sift

How to save Sift feature vector for classification using Neural network

由 妖精的绣舞 提交于 2019-12-30 07:34:07
问题 Matlab implementation of SIFT features were found from http://www.cs.ubc.ca/~lowe/keypoints/. with the help of stackoverflow. I want to save features to a .mat file. Features are roundness, color, no of white pixel count in the binary image and sift features. For the sift features I took descriptors in above code { [siftImage, descriptors, locs] = sift(filteredImg) } So my feature vector now is FeaturesTest = [roundness, nWhite, color, descriptors, outputs]; When saving this to .mat file

Training of SVM classifier using SIFT features

由 徘徊边缘 提交于 2019-12-29 07:17:30
问题 please i like to classify a set of image in 4 class with SIFT DESCRIPTOR and SVM. Now, using SIFT extractor I get keypoints of different sizes exemple img1 have 100 keypoints img2 have 55 keypoints.... how build histograms that give fixed size vectors with matlab 回答1: In this case, perhaps dense sift is a good choice. There are two main stages: Stage 1 : Creating a codebook. Divide the input image into a set of sub-images. Apply sift on each sub-image. Each key point will have 128 dimensional

SIFT/SURF、haar特征、广义hough变换的特性对比分析[z]

由 南笙酒味 提交于 2019-12-26 03:31:07
SIFT/SURF基于灰度图， 一、首先建立图像金字塔，形成三维的图像空间，通过Hessian矩阵获取每一层的局部极大值，然后进行在极值点周围26个点进行NMS，从而得到粗略的特征点，再使用二次插值法得到精确特征点所在的层（尺度），即完成了尺度不变。 二、在特征点选取一个与尺度相应的邻域，求出主方向，其中SIFT采用在一个正方形邻域内统计所有点的梯度方向，找到占80%以上的方向作为主方向；而SURF则选择圆形邻域，并且使用活动扇形的方法求出特征点主方向，以主方向对齐即完成旋转不变。 三、以主方向为轴可以在每个特征点建立坐标，SIFT在特征点选择一块大小与尺度相应的方形区域，分成16块，统计每一块沿着八个方向占的比例，于是特征点形成了128维特征向量，对图像进行归一化则完成强度不变；而SURF分成64块，统计每一块的dx，dy，|dx|，|dy|的累积和，同样形成128维向量，再进行归一化则完成了对比度不变与强度不变。 haar特征也是基于灰度图， 首先通过大量的具有比较明显的haar特征（矩形）的物体图像用模式识别的方法训练出分类器，分类器是个级联的，每级都以大概相同的识别率保留进入下一级的具有物体特征的候选物体，而每一级的子分类器则由许多haar特征构成（由积分图像计算得到，并保存下位置），有水平的、竖直的、倾斜的，并且每个特征带一个阈值和两个分支值，每级子分类器带一个总的阈值

SIFT and Keypoint [closed]

由 风流意气都作罢 提交于 2019-12-25 18:54:06
问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . I would like to ask how is SIFT algorithm done in C++. I understand that SIFT extracts keypoints. In that case, what will be the value represented by the keypoints extracted by SIFT algorithm? And how can I compare and detect similar keypoints? I will be using SIFT in my thesis.

cv::Mat memory is not released even after calling release()?

由 岁酱吖の 提交于 2019-12-25 16:56:22
问题 I've written a method where an image descriptor (like OpenCV SIFT or VLFeat Dense SIFT) computes the descriptors for a set of images (save in std::vector<std::string> files ). The descriptor is called through ComputeDescriptors(image, descriptorMatrix) where it fills descriptorMatrix with the descriptors computed from. Then I randomly pick samples (usually 50) descriptors and push the sampledDescriptors matrix in the returned std::vector<cv::Mat1f> descriptors . This is the code: void

Bag of Visual Words: what is a reasonable word (vector) dimension?

由 半世苍凉 提交于 2019-12-25 07:59:59
问题 In the Bag of Features/Visual Words paradigm we have a vector V in k -dimensions, where V[i]=j if the i -th centroid (obtained by k -means algorithm) is the closest one among all the k -centroids for j visual descriptors (e.g. SIFT descriptors). AFAIK, the resulting visual vector is very sparse (it means that most of entries are 0-value) since k is really big, but my question is: what is a reasonable value for k (and so the vector size)? Hundreds of dimensions? Thousands? Especially

How to crete a SIFT's descriptors database

由 别说谁变了你拦得住时间么 提交于 2019-12-25 04:33:06
问题 How do I create a database of SIFT descriptors (of images)? My intention is to implement a supervisioned training set on Support Vector Machine. 回答1: Which kind of images do you need? If you don`t care, you can just download some public computer vision dataset like http://lear.inrialpes.fr/~jegou/data.php#holidays which offers both images and already computed SIFTs from its regions. Or try other datasets, for instance, from http://www.cvpapers.com/datasets.html Other possibility is just to

Open CV : Assertion failed while implementing SIFT

由 谁都会走 提交于 2019-12-25 04:27:10
问题 I am trying to implement SIFT in studio 2013 and opencv 2.4. And I am getting the following error. OpenCV Error: Assertion failed (dims <= 2 && data && (unsigned)i0 < (unsigned)size.p[0] && (unsigned)(i1*DataType<_Tp>::channels) < (unsigned)(size.p[1]*channels()) && ((((sizeof(size_t)<<28)|0x8442211) ((DataType<_Tp>::depth) & ((1 << 3) - 1))*4) & 15) == elemSize1()) in unknown function, file C:\opencv231\build\include\opencv2/core/mat.hpp, line 552 I think the error is in this code : void

Open CV : Assertion failed while implementing SIFT

由 ﹥＞﹥吖頭↗ 提交于 2019-12-25 04:27:06
问题 I am trying to implement SIFT in studio 2013 and opencv 2.4. And I am getting the following error. OpenCV Error: Assertion failed (dims <= 2 && data && (unsigned)i0 < (unsigned)size.p[0] && (unsigned)(i1*DataType<_Tp>::channels) < (unsigned)(size.p[1]*channels()) && ((((sizeof(size_t)<<28)|0x8442211) ((DataType<_Tp>::depth) & ((1 << 3) - 1))*4) & 15) == elemSize1()) in unknown function, file C:\opencv231\build\include\opencv2/core/mat.hpp, line 552 I think the error is in this code : void

Using openCV to implement SIFT in image

由 家住魔仙堡 提交于 2019-12-25 01:46:52
问题 I have tried to implement SIFT with openCV and I have refer to these links link1 and link2. Besides, I have also read the paper about SIFT written by Lowe. I have some problems about the code in link1 and link2. cv::SiftFeatureDetector detector( 0.05, 5.0 ); cv::SiftDescriptorExtractor extractor( 3.0 ); I can't totally understand the parameter in the above function. If I modify the first function to cv::SiftFeatureDetector detector( 0.05, 10.0 ); , there is a running time OpenCV Error
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