Scale Invariant Feature Transform (SIFT) is a method for extracting distinctive invariant feature from images [1]. SIFT has been applied to many problems such as face recognition and object recognition [18], [19], [20], [21]. We have analyzed performance of SIFT using Euclidean distance as a matching algorithm. Further the matching rate can be enhanced/improved by changing distance calculation ...

This paper presents a novel object recognition method, of a mobile robot, by combining scale invariant feature transform (SIFT) and de-speckle filtering to enhance the recognition capability. The main idea of the proposed algorithm is to use SIFT programming to identify other robots after de-speckle filtering process to remove outside noise. Since a number of features are much larger than neede...

9 ABSTRACT: Aiming at long registration time and mismatch problems resulted by images that exist local region similar or generated SIFT vector similar in the traditional SIFT registration method, a fine registration method based on geometric constraints of block and parallel architecture is put forward. This method using SIFT algorithm parallelly extract feature points to calculate initial tran...

Traditional trademark image retrieval algorithm only using global feature easily makes mistaken retrieval, and scale invariant feature transform (SIFT) features have limited descriptive ability for image contour and high algorithm complexity. This paper proposes a trademark retrieval algorithm combining the image global features and local features. In this paper, we will firstly, extract Zernik...

Briefly, what approach or combination of approaches did you test in each of your submitted runs? • all runs: video decoding using ffmpeg library, sampling every 25th frame. • F_X_NO_TNO-ANNMSK_1: standard SIFT key point detection, exhaustive search using FLANN matching with query key points inside query mask. • F_X_NO_TNO-ANNMSK_3: standard SIFT key point detection, exhaustive search using FLAN...

This paper proposes an efficient three fold stratified SIFT matching for iris recognition. The objective is to filter wrongly paired conventional SIFT matches. In Strata I, the keypoints from gallery and probe iris images are paired using traditional SIFT approach. Due to high image similarity at different regions of iris there may be some impairments. These are detected and filtered by finding...