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Learning to operate medical equipment is one of the essential skills for providing efficient treatment to patients. One of the current problems faced by many medical institutions is the lack or shortage of specialized infrastructure for medical practitioners to conduct hands-on training. Medical equipment is mostly used for patients, limiting training time drastically. Virtual simulation can help alleviate this problem by providing the virtual embodiment of the medical facility in an affordable manner. This paper reports the current results of an ongoing project aimed at providing virtual reality-based technical training on various medical equipment to radiophysicist trainees. In particular, we introduce a virtual reality (VR) prototype of a linear accelerator simulator for oncological radiotherapy training. The paper discusses the main challenges and features of the VR prototype, including the system design and implementation. A key factor for trainees’ access and usability is the user interface, particularly tailored in our prototype to provide a powerful and versatile yet friendly user interaction.

This article was withdrawn and retracted by the Journal of Fundamental and Applied Sciences and has been removed from AJOL at the request of the journal Editor in Chief and the organisers of the conference at which the articles were presented (www.iccmit.net). Please address any queries to editor@jfas.info.

This article was withdrawn and retracted by the Journal of Fundamental and Applied Sciences and has been removed from AJOL at the request of the journal Editor in Chief and the organisers of the conference at which the articles were presented (www.iccmit.net). Please address any queries to editor@jfas.info.

In a handwriting recognition problem, characters can be represented using chain codes. The main problem in representing characters using chain code is optimizing the length of the chain code. This paper proposes to use randomized algorithm to minimize the length of Freeman Chain Codes (FCC) generated from isolated handwritten characters. Feedforward neural network is used in the classification stage to recognize the image characters. Our test results show that by applying the proposed model, we reached a relatively high accuracy for the problem of isolated handwritten when tested on NIST database., {"references":["Tay, Y.H., Offline Handwriting Recognition using Artificial Neural\nNetwork and Hidden Markov Model. PhD Thesis. Universiti Teknologi\nMalaysia, 2002.","Al-Rashaideh, H., Preprocessing Phase for Arabic Word Handwritten\nRecognition. Information Transmission in Computer Network. 2006.","Steinherz, T., Rivlin, E., Intrator, N., Off-Line Cursive Script Word\nRecognition - A Survey. International Journal on Document Analysis and\nRecognition. 1999.","Mingqiang, Y., Kidiyo, K., Joseph, R., A Survey of Shape Feature\nExtraction Techniques. Book Pattern Recognition (Edited by: Peng-\nYeng Yin). November 2008. IN-TECH.","Wegener, I., Towards a Theory of Randomized Search Heuristics.\nLecturer Notes in Computer Science. Springer. 2003.","Ravichandran, A., An Algorithm for Thinning Noisy Images.\nInternational Conference on Acoustics Speech and Signal Processing,\n1990.","Alba, E., Chicana, J.F., Training Neural network with GA Hybrid\nAlgorithm. Proc of GECCO-04. Seattle, Washington. 2004.","Chen, T-P, Tian.J-X., The Research of Artificial Neural Network as a\nTool of Adjustment Prediction in Eco-City Construction. Proceedings of\nthe Fourth International Conference on Machine Learning and\nCybernetics, Guangzhou, 18-21 August 2005."]}

In this study, a Loop Back Algorithm for component connected labeling for detecting objects in a digital image is presented. The approach is using loop back connected component labeling algorithm that helps the system to distinguish the object detected according to their label. Deferent than whole window scanning technique, this technique reduces the searching time for locating the object by focusing on the suspected object based on certain features defined. In this study, the approach was also implemented for a face detection system. Face detection system is becoming interesting research since there are many devices or systems that require detecting the face for certain purposes. The input can be from still image or videos, therefore the sub process of this system has to be simple, efficient and accurate to give a good result., {"references":["Humberto Sossa, Giovanni Guzmán, \"New Method to Count Objects\ninto an Image\", icpr, 15th International Conference on Pattern\nRecognition (ICPR'00) - Volume 1, 2000, pp.1470.","Dae Ro Lee, Seung Hun Jin, Pham Cong Thien and Jae Wook Jeon,\n\"FPGA based Connected Component Labeling\", International\nConference on Control, Automation and Systems, ICROS, 2007,\npp2313-2317.","R. Sotelo, \"New methodology to determine the number of objects into an\nimage\", M. Sc. Thesis, Centro de Investigación en Computación - IPN,\nMay 2000, In Spanish.","Yang Yang, David Zhang, \"A novel line scan clustering algorithm for\nidentifying connected components in digital images\", Elsevier 2003,\nImage and Vision Computing 21 (2003), pp. 459-472.","L.D. Stefano, A. Bulgarelli, \"A simple and efficient connected\ncomponents labeling algorithm\", International Conference on Image\nAnalysis and Processing 1999, pp 322-327.","R. Gonzales, R. Woods, \"Digital Image Processing\", Addison-Wesley,\nReading, MA, 1992, pp. 42-45.","R. Haralick, L. Shapiro, \"Computer and Robot Vision\", 1, Addison-\nWesley, Reading, MA, 1992, pp. 33-37.","R. Klette, P. Zamperoni, \"Hand Book of Image Processing Operators\",\nWiley, New York, 1996, pp. 314-319.","R.K.R. Jain, B.G. Schunck, \"Machine Vision\", McGraw-Hill, 1995, pp.\n44-45.\n[10] A. Rosenfeld, A.C. Kak, \"Digital Picture Processing\", 2, Academic\nPress, New York, 1982, pp. 241-242.\n[11] Helmuth Spath, \"Cluster Analysis Algorithms\", Ellis Horwood, 1980.\n[12] Jonas Gomes, Luis Velho, \"Image Processing for Computer Graphic\",\nSpringer, 1997.\n[13] P. Peer, F. Solina, \"An automatic human face detection method\", in\nProc. 4th Computer Vision Winter Workshop (CVWW), Rastenfeld,\nAustria, Feb. 1999, pp. 122-130.\n[14] G. Kukharev, A. Novosielski, \"Visitor identification-elaborating real\ntime face recognition system\", in Proc.12th Winter School on Computer\nGraphics (WSCG), Plzen, Czech Republic, Feb. 2004, pp. 157-164.\n[15] K. Sobottka, I. Pitas, \"Segmentation and tracking of faces in color\nimages\", in Proc. IEEE Conf. on Automatic Face and Gesture\nRecognition (FG), Killington, USA, Oct. 1996, pp. 236-241.\n[16] Ramesh Jain and Rangachar Kasturi and Brian G. Shunck, \"MACHINE\nVISION\", McGraw-Hill Inc, International Editions 1995, pp.44-47.\n[17] Udo Ahlvers, Ruben Rajagopalan, Udo Z¨olzer, \"MODEL-FREE FACE\nDETECTION AND HEAD TRACKING WITH MORPHOLOGICAL\nHOLE MAPPING\", Proc. 13th European Signal Processing Conference\n(EUSIPCO'05), Antalya, Turkey, September 4-8, 2005.\n[18] R. Haralick, L. Shapiro, \"Computer and Robot Vision\", 1, Addison-\nWesley, Reading, MA, 1992, pp. 33-37.\n[19] R. Klette, P. Zamperoni, \"Hand Book of Image Processing Operators\",\nWiley, New York, 1996, pp. 314-319.\n[20] H.S.M. Dillencourt, M. Tamminen, \"A general approach to connected\ncomponent labeling for arbitrary image representations\", Journal of the\nACM 39 (2), 1992, pp 253-280.\n[21] R.L.O. Zuniga, L. Shapiro, \"A new connected components algorithm for\nvirtual memory computers\", Computer Vision, Graphics and Image\nProcessing 22, 1983, pp 287-300.\n[22] A. Rosenfeld, A.C. Kak, \"Digital Picture Processing\", 2, Academic\nPress, New York, 1982, pp. 241-242.\n[23] A. Rosenfeld, J. Pfaltz, \"Sequential operations in digital picture\nprocessing\", Journal of the ACM 13 (4), 1966, pp 471-494.\n[24] H. Samet, M. Tamminen, \"An improved approach to connected\ncomponent labeling of images\", Proceedings of CVPR-86, 1986, pp\n312-318.\n[25] X.D. Yang, \"An improved algorithm for labeling connected components\nin a binary image\", CVIP (1992)."]}