Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras

In this paper we propose a fuzzy method to detect free paths in real-time using digital stereo images. It is based on looking for linear variations of depth in disparity maps, which are obtained by processing a pair of rectified images from two stereo cameras. By applying least-squares fitting over groups of disparity maps columns to a linear model, free paths are detected by giving a certainty using a fuzzy rule. Experimental results on real outdoor images are also presented.Nuria Ortigosa acknowledges the support of Universidad Polit'ecnica de Valencia under grant FPI-UPV 2008. Samuel Morillas acknowledges the support of Spanish Ministry of Education and Science under grant MTM 2009-12872-C02-01.Ortigosa Araque, N.; Morillas Gómez, S.; Peris Fajarnes, G.; Dunai Dunai, L. (2012). Fuzzy Free Path Detection based on Dense Disparity Maps obtained from Stereo Cameras. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 20(2):245-259. doi:10.1142/S0218488512500122S245259202Grosso, E., & Tistarelli, M. (1995). Active/dynamic stereo vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(9), 868-879. doi:10.1109/34.406652Wedel, A., Badino, H., Rabe, C., Loose, H., Franke, U., & Cremers, D. (2009). B-Spline Modeling of Road Surfaces With an Application to Free-Space Estimation. IEEE Transactions on Intelligent Transportation Systems, 10(4), 572-583. doi:10.1109/tits.2009.2027223Bloch, I. (2005). Fuzzy spatial relationships for image processing and interpretation: a review. Image and Vision Computing, 23(2), 89-110. doi:10.1016/j.imavis.2004.06.013Keller, J. M., & Wang, X. (2000). A Fuzzy Rule-Based Approach to Scene Description Involving Spatial Relationships. Computer Vision and Image Understanding, 80(1), 21-41. doi:10.1006/cviu.2000.0872Moreno-Garcia, J., Rodriguez-Benitez, L., Fernández-Caballero, A., & López, M. T. (2010). Video sequence motion tracking by fuzzification techniques. Applied Soft Computing, 10(1), 318-331. doi:10.1016/j.asoc.2009.08.002Morillas, S., Gregori, V., & Hervas, A. (2009). Fuzzy Peer Groups for Reducing Mixed Gaussian-Impulse Noise From Color Images. IEEE Transactions on Image Processing, 18(7), 1452-1466. doi:10.1109/tip.2009.2019305Poloni, M., Ulivi, G., & Vendittelli, M. (1995). Fuzzy logic and autonomous vehicles: Experiments in ultrasonic vision. Fuzzy Sets and Systems, 69(1), 15-27. doi:10.1016/0165-0114(94)00237-2Alonso, J. M., Magdalena, L., Guillaume, S., Sotelo, M. A., Bergasa, L. M., Ocaña, M., & Flores, R. (2007). Knowledge-based Intelligent Diagnosis of Ground Robot Collision with Non Detectable Obstacles. Journal of Intelligent and Robotic Systems, 48(4), 539-566. doi:10.1007/s10846-006-9125-6McFetridge, L., & Ibrahim, M. Y. (2009). A new methodology of mobile robot navigation: The agoraphilic algorithm. Robotics and Computer-Integrated Manufacturing, 25(3), 545-551. doi:10.1016/j.rcim.2008.01.008Sun, H., & Yang, J. (2001). Obstacle detection for mobile vehicle using neural network and fuzzy logic. Neural Network and Distributed Processing. doi:10.1117/12.441696Ortigosa, N., Morillas, S., & Peris-Fajarnés, G. (2010). Obstacle-Free Pathway Detection by Means of Depth Maps. Journal of Intelligent & Robotic Systems, 63(1), 115-129. doi:10.1007/s10846-010-9498-4Picton, P. D., & Capp, M. D. (2008). Relaying scene information to the blind via sound using cartoon depth maps. Image and Vision Computing, 26(4), 570-577. doi:10.1016/j.imavis.2007.07.005Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330-1334. doi:10.1109/34.888718Scharstein, D., & Szeliski, R. (2002). International Journal of Computer Vision, 47(1/3), 7-42. doi:10.1023/a:1014573219977Felzenszwalb, P. F., & Huttenlocher, D. P. (2006). Efficient Belief Propagation for Early Vision. International Journal of Computer Vision, 70(1), 41-54. doi:10.1007/s11263-006-7899-4Qingxiong Yang, Liang Wang, Ruigang Yang, Stewenius, H., & Nister, D. (2009). Stereo Matching with Color-Weighted Correlation, Hierarchical Belief Propagation, and Occlusion Handling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3), 492-504. doi:10.1109/tpami.2008.99Zitnick, C. L., & Kang, S. B. (2007). Stereo for Image-Based Rendering using Image Over-Segmentation. International Journal of Computer Vision, 75(1), 49-65. doi:10.1007/s11263-006-0018-8Hartley, R., & Zisserman, A. (2004). Multiple View Geometry in Computer Vision. doi:10.1017/cbo9780511811685Lee, C. C. (1990). Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 404-418. doi:10.1109/21.52551C. Fodor, J. (1993). A new look at fuzzy connectives. Fuzzy Sets and Systems, 57(2), 141-148. doi:10.1016/0165-0114(93)90153-9Nalpantidis, L., & Gasteratos, A. (2010). Stereo vision for robotic applications in the presence of non-ideal lighting conditions. Image and Vision Computing, 28(6), 940-951. doi:10.1016/j.imavis.2009.11.011BOHANNON, R. W. (1997). Comfortable and maximum walking speed of adults aged 20—79 years: reference values and determinants. Age and Ageing, 26(1), 15-19. doi:10.1093/ageing/26.1.1

Rethinking globalised resistance : feminist activism and critical theorising in international relations

This article argues that a feminist approach to the 'politics of resistance' offers a number of important empirical insights which, in turn, open up lines of theoretical inquiry which critical theorists in IR would do well to explore. Concretely, we draw on our ongoing research into feminist 'anti-globalisation' activism to rethink the nature of the subject of the politics of resistance, the conditions under which resistance emerges and how resistance is enacted and expressed. We begin by discussing the relationship of feminism to critical IR theory as a way of situating and explaining the focus and approach of our research project. We then summarise our key empirical arguments regarding the emergence, structure, beliefs, identities and practices of feminist 'anti-globalisation' activism before exploring the implications of these for a renewed critical theoretical agenda in IR

Corresponding author:

Abstract An approach to automate the extraction and measurement of roots in minirhizotron images is presented. Two-dimensional matched filtering is followed by local entropy thresholding to produce binarized images from which roots are detected. After applying a root classifier to discriminate fine roots from unwanted background objects, a root labeling method is implemented to identify each root in the image. Once a root is detected, its length and diameter are measured using Dijkstra’s algorithm for obtaining the central curve and the Kimura-Kikuchi-Yamasaki method for measuring the length of the digitized path. Experimental results from a collection of peach (Prunus persica) root images demonstrate the effectiveness of the approach. Key words root detection, minirhizotron images, matched filtering, thresholding, AdaBoost, Free-man algorith

Environmetrics of synfuels. I. Processing the automated PDP-11 data components for the UMD gasifier facility

This report summarizes the techniques and procedures used to handle automated data collected at the University of Minnesota-Duluth (UMD) campus coal gasification facility. This facility, which is partially funded by the Department of Energy, is being evaluated by scientists at Oak Ridge National Laboratory (ORNL) for its potential health and environmental effects. Automatic data collections and manually collected and sample results data are used for this assessment. A data management project at ORNL handles these and other UMD data for the Gasifiers in Industry Program (GIIP). Specifically, this report documents the procedures developed within the data management project for handling two categories of automated data: (1) process and (2) environmental. The examples included use actual data from the first one and a half years of gasifier operation

Multi-camera Scene Reconstruction via Graph Cuts

We address the problem of computing the 3-dimensional shape of an arbitrary scene from a set of images taken at known viewpoints