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Comparative Analysis of Techniques for the Recognition of Stabbed Wound and Accidental Wound Patterns
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 13 |
| Year of Publication: 2018 |
| Authors: Dayanand G. Savakar, Anil Kannur |
10.5120/ijca2018917769
|
Dayanand G. Savakar, Anil Kannur . Comparative Analysis of Techniques for the Recognition of Stabbed Wound and Accidental Wound Patterns. International Journal of Computer Applications. 182, 13 ( Sep 2018), 34-41. DOI=10.5120/ijca2018917769
Abstract
The Paper proposed a comparative analysis of wound patterns for the process of recognition whether the wound is stabbed wounds using any sharp metals or accidental wounds. The analysis is on the basis of characteristics of wounds in terms of parameters like shape, size and crime scene. And this paper also presents analysis of different segmentation techniques, possible better combination of features to extract for the recognition and finally analysis on different recognition methodologies. Different schemas of recognition are presented in which combination of different segmentation algorithms, features vectors and two approaches of classifiers, and also the comparative analysis of these schemas is discussed. Based on comparative analysis, the combination of three stage techniques of recognition has given results in diverse. From these schemas of recognition, the structural method has given better results compared to the other schemas on the available database of 500 images of pattern consisting of stabbed wounds and accidental wounds. The authenticated experiments out-turns the superiority of the proposed approach over the other approach considered in this work and also compares and suggest the false positive recognition verses false negative recognition. The proposed methodology has given better results compared to traditional method and will be helpful in forensic and crime investigation.
References
- Javaregowda Vinay et.al, (2017), “A Study on Postmortem Wound Dating by Grossand Histopathological Examination of Abrasions”, Am J Forensic Med Pathol Vol 38, pp: 167–173
- Naveen Tokas et al, (2016) “Comparison of Digital Image Segmentation Techniques- A Research Review”, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, pp. 215-220
- Xuan Zhou, Jiajun Wang (2015), “Feature Selection for Image Classification Based on a New Ranking Criterion”, Journal of Computer and Communications, Vol 3, pp: 74-79
- Dayanand G Savakar, Anil Kannur (2015) “A Genetic algorithm and Bayesian approach for identification & classification of weapon based on the stab wound patterns caused by different sharp metal”, International Journal of Computer Engineering and Applications, Volume IX, Issue I, pp: 01-12.
- Dayanand G. Savakar and Anand Ghuli, (2015), “Digital Watermarking as distributed noise by DWT, Fast Fourier Transformation and Fast Walsh-Hadamard Transform to study the sensitivity between Robustness and Fidelity”, International Journal of Computer Application, Issue 5, Vol.1, pp 102-107.
- Dayanand G. Savakar and Anand Ghuli, (2014), “Digital Watermarking-A Combined Approach by DWT, Chirp-Z and Fast Walsh-Hadamard Transform”, International Journal of Computer Technology and Applications (IJCTA), Vol. 5 No.6, pp 2006-2010, ISSN 2229-6093.
- Song Bo, (2012) “Automated wound identification system based on image segmentation and Artificial Neural Networks”, IEEE International Conference on Bioinformatics and Biomedicine, pp: 11-16.
- B.S.Anami, Sunanda Biradar and D.G.Savakar, (2013), “Identification and Classification of Similar looking food grains”, International Conference on Communication and Electronics System Design, ICCESD.
- Gitto L., Vullo A., Demari G.M., (2012) “Identification of the murder weapon by the analysis of a typical pattern of sharp force injury”, Italian Journal of Legal Medicine, Vol: 01, Issue No. 1, pp: 04-14.
- Ying Bai; Dali Wang, (2011)"Evaluate and identify optimal weapon systems using fuzzy multiple criteria decision making", Proceedings of IEEE International Conference on Fuzzy Systems (FUZZ), pp: 1510-1515.
- Suapang P., et.al..,(2011), ”Tool and Firearm Identification System Based on Image Processing”, Proceedings of 11th International Conference on Control, Automation and Systems (ICCAS), pp: 178 – 182
- Kaliszan M., Karnecki K., Akçan R., (2011) “Striated abrasions from a knife with non-serrated blade—identification of the instrument of crime on the basis of an experiment with material evidence”, International Journal of Legal Medicine, Vol: 125, Issue No. 5, pp: 745–748
- Ajay Kumar N, et.al., (2011) “Automated human identification using ear imaging”, Journal of Pattern Identification, pp: 1-13.
- Basavaraj S. Anami and Dayanand G. Savakar, (2011), “Suitability of Feature Extraction Methods in Recognition and Classification of Grains, Fruits and Flowers”, International Journal of Food Engineering, Volume 7, Issue 1, Article 9, pp: 1-28, Publisher: Berkeley Electronic Press, Berkeley, U.S.A.
- Francisco Veredas, et.al., (2010)”Binary Tissue Classification on Wound Images With Neural Networks and Bayesian Classifiers”, IEEE transactions on medical imaging, Vol: 29, Issue 2, pp: 410-426.
- B. S. Anami, Dayanand G. Savakar, (2009), “Effect of Foreign Bodies on Identification and Classification of Bulk Food Grains Images”, Journal of Applied Computer Science and Mathematics, Vol 3(6), pp:77- 83.
- F.A. Andaló, A.V. Miranda, A.X.Falcão, (2009) ,” Shape feature extraction and description based on tensor scale”, Journal of Pattern Recognition, Elsevier Ltd, pp:1-11.
- Jamieson A., Monessen A., (2009) “Wiley Encyclopedia of Forensic Sciences”, Wiley & Sons Ltd Edition, UK, pp: 2647-2649.
- M. Brandon Westover and Joseph A. O’Sullivan, (2008) “Achievable Rates for Pattern Identification”, IEEE transactions on Information Theory, Vol: 54, Issue No. 1, pp: 299-320.
- Li Dongguang, (2008) “Firearm Identification System Based on Ballistics Image Processing”, Proceedings of CISP '08, Congress on Image and Signal Processing Vol: 3, pp: 149 – 154
- Jie Liu1, Jigui Sun, Shengsheng Wang, (2006) “Pattern Identification: An overview”, IJCSNS International Journal of Computer Science and Network Security, Vol:6, Issue No.6, pp: 57-61
- Qi Peter Li, and Biing-Hwang Juang, (2006) “Study of a Fast-Discriminative Training Algorithm for Pattern Identification”, IEEE transactions on neural networks, Vol: 17, Issue No. 5, pp-1212-1221
- T. Plattner, B. Kneubuehl, M. Thali, U. Zollinger, (2003) “Gunshot residue patterns on skin in angled-contact and near contact gunshot wounds”, Forensic Science International, Elsevier publication, Vol. 138, pp:68-74.
- Candida Ferreira, (2001) “Gene Expression Programming: A New Adaptive Algorithm for Solving Problems”, Journal of Complex Systems, Vol. 13, issue 2, pp: 87-129.
- Hugar B.S. et al., (2012) “Study of defense injuries in homicidal deaths – An autopsy study”, Journal of Legal Medicine, Vol: 19, Issue No. 4, pp: 207-215.
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