HarrisZ+^+: Harris Corner Selection for Next-Gen Image Matching Pipelines

Due to its role in many computer vision tasks, image matching has been subjected to an active investigation by researchers, which has lead to better and more discriminant feature descriptors and to more robust matching strategies, also thanks to the advent of the deep learning and the increased computational power of the modern hardware. Despite of these achievements, the keypoint extraction process at the base of the image matching pipeline has not seen equivalent progresses. This paper presents HarrisZ$^+$, an upgrade to the HarrisZ corner detector, optimized to synergically take advance of the recent improvements of the other steps of the image matching pipeline. HarrisZ$^+$ does not only consists of a tuning of the setup parameters, but introduces further refinements to the selection criteria delineated by HarrisZ, so providing more, yet discriminative, keypoints, which are better distributed on the image and with higher localization accuracy. The image matching pipeline including HarrisZ$^+$, together with the other modern components, obtained in different recent matching benchmarks state-of-the-art results among the classic image matching pipelines. These results are quite close to those obtained by the more recent fully deep end-to-end trainable approaches and show that there is still a proper margin of improvement that can be granted by the research in classic image matching methods

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Is there anything new to say about SIFT matching?

SIFT is a classical hand-crafted, histogram-based descriptor that has deeply influenced research on image matching for more than a decade. In this paper, a critical review of the aspects that affect SIFT matching performance is carried out, and novel descriptor design strategies are introduced and individually evaluated. These encompass quantization, binarization and hierarchical cascade filtering as means to reduce data storage and increase matching efficiency, with no significant loss of accuracy. An original contextual matching strategy based on a symmetrical variant of the usual nearest-neighbor ratio is discussed as well, that can increase the discriminative power of any descriptor. The paper then undertakes a comprehensive experimental evaluation of state-of-the-art hand-crafted and data-driven descriptors, also including the most recent deep descriptors. Comparisons are carried out according to several performance parameters, among which accuracy and space-time efficiency. Results are provided for both planar and non-planar scenes, the latter being evaluated with a new benchmark based on the concept of approximated patch overlap. Experimental evidence shows that, despite their age, SIFT and other hand-crafted descriptors, once enhanced through the proposed strategies, are ready to meet the future image matching challenges. We also believe that the lessons learned from this work will inspire the design of better hand-crafted and data-driven descriptors

Editorial for Special Issue "Fine Art Pattern Extraction and Recognition"

Cultural heritage, especially the fine arts, plays an invaluable role in the cultural, historical, and economic growth of our societies. Works of fine arts are primarily developed for aesthetic purposes and are mainly expressed through painting, sculpture, and architecture. In recent years, owing to technological improvements and drastic cost reductions, a large-scale digitization effort has been made, which has led to the increasing availability of large, digitized fine art collections. Coupled with recent advances in pattern recognition and computer vision, this availability has provided, especially researchers in these fields, with new opportunities to assist the art community by using automatic tools to further analyze and understand works of fine arts. Among other benefits, a deeper understanding of the fine arts has the potential to make works more accessible to a wider population, both in terms of fruition and creation, thus supporting the spread of culture

DNA-methylation dependent regulation of embryo-specific 5S ribosomal DNA cluster transcription in adult tissues of sea urchin Paracentrotus lividus

We have previously reported a molecular and cytogenetic characterization of three different 5S rDNA clusters in the sea urchin Paracentrotus lividus and recently, demonstrated the presence of high heterogeneity in functional 5S rRNA. In this paper, we show some important distinctive data on 5S rRNA transcription for this organism. Using Single Strand Conformation Polymorphism (SSCP) analysis, we demonstrate the existence of two classes of 5S rRNA, one which is embryo-specific and encoded by the smallest (700bp) cluster and the other which is expressed at every stage and encoded by longer clusters (900 and 950bp). We also demonstrate that the embryo-specific class of 5S rRNA is expressed in oocytes and embryonic stages and is silenced in adult tissue and that this phenomenon appears to be due exclusively to DNA methylation, as indicated by sensitivity to 5-azacytidine, unlike Xenopus where this mechanism is necessary but not sufficient to maintain the silenced status