问题,简化和近似了DoH(Determinant of Hessian),这样不仅保证了算法结果的稳定性,也提高了计算效率。但是SURF这样的近似简化过程,损失了图像中的一些渐变信息。对SURF算子进行了改进,在其处理过...SURF算子为了改善SIFT的计算复杂度高的问题,简化和近似了DoH(Determinant of Hessian),这样不仅保证了算法结果的稳定性,也提高了计算效率。但是SURF这样的近似简化过程,损失了图像中的一些渐变信息。对SURF算子进行了改进,在其处理过程中加入了渐变的信息。实验结果表明,提出的G-SURF(Gradual-SURF)算子可以获得更稳定的效果,并且同时计算复杂度也有所改善。展开更多
文摘This article presents a good robust and real-time system scheme of the mobile robot obstacle detection and navigation, which principle of work is based on the feature descriptor SURF. In this scheme, firstly, the image information of the mobile robot path was captured by the binocular camera; then the feature points were extracted and corresponding matched using SURF to the binocular images as the undetected obstacles; finally fixed the position of the objective by the parallax between the matching points combining with the binocular vision calibration model. Theoretical derivation and experimental results show that this scheme is more accurate for the detection and navigation of the interest points. It has fast matching speed and high accuracy and low error. So, it has certain practical effect and popularizing value for the mobile robot real-time obstacle avoidance and navigation.
基金supported by the National Natural Science Foundation of China (60802043)the National Basic Research Program of China(973 Program) (2010CB327900)
文摘Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature（SURF）,and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object＇s position.At last,the study makes registration experiment by scale invariant feature transform（SIFT）,SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi（KLT） 3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping（vSLAM） in a period of time.
文摘Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.
文摘要实现指纹识别技术,必须使用图像特征的提取技术,尺度不变特征变换(ScaleInvariantFeature Transform,SIFT)和加速稳健特征(Speeded Up Robust Features,SURF)是目前运用比较广泛的两种图像特征提取算法。为验证哪种算法在指纹识别领域更适用,文章使用同一手指指纹的多张照片为图库,分别使用两种方法进行同一图自我匹配、指纹角度、范围不同图片匹配及较为模糊的图片匹配等实验。通过实验得出:虽然SIFT算法比SURF算法耗时更长,但是使用SIFT算法的运算量小于SURF算法,因此两种算法花费的时间近乎相等;在指纹对比库的创建方面,SIFT算法要优于SURF算法;在指纹匹配识别过程中,无论是同一张图片自我匹配,还是指纹角度和范围不同的图片进行匹配、指纹不清晰的图片进行匹配,SIFT算法的成功匹配点比SURF算法的成功匹配点分布更均匀,且数量相近或更多。可见,在指纹识别系统中,使用SIFT算法比SURF算法更合适。
文摘针对传统图像匹配算法计算量大、耗时长等缺陷,提出一种基于SURF(speeded up robust features)的图像特征点快速匹配算法.首先对图像采用SURF算法提取特征点;然后通过Haar小波变换确定特征点的主方向和特征点描述子,使用优化的最近邻搜索算法(best bin first,BBF)进行特征点匹配;最后根据实际需要选取相似度最高的前n对匹配点进行对比实验.实验结果表明:该算法鲁棒性强,速度快,匹配准确性高,具有较大的应用价值.
文摘为解决车辆压线行驶或发生交通事故时占用多个车道而无法采集到完整的车辆图像问题,基于SURF(speed up robust features)算法和最佳缝合线的思想,提出一种车道图像序列拼接方法。首先根据车辆运动轨迹提出寻找最优对应图像算法,用于找出近似同步拍摄图像;然后用SURF检测图像中的关键点对图像进行粗配准,用RANSAC方法去除误配准点实现精配准;最后利用最佳缝合线方法沿图像的重叠区域进行分割拼接。实验表明:利用车道图像序列拼接方法能找出近似同步拍摄图像,准确计算出图像的重叠区域,避免缝合线经过运动区域,解决了拼接过程中产生的重影、裂缝等问题,合成了质量较高的多车道图像,以此确保获得完整的车辆图像。
文摘SURF算子为了改善SIFT的计算复杂度高的问题,简化和近似了DoH(Determinant of Hessian),这样不仅保证了算法结果的稳定性,也提高了计算效率。但是SURF这样的近似简化过程,损失了图像中的一些渐变信息。对SURF算子进行了改进,在其处理过程中加入了渐变的信息。实验结果表明,提出的G-SURF(Gradual-SURF)算子可以获得更稳定的效果,并且同时计算复杂度也有所改善。
