Scale-Invariant Feature Transform (SIFT) is being investigated more and more to realize a less-constrained hand vein recognition system. Contrast enhancement (CE), compensating for deficient dynamic range aspects, is a must for SIFT based framework to improve the performance. However, evidence of negative influence on SIFT matching brought by CE is analysed by our experiments. We bring evidence...

The objective of this work is to provide an experimental numerical technique that combines the use of computer vision through the SIFT technique (Scale Invariant Features Transform) with a meshless numeric method to determine displacement and strain fields on the surface of a deformable material. The experimental part of this technique is based on the capture of images during the deformation pr...

Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we propose using the combination of Affine Scale Invariant Feature Transform (SIFT) and Probabilistic Similarity for face recognition under a large viewpoint change. Affine ...

In this paper, we propose a novel approach for feature generation by appropriately fusing KAZE and SIFT features. We then use this feature set along with Minimal Complexity Machine(MCM) for object classification. We show that KAZE and SIFT features are complementary. Experimental results indicate that an elementary integration of these techniques can outperform the state-of-the-art approaches.

We propose a considerably faster approximation of the well known SIFT method. The main idea is to use efficient data structures for both, the detector and the descriptor. The detection of interest regions is considerably speed-up by using an integral image for scale space computation. The descriptor which is based on orientation histograms, is accelerated by the use of an integral orientation h...

Run No. Run ID Run Description infMAP (%) training on TV09 data (type: A) 1 IUPR-VW-TV SIFT visual words with SVMs 8.5 2 IUPR-ADAPT-TV SIFT visual words with PA1SD 5.1 combined training on YouTube and TV09 data (type: C) 3 IUPR-VW+TT-TV SIFT visual words with SVMs, fused with TubeTagger concept detection scores 8.3 4 IUPR-ADAPT-YT SIFT visual words with PA1SD, trained on YouTube, adapted to TV0...

11 Existing algorithms based on scale invariant feature transform (SIFT) and Harris corners such as edge-driven 12 dual-bootstrap iterative closest point and Harris-partial intensity invariant feature descriptor (PIIFD) respectivley have 13 been shown to be robust in registering multimodal retinal images. However, they fail to register color retinal 14 images with other modalities in the presen...

As the individual identification, access control and security appliance issues attract much attention, face recognition applications are more and more popular. The challenge of face recognition is that the performance is mainly constrained by the variations of illumination, expression, pose and accessory. And most algorithms which were proposed in recent years focused on how to conquest these c...