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Abstract

Lane detection is one of the key steps for developing driver assistance and vehicle automation features. A number of techniques are available for lane detection as part of computer vision tools to perform lane detection with different levels of accuracies. In this paper a unique method has been proposed for lane detection based on dynamic origin (DOT). This method provides better flexibility to adjust the outcome as per the specific needs of the intended application compared to other techniques. As the method offers better degree of control during the lane detection process, it can be adapted to detect lanes in varied situations like poor lighting or low quality road markings. Moreover, the Piecewise Linear Stretching Function (PLSF) has also been incorporated into the proposed method to improve the contrast of the input image source. Adding the PLSF method to the proposed lane detection technique, has significantly improved the accuracy of lane detection when compared to hough transform method from 87.88% to 98.25% in day light situations and from 94.15% to 97% in low light situations.
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Bibliography

[1] V. Gaikwad, S. Lokhande, “Lane departure identification for advanced driver assistance,” IEEE Transactions on Intelligent Transportation Systems., 2015, 16(2): 910–918.
[2] Sandipann P. Narote, Pradnya N. Bhujbal, Abbhilasha S. Narote, Dhiraj M. Dhane, “A review of recent advances in lane detection and departure warning,” System. Pattern Recognition, 2018, 73:216-234.
[3] P.C. Wu, C. Chang, C.H. Lin, “Lane mark extraction for automobiles under complex conditions,” Pattern Recognition, 2014, 47: 2756–2767.
[4] C. Mu, X. Ma, “Lane detection based on object segmentation and piecewise fitting,” Telkomnika Indonesian Journal of Electrical Engineering, 2014, 12(5):3491–3500.
[5] CALTECH database http://www.vision.caltech.edu/archive.html
[6] Y. Dong, J. Xiong, L. Li, J. Yang “Lane detection based on object segmentation and piecewise fitting,” ICCP proceedings, 2012, 461–464.
[7] P. Hsiao, C.W. Yeh, S. Huang, L.C. Fu, “Portable vision based real time lane departure warning system day and night,” IEEE Transactions on Vehicular Technology,2009, 58(4):2089–2094.
[8] Prashanth Viswanath, Pramod Swami, “A Robust and Real -Time Image Based Lane Departure Warning System,” IEEE International Conference on Consumer Electronics, 2016.
[9] Minghua Niu, Jianmin Zhang, Gen Li, “Research on the Algorithms of Lane Recognition based on Machine Vision,” International Journal of Intelligent Engineering and Systems, 2015, 8(4).
[10] Gulivindala Suresh,Chanamallu Srinivasa Rao, “Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features,” International Journal of Intelligent Engineering and Systems, 2019, 12(2).
[11] C.R. Jung, C.R. Kelber, “Lane following and lane departure using a linear parabolic mode,” Image and Vision Computing, 2005, 23(13):1192–1202.
[12] D. Kragic, L. Petersson and H.I. Christensen, “Visually guided manipulation tasks,” Robotics and Autonomous Systems, 2002, 40(2/3):193-203.
[13] J.W. Lee, “A machine vision system for lane departure detection. Computing,” Vision Image Understanding, 2002, 86(1): 52–78.
[14] J. Melo, A. Naftel, A. Bernardino, J. Santos, “Detection and classification of highway lanes using vehicle motion trajectories,” IEEE Transactions on Intelligent Transportation Systems, 2006, 7(2): 188–200.
[15] Chaiwat Nuthong; Theekapun Charoenpong, “Lane detection using smoothing,” 3rd International Congress on Image and Signal Processing, 2010, 989-993.
[16] Bing Yu; Weigong Zhang; Yingfeng Cai, “A Lane Departure Warning System Based on Machine Vision,” Proceeding IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008, 197-201.
[17] J.G. Wang, C. Lin, S. Chen, “Applying fuzzy method to vision-based lane detection and departure warning system,” Expert Systems with Applications, 2010, 3(1):113–126.
[18] H. Xu, H. Li, “Study on a robust approach of lane departure warning algorithm,” IEEE International Conference on Signal Processing System (ICSPS), 2010, 201–204.
[19] S. Srivastava, M. Lumb, R. Singal, “Improved Lane Detection using Hybrid Median Filter and Modified Hough Transform,” International Journal of Advanced Research in Computer Science and Software Engineering, 2014, 4(1): 30–37.
[20] H. Aung, M.H. Zaw, “Video based lane departure warning system using hough transform,” International Conference on Advances in Engineering and Technology (ICAET), 2010, 85–88.
[21] X. An, E. Shang, J. Song, J. Li, H. He, “Real-time lane departure warning system based on a single fpga,” Eurasip Journal on Image and Video Processing, 2013,38(1–18).
[22] J. Son, H. Yoo, S. Kim, K. Sohn, “Real-time illumination invariant lane detection for lane departure warning system,” Expert Systems with Applications, 2015, 42(4):1816–1824.
[23] Y. Wang, D. Shen, E.K. Teoh, “Lane detection using spline model,” Pattern Recognition, 2000, 21(9): 677–689.
[24] C.J. Lin, J.G. Wang, S.M. Chen, C.Y. Lee, “Design of a lane detection and departure warning system using functional link-based neuro-fuzzy network,” IEEE International Conference on Fuzzy System (FUZZ), 2010, 1–7.
[25] Q. Lin, Y. Han, H. Hahn, “Real time lane detection based on extended edge-linking algorithm,” IEEE International Conference on Computer Research and Development, 2010, 725–730.
[26] C. Tu, B.V. Wyk, Y. Hamam, K. Djouni, S. Du, “Vehicle Position Monitoring using,” Hough Transform.IERI Procedia, 2013;4:316–322.
[27] E. Salari, D. Ouyang, “Camera-based forward collision and lane departure warning system using svm,” IEEE 56th International Midwest Symp. On Circuits and Systems (MWSCAS), 2013,1278–1281.
[28] A.S. Aguadoa, Eugenia, Montie and M. S. Nixonc, “Invariant characterisation of the Hough transform for pose estimation of arbitrary shapes,” Pattern Recognition, 2002, 35(5):1083-1097.
[29] Borkar, M. Hayes, M. Smith, “Robust lane detection and tracking with ransac and kalman filter,” 16th IEEE International Conference on Image Processing (ICIP), 2009, 3261–3264.
[30] N. Madrid, P. Hurtik “Lane departure warning for mobile devices based on a fuzzy representation of images,” Fuzzy Sets System, 2016, 291:144–159.
[31] P.Maya, C.Tharini, “Performance Analysis of Lane Detection Algorithm using Partial Hough Transform,” 21st International Arab Conference on Information Technology (ACIT'2020), 2020, Egypt.
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Authors and Affiliations

P. Maya
1
C. Tharini
2

  1. B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
  2. B S Abdur Rahman Crescent Institute of Science and Technology,Chennai, India
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Abstract

The shock is a general, non-specific pathological process, caused by the sudden action of very brutal pathogens, a situation for which the body has no reserves for qualitative and quantitative compensation-adaptation. The objective of our experiment was to make an evaluation of the changes in some hematological and biochemical parameters of the blood, during some hypovolemic evolutions, in the rabbits. Twenty New Zealand White rabbits we used. An IDEXX ProCyte Dx Hematology Analyzer was applied to perform hematological determinations. An IDEXX VetTest Chemistry Analyzer was used to perform blood biochemistry determinations. The data obtained were statistically analyzed, calculating the Media and Standard Deviation (SD), using the Microsoft Excel application. At the same time, the statistical significance of the differences between the batches was calculated based on the t test (Student) using the Microsoft Excel application. The study revealed a decrease in the number of red blood cells and leukocytes per unit volume of blood (p<0.05) in the case of group 2 and an increase in glucose, triglycerides (p<0.05).
Experimental hypovolemia induced in the conditions of our experiment determined: an obvious posthemorrhagic anemia, a significant leukopenia mainly 6 hours after the production of hypovolemic shock and a significant hyperglycemia, manifested mainly 12 hours after the induction of hypovolemia.
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Bibliography


Bailey JM (1985) Prostaglandins, leukotrienes, and lipoxins: biochemistry, mechanism of action, and clinical applications. Bailey JM (ed) Plenum Press, New York and London, pp 705.

Capone A, Safar P, Stezoski SW, Peitzman A, Tisherman S (1995) Uncontrolled hemorrhagic shock outcome model in rats. Resuscitation 29: 143-152.

Cho SD, Holcomb JB, Tieu BH, Englehart MS, Morris MS, Karahan ZA, Underwood SA, Muller PJ, Prince MD, Medina L, Sondeen J, Shults C, Duggan M, Tabbara M, Alam HB & Schreiber MA (2009) Reproducibility of an animal model simulating complex combat-related injury in a multiple-institution format. Shock 31: 87-96.

Ghiţă M, Cotor G, Viţălaru AB, Brăslaşu D (2015) Comparative study on the effect of prednisone and dexamethasone on leukocytes in rabbit. J Biotechnology 208: 92.

Gutierrez G, Reines HD, Wulf-Gutierrez ME (2004) Clinical review: hemorrhagic shock. Crit Care 8: 373-381.

Hamar J, Kovach AG, Reivich M, Nyary I, Durity F (1979) Effect of phenoxybenzamine on cerebral blood flow and metabolism in the ba-boon during hemorrhagic shock. Stroke 10: 401-407.

Holzrichter D, Burk A, Korn U, Burk R (1983) The rise of blood sugar as parameter for the degree of severity of hemorrhagic shock in the rabbit. Arch Orthop Trauma Surg 102: 73-77.

Holzrichter D, Meiss L, Behrens S, Mickley V (1987) The rise of blood sugar as an additional parameter in traumatic shock. Arch Orthop Trauma Surg 106: 319-322.

Humphreys PW, Joels N (1985) Arterial pressure maintenance after haemorrhage in the pregnant rabbit. J Physiol 366: 17-25.

Kovách AG, Mitsányi A, Monos E, Nyáry I, Sulyok A (1972) Control of organ blood flow following hemorrhage. Adv Exp Med Biol 33: 1-17.

Majde JA (2003) Animal models for hemorrhage and resuscitation research. J Trauma 54: 100-105.

Nunez TC, Cotton BA (2009) Transfusion therapy in hemorrhagic shock. Curr Opin Crit Care 15: 536-541.

Porter AE, Rozanski EA, Sharp CR, Dixon KL, Price LL, Shaw SP (2013) Evaluation of the shock index in dogs presenting as emergencies. J Vet Emerg Crit Care (San Antonio) 23: 538-544.

Porth CM (2005) Pathophysiology: Concepts of Altered Health States. 7th ed., Philadelphia: Lippincott, Williams & Wilkins.

Rao KV. (1999) Multiple comparison test procedures. In: Balakrishnan N (ed.). Biostatistics, 1st ed., New Delhi, India, Jaypee Brothers Medical Publishers, p 273-284.

Slauson OS, Cooper BJ. (2002) Mechanisms of diseases. 3rd ed., Mosby (Elsevier), Philadelphia.

Sondeen JL, Dubick MA, Holcomb JB, Wade CE (2007) Uncontrolled hemorrhage differs from volume- or pressure-matched controlled hemorrhage in swine. Shock 28: 426-433.

Tabsh K, Rudelstorfer R, Nuwayhid B, Assali NS (1986) Circulatory responses to hypovolemia in the pregnant and nonpregnant sheep after pharmacologic sympathectomy, Am J Obstet Gynecol 154(2): 411-419.

Tsukamoto T, Pape HC (2009) Animal models for trauma research: what are the options? Shock 31: 3-10.

Yu YH, Zhao KS, Gong SP (2008) Effect of limited volume resuscitation on hemodynamic changes in pregnant rabbit with hemorrhagic shock. Zhonghua Fu Chan Ke Za Zhi 43: 50-53.

Weiss A, Loh G (1999) Allgemeine Pathologie. Fachhschaft Tiermedizin Skript. Iustus Liebig Universitat Giessen.
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Authors and Affiliations

G. Cotor
1
G. Zagrai
2
G. Gâjâilă
1
M. Ghiță
1
A.M. Ionescu
1
A. Damian
2
A.M. Zagrai (Măierean)
2
Ș. Dragosloveanu
3
D.C. Cotor
2 3

  1. Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine, Bucharest-050097, Splaiul Independentei 105, Bucharest, Romania
  2. Faculty of Veterinary Medicine, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca-400372, Calea Manastur 3-5, Cluj-Napoca, Romania
  3. Clinical Hospital of Orthopedics, Traumatology and Osetoarticular TB “Foișor”, Bucharest-030167, Bd. Ferdinand nr. 35-37, Romania
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Abstract

In this paper, we show that signal sampling operation can be considered as a kind of all-pass filtering in the time domain, when the Nyquist frequency is larger or equal to the maximal frequency in the spectrum of a signal sampled. We demonstrate that this seemingly obvious observation has wideranging implications. They are discussed here in detail. Furthermore, we discuss also signal shaping effects that occur in the case of signal under-sampling. That is, when the Nyquist frequency is smaller than the maximal frequency in the spectrum of a signal sampled. Further, we explain the mechanism of a specific signal distortion that arises under these circumstances. We call it the signal shaping, not the signal aliasing, because of many reasons discussed throughout this paper. Mainly however because of the fact that the operation behind it, called also the signal shaping here, is not a filtering in a usual sense. And, it is shown that this kind of shaping depends upon the sampling phase. Furthermore, formulated in other words, this operation can be viewed as a one which shapes the signal and performs the low-pass filtering of it at the same time. Also, an interesting relation connecting the Fourier transform of a signal filtered with the use of an ideal low-pass filter having the cut frequency lying in the region of under-sampling with the Fourier transforms of its two under-sampled versions is derived. This relation is presented in the time domain, too.

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Authors and Affiliations

Andrzej Borys
ORCID: ORCID

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