This paper introduces a high-speed all-hardware scale-invariant feature transform (SIFT)
architecture with parallel and pipeline technology for real-time extraction of image features …

Image feature extraction constitutes a fundamental task in robotic vision applications. Scale-
Invariant Feature Transform (SIFT) has been widely used as a robust method for detecting …

Visual feature extraction is a key technology of computer vision for intelligent video
processing. Efficient feature extraction is a fundamental problem in computer vision …

This paper proposes a novel hardware design method of scale-invariant feature transform
(SIFT) algorithm for implementation on field-programmable gate array (FPGA). To reduce the …

Full-HD video has drawn more and more attention in advanced computer vision applications
which rely on more details in image. Benefit from high resolution input, local feature based …

With the growth of multimedia data generation and consumption, image-based data
analytics plays an increasingly important role in big data analytics systems. For image …

Scale and rotation invariant salient point detection and matching algorithms are variously
used in computer vision applications such as image matching, 3D localization and pose …

Scale-invariant feature transform (SIFT) is considered one of the best algorithms to get
feature points in an image. It maintains the accuracy in results even in image scaling …

Abnormal events in public places can be detected in real time by analyzing the video
sequences from video posts, web or surveillance cameras. Sudden changes of speed …

Due to its computational complexity, the Scale-Invariant Feature Transform (SIFT) algorithm
poses a challenge for use in embedded applications. To meet real-time at low power …