This work investigates performance of recent feature-based matching
techniques when applied to registration of underwater images.
Matching methods are tested versus diff erent contrast en¬hancing
pre-processing of images. As a result of the performed experiments for
various dominat¬ing in images underwater artifacts and present deformation,
the outper¬forming preprocessing, detection and description
methods are proposed

García, R.

Kudzinava, M.

Martí, J.

This work investigates performance of recent feature-based matching

techniques when applied to registration of underwater images.

Matching methods are tested versus diff erent contrast en¬hancing

pre-processing of images. As a result of the performed experiments for

various dominat¬ing in images underwater artifacts and present deformation,

the outper¬forming preprocessing, detection and description

methods are proposed

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Feature-based matching of underwater images

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INSTRUMENTATION VIEWPOINTSessions96Figure 3. A356 T6 anodized.Figure 4. a) Nyquist plots for A356 T6 and A356 T6 anodized; B) Nyquist plot for A356 T6 .4. Conclusions• The A356 components obtained by SLC with T6 treatment can highly improve corrosion resistance by anodizing despite the non-uniform thickness.The anodizing possibility of these components off ers new perspec-tives to obtain components by SSM processes.5. References[1] A. Forn, J.A. Picas, M.T. Baile, S. Menargues, V.G. García, “Anodic oxide layer formation on A357 aluminium alloy produced by thixocasting”, Solid  State Phenomena, vol. 116-117, pp 80 -83, 2006.[2] J.A.Picas, E.Martín, M.T.Baile, E.Rupérez A.Forn, “Hard anodizing of alu-minium matrix composite A6061/(Al2O3)p for wear and corrosion resistance improvement”, Proceedings 10th International Conference on Plasma Surface Engineering, Germany (2006).[3] A.Forn, E.Rupérez, M.T.Baile, M.Campillo, S.Menargues, I.Espinosa “Corro-sion behaviour of L2630 (EN-AC 46500) Aluminium Alloy by Semi- Solid Reo-casting”, proceedings  Esaform2007, Zaragoza, (2007).AcknowledgementsThe authors express their gratitude to the Comisión Interministerial de Ciencia y Tecnología (CICYT) of Spain for fi nancial support under the DPI2005-02456 project .FEATURE-BASED MATCHING OF UNDERWATER IMAGESM. Kudzinava,  R. Garcia,  J. MartíSpain, 17071, Girona, UdG, Campus Montilivi, Building P-IV+34 972 419 812,  {kudinova,rafa,joanm}@eia.udg.esKeywords: underwater imaging, lighting artifacts, image matching.This work investigates performance of recent feature-based match-ing techniques when ap¬plied to registration of underwater images. Matching methods are tested versus diff erent contrast en¬hancing pre-processing of images. As a result of the performed experiments for various dominat¬ing in images underwater artifacts and present defor-mation, the outper¬forming preprocessing, detection and descrip¬tion methods are proposed. 1. IntroductionUnderwater vehicles are usually equipped with video cameras to pro-vide a visual feedback of the seafl oor. In this scope matching of im-ages acquired under water has several important applica¬tions, such as photo-mosaicing, depth estima¬tion, motion tracking, etc. Fea-ture-based matching of two overlapping images consists in detect-ing salient features in each image, de¬scribing the detected features and actual matching of descriptors. Complexity of the matching task consists in overcoming the geo¬metric deformation and photomet-ric diff erences between images. The water medium introduces even more diffi  culties for matching techniques comparing to overland. Underwater images suff er from eff ects such as diff usion, scatter and caustics. Moreover, there is a wider range of possible deformations due to less controllable camera movements. All these diff erences should be overcome by robustness and invariance of the detection and description methods applied to match the images.In this work, several experiments have been carried out. Two descrip-tors, SIFT [1] and SURF [2], were tested in conjunction with fi ve dif-ferent detectors. Three classical detectors, Harris [3], Hessian [4] and Laplacian [5], were used in their straightforward form, which is not invariant to scale. The two other detectors, DoG and FastHessian, are the original detectors of SIFT and SURF, respectively. As opposed to the previous three detectors, they perform multi-scale detection. Several matching methods, represented by possible combinations of detec¬tor and descriptor, were tested on 80 image pairs from four underwater sequences. In all cases RANSAC [6] was used to estimate homo¬graphies. Initial matches following the esti¬mated homogra-phy were accepted as correct correspondences, or inliers, while the rest of the matches were rejected as outliers. M11INSTRUMENTATION VIEWPOINTSessions97SIFT and SURF proved to be effi  cient for images of overland scenes, often failed to match images acquired under water without special preprocess¬ing of the latter. However, undesired underwater ar-tifacts, such as diff usion, blur, scatter, noise, caustics and artifi cial lighting, displayed by Fig.1, which obstacle successful matching of underwater frames, can be sup¬pressed to a considerable extent by applying special pre-processing to the images. All matching techniques were tested versus diff er¬ent conversion to grayscale, such as the blue chan¬nel from the RGB image, luma channel after con¬version to YIQ, or the fi rst principal component after PCA of the image. Next, dif¬ferent contrast enhancement techniques were ap-plied, including normalization, equalization and CLAHE [7]. Figure 1. Underwater eff ects: diff usion, non-uniform lighting, caustics.For each confi guration of pre-processing, detection and descrip-tion, the number of successfully matched image pairs per sequence was counted, as well as the percentage of outliers, averaged through the evaluated matched pairs. The bigger this percentage is, the more diffi  cult it is to estimate the motion.2. Results and Discussion Performed tests show that classical conversion to grayscale via luma component provides better results than selecting the dominant color channel or applying PCA. Among the contrast enhance¬ment methods CLAHE appeared to be in general the most eff ective when dealing with underwater eff ects. Table I summarizes the outperform-ing pre-preprocessing for each eff ect.UnderwaterArtifactsConversion to GrayscaleContrast En-hancementClutter, Blur,Low contrastYIQ CLAHENon-uniformLightningYIQ NormalizationCLAHECausticPatternsYIQPCANormalizationTable I. Recommended preprocessing for diff erent underwater ar-tifacts.When photometric eff ects can be partially suppressed by special pre-processing, geometric de¬formations should be overcome solely by the matching technique. SIFT and SURF descriptors are fully invariant to translation and rotation of images, and, when used with DoG and FastHessian, they are also invariant to scale. Fig.2 illus¬trates these types of deformations. Moreover, since each descriptor itself covers change of scale to some degree, both of them demonstrate good results when applied to keypoints detected by Harris, Hessian and Laplacian if scale change between images is not signifi cant.  Figure 2. Geometric deformations: translation and rotation, scale.detection and speed– show good localization accuracy. DoG appears to be robust against signifi cant oscillations in image intensities. Table II summarizes the outperforming detection and description methods depending on complexity and amount (“S”=signifi cant, “I”= insignifi -cant) of deformation present between images.Deformation 1 2 3 4Translation & Rotation S S S SChange in Scale I S I SAffine Deformation I I S SProjective Deformation I I S SDetectorHessianFastHessianHessianDoGDescriptorSURFSURFSIFTSIFTTable II. Recommended detector and descriptor depending on com-plexity of deformation.3. ConclusionsPerformed tests proved that recent feature-based image matching techniques provide a good basis to deal with underwater images. SIFT and SURF descriptors demonstrated good performance when used with non-scale-invariant detectors under restriction of slight scale deformation. Hessian detector outper¬formed Harris and La-placian. SURF appeared to be more discriminative providing higher than SIFT percentage of inliers. However, SIFT outperforms SURF in terms of robustness to affi  ne and projec¬tive deformations, thus be-ing the best method for loop closing detection when constructing the mosaic.4. References[1] D. Lowe, Distinctive image features from scale-invariant keypoints, Interna-tional Journal of Computer Vision, 60(2): 91-110, 2004.[2] H. Bay et al., SURF: Speeded up robust features, Proc. ECCV’06: 404-417, 2006.[3] C. Harris, Determination of ego-motion from matched points, Proc. Alvey Conference, 1987.[4] P. Beaudet, Rotationally invariant image operators, Proc. 4th ICPR: 579-583, 1978.[5] T. Lindeberg, Feature detection with automatic scale selection, Tech. Re-port, 1998.[6] R. Hartley and A. Zisserman, Multiple view geome¬try in computer vision, 2000.[7] K. Zuiderveld, Contrast Limited Adaptive Histogram Equalization, 1994.Acknowledgement: This work has been funded in part by MEC un-der grant CTM2004-04205. MK has been funded by MEC under FPU grant AP2005-4884.However, in our experiments camera motion is not constrained. Thus, image pairs are often warped by more complex deformations (affi  ne or projective). SIFT occurs to be robust to wider range of these defor-mations. Only full SIFT was able to match all the tested image pairs, when full SURF failed for 5% of them. However, when SURF is able to match the image pair it outperforms SIFT in terms of percentage of inliers at least by 10%. Among single-scale detectors, Hessian out-performs Harris and Laplacian. On the other hand, DoG and FastHes-sian –being approximations to Laplacian and Hessian for multi-scale M11

Feature-based matching of underwater images

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