Description: We show that i s possible to estimate depth from two wide baseline images using a dense descriptor. Our local descriptor, called DAISY, is very fast and efficient to compute. It depends on histograms of gradients like SIFT and GLOH but uses a Gaussian weighting and circula-We show that it is possible to estimate depth from two wide baseline images using a dense descriptor. Our local descriptor, called DAISY, is very fast and efficient to compute. It depends on histograms of gradients like SIFT and GLOH but uses a Gaussian weighting and circula Platform: |
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Author:na |
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Description: 使用gpu、cpu并行进行sift算子计算匹配，能够在原来的基础上加速处理，但对显卡要求较高，具体环境配置使用方法可以参照mannual-SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors.
SiftGPU is inspired by Andrea Vedaldi s sift++[2] and Sudipta N Sinha et al s GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified.
SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided.
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Author:周金强 |
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Description: SIFT图像特征提取的图像预处理步骤：构建图像构建高斯金字塔，相邻层相减得到DOG金字塔，在DOG金字塔3x3x3的邻域内寻找局部极值点，供进一步计算SIFT特征描述子使用。工程运行于VS2008环境，需要OpenCV支持。Debug目下exe文件可以直接双击运行查看结果。-SIFT image feature extraction image preprocessing steps: build image Gaussian pyramid, subtracting the adjacent layer get to DOG pyramid, in the DOG pyramid 3x3x3 neighborhood of finding local extreme point for further calculations using the SIFT feature descriptor. This project runs under VS2008 with OpenCV. Double click the exe file under Debug folder to check the final result. Platform: |
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Description: GPU版的klt光流算法，能够快速的完成sift 算法
，需要显卡支持-SIFTGPU is an implementation of SIFT for GPU. SiftGPU uses GPU to process pixels and features
parallely in Gaussian pyramid construction, DoG keypoint detection and descriptor generation
for SIFT. Compact feature list is efficiently build through a GPU/CPU mixed reduction.
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Size: 5332992 |
Author:wangxi |
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Description: SIFT 由特征提取，特征描述符描述和特征匹配 3 部分构成，该算子特征提取数目庞大，建立特征描述符运算
量高，导致算法效率低。提出了一种 SEC( SIFT-Edge-Corner) 算法，在图像尺度空间提取角点代替 SIFT 特征点，并根
据角点是边缘曲率极值理论，预先采用 Canny 算子得到高斯边缘图像金字塔，再提取角点并进行尺度选择。实验结
果表明: 该算法在保障高准确率的前提下大幅度提高特征提取效率-By the SIFT feature extraction, feature descriptions and feature matching descriptors 3 parts, the large number of feature extraction operator established feature descriptor computation high, resulting in low efficiency of the algorithm. Presents a SEC (SIFT-Edge-Corner) algorithm, the image scale space instead of SIFT feature extraction corner points and corner points based on extreme value theory is an edge curvature in advance using Canny operator edge image obtained Gaussian pyramid, and then extract corner point and scale selection. Experimental results show that: the algorithm protect high accuracy under the premise of feature extraction efficiency greatly improved Platform: |
Size: 231424 |
Author:焦婷 |
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Description: This a MATLAB implementation of the SIFT keypoint detector and descriptor -do_gaussian: generate Gaussian scale space of input image
do_diffofg: generate Difference of Gaussian (DoG) scale space
do_localmax: local extrema as the potential keypoints
do_extrefine: refine the keypoints by discarding the ones with low contrast and along an edge
do_orientation: compute the orientation of a support region of keypoint
do_descriptor: compute the descriptor of a keypoint based on image gradients.
do_match: match two images based on the nearest neighbor principle and spatial consistency.
do_sift: generate the SIFT descriptors for a given input image. It basically s all the functions above.
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Size: 1257472 |
Author:崔雪红 |
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Description: This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and
bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD)
with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of
image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances.
Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation(This paper presents a novel active contour model in a variational level set formulation for simultaneous segmentation and
bias field estimation of medical images. An energy function is formulated based on improved Kullback-Leibler distance (KLD)
with likelihood ratio. According to the additive model of images with intensity inhomogeneity, we characterize the statistics of
image intensities belonging to each different object in local regions as Gaussian distributions with different means and variances.
Then, we use the Gaussian distribution with bias field as a local region descriptor in level set formulation for segmentation and
bias field correction of the images with inhomogeneous intensities. Therefore, image segmentation and bias field estimation are
simultaneously achieved by minimizing the level set formulation) Platform: |
Size: 2441216 |
Author:song86
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Search - gaussian descriptor