c++ - Increasing the number of detected features in SIFT will …
2016年7月20日 · nfeatures – The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast) contrastThreshold – The contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
computer vision - Dense sift vs sift? - Stack Overflow
2011年12月8日 · Dense SIFT collects more features at each location and scale in an image, increasing recognition accuracy accordingly. However, computational complexity will always be an issue for it (in relation to normal SIFT).
OpenCV Python and SIFT features - Stack Overflow
2011年7月17日 · I can't say whether this is the reason SIFT was not available via Python for OpenCV 2.3 (as the original question asks). However, the patent which was preventing SIFT from being included in OpenCV expired on 2020-03-06. The distributed build of OpenCV now includes SIFT since version 4.4.0, accessible via cv2.SIFT_create() in Python.
SIFT features and classification of images? - Stack Overflow
2018年1月18日 · To make v for a given image, the simplest approach is to assign v[j] the proportion of SIFT descriptors that are closest to the jth cluster centroid. This means the length of V is K, so it is independent of the number of SIFT features that are detected in the image. Concretely, suppose you've done K means clustering with K = 100.
What is the difference between dense SIFT and HoG?
2020年6月10日 · I am new to Computer Vision. I am studying Dense SIFT and HOG. For dense SIFT, the algorithm just considers every point as an interesting point and computes its gradient vector. HOG is another way to describe an image with a gradient vector. I think Dense SIFT is a special case for HOG.
Transforming an image before extracting SIFT features
2015年9月24日 · First approach: extract SIFT features from the target image, match them to SIFT features in the scene image and compute a homography. Second approach: transform the target image in various ways to simulate changes of perspective: ...before extracting SIFT features from each transform.
python - Kmeans clustering for sift features - Stack Overflow
2014年12月30日 · I performed sift (scale invariant feature transform) for all those images (using David Lowe's sift demo) and have obtained the images' respective (3000) keypoint features. Now I have to perform the k-means clustering for the 3000 images' keypoint features.
Training of SVM classifier using SIFT features - Stack Overflow
2013年9月27日 · Apply dense sift as in step 2 in stage 1. For each key point's vector find the index of it's "best match" vector in the codebook matrix C (can be the minimum in the Euclidian distance) . Increase the corresponding bin to this index in h by 1.
Selecting strongest SIFT features for face recognition
Using SIFT features, one popular way is to create a Bag of Visual Words framework where you take all of the features detected from all of the faces and you create a dictionary, usually with k-Means. Once you find these clusters, for each face you figure out which feature maps to which cluster then build a histogram.
Opencv Feature vector size of SiFT - Stack Overflow
2013年7月17日 · You are right, SIFT descriptor is a 128 dimensional feature. SIFT descriptor is computed for every key-point detected in the image. Before computing descriptor, you probably used a detector (as Harris, Sift or Surf Detector) to detect points of interest. Detecting key-points and computing descriptors are two independent steps!