LMFD: lightweight multi-feature descriptors for image stitching

Abstract

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains.

Key-Point-Descriptor-Based Image Quality Evaluation in Photogrammetry Workflows

“…This study proposes a novel approach to image quality evaluation in photogrammetric workflows through the use of key point descriptors. Key point descriptors like SIFT (Scale-Invariant Feature Transform) [54,55], SURF (Speeded-Up Robust Features) [56], BRISK (Binary Robust Invariant Scalable Keypoints) [57], ORB (Oriented FAST and Rotated BRIEF) [58], KAZE [59], FREAK (Fast Retina Keypoint) [60], and SuperPoint [61] encapsulate robust and distinctive information about local features in images [62][63][64]. These descriptors are not only pivotal for object recognition and image matching, but also have the potential to indicate image quality based on the characteristics of the features they extract.…”