Template matching is a technique widely used for finding patterns in digital images. An efficient template matching algorithm should be able to detect template instances that have undergone geometric transformations. Similarly, a color template matching should be able to deal with color constancy problem. Recently we have proposed a new grayscale template matching algorithm named Ciratefi, inva...

Here we present performance evaluation of different routing protocols such as SIFT, GPSR and GOSR using different mobility models like Fluid Traffic Model (FTM), Intelligent Driver Model and Random Waypoint Model (RWM) with Intersection Management (IDM-IM). We present simulation results that demonstrate the importance of choosing a mobility model in the simulation of a Vehicular Network Protoco...

In order to ensure that SAR scene matching aided navigation system can acquire the position errors and yawing errors simultaneously, we propose an image matching algorithm based on Scale Invariant Feature Transform (SIFT). However, the SIFT is proposed for optical image, and its performance degrades when used in SAR image. To enhance the adaptability of SIFT, two ways are employed. One is the a...

Image matching is a key part of many remote sensing image processing and image analysis. The traditional gray correlation matching algorithm based on corner point because they do not have the rotational invariance requires manual intervention to roughly match can not be automated. SIFT (Scale invariant feature transform) algorithm to solve the image rotation, scaling and other issues, but for t...

The Sorting Intolerant from Tolerant (SIFT) algorithm predicts the effect of coding variants on protein function. It was first introduced in 2001, with a corresponding website that provides users with predictions on their variants. Since its release, SIFT has become one of the standard tools for characterizing missense variation. We have updated SIFT's genome-wide prediction tool since our last...

Most existing image indexing techniques rely on Scale Invariant Feature Transformation (SIFT) for extracting local point features. Applied to individual image, SIFT extracts hundreds of numerical vectors. The vectors are quantized and stored in tree-like data structures for fast search. SIFTbased indexing can exhibit weakness under certain non-rigid transformations, which are common among real ...

To compose the wide visual angle and high resolution image from the sequence of images which have overlapping region in the same scene quickly and correctly, an improved SIFT algorithm which is based on D2oG interest point detector was proposed. It extracted the image feature points and generated corresponding feature descriptors by improved SIFT algorithm. Then, using the random consistency (R...

From 1970, research on automated face recognition has been on the rise. Since then many techniques and algorithms have been designed each one trying to provide better efficiency than the earlier one. This field of biometric analysis has found its use in many practical applications and with rising technologies each day, its exhaustive use in future is also expected. In this paper we have studied...

In the domain of object recognition, the SIFT feature [1] is known to be a very successful local invariant feature. The performance of the recognition task using SIFT features is very robust and also can be done in real-time. This project present an approach that adopt the SIFT feature for the task of face detection. A feature database is created for the detection of generic face features and a...

Growth of videos in today’s Internet usage is extensive. Different types of videos will be available in the Internet which among them are lecture videos. Students can make use of these videos, so there is a need to develop an automated system to search the required content only, rather than wasting the time in viewing the complete video. This can be developed into automated system, required ste...