Abstract:Using a classical result on algebraic invariants of the unimodular group, we present in this paper some basic geometric affine invariant quantities, and we use them to construct some distinctive descriptors for object detection. Although full affine invariance cannot be guaranteed due to noncommutativity of camera blur with affine maps and the domain problem (that is, the difficulty of finding an affine covariant domain), the proposed descriptors behave more robustly than SIFT with respect to affine deformatio… Show more
“…Finally, the gradient orientation distribution in this patch is encoded into a 128 elements feature, the so-called SIFT descriptor. We shall not discuss further the constitution of the descriptor and refer to the abundant literature [35,30,36,17,33,18]. For a detailed description of the SIFT method we refer the reader to [37].…”
Section: Sift Overviewmentioning
confidence: 99%
“…The literature on SIFT focuses on variants, alternatives and accelerations [3,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33]. A majority of them use the scale-space keypoints as defined in the SIFT method.…”
The most popular image matching algorithm SIFT, introduced by D. Lowe a decade ago, has proven to be sufficiently scale invariant to be used in numerous applications. In practice, however, scale invariance may be weakened by various sources of error inherent to the SIFT implementation affecting the stability and accuracy of keypoint detection. The density of the sampling of the Gaussian scalespace and the level of blur in the input image are two of these sources. This article presents a numerical analysis of their impact on the extracted keypoints stability. Such an analysis has both methodological and practical implications, on how to compare feature detectors and on how to improve SIFT. We show that even with a significantly oversampled scale-space numerical errors prevent from achieving perfect stability. Usual strategies to filter out unstable detections are shown to be inefficient. We also prove that the effect of the error in the assumption on the initial blur is asymmetric and that the method is strongly degraded in presence of aliasing or without a correct assumption on the camera blur.
“…Finally, the gradient orientation distribution in this patch is encoded into a 128 elements feature, the so-called SIFT descriptor. We shall not discuss further the constitution of the descriptor and refer to the abundant literature [35,30,36,17,33,18]. For a detailed description of the SIFT method we refer the reader to [37].…”
Section: Sift Overviewmentioning
confidence: 99%
“…The literature on SIFT focuses on variants, alternatives and accelerations [3,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33]. A majority of them use the scale-space keypoints as defined in the SIFT method.…”
The most popular image matching algorithm SIFT, introduced by D. Lowe a decade ago, has proven to be sufficiently scale invariant to be used in numerous applications. In practice, however, scale invariance may be weakened by various sources of error inherent to the SIFT implementation affecting the stability and accuracy of keypoint detection. The density of the sampling of the Gaussian scalespace and the level of blur in the input image are two of these sources. This article presents a numerical analysis of their impact on the extracted keypoints stability. Such an analysis has both methodological and practical implications, on how to compare feature detectors and on how to improve SIFT. We show that even with a significantly oversampled scale-space numerical errors prevent from achieving perfect stability. Usual strategies to filter out unstable detections are shown to be inefficient. We also prove that the effect of the error in the assumption on the initial blur is asymmetric and that the method is strongly degraded in presence of aliasing or without a correct assumption on the camera blur.
“…Finally, the gradient orientation distribution in this patch is coded into a 128 elements feature, the so-called SIFT descriptor. We shall not discuss further the constitution of the descriptor and refer to the abundant literature [17,18,30,[33][34][35].…”
The most popular image matching algorithm SIFT, introduced by D. Lowe a decade ago, has proven to be sufficiently scale invariant to be used in numerous applications. In practice, however, scale invariance may be weakened by various sources of error. The density of the sampling of the Gaussian scale-space and the level of blur in the input image are two of these sources. This article presents an empirical analysis of their impact on the extracted keypoints stability. We prove that SIFT is really scale and translation invariant only if the scale-space is significantly oversampled. We also demonstrate that the threshold on the difference of Gaussians value is inefficient for eliminating aliasing perturbations.
“…Pang et al [21] replaced SIFT by SURF [3] in the ASIFT algorithm to reduce the computation time. Sadek et al [23] present a new affine covariant descriptor based on SIFT which can be used with or without view synthesis. Detection of the MSERs on the scale space pyramid was proposed by Forssén and Lowe [9].…”
Abstract-Wide-baseline matching focussing on problems with extreme viewpoint change is considered. We introduce the use of view synthesis with affine-covariant detectors to solve such problems and show that matching with the Hessian-Affine or MSER detectors outperforms the state-of-the-art ASIFT [19].To minimise the loss of speed caused by view synthesis, we propose the Matching On Demand with view Synthesis algorithm (MODS) that uses progressively more synthesized images and more (time-consuming) detectors until reliable estimation of geometry is possible. We show experimentally that the MODS algorithm solves problems beyond the state-of-the-art and yet is comparable in speed to standard wide-baseline matchers on simpler problems.Minor contributions include an improved method for tentative correspondence selection, applicable both with and without view synthesis and a view synthesis setup greatly improving MSER robustness to blur and scale change that increase its running time by 10% only.
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