The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that c...The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced.This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images.The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression.This paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during transmission.By employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious actions.Furthermore,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been accomplished.For applications involving the transmission and sharing of images across a network.The suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture transfer.An effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of responsibilities.This scheme ensures a large reduction in computational and energy requirements while minimizing image quality loss.This approach employs multi-scale characteristics to improve the resistance of signatures against image deterioration.The proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.展开更多
行人检测在机器人、驾驶辅助系统和视频监控等领域有广泛的应用,该文提出一种基于显著性检测与方向梯度直方图-非负矩阵分解(Histogram of Oriented Gradient-Non-negative Matrix Factorization,HOG-NMF)特征的快速行人检测方法。采用...行人检测在机器人、驾驶辅助系统和视频监控等领域有广泛的应用,该文提出一种基于显著性检测与方向梯度直方图-非负矩阵分解(Histogram of Oriented Gradient-Non-negative Matrix Factorization,HOG-NMF)特征的快速行人检测方法。采用频谱调谐显著性检测提取显著图,并基于熵值门限进行感兴趣区域的提取;组合非负矩阵分解和方向梯度直方图生成HOG-NMF特征;采用加性交叉核支持向量机方法(Intersection Kernel Support Vector Machine,IKSVM)。该算法显著降低了特征维数,在相同的计算复杂度下明显改善了线性支持向量机的检测率。在INRIA数据库的实验结果表明,该方法对比HOG/线性SVM和HOG/RBF-SVM显著减少了检测时间,并达到了满意的检测率。展开更多
针对传统基于梯度方向直方图特征检测算法对解决目标模型单一、发生形变、存在遮挡及目标受干扰下定位困难的问题,提出一种基于HOG特征混合模型结合隐SVM的感兴趣目标检测算法。首先利用用训练图像的HOG特征金字塔表示得到包含感兴趣目...针对传统基于梯度方向直方图特征检测算法对解决目标模型单一、发生形变、存在遮挡及目标受干扰下定位困难的问题,提出一种基于HOG特征混合模型结合隐SVM的感兴趣目标检测算法。首先利用用训练图像的HOG特征金字塔表示得到包含感兴趣目标根模型、部件模型和对应可变形部件特征表示,该模型不仅描述目标的整体轮廓,而且能够捕捉到更为精细的目标部件轮廓,在一定程度上提高了检测算法在目标姿态复杂情况下的鲁棒性。然后利用HOG特征混合特征训练部件检测分类器LSVM(Latent Support Vector Machine)。最后通过动态规划和距离转换算法在测试图上扫描出与可变形部件模型相匹配的区域,实现感兴趣目标的检测定位。经过多组实验结果表明,所提出的算法能较好地解决目标在发生较大形变和存在遮挡等复杂姿态下的定位问题。展开更多
文摘The act of transmitting photos via the Internet has become a routine and significant activity.Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced.This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images.The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression.This paper introduces a content-based image authentication mechanism that is suitable for usage across an untrusted network and resistant to data loss during transmission.By employing scale attributes and a key-dependent parametric Long Short-Term Memory(LSTM),it is feasible to improve the resilience of digital signatures against image deterioration and strengthen their security against malicious actions.Furthermore,the successful implementation of transmitting biometric data in a compressed format over a wireless network has been accomplished.For applications involving the transmission and sharing of images across a network.The suggested technique utilizes the scalability of a structural digital signature to attain a satisfactory equilibrium between security and picture transfer.An effective adaptive compression strategy was created to lengthen the overall lifetime of the network by sharing the processing of responsibilities.This scheme ensures a large reduction in computational and energy requirements while minimizing image quality loss.This approach employs multi-scale characteristics to improve the resistance of signatures against image deterioration.The proposed system attained a Gaussian noise value of 98%and a rotation accuracy surpassing 99%.
文摘行人检测在机器人、驾驶辅助系统和视频监控等领域有广泛的应用,该文提出一种基于显著性检测与方向梯度直方图-非负矩阵分解(Histogram of Oriented Gradient-Non-negative Matrix Factorization,HOG-NMF)特征的快速行人检测方法。采用频谱调谐显著性检测提取显著图,并基于熵值门限进行感兴趣区域的提取;组合非负矩阵分解和方向梯度直方图生成HOG-NMF特征;采用加性交叉核支持向量机方法(Intersection Kernel Support Vector Machine,IKSVM)。该算法显著降低了特征维数,在相同的计算复杂度下明显改善了线性支持向量机的检测率。在INRIA数据库的实验结果表明,该方法对比HOG/线性SVM和HOG/RBF-SVM显著减少了检测时间,并达到了满意的检测率。
文摘针对传统基于梯度方向直方图特征检测算法对解决目标模型单一、发生形变、存在遮挡及目标受干扰下定位困难的问题,提出一种基于HOG特征混合模型结合隐SVM的感兴趣目标检测算法。首先利用用训练图像的HOG特征金字塔表示得到包含感兴趣目标根模型、部件模型和对应可变形部件特征表示,该模型不仅描述目标的整体轮廓,而且能够捕捉到更为精细的目标部件轮廓,在一定程度上提高了检测算法在目标姿态复杂情况下的鲁棒性。然后利用HOG特征混合特征训练部件检测分类器LSVM(Latent Support Vector Machine)。最后通过动态规划和距离转换算法在测试图上扫描出与可变形部件模型相匹配的区域,实现感兴趣目标的检测定位。经过多组实验结果表明,所提出的算法能较好地解决目标在发生较大形变和存在遮挡等复杂姿态下的定位问题。