Applied Graph Theory in Computer Vision and Pattern Recognition

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Validity of an automatic measure protocol in distal femur for allograft selection from a three-dimensional virtual bone bank system

Osteoarticular allograft is one possible treatment in wide surgical resections with large defects. Performing best osteoarticular allograft selection is of great relevance for optimal exploitation of the bone databank, good surgery outcome and patient’s recovery. Current approaches are, however, very time consuming hindering these points in practice. We present a validation study of a software able to perform automatic bone measurements used to automatically assess the distal femur sizes across a databank. 170 distal femur surfaces were reconstructed from CT data and measured manually using a size measure protocol taking into account the transepicondyler distance (A), anterior-posterior distance in medial condyle (B) and anterior-posterior distance in lateral condyle (C). Intra- and inter-observer studies were conducted and regarded as ground truth measurements. Manual and automatic measures were compared. For the automatic measurements, the correlation coefficients between observer one and automatic method, were of 0.99 for A measure and 0.96 for B and C measures. The average time needed to perform the measurements was of 16 h for both manual measurements, and of 3 min for the automatic method. Results demonstrate the high reliability and, most importantly, high repeatability of the proposed approach, and considerable speed-up on the planning.

Driving the refactoring of Java Enterprise applications by evaluating the distance between application elements

Java Enterprise Applications (JEAs) are complex systems composed using various technologies that in turn rely on languages other than Java, such as XML or SQL. Given the complexity of these applications, the need to reverse engineer them in order to support further development becomes critical. In this paper we show how it is possible to split a system into layers and how is possible to interpret the distance between application elements in order to support the refactoring of JEAs. The purpose of this paper is to explore ways to provide suggestions about the refactoring operations to perform on the code by evaluating the distance between layers and elements belonging those layers. We split JEAs into layers by considering the kinds and the purposes of the elements composing the application. We measure distance between elements by using the notion of the shortest path in a graph. Also we present how to enrich the interpretation of the distance value with enterprise pattern detection in order to refine the suggestion about modifications to perform on the code.

ROSINA/DFMS capabilities to measure isotopic ratios in water at comet 67P/Churyumov–Gerasimenko

The likelihood that comets may have delivered part of the water to Earth has been reinforced by the recent observation of the earth-like D/H ratio in Jupiter-family comet 103P/Hartley 2 by Hartogh et al. (2011). Prior to this observation, results from several Oort cloud comets indicated a factor of 2 enrichment of deuterium relative to the abundance at Earth. The European Space Agency’s Rosetta spacecraft will encounter comet 67P/Churyumov-Gerasimenko, another Jupiter-family comet of likely Kuiper belt origin, in 2014 and accompany it from almost aphelion to and past perihelion. Onboard Rosetta is the Rosetta Orbiter Spectrometer for Ion and Neutral Analysis (ROSINA) which consists of two mass spectrometers and a pressure sensor [Balsiger et al. 2007]. With its unprecedented mass resolution, for a space-borne instrument, the Double Focusing Mass Spectrometer (DFMS), one of the major subsystems of ROSINA, will be able to obtain unambiguously the ratios of the isotopes in water from in situ measurements in the coma around the comet. We will discuss the performance of this sensor on the basis of measurements of the terrestrial hydrogen and oxygen isotopic ratios performed with the flight spare instrument in the lab. We also show that the instrument on Rosetta is capable of measuring the D/H even in the very low density water background released by the spacecraft. This capability demonstrates that ROSINA should obtain very sensitive measurements of these ratios in the cometary environment. These measurements will allow detection of fractionation as function of the distance from the nucleus as well as fractionation due to mechanisms that are correlated with heliocentric distance.