This volume of Springer’s Lecture Notes in computer Science series comprises th the scienti?c proceedings of the 10 International Workshop on Digital Mammography (IWDM), which was held June 16–18, 2010 in Girona, Ca...
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ISBN:
(数字)9783642136665
ISBN:
(纸本)9783642136658
This volume of Springer’s Lecture Notes in computer Science series comprises th the scienti?c proceedings of the 10 International Workshop on Digital Mammography (IWDM), which was held June 16–18, 2010 in Girona, Cata- nia. The IWDMmeetingstraditionallybringtogetheradiversesetofresearchers (physicists, mathematicians, computer scientists, engineers), clinicians (radi- ogists, surgeons) and representatives of industry, who are jointly committed to developing technology, not just for its own sake, but to support clinicians in the early detection and subsequent patient management of breast cancer. The IWDM conference series was initiated at a 1993 meeting of the SPIE Medical Imaging Symposium in San Jose, CA, with subsequent meetings hosted every two yearsbyresearchersaroundthe world. Former workshopswereheld in York, England (1994), Chicago, IL USA (1996), Nijmegen, The Netherlands (1998), Toronto, Canada (2000), Bremen, Germany (2002), Durham, NC, USA (2004), Manchester, UK (2006) and Tucson, AZ USA (2008). Each of these scienti?c events was combined with very successful and focused industrial and research exhibits, which demonstrated the milestones of digital mammography over the years. A total number of 141 paper submissions from 21 countries were received. Each of these four-page abstract submissions was reviewed in a blind process by at least two members of the Scienti?c Committee, which led to a ?nal selection of 46 oral presentations and 57 posters during the two and one-half days of scienti?c sessions.
The ability of a robot to build a persistent, accurate, and actionable model of its surroundings through sensor data in a timely manner is crucial for autonomous operation. While representing the world as a point clou...
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The ability of a robot to build a persistent, accurate, and actionable model of its surroundings through sensor data in a timely manner is crucial for autonomous operation. While representing the world as a point cloud might be sufficient for localization, denser scene representations are required for obstacle avoidance. On the other hand, higher-level semantic information is often crucial for breaking down the necessary steps to autonomously complete a complex task, such as cooking. So the looming question is, What is a suitable scene representation for the robotic task at hand? This survey provides a comprehensive review of key approaches and frameworks driving progress in the field of robotic spatial perception, with a particular focus on the historical evolution and current trends in representation. By categorizing scene modeling techniques into three main types—metric, metric–semantic, and metric–semantic–topological—we discuss how spatial perception frameworks are transitioning from building purely geometric models of the world to more advanced data structures incorporating higher-level concepts, such as the notion of object instances and places. Special emphasis is placed on approaches for real-time simultaneous localization and mapping, their integration with deep learning for enhanced robustness and scene understanding, and their ability to handle scene dynamicity as some of the hottest topics of interest driving robotics research today. We conclude with a discussion of ongoing challenges and future research directions in the quest to develop robust and scalable spatial perception systems suitable for long-term autonomy.
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