Ridges and valleys are the principle geometric features for their diverse applications, especially in image analysis problems such as segmentation, object detection, etc. Numerous characterizations have contributed to formalize the ridge and valley theory. The significance of each characterization rely however on its practical usefulness in a particular application. The objective comparison and evaluation of ridgeness/valleyness characterized as thin and complex image structure is thus crucially important, for choosing, which parameter's values correspond to the optimal configuration to obtain accurate results and best performance. This paper presents a supervised and objective comparison of different filtering-based ridge detectors. Moreover, the optimal parameter configuration of each filtering techniques have been objectively investigated.
Ridges (resp., valley) are the useful geometric features due to their wide varieties of applications, mainly in image analysis problems such as object detection, image segmentation, scene understanding, etc. Many characterizations have contributed to formalize the ridge notion. The signification of each characterization rely however on its actual application. The objective analysis of ridge characterized as thin and complex image structure is thus essentially important, for choosing which parameter's values correspond to the suitable configuration to obtain accurate results and optimal performance. In this article an extensive analysis followed by a supervised and objective comparison of different filtering-based ridge detection techniques is led. Furthermore, the optimal parameter configuration of each filtering techniques aimed for image salient feature analysis tool have been objectively investigated, where each chosen filter's parameters corresponds to the width of the desired ridge or valley. At last, the comparative evaluations and analysis results are reported on both synthetic images, distorted with various types of noises and real images.
Keypoint(s) or corners, as a stable feature possessing the defined characteristics of a robust point of interest remain an active research field for machine vision researchers due to its applications in motion capture, image matching, tracking, image registration, 3D reconstruction, and object recognition. There exist different techniques for keypoint detection; this paper focuses on direct computation on the gray-level analysis of interest point detection because of its straightforward implementation. In this contribution, an objective comparison of 12 state-of-the-art keypoint detection techniques; an application to feature matching have been executed in the context of underwater video sequences. These videos contain noise and all geometric and/or photometric transformations. Experiments are led on 5 videos containing in total 10 000 frames, evaluating the repeatability of the keypoints detectors. These detectors are evaluated on these complex videos by computing statistics-based repeatability, but also as a function of the Zero-Mean Normalized Cross-Correlation (ZNCC) scores.
Among the common image structures, line feature is the extensively used geometric structure for various image processing applications, including the analysis of biomedical image with blood vessels highlighting, graph-shape structures, cracks detection, satellite images or remote sensing data. Multi-scale processing of line feature is essentially required for the extraction of more relevant information or line structures of heterogeneous widths. In this paper, a multi-scale filtering-based line detection approach using second-order semi-Gaussian anisotropic kernel is proposed. Meanwhile, a strategy is introduced to calculate the strength of the observed line feature across the different scales. The proposed technique is evaluated on real images by using their tied hand-labeled images. Finally, the experimental results and comparison of images containing different line feature widths with state-of-the-art techniques have sufficiently supported the effectiveness of our technique.
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