Texture is a powerful cue for describing structures that show a high degree of similarity in their image intensity patterns. This paper describes the use of Self-Invariant Feature Transform (SIFT), both as low-level and high-level descriptors, applied to differentiate the tissues present in breast US images. For the low-level texture descriptors case, SIFT descriptors are extracted from a regul...

The SIFT algorithm produces keypoint descriptors. This paper analyzes that the SIFT algorithm generates the number of keypoints when we increase a parameter (number of sublevels per octave). SIFT has a good hit rate for this analysis. The algorithm was tested over a specific data set, and the experiments were conducted to increase the performance of SIFT in terms of accuracy and efficiency so a...

In this paper, we consider the problem of segmenting an image into sky and non-sky regions, typically referred to as horizon line detection or skyline extraction. Specifically, we present a new approach to horizon line detection by coupling machine learning with dynamic programming. Given an image, the Canny edge detector is applied first and keeping only those edges which survive over a wide r...

In this paper, a novel framework is presented to recover the 3D shape information of a complex surface using its texture-less stereo images. First a linear and generalized Lambertian model is proposed to obtain the depth information by shape from shading (SfS) using an image from stereo pair. Then this depth data is corrected by integrating scale invariant features (SIFT) indexes. These SIFT in...

Scale Invariant Feature Transform is a widely used image descriptor, which is distinctive and robust in real-world applications. However, the high dimensionality of this descriptor causes computational inefficiency when there are a large number of points to be processed. This problem has led to several attempts at developing more compact SIFT-like descriptors, which are suitable for faster matc...

A novel digital image stabilization approach using Harris and Scale Invariant Feature Transform (SIFT) was presented in this article. Using SIFT in digital image stabilization, too many feature points and matches were extracted, but some of them were not so stable. Using these feature points and matches can not only increase the computational effort, but also enhance the wrong matching probabil...

This paper provides a new approach to recognize facial expressions. In this paper, facial expression recognition is based on appearance based features or we can say that low level features. We used two different approaches to categories the expression into seven different classes. These classifications based on Scale Invariant Feature Transform (SIFT) and Local Gabor Binary Filter (LGBP). First...

We present a mobile image matching application for Android mobile phone called Virtual Telescope to assist users in viewing distant buildings. The application automatically augments photo's field of view by retrieving replacement images and satellite images from the Internet. The application adopts content based image retrieval method and allow user two choices of feature descriptor, namely Sca...