In this survey, we give an overview of invariant interest point detectors, how they evolvd over time, how they work, and what their respective strengths and weaknesses are. We begin with defining the properties of the ideal local feature detector. This is followed by an overview of the literature over the past four decades organized in different categories of feature extraction methods. We then provide a more detailed analysis of a selection of methods which had a particularly significant impact on the research field. We conclude with a summary and promising future research directions.

https://www.nowpublishers.com/article/DownloadSummary/CGV-017

Local Invariant Feature Detectors: A Survey

‘Invariant regions’ are self-adaptive image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. Such regions can be extracted directly from a single image. They are then described by a set of invariant features, which makes it relatively easy to match them between views, even under wide baseline conditions. In this contribution, two methods to extract invariant regions are presented. The first one starts from corners and uses the nearby edges, while the second one is purely intensity-based. As a matter of fact, the goal is to build an opportunistic system that exploits several types of invariant regions as it sees fit. This yields more correspondences and a system that can deal with a wider range of images. To increase the robustness of the system, two semi-local constraints on combinations of region correspondences are derived (one geometric, the other photometric). They allow to test the consistency of correspondences and hence to reject falsely matched regions. Experiments on images of real-world scenes taken from substantially different viewpoints demonstrate the feasibility of the approach.

/pdf/matching-widely-separated-views-based-on-affine-invariant-3vevjwojdj.pdf

Matching Widely Separated Views Based on Affine Invariant Regions

An unified approach for a simultaneous and cooperative estimation of defocus blur and spatial shifts

The limitations of estimating structure from either stereo or motion alone can be addressed by the use of stereo image sequences; however, many existing techniques for processing stereo sequences rely on having accurate feature correspondences initially. We present an iterative feature matching and shape reconstruction algorithm. The proposed method simultaneously resolves matching ambiguities by multiple hypothesis testing and develops an incrementally accurate and dense representation of the reconstructed object. This approach uses minimal domain specific constraints and can be easily generalised. The method's potential is demonstrated in a space vision application.

/pdf/incremental-shape-reconstruction-using-stereo-image-q48okixl7e.pdf

Incremental shape reconstruction using stereo image sequences

Local channel scour is a problem of substantial importance in hydraulic engineering practice. Scour occurring around hydraulic structures has serious consequences in terms of damage to the structure and to human safety. The mechanisms of scour are complex and dynamic. This has resulted in most of the studies dealing with local scour to be experimental in nature. The amount of erosion has generally been quantified by noting the depth of scour at various locations in the bed. When the flow is unsteady or quasi-steady, determining the instantaneous depth is not a trivial task and involves a three-dimensional analysis of the scour field. Furthermore, the depth measurement has to be non-intrusive. The present study aims at developing a practical video image analysis for use in three-dimensional scour depth measurement. To acquire images, two cameras with similar specifications were mounted over the model of the eroded bed and the two images were simultaneously captured to simulate stereoscopic viewing. The com...

Computational stereoscopy for three-dimensional scour depth measurement in channels

This paper presents a one-stage stereo algorithm that yields 3D planar surface patches directly from matching image patch intensity information. The method allows an arbitrary rotation and translation between the cameras; it is not limited to parallel-axis, narrow-baseline, or vergent geometries. The key to the approach is to match image patches that have positions, shapes, sizes, orientations, and samplings consistent with a hypothesized surface patch and with each other. The match error then reflects only the mismatch of patch contents and not the mismatch of patch geometries or samplings. The algorithm is quantitatively evaluated against ground truth on real images with difficult viewing geometries, and demonstrates an average accuracy of about 1% in estimating surface depths and 10/spl deg/ in estimating surface normals.

Patch-based stereo in a general binocular viewing geometry

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Patch-based stereo in a general binocular viewing geometry