The SIFT (Scale Invariant Feature Transform) is a computer vision algorithm that is used to detect and describe the local image features. The SIFT features are robust to changes in illumination, noise, and minor changes in viewpoint. The SIFT features have been used object recognition, image retrieval and matching, and so on.. The research of SIFT descriptors and improved SIFT descriptors is im...

We propose reliable outdoor object detection on mobile phone imagery from off-the-shelf devices. With the goal to provide both robust object detection and reduction of computational complexity for situated interpretation of urban imagery, we propose to apply the ’Informative Descriptor Approach’ on SIFT features (i-SIFT descriptors). We learn an attentive matching of i-SIFT keypoints, resulting...

We first propose in this paper a new oRGB-SIFT descriptor, and then integrate it with other color SIFT features to produce the Color SIFT Fusion (CSF) and the Color Grayscale SIFT Fusion (CGSF) methods for image category classification. The effectiveness of our proposed representation and methods are evaluated on three representative, large scale, and grand challenging datasets. The experimenta...

Iris is one of the most reliable biometric traits due to its stability and randomness. Iris is transformed to polar coordinates by the conventional recognition systems. They perform well for the cooperative databases, but the performance deteriorates for the non-cooperative irises. In addition to this, aliasing effect is introduced as a result of transforming iris to polar domain. In this thesi...

Action representation for robust human activity recognition is still a challenging problem. This thesis proposed a new feature for human activity recognition named SIFTMotion Estimation (SIFT-ME). SIFT-ME is derived from SIFT correspondences in a sequence of video frames and adds tracking information to describe human body motion. This feature is an extension of SIFT and is used to represent bo...

Why has SIFT been so successful? Why its extension, DSP-SIFT, can further improve SIFT? Is there a theory that can explain both? How can such theory benefit real applications? Can it suggest new algorithms with reduced computational complexity or new descriptors with better accuracy for matching? We construct a general theory of local descriptors for visual matching. Our theory relies on concep...

Action representation for robust human activity recognition is still a challenging problem. This thesis proposed a new feature for human activity recognition named SIFTMotion Estimation (SIFT-ME). SIFT-ME is derived from SIFT correspondences in a sequence of video frames and adds tracking information to describe human body motion. This feature is an extension of SIFT and is used to represent bo...

SIFT is an image local feature description algorithm based on scale-space. Due to its strong matching ability, SIFT has many applications in different fields, such as image retrieval, image stitching, and machine vision. After SIFT was proposed, researchers have never stopped tuning it. The improved algorithms that have drawn a lot of attention are PCA-SIFT, GSIFT, CSIFT, SURF and ASIFT. In thi...

A robust image matching algorithm using a set of selected SIFT descriptors is investigated in this work. We first utilize the colorbased segmentation method and the watershed algorithm to separate foreground and background regions in images and then search the corresponding SIFT descriptors along foreground contours. These selected SIFT descriptors can offer more robust and stable image matchin...