High-quality feature matching is a critical prerequisite in a wide range of applications. Most contemporary methods concentrate on detecting keypoints or line features for matching, which have achieved adequate results. However, in some low-texture environments where these features are both lacking, previously used approaches may result in an insufficient number of matches. Besides, in repeated-texture environments, feature matching is also a challenging task. As a matter of fact, there exist numerous irregular curves that can be detected in all kinds of images, including low-texture and repeated-texture scenes, which inspires us to move a step further and dig into the research of curves. In this paper, we propose an accurate method to match invariant features from irregular curves. Our method consists of two stages, the first of which is to match the curves as accurately as possible by an elaborate three-step matching strategy. The second is to extract the matching features with the presented self-adaptive curve fitting approach. Experiments have shown that the matching performances of our features in ordinary scenes are comparable to previous keypoints. Particularly, our features can outperform the keypoints in low-texture and repeated-texture scenes.

Autonomous underwater vehicles must plan through observations affected by color cast, low illumination, scattering, and blur. This study develops a reproducible framework that converts the expert pixel annotations of the Segmentation of Underwater IMagery (SUIM) dataset into binary free-water/obstacle traversability labels, trains SegFormer-B0 with U-Net and DeepLabv3 baselines, estimates pixel-wise softmax entropy, and fuses obstacle probability with uncertainty into a grid risk field for A* replanning. Navigation under perceptual uncertainty is posed as a shortest-path problem on a 32 × 32 image-plane grid: the per-cell obstacle probability and entropy obtained from the network outputs enter the A* cost, so ambiguous cells are penalized without using labels at run time. All navigation experiments are image-plane grid simulations in which the vehicle cell is known exactly; real inertial navigation, localization error, and vehicle dynamics are not included. Four deterministic degradation levels are applied to held-out images, and 300 paired navigation scenarios with fixed start and goal cells are built from 60 test scenes, both for static planning and for a receding-horizon loop with limited sensing and route hysteresis. SegFormer-B0 reaches 0.913 obstacle-class IoU and 0.954 F1 on Clear and retains 0.843 obstacle-class IoU and 0.915 F1 at L4, with an expected calibration error below 0.04; retraining with five seeds confirms the backbone ranking from Clear to L3 but shows a large spread at L4 (0.733 ± 0.085 obstacle-class IoU). Mean normalized entropy increases from 0.373 to 0.414, while the top-10% hit rate in boundary or error regions increases from 29.4% to 34.9%. Risk-aware A* improves safety success over probability-cost A* on all conditions and reduces unsafe crossing by 2.86–10.23% with a mean path-length cost below 1.27%; the gain is small but consistent (8 of 8 discordant scenarios, McNemar p = 0.008) and shrinks when the baseline cost scale is tuned on validation data. The segmentation backbone has a larger effect (72.0% vs. 56.0% safe routes against DeepLabv3), and route hysteresis removes 96% of route switches in receding-horizon replanning.