Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Evaluation of modern camera calibration techniques for conventional diagnostic X-ray imaging settings

[EN] We explore three different alternatives for obtaining intrinsic and extrinsic parameters in conventional diagnostic X-ray frameworks: the direct linear transform (DLT), the Zhang method, and the Tsai approach. We analyze and describe the computational, operational, and mathematical background differences for these algorithms when they are applied to ordinary radiograph acquisition. For our study, we developed an initial 3D calibration frame with tin cross-shaped fiducials at specific locations. The three studied methods enable the derivation of projection matrices from 3D to 2D point correlations. We propose a set of metrics to compare the efficiency of each technique. One of these metrics consists of the calculation of the detector pixel density, which can be also included as part of the quality control sequence in general X-ray settings. The results show a clear superiority of the DLT approach, both in accuracy and operational suitability. We paid special attention to the Zhang calibration method. Although this technique has been extensively implemented in the field of computer vision, it has rarely been tested in depth in common radiograph production scenarios. Zhang¿s approach can operate on much simpler and more affordable 2D calibration frames, which were also tested in our research. We experimentally confirm that even three or four plane-image correspondences achieve accurate focal lengths.This work was carried out with the support of Information Storage S. L., University of Valencia (Grant #CPI-15170), CSD2007-00042 Consolider Ingenio CPAN (Grant #CPAN13TR01), Spanish Ministry of Industry, Energy and Tourism (Grant #TSI-100101-2013-019), IFIC (Severo Ochoa Centre of Excellence #SEV-2014-0398), and Dr. Bellot's medical clinic.Albiol Colomer, F.; Corbi, A.; Albiol Colomer, A. (2017). Evaluation of modern camera calibration techniques for conventional diagnostic X-ray imaging settings. Radiological Physics and Technology. 10(1):68-81. https://doi.org/10.1007/s12194-016-0369-yS6881101Selby BP, Sakas G, Groch W-D, Stilla U. Patient positioning with X-ray detector self-calibration for image guided therapy. Aust Phys Eng Sci Med. 2011;34:391–400.Markelj P, Likar B. Registration of 3D and 2D medical images. PhD Thesis, University of Ljubljana; 2010.Miller T, Quintana E. Stereo X-ray system calibration for three-dimensional measurements. Springer, 2014. pp. 201–207.Rougé A, Picard C, Ponchut C, Trousset Y. Geometrical calibration of X-ray imaging chains for three-dimensional reconstruction. Comput Med Imaging Graph. 1993; 295–300.Trucco E, Verri A. Introductory techniques for 3-D computer vision. Prentice Hall Englewood Cliffs, 1998.Moura DC, Barbosa JG, Reis AM, Tavares JMRS. A flexible approach for the calibration of biplanar radiography of the spine on conventional radiological systems. Comput Model Eng Sci. 2010; 115–137.Schumann S, Thelen B, Ballestra S, Nolte L-P, Buchler P, Zheng G. X-ray image calibration and its application to clinical orthopedics. Med Eng Phys. 2014;36:968–74.Selby B, Sakas G, Walter S, Stilla U. Geometry calibration for X-ray equipment in radiation treatment devices. 2007. pp. 968–974.de Moura DC, Barbosa JMG, da Silva Tavares JMR, Reis A. Calibration of bi-planar radiography with minimal phantoms. In: Symposium on Informatics Engineering. 2008. pp. 1–10.Medioni G, Kang SB. Emerging topics in computer vision. Prentice Hall. 2004.Bushong S. Radiologic science for technologists: physics, biology, and protection. Elsevier. 2012.Rowlands JA. The physics of computed radiography. Phys Med Biol. 2002;47:123–66.Dobbins JT, Ergun DL, Rutz L, Hinshaw DA, Blume H, Clark DC. DQE(f) of four generations of computed radiography acquisition devices. Med Phys. 1995;22:1581–93.Hartley R. Self-calibration from multiple views with a rotating camera. In: European Conference on Computer Vision. 1994. pp. 471–478.Tsai R. A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J Robot Autom. 1985;3(4):323–44.Hartley R, Zisserman A. Multiple view geometry in computer vision. Cambridge University Press. 2004.Zhang Z. A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell. 2000;22:1330–4.Remondino F, Fraser C. Digital camera calibration methods: considerations and comparisons. Symposium Image Eng Vis Metrol. 2006;36:266–72.Zollner H, Sablatnig R. Comparison of methods for geometric camera calibration using planar calibration targets. In: Digital Imaging in Media and Education. 2004. pp. 237–244

Reviews

Judith Jeffcoate, Multimedia in Practice ‐Technology and Applications, BCS Practitioner Series, Prentice‐Hall International, 1995. ISBN: 0–13–123324–6. £24.95

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Measurement with Persons: A European Network

The European ‘Measuring the Impossible’ Network MINET promotes new research activities in measurement dependent on human perception and/or interpretation. This includes the perceived attributes of products and services, such as quality or desirability, and societal parameters such as security and well-being. Work has aimed at consensus about four ‘generic’ metrological issues: (1) Measurement Concepts & Terminology; (2) Measurement Techniques: (3) Measurement Uncertainty; and (4) Decision-making & Impact Assessment, and how these can be applied specificallyto the ‘Measurement of Persons’ in terms of ‘Man as a Measurement Instrument’ and ‘Measuring Man.’ Some of the main achievements of MINET include a research repository with glossary; training course; book; series of workshops;think tanks and study visits, which have brought together a unique constellation of researchers from physics, metrology,physiology, psychophysics, psychology and sociology. Metrology (quality-assured measurement) in this area is relativelyunderdeveloped, despite great potential for innovation, and extends beyond traditional physiological metrology in thatit also deals with measurement with all human senses as well as mental and behavioral processes. This is particularlyrelevant in applications where humans are an important component of critical systems, where for instance health andsafety are at stake

Challenging the Computational Metaphor: Implications for How We Think

This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think

Model AI Assignments 2018

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of seven AI assignments from the 2018 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu