This paper describes a method in which SIFT features on and around the horizon are used for image classification (matching or recognition). Ordinarily, transformational geometry relating two images is recovered from a set of point correspondances – but correspondance formation may be improved and accelerated using geometric constraints. The proposed solution to this chicken/egg problem is to fi...

The paper describes our methods of three runs for the participation to the plant identification task of ImageCLEF2013.We use three kind of image features to do identification, which are spatial principal component analysis of census transform histograms (SPACT) ,a descriptor based on the global shape feature and scale invariant feature transform (SIFT). And the classifier we employed is the Sup...

The selection of matching method is critical to the scene matching navigation system, as it determines the accuracy of navigation. A coarse-to-fine matching method, which combines the area-based and feature-based matching method, is presented to meet the requirements of navigation, including the real-time performance, the sub-pixel accuracy and the robustness. In the coarse matching stage, the ...

Existing feature descriptor-based methods on retinal image registration are mainly based on scale-invariant feature transform (SIFT) or partial intensity invariant feature descriptor (PIIFD). While these descriptors are many times being exploited, they have not been applied to color fundus and optical coherence tomography (OCT) fundus image pairs. OCT fundus images are challenging to register a...

In this paper, we introduce a new dataset for offline Arabic handwriting recognition. The aim is to collect a large dataset of isolated Arabic words that covers all letters of the alphabet in all possible shapes using a small number of simple words. The end goal is to obtain a very large database of segmented letter images, which can be used to build and evaluate Arabic handwriting recognition ...

Based on feature matching of car tail text information, a novel approach for vehicle identification is proposed in this paper. Our method creatively implements Scale-Invariant Feature Transform (SIFT) to extract distinctive invariant features from car tail text sub-images, which is a new application of SIFT. In our approach, firstly, the coordinate and the content of a vehicle’s license plate a...

This paper introduces a process where fusion features assist matching scale invariant feature transform (SIFT) image features with high contrast scenes where areas would be under or over exposed in a single image. The process uses three quality measures to select features from a series of differently exposed images and weight the featuresin favour of those areas that are well exposed. The resul...

Nodes in sensor networks often encounter spatially-correlated contention, where multiple nodes in the same neighborhood all sense an event they need to transmit information about. Furthermore, in many sensor network applications, it is sufficient if a subset of the nodes that observe the same event report it. We show that traditional carrier-sense multiple access (CSMA) protocols for sensor net...

In this paper we present a technique which infers interframe motion by tracking SIFT features through consecutive frames: feature points are detected and their stability is evaluated through a combination of geometric error measures and fuzzy logic modelling. Our algorithm does not depend on the point detector adopted prior to SIFT descriptor creation: therefore performance have been evaluated ...

A common type of digital image forgery is the duplication of a region in the same image to conceal something in a captured scene. The detection of region duplication forgeries has been recently addressed using methods based on SIFT features that provide points of the regions involved in the tampering and also the parameters of the geometric transformation between both regions. However, consider...