Search results for "Escriptors"
showing 10 items of 66 documents
Improving SIFT-based descriptors stability to rotations
2010
Image descriptors are widely adopted structures to match image features. SIFT-based descriptors are collections of gradient orientation histograms computed on different feature regions, commonly divided by using a regular Cartesian grid or a log-polar grid. In order to achieve rotation invariance, feature patches have to be generally rotated in the direction of the dominant gradient orientation. In this paper we present a modification of the GLOH descriptor, a SIFT-based descriptor based on a log-polar grid, which avoids to rotate the feature patch before computing the descriptor since predefined discrete orientations can be easily derived by shifting the descriptor vector. The proposed des…
THE PURPOSING OF NEW COMPOUNDS OR THE RE-PURPOSING OF OLD DRUGS BY MEANS OF MULTIVARIATE ANATYSIS ON MOLECULAR DESCRIPTORS
2010
A Quantitative Model for Alkane Nucleophilicity Based on C−H Bond Structural/Topological Descriptors
2020
A first quantitative model for calculating the nucleophilicity of alkanes is described. A statistical treatment was applied to the analysis of the reactivity of 29 different alkane C−H bonds towards in situ generated metal carbene electrophiles. The correlation of the recently reported experimental reactivity with two different sets of descriptors comprising a total of 86 parameters was studied, resulting in the quantitative descriptor‐based alkane nucleophilicity (QDEAN) model. This model consists of an equation with only six structural/topological descriptors, and reproduces the relative reactivity of the alkane C−H bonds. This reactivity can be calculated from parameters emerging from th…
Shape matching, shape retrieval
2016
This thesis concerns shape matching and shape retrieval. It describes four contributions to thisdomain. The first is an improvement of the k-means method, in order to find the best partition ofvoxels inside a given shape ; these best partitions permit to match shapes using an optimal matchingin a bipartite graph. The second contribution is the fusion of two descriptors, one local, the otherglobal, with the product rule. The third contribution considers the complete graph, the vertices ofwhich are the shapes in the database and the query. Edges are labelled with several distances,one per descriptor. Then the method computes, with linear programming, the convex combinationof distances which m…
Development of Sicilian bean core collection using morphological descriptors
2018
Different species and varieties of bean, spread in Sicily, are representative of local agricultural practices, as result of a careful exploration. Many landraces have become obsolete due to the spread of commercial varieties, but are still cultivated in small areas of Nebrodi Mountains (ME-Italy) and are endangered. The Sicilian bean landraces are often poorly known but represent a genetic heritage to be preserve and to enhance. The ex situ conservation of Sicilian bean landraces was carried out in “Living Plants Germplasm Bank” of Ucria (ME-Italy), founded by the Nebrodi Regional Park, and in “Sicilian Plant Germplasm Repository” of STEBICEF Department - University of Palermo. Within ex si…
An evaluation of recent local image descriptors for real-world applications of image matching
2019
This paper discusses and compares the best and most recent local descriptors, evaluating them on increasingly complex image matching tasks, encompassing planar and non-planar scenarios under severe viewpoint changes. This evaluation, aimed at assessing descriptor suitability for real-world applications, leverages the concept of approximated overlap error as a means to naturally extend to non-planar scenes the standard metric used for planar scenes. According to the evaluation results, most descriptors exhibit a gradual performance degradation in the transition from planar to non-planar scenes. The best descriptors are those capable of capturing well not only the local image context, but als…
Is There Anything New to Say About SIFT Matching?
2020
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. Th…
Guest editorial: Local image descriptors in computer vision
2020
...This Special Issue includes seven original research papers that cover diverse and significant aspects of local image descriptor research. In particular, the order in which papers appear reflects the main phase they address, in an ideal computational pipeline starting with the localisation of salient points in an image and ending with the incorporation of spatial and temporal features in descriptor construction....
Global Archaelogical Mosaicing for Underwater Scenes
2006
This contribution regards the mosaicing of seabed landscapes, in order to represent higher resolution photos of whole sites with wrecks in a fast and safe fashion. A stereo vision system has been arranged by adding two cameras to the payload aboard a Remotely Operated Vehicle. A number of problems arise due to poor luminosity, cloudy water, water distortion and presence of artifacts. A robust algorithm has been defined to reduce the radial distortion of the camera lenses and to enhance the results.
3D objects descriptors methods: Overview and trends
2017
International audience; Object recognition or object's category recognition under varying conditions is one of the most astonishing capabilities of human visual system. The scientists in computer vision have been trying for decades to reproduce this ability by implementing algorithms and providing computers with appropriate tools. Hence, several intelligent systems have been proposed. To act in this field, numerous approaches have been proposed. In this paper we present an overview of the current trend in 3D objects recognition and describe some representative state of the art methods, highlighting their limits and complexity.