A fast image registration approach based on SIFT key-points applied to super-resolution

Image and Video Technology – PSIVT 2013 Workshops

Sugimoto

Kenmochi³

et al. 2014

Abstract. Rigid image registration is an essential image processing task, with a large body of applications. This problem is usually formulated in the continuous domain, most often in an optimization framework. This strategy is suitable whenever the estimated transformation is associated with interpolation. In the case of purely discrete applications, e.g., for template-based segmentation or classification, it is however preferable to avoid digitizing the result again after transformation. In this article, we deal with this point of view in the 2D case. Based on a fully discrete framework, we explicitly explore the parameter space of rigid transformations. This exploration leads to a local search scheme that can be involved in combinatorial optimization strategies.

Section: Local Search Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Discrete Rigid Transformation Graph Search for 2D Image Registration

Image and Video Technology – PSIVT 2013 Workshops

Sugimoto

Kenmochi³

et al. 2014

“…Concerning the initial solution, namely a seed, for the local search algorithm, we 515 use the first and second order central moments for binary images [21], and a SIFT feature based method [22,23] for grey-level images.…”

Section: Experiments: Image Registrationmentioning

confidence: 99%

Discrete rigid registration: A local graph-search approach

Discrete Applied Mathematics

Kenmochi²,

Sugimoto

et al. 2017

International audienceImage registration has become a crucial step in a wide range of imaging domains, from computer vision to computer graphics. The core of image registration consists of determining the transformation that induces the best mapping between two images. This problem is ill-posed; it is also difficult to handle, due to the high size of the images and the high dimension of the transformation parameter spaces. Computing an actually optimal solution is practically impossible when transformations are assumed continuous (i.e., defined on R n). In this article, we initiate the exploration of a new way of considering image registration. Since digital images are basically defined in a discrete framework (i.e., in Z n), the transformation spaces – despite a potentially high complexity – actually remain finite, allowing for the development of explicit exploration of the parameter space via discrete optimization schemes. We propose an analysis of the very basis of registration, by considering rigid registration between 2D images. We show, in particular, how this problem can be handled in a fully discrete fashion, by computing locally the combinatorial structure of the parameter space of discrete rigid transformations, and by navigating on-the-flight within this space via gradient descent paradigms. This registration framework is applied in real imaging cases, emphasising the relevance of our approach, and the potential usefulness of its further extension to higher dimension images and richer transformations. ✩ The research leading to these results has received funding from the French Agence Nationale de la Recherche (Grant Agreement ANR-2010-BLAN-0205) and the Programme d'Investissements d'Avenir (LabEx Bézout

“…The DRT graph can be used in a local fashion, e.g., in pattern-based strategies, as proposed in [19] for analysing the topological invariance of digital images under arbitrary rigid transformations. Beyond the theoretical aspects of the DRT graph, its high-order polynomial complexity makes it difficult to generate the whole graph for large images, and to use it directly in imaging applications such as registration or warping [1,9,22,29].…”

Section: Introductionmentioning

confidence: 99%

On 2D constrained discrete rigid transformations

Kenmochi

Passat

et al. 2014

Ann Math Artif Intell

Rigid transformations are involved in a wide range of digital image processing applications. In such a context, they are generally considered as continuous processes, followed by a digitization of the results. Recently, rigid transformations on Z 2 have been alternatively formulated as a fully discrete process. Following this paradigm, we investigate – from a combinatorial point of view – the effects of pixel-invariance constraints on such transformations. In particular we describe the impact of these constraints on both the combi-natorial structure of the transformation space and the algorithm leading to its generation