The Conference on Historic Site Archaeology Papers 1980 - Volume 15

This multi-article volume was edited by Stanley South of the South Carolina Institute of Archaeology and Anthropology. Contents: The Chairman\u27s Final Report for the Final Volume of this Series - Stanley South.....ii Population and Trade: An Economic Mathematical Model - GOGGIN AWARD - Timothy William Jones.....1 Archeological Patterning on the Frontier: the Functional Significance of the Carolina and Frontier Patterns - Kenneth E. Lewis.....50 Pattern in Pattern Recognition? - Marc G. Stevenson.....57 The Early Fur Trade Artifact Pattern - Michael R.A. Foresman.....71 Excavation of a Whiskey Still in Northeast Georgia - Patrick H. Garrow.....91 Early Nineteenth Century Blast Furnace Charcoals: Analysis and Economics - John R. White.....106 A Taxonomy for Square Cut Nails - Donna L. Benson.....123 Historical Archaeology at an Union Pacific Railroad Station in the Red Desert of Wyoming - William B. Fawcett, Jr. and Ken Erickson.....153 The Sepulveda Project: Archaeological Investigations at the El Pueblo de Los Angeles State Historic Park, Los Angeles, California - Janet Hightower.....177 Recollections of Jim Howard: A Scholarly Antagonist - Melburn D. Thurman.....190https://scholarcommons.sc.edu/archanth_historic_site_arch_conf_papers/1014/thumbnail.jp

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end

A discussion on the validation tests employed to compare human action recognition methods using the MSR Action3D dataset

This paper aims to determine which is the best human action recognition method based on features extracted from RGB-D devices, such as the Microsoft Kinect. A review of all the papers that make reference to MSR Action3D, the most used dataset that includes depth information acquired from a RGB-D device, has been performed. We found that the validation method used by each work differs from the others. So, a direct comparison among works cannot be made. However, almost all the works present their results comparing them without taking into account this issue. Therefore, we present different rankings according to the methodology used for the validation in orden to clarify the existing confusion.Comment: 16 pages and 7 table