Shape-Based Image Matching Using Heat Kernels and Diffusion Maps

Abstract

ABSTRACT:2D image matching problem is often stated as an image-to-shape or shape-to-shape matching problem. Such shape-based matching techniques should provide the matching of scene image fragments registered in various lighting, weather and season conditions or in different spectral bands. Most popular shape-to-shape matching technique is based on mutual information approach. Another wellknown approach is a morphological image-to-shape matching proposed by Pytiev. In this paper we propose the new image-to-shape matching technique based on heat kernels and diffusion maps. The corresponding Diffusion Morphology is proposed as a new generalization of Pytiev morphological scheme. The fast implementation of morphological diffusion filtering is described. Experimental comparison of new and aforementioned shape-based matching techniques is reported applying to the TV and IR image matching problem.

Change Detection via Morphological Comparative Filters

“…This pipeline contains comparative filtering on image pyramid, calculation of morphological difference map, binarization, extraction of change proposals and testing change proposals using local morphological correlation coefficient. We implement this pipeline based on both guided contrasting filters and morphological diffusion filters previously proposed in (Vizilter et al, 2014) for shape-based matching. Qualitative experiments with guided contrasting filtering in different change detection tasks demonstrate that they provide the reasonable scene change proposals and demonstrate the enough robustness relative to changes in lighting and other image acquisition conditions.…”

“…In this paper we present a new change detection technique based on generalized ideas of Morphological Image Analysis (MIA) proposed by Pyt'ev (Pyt'ev, 1993) and further developed in (Evsegneev, Pyt'ev, 2006;Vizilter, Zheltov, 2012;Pyt'ev, 2013;Vizilter et al, 2014). Let's note that terms "morphology", "morphological filter" and "morphological analysis" refer to Mathematical Morphology (MM) proposed by Serra (Serra,1982) as well as to MIA.…”

“…But the diffusion filtering with such heat kernel (18) is a time-consuming procedure. So, we prefer the diffusion filters proposed in (Vizilter et al, 2014) and based on point feature descriptor iLBP (intensity + LBP):…”

“…It is based on notions of "heat kernels" and "heat dissipation" introduced in (Belkin, Niyogi, 2001) in the context of Laplacian Eigenmap technique for nonlinear dimensionality reduction. In (Vizilter et al, 2014) the generalized MIA formalism called Diffusion Morphology (DM) was proposed based on heat kernels and diffusion maps. It was developed as an image-to-shape matching technique, but it looks reasonable to try this approach in the change detection task too.…”

“… new algorithmic pipeline for change detection in remote sensing data;  implementation of change detection scheme based on both guided contrasting and diffusion morphology previously proposed in (Vizilter et al, 2014).…”