Abstract The widely adopted image matching approach remains dependent on exhaustive matching of local features across images. Existing methods aiming to improve efficiency either approximate nearest neighbor (NN) search, compromising accuracy, or apply filtering only after establishing tentative matches, which restricts potential efficiency gains. We challenge the assumption that exhaustive NN search is necessary by proposing a more efficient hierarchical approach that maintains matching accuracy without relying on full-scale NN search. Our key insight is that efficiently identifying sufficiently similar, geometrically meaningful feature matches-rather than the most similar but geometrically random ones-can improve or maintain performance at a lower computational cost. We propose a novel method, Group-Guided Nearest Neighbors (GGNN), which matches groups of features first and then matches individual features only within these matched groups. This hierarchical pipeline reduces the computational complexity of feature matching from $$\theta (n^2)$$ to $$\theta (n \sqrt{n})$$ , significantly improving efficiency. Experimental results on homography estimation demonstrate that GGNN outperforms standard NN search while achieving performance comparable to state-of-the-art methods. Additionally, we formulate GGNN as a general framework, where conventional NN search is a special case with a single global feature group. This formulation provides a continuum of feature matching methods with varying computational costs, enabling automatic selection based on a given time budget.

Hyperspectral imaging systems provide dense spectral information on the scene under investigation by collecting data from a high number of contiguous bands of the electromagnetic spectrum. The low spatial resolutions of these sensors frequently give rise to the mixing problem in remote sensing applications. Several unmixing approaches are developed in order to handle the challenging mixing problem on perspective images. On the other hand, omnidirectional imaging systems provide a 360‐degree field of view in a single image at the expense of lower spatial resolution. In this study, we propose a novel imaging system which integrates hyperspectral cameras with mirrors so on to yield catadioptric omnidirectional imaging systems to benefit from the advantages of both modes. Catadioptric images, incorporating a camera with a reflecting device, introduce radial warping depending on the structure of the mirror used in the system. This warping causes a non‐uniformity in the spatial resolution which further complicates the unmixing problem. In this context, a novel spatial–contextual unmixing algorithm specifically for the large field of view of the hyperspectral imaging system is developed. The proposed algorithm is evaluated on various real‐world and simulated cases. The experimental results show that the proposed approach outperforms compared methods.

This work is supported by EPSRC research grants, HMGCC, TUBITAK researcher exchange grant, the IST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886.

We describe a pipeline for structure-from-motion with mixed camera types, namely omni directional and perspective cameras. The steps of the pipeline can be summarized as calibration, point matching, pose estimation, triangulation and bundle adjustment. For these steps, we either propose improved methods or modify existing perspective camera methods to make the pipeline more effective and automatic when employed for hybrid camera systems.