Techniques for keypoint detection and matching between endoscopic images

Abstract

The detection and description of local image features is fundamental for different computer vision applications, such as object recognition, image content retrieval, and structure from motion. In the last few years the topic deserved the attention of different authors, with several methods and techniques being currently available in the literature. The SIFT algorithm, proposed in [2], gained particular prominence because of its simplicity and invariance to common image transformations like scaling and rotation. Unfortunately the approach is not able to cope with non-linear image deformations caused by radial lens distortion. The invariance to radial distortion is highly relevant for applications that either require a wide field of view (e.g. panoramic vision), or employ cameras with specific optical arrangements enabling the visualization of small spaces and cavities (e.g. medical endoscopy). One of the objectives of this thesis is to understand how radial distortion impacts the detection and description of keypoints using the SIFT algorithm. We perform a set of experiments that clearly show that distortion affects both the repeatability of detection and the invariance of the SIFT description. These results are analyzed in detail and explained from a theoretical viewpoint. In addition, we propose a novel approach for detection and description of stable local features in images with radial distortion. The detection is carried in a scale-space image representation built using an adaptive gaussian filter that takes into account distortion, and the feature description is performed after implicit gradient correction using the derivative chain rule. Our approach only requires a rough modeling of the radial distortion function and, for moderate levels of distortion, it outperforms the application of the SIFT algorithm after explicit image correction.