Skeleton Extraction from Polygonal Meshes

Tato bakalářská práce se zabývá extrakcí kostry z modelu třírozměrného tělesa. Z existujících metod byla vybrána extrakce kostry pomocí os lokální válcové symetrie (ROSA - rotational symmetry axis), která je velmi odolná ke ztrátám vstupních dat a je vhodná pro tělesa složená z obecně válcových částí a jejich spojů (jako jsou například lidská a zvířecí těla). Bylo optimalizováno vyhledávání os lokální válcové symetrie. V experimentech byl zkoumán vliv vlastností tělesa (jako např. ztráta vstupních dat, zvrásnění povrchu, tvar) a vliv parametrů metody na kostru.This bachelor's thesis deals with skeleton extraction from 3D objects. The presented technique for skeleton extraction from incomplete points cloud is based on ROSA (Rotational Symmetry Axis) method, while the searching for rotational symmetry axis was optimized. The method is resistant to missing data and is suitable for objects made from cylindrical parts and joints of that parts (for example human and animals bodies). In experiments, both the influence of object properties (for example missing data, chill mark, shape) and the influence of method's parameters were examined.

Extracting curve-skeletons from digital shapes using occluding contours

Curve-skeletons are compact and semantically relevant shape descriptors, able to summarize both topology and pose of a wide range of digital objects. Most of the state-of-the-art algorithms for their computation rely on the type of geometric primitives used and sampling frequency. In this paper we introduce a formally sound and intuitive definition of curve-skeleton, then we propose a novel method for skeleton extraction that rely on the visual appearance of the shapes. To achieve this result we inspect the properties of occluding contours, showing how information about the symmetry axes of a 3D shape can be inferred by a small set of its planar projections. The proposed method is fast, insensitive to noise, capable of working with different shape representations, resolution insensitive and easy to implement

Morphological feature extraction for statistical learning with applications to solar image data

Abstract: Many areas of science are generating large volumes of digital image data. In order to take full advantage of the high-resolution and high-cadence images modern technology is producing, methods to automatically process and analyze large batches of such images are needed. This involves reducing complex images to simple representations such as binary sketches or numerical summaries that capture embedded scientific information. Using techniques derived from mathematical morphology, we demonstrate how to reduce solar images into simple ‘sketch ’ representations and numerical summaries that can be used for statistical learning. We demonstrate our general techniques on two specific examples: classifying sunspot groups and recognizing coronal loop structures. Our methodology reproduces manual classifications at an overall rate of 90 % on a set of 119 magnetogram and white light images of sunspot groups. We also show that our methodology is competitive with other automated algorithms at producing coronal loop tracings and demonstrate robustness through noise simulations. 2013 Wile

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing