We propose a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of contextual self-dissimilarity reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover, our approach extends to contextual information the local self-dissimilarity notion embedded in established detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and localization accuracy. As the key principle and machinery of our method are amenable to a variety of data kinds, including multi-channel images and organized 3D measurements, we delineate how to extend the basic formulation in order to deal with range and RGB-D images, such as those provided by consumer depth cameras.
The Maximal Self-Dissimilarity Interest Point Detector / Tombari, Federico; Di Stefano, Luigi. - In: IPSJ TRANSACTIONS ON COMPUTER VISION AND APPLICATIONS. - ISSN 1882-6695. - ELETTRONICO. - 7:(2015), pp. 175-188. [10.2197/ipsjtcva.7.175]
The Maximal Self-Dissimilarity Interest Point Detector
TOMBARI, FEDERICO;DI STEFANO, LUIGI
2015
Abstract
We propose a novel interest point detector stemming from the intuition that image patches which are highly dissimilar over a relatively large extent of their surroundings hold the property of being repeatable and distinctive. This concept of contextual self-dissimilarity reverses the key paradigm of recent successful techniques such as the Local Self-Similarity descriptor and the Non-Local Means filter, which build upon the presence of similar - rather than dissimilar - patches. Moreover, our approach extends to contextual information the local self-dissimilarity notion embedded in established detectors of corner-like interest points, thereby achieving enhanced repeatability, distinctiveness and localization accuracy. As the key principle and machinery of our method are amenable to a variety of data kinds, including multi-channel images and organized 3D measurements, we delineate how to extend the basic formulation in order to deal with range and RGB-D images, such as those provided by consumer depth cameras.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.