视觉假体图象处理策略和算法研究
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摘要
我国约有877万人有视觉功能障碍。通过神经电刺激视觉通路中各段可以产生视觉感觉,也被称为光幻视,它能够传递有限但是十分重要的视觉信息,帮助盲人恢复基本视觉。研究人工视觉假体来恢复盲人视觉功能是当今科学界面临的挑战之一,其可行性已被欧美科学家所证实。本文是国家973项目《视觉功能修复的基础理论与相关科学问题研究》的人工视觉图像处理部分。
本文首先提出了基于不同复杂度图像的人工视觉图像处理策略,对不同图像处理策略使用的算法进行了介绍。随后,本文介绍了对形体和简单景物识别的人工视觉仿真实验,为视觉假体图像处理系统的设计提供理论支持。然后,本文介绍了自主搭建的一套基于DM642,适用于各类视觉假体的图像系统,系统可以采用CMOS图像传感器,也可以采用CCD作为图像捕获设备,可以满足各类视觉假体图像处理方法的要求。最后,论文介绍了系统的软件编写流程并对各类图像算法在平台上进行了初步的性能评估,结果表明所设计系统完全能满足第一代视觉假体需要。
There are 8.77 million persons with visual disabilities in 2006. The neural electrical stimulation could be provided at various stages to restore the vision. Each of the proposed approaches is expected to artificially generate visual sensations called phosphenes, which convey limited but crucial functional visual information. Its feasibility has already been confirmed by some Euramerican scientists. This paper is the subproject of the“973”project《Basic Research on the Key Issues of Visual Function Recovery For Blindness》
.Firstly, we provide various image processing strategies used for visual prostheses based on the complexity of various images, and we Introduction the algorithm used in strategies. Secondly, this paper introduce the experiment of pixelized images of objects and simple scenes recognition using simulated prosthetic vision, We hope these results will be helpful for the researchers of visual prosthesis in rehabilitation of image recognition for blind implanted with limited numbers of stimulating electrodes. Then, we introduce a set of image processing system developed by our team based on DM642, which could applied to all types of visual prostheses. The system could uses CMOS sensor and CCD sensor as image-grabbing equipment, so that it is feasible to adapt to various image processing strategies. Finally, we present the method of the flow of software development for which are implemented on our platform, and conducted a preliminary assessment of the performance. It was found that the system can meet the demand of the first generation visual prostheses.
本文首先提出了基于不同复杂度图像的人工视觉图像处理策略,对不同图像处理策略使用的算法进行了介绍。随后,本文介绍了对形体和简单景物识别的人工视觉仿真实验,为视觉假体图像处理系统的设计提供理论支持。然后,本文介绍了自主搭建的一套基于DM642,适用于各类视觉假体的图像系统,系统可以采用CMOS图像传感器,也可以采用CCD作为图像捕获设备,可以满足各类视觉假体图像处理方法的要求。最后,论文介绍了系统的软件编写流程并对各类图像算法在平台上进行了初步的性能评估,结果表明所设计系统完全能满足第一代视觉假体需要。
There are 8.77 million persons with visual disabilities in 2006. The neural electrical stimulation could be provided at various stages to restore the vision. Each of the proposed approaches is expected to artificially generate visual sensations called phosphenes, which convey limited but crucial functional visual information. Its feasibility has already been confirmed by some Euramerican scientists. This paper is the subproject of the“973”project《Basic Research on the Key Issues of Visual Function Recovery For Blindness》
.Firstly, we provide various image processing strategies used for visual prostheses based on the complexity of various images, and we Introduction the algorithm used in strategies. Secondly, this paper introduce the experiment of pixelized images of objects and simple scenes recognition using simulated prosthetic vision, We hope these results will be helpful for the researchers of visual prosthesis in rehabilitation of image recognition for blind implanted with limited numbers of stimulating electrodes. Then, we introduce a set of image processing system developed by our team based on DM642, which could applied to all types of visual prostheses. The system could uses CMOS sensor and CCD sensor as image-grabbing equipment, so that it is feasible to adapt to various image processing strategies. Finally, we present the method of the flow of software development for which are implemented on our platform, and conducted a preliminary assessment of the performance. It was found that the system can meet the demand of the first generation visual prostheses.
引文
[1] Uits Conference Digest of Technical Papers, pp 218 –219, February 2004.
[2] Maynard E.M. Visual prostheses. Annual Review of Biomedical Engineering, 2001, 3, pp. 145–68
[3] Brelén M. E., Duret F., Gérard B., Delbeke J., Veraart C. Creating a meaningful visual perception in blind volunteers by optic nerve stimulation. J. Neural Eng., 2005, 2, S22–28
[4] Delbeke J., Oozeer M., Veraart C. Position, size and luminosity of phosphenes generated by direct optic nerve stimulation. Vision Research, 2003, 43, pp. 1091–1102
[5] Brindley G.S., Donaldson P.E., Falconer M.A., Rushton D.N. The extent of the region of occipital cortex that when stimulated gives phosphenes fixed in the visual field. J. Physiol., 1972, 225, pp. 57–58
[6] Brindley G.S., Lewin W. Short- and long-term stability of cortical electrical phosphenes. J. Physiol., 1968, 196, pp. 479–93
[7] Dobelle W., Mladejovsky M. Phosphenes produced by electrical stimulation of human occipital cortex, and their application to the development of a prosthesis for the blind. J. Physiol. 1974, 243, pp. 553–76
[8] Dobelle W., Quest D., Antunes J., Roberts T., Girvin J. Artificial vision for the blind by electrical stimulation of the visual cortex. Neurosurgery, 1979, 5, pp. 21–27
[9] Humayun MS, Dagnelie G, Greenberg RJ, Propst RH, Phillips DH, Visual perception elicited by electrical stimulation of retina in blind humans, Arch. Ophthalmol., 1996, 114, 40–46
[10] Chow A.Y., Chow V.Y. Subretinal electrical stimulation of the rabbit retina. Neurosci Lett, 1997, 225, pp. 13–16
[11] Rizzo J.F. III, etc. Perceptual efficacy of electrical stimulation of human retina with a microelectrode array during short-term surgical trials. Investigative Ophthalmology & visual science, 2003, 44(12), pp. 5362
[12] Schmidt EM, Bak MJ, Hambrecht FT,Kufta CV, O’Rourk DK, VallabhanathP. 1996. Feasibility of a visual prosthesis for the blind based on intracortical microstimulation of the visual cortex. Brain 119:507–22
[13] Chow A.Y., Chow V.Y. Subretinal electrical stimulation of the rabbit retina. Neurosci Lett, 1997, 225, pp. 13–16
[14] Zrenner E., Stett A., Weiss S., etc. Can subretinal microphotodiodes successfully replace degenerated photoreceptors? Vision Res, 1999, 39, pp. 2555–2567
[15] Rizzo J.F. III, etc. Perceptual efficacy of electrical stimulation of human retina with a microelectrode array during short-term surgical trials. Investigative Ophthalmology & visual science, 2003, 44(12), pp. 5362
[16] W Liu, and MS Humayun, “Retinal Prosthesis,” IEEE International Solid-State Circ Humayun M.S., Weiland J.D., Fujii G.Y., etc. Visual perception in a blind subject with a chronic microelectronic retinal prosthesis. Vision Res, 2003, 43, 71pp. 2573–2581
[17] Naples GG, Mortimer JT, Scheiner A, Sweeney JD. 1988. A spiral nerve cuff electrode for peripheral nerve stimulation. IEEE Trans. Biomed. Eng. 35:905–16
[18] Sweeney JD, Mortimer JT. 1986. An asymmetric two electrode cuff for generation of unidirectional propagated action potentials. IEEE Trans. Biomed. Eng.33:541–49
[19] Ungar IJ, Mortimer JT, Sweeney JD. 1986. Generation of unidirectionally propagating action potentials using a monopolar electrode cuff. Ann. Biomed. Eng.14:437–50
[20] Walter JS, Griffith P, Sweeney J, Scarpine V, Bidnar M, et al. 1997. Multielectrode nerve cuff stimulation of the median nerve produces selective movements in a raccoon animal model. J. Spinal Cord Med. 20:233–43
[21] Veraart C., Raftopoulos C., Mortimer J.T., etc. “Visual sensations produced by optic nerve stimulation using an implanted self-sizing spiral cuff electrode”, Brain Research, 1998, 813, pp. 181–186
[22] Hanines D.E. Neuroanatomy – An Atlas of Structures, Sections, and Systems
[23] Humayun M.S., de Juan E Jr, Weiland J.D., etc. Pattern electrical stimulation of the human retina. Vision Res, 1999, 39, pp. 2569–2576
[24] Cha K, Horch KW, Normann RA and Boman DK. Reading speed with a pixelized vision system. Journal of the Optical Society of America. 1992;A 9:673–677.
[25] Hayes JS, Yin VT, Piyathaisere D, Weiland JD, Humayun MS, Dagnelie G. Visually guided performance of simple tasks using simulated prosthetic vision. Artif Organs 2003;27: 1016–28
[26] Dagnelie G, Thompson RW, Barnett GD, Zhang WQ. Visual perception and performance under conditions simulating prosthetic vision. Perception 2000;29:84.
[27] Xiaomei Yang, Chenghu Zhou. Analysis of the Complexity of Remote Sensing Image and Its Role on Image Classification. Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International.2000; vol.5, 2179-2181
[28] GUO Zheng, CAI Zhi. Two Dimensional Pattern Complexity. Journal of Fudan University. 2005; 44(2), P332~337
[29] R. Gonzales and R. Woods Digital Image Processing, Addison-Wesley Publishing Company, 1992, pp 443 - 452.
[30] A. Jain Fundamentals of Digital Image Processing, Prentice-Hall, 1986, p 408.
[31] Bai J ing. Simulation and Modelling of Physiological Systems [M ].Beijing: Tsinghua University Press, 1994. [白净. 生理系统的仿真与建模[M ]. 北京:清华大学出版社, 1994. ]
[32] Privitera C M, Stark L W. Algorithms for defining visual region of interest: comparison with eye fixations [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2000, 22 (9) : 970～981.
[33] A Model of Saliency-Based Visual Attention for Rapid Scene AnalysisLaurent Itti, Christof Koch, and Ernst Niebur; IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 11, NOVEMBER 1998
[34] Itti L, Koch C. A saliency2based search mechanism for overt and covert shifts of visual attention[J]. Vision Research, 2000, 40 (10-12) : 1489～1506.
[35] Mannan S K, Ruddock K H. WoodingD S. The relationship between the locations of spatial features and those of fixations made during visual examination of briefly p resented images [J]. Spatial Vision,1996, 10 (3) : 165～188.
[36] Gilbert C D, Wiesel T N. Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex [J]. Journal ofNeuroscience, 1989, 9 (7) : 2432～2442.
[37] Levitt J B, Lund J S. Contrast dependence of contextual effects in primate visual cortex [J]. Nature, 1997, 387 (6628) : 73～76.
[38] Sillito A M, Grieve K L, Jones H E, et al. Visual cortical mechanisms detecting focal orientation discontinuities [J]. Nature,1995, 378 (6556) : 492～496.
[39] ZengerB, SagiD. Isolating excitatory and inhibitory nonlinear spatial interactions involved in contrast detection [J]. Vision Research,1996, 36 (16) : 2497～2513.
[40] Laurent Itti,Christ of KochFeature .Combination strategies for saliency-based visual attention systems Journal of Electronic Imaging 10(1), 161–169 (January 2001).
[41] Legge G.E., Pelli D.G et al. (1985). Psychophysics of reading. I. Normal vision. Vision Research 25 (2), 239–252.
[42] Legge, G.E. et al. (1989). Psychophysics of reading. VIII: The Minnesota Low-Vision Reading Test. Optometry and Vision Science 66(12), 843–853.
[43] Chai X et al. (2007). Recognition of pixelized Chinese character using simulated prosthetic vision. Artif Organs 31(3), 175-182.
[44] Cha K, Horch KW. (1992). Normann RA. Simulation of a phosphenebased visual field: visual acuity in a pixelized vision system. Ann Biomed Eng 20, 439–449.
[45] Cha K, Horch KW et al. (1992). Reading speed with a pixelized vision system. J Opt Soc Am 9, 673–7.
[46] Cha K et al. (1992). Mobility performance with a pixelized vision system. Vision Res 32, 1367–72.
[47] Hayes J. S. et al. (2003). Visually guided performance of simple tasks using simulated prosthetic vision. Artif. Organs 27, 1016–1028.
[48] Dagnelie, G. et al. (2007). Real and Virtual mobility performance in simulated prosthetic vision. Journeal of Neural Engineering 4, S92-S101.
[49] Boyle, J. R. et al.(2001). Challenges in Digital Imaging for Artificial Human Vision. Proceedings of SPIE 4299.
[50] Thompson R, et al.(2003). Facial recognition using simulated prosthetic pixelized vision. Investigative Ophthalmology& Vision Science, 44 (11), 5035-5042.
[51] Farag A, and Delp E. J. (1986). Some experiments with histogram-based segmentation. Proceedings of the 1986 Conference on Intelligent.Systems and Machines, Oakland University,Rochester, Michigan,.251-256.
[52] J.N. Kapur et al. (1985). A new method for gray level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, CVGIP 29, 273-285.
[53] Otsu N. (1979). A threshold selection method from gray-level histograms. IEEE Tkansactions on Systems, Man, and Cybernetics, SMC 9( 1)
[54] Nalwa V. S. (1993). A guided tour of computer vision. Addison-Wesley Longman Publishing Co., Inc., Boston, MA.
[55] Gilbert C D, Wiesel T N. (1989). Columnar specificity of intrinsic horizontal and cortical connections in cat visual cortex. Journal of Neuroscience 9 (7), 2432-2442.
[56] Levitt J B, Lund J S.(1997). Contrast dependence of contextual effects in primate visual cortex. Nature 387 (6628), 73-76.
[57] Sillito A M. et al. (1995). Visual cortical mechanisms detecting focal orientation discontinuities. Nature 378 (6556), 492-496.
[58] Zenger B, Sagi D. (1996). Isolating excitatory and inhibitory nonlinear spatial interactions involved in contrast detection. Vision Research 36 (16), 2497-2513.
[59] Dowling J et al. (2004). Mobility enhancement and assessment for a visual prosthesis. In: Proceedings of SPIE: Medical Imaging[ C ] , San Jose CA, USA, 5369, 780-791
[60] Snaith Martin et al. (1998). A low-cost system using spare vision for navigation in the urban environment. Image Vision and Computing 16,225-238
[61] 瑞泰 DSP,ICETEK-DM642-B 评估板使用手册
[62] Texas Instruments, TMS320DM642 Technical Overview, Sept.2002
[63] Texas Instruments, TMS320DM642 DIGITAL MEDIA PROCESSOR PRODUCT PREVIEW, 2002. 7
[64] Texas Instruments, TMS320DM642 Digital Media Processor Product Bulletin, 2002
[65] Texas Instrument, TM320DM642 Datasheet, Jul 2003
[66] TI, TMS320C64x DSP Video Port/VCXO Interpolated Control (VIC) Port Reference Guide, 2003
[67] TI, TMS320C6000 DSP Inter-Integrated Circuit (I2C) Module Reference Guide, 2002
[68] OminVision, OV6650 Datasheet
[69] Texas Instrument, DSP/BIOS Quick Start Reference Guide,l999
[70] TMS320C6000 Optimizing Compiler User's Guide. Texas Instruments Inc.,Oct. 2002
[71] TI, The TMS320DM642 Video Port Mini-Driver, Application Report, SPRA918A, August, 2003
[2] Maynard E.M. Visual prostheses. Annual Review of Biomedical Engineering, 2001, 3, pp. 145–68
[3] Brelén M. E., Duret F., Gérard B., Delbeke J., Veraart C. Creating a meaningful visual perception in blind volunteers by optic nerve stimulation. J. Neural Eng., 2005, 2, S22–28
[4] Delbeke J., Oozeer M., Veraart C. Position, size and luminosity of phosphenes generated by direct optic nerve stimulation. Vision Research, 2003, 43, pp. 1091–1102
[5] Brindley G.S., Donaldson P.E., Falconer M.A., Rushton D.N. The extent of the region of occipital cortex that when stimulated gives phosphenes fixed in the visual field. J. Physiol., 1972, 225, pp. 57–58
[6] Brindley G.S., Lewin W. Short- and long-term stability of cortical electrical phosphenes. J. Physiol., 1968, 196, pp. 479–93
[7] Dobelle W., Mladejovsky M. Phosphenes produced by electrical stimulation of human occipital cortex, and their application to the development of a prosthesis for the blind. J. Physiol. 1974, 243, pp. 553–76
[8] Dobelle W., Quest D., Antunes J., Roberts T., Girvin J. Artificial vision for the blind by electrical stimulation of the visual cortex. Neurosurgery, 1979, 5, pp. 21–27
[9] Humayun MS, Dagnelie G, Greenberg RJ, Propst RH, Phillips DH, Visual perception elicited by electrical stimulation of retina in blind humans, Arch. Ophthalmol., 1996, 114, 40–46
[10] Chow A.Y., Chow V.Y. Subretinal electrical stimulation of the rabbit retina. Neurosci Lett, 1997, 225, pp. 13–16
[11] Rizzo J.F. III, etc. Perceptual efficacy of electrical stimulation of human retina with a microelectrode array during short-term surgical trials. Investigative Ophthalmology & visual science, 2003, 44(12), pp. 5362
[12] Schmidt EM, Bak MJ, Hambrecht FT,Kufta CV, O’Rourk DK, VallabhanathP. 1996. Feasibility of a visual prosthesis for the blind based on intracortical microstimulation of the visual cortex. Brain 119:507–22
[13] Chow A.Y., Chow V.Y. Subretinal electrical stimulation of the rabbit retina. Neurosci Lett, 1997, 225, pp. 13–16
[14] Zrenner E., Stett A., Weiss S., etc. Can subretinal microphotodiodes successfully replace degenerated photoreceptors? Vision Res, 1999, 39, pp. 2555–2567
[15] Rizzo J.F. III, etc. Perceptual efficacy of electrical stimulation of human retina with a microelectrode array during short-term surgical trials. Investigative Ophthalmology & visual science, 2003, 44(12), pp. 5362
[16] W Liu, and MS Humayun, “Retinal Prosthesis,” IEEE International Solid-State Circ Humayun M.S., Weiland J.D., Fujii G.Y., etc. Visual perception in a blind subject with a chronic microelectronic retinal prosthesis. Vision Res, 2003, 43, 71pp. 2573–2581
[17] Naples GG, Mortimer JT, Scheiner A, Sweeney JD. 1988. A spiral nerve cuff electrode for peripheral nerve stimulation. IEEE Trans. Biomed. Eng. 35:905–16
[18] Sweeney JD, Mortimer JT. 1986. An asymmetric two electrode cuff for generation of unidirectional propagated action potentials. IEEE Trans. Biomed. Eng.33:541–49
[19] Ungar IJ, Mortimer JT, Sweeney JD. 1986. Generation of unidirectionally propagating action potentials using a monopolar electrode cuff. Ann. Biomed. Eng.14:437–50
[20] Walter JS, Griffith P, Sweeney J, Scarpine V, Bidnar M, et al. 1997. Multielectrode nerve cuff stimulation of the median nerve produces selective movements in a raccoon animal model. J. Spinal Cord Med. 20:233–43
[21] Veraart C., Raftopoulos C., Mortimer J.T., etc. “Visual sensations produced by optic nerve stimulation using an implanted self-sizing spiral cuff electrode”, Brain Research, 1998, 813, pp. 181–186
[22] Hanines D.E. Neuroanatomy – An Atlas of Structures, Sections, and Systems
[23] Humayun M.S., de Juan E Jr, Weiland J.D., etc. Pattern electrical stimulation of the human retina. Vision Res, 1999, 39, pp. 2569–2576
[24] Cha K, Horch KW, Normann RA and Boman DK. Reading speed with a pixelized vision system. Journal of the Optical Society of America. 1992;A 9:673–677.
[25] Hayes JS, Yin VT, Piyathaisere D, Weiland JD, Humayun MS, Dagnelie G. Visually guided performance of simple tasks using simulated prosthetic vision. Artif Organs 2003;27: 1016–28
[26] Dagnelie G, Thompson RW, Barnett GD, Zhang WQ. Visual perception and performance under conditions simulating prosthetic vision. Perception 2000;29:84.
[27] Xiaomei Yang, Chenghu Zhou. Analysis of the Complexity of Remote Sensing Image and Its Role on Image Classification. Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International.2000; vol.5, 2179-2181
[28] GUO Zheng, CAI Zhi. Two Dimensional Pattern Complexity. Journal of Fudan University. 2005; 44(2), P332~337
[29] R. Gonzales and R. Woods Digital Image Processing, Addison-Wesley Publishing Company, 1992, pp 443 - 452.
[30] A. Jain Fundamentals of Digital Image Processing, Prentice-Hall, 1986, p 408.
[31] Bai J ing. Simulation and Modelling of Physiological Systems [M ].Beijing: Tsinghua University Press, 1994. [白净. 生理系统的仿真与建模[M ]. 北京:清华大学出版社, 1994. ]
[32] Privitera C M, Stark L W. Algorithms for defining visual region of interest: comparison with eye fixations [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence, 2000, 22 (9) : 970～981.
[33] A Model of Saliency-Based Visual Attention for Rapid Scene AnalysisLaurent Itti, Christof Koch, and Ernst Niebur; IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 11, NOVEMBER 1998
[34] Itti L, Koch C. A saliency2based search mechanism for overt and covert shifts of visual attention[J]. Vision Research, 2000, 40 (10-12) : 1489～1506.
[35] Mannan S K, Ruddock K H. WoodingD S. The relationship between the locations of spatial features and those of fixations made during visual examination of briefly p resented images [J]. Spatial Vision,1996, 10 (3) : 165～188.
[36] Gilbert C D, Wiesel T N. Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex [J]. Journal ofNeuroscience, 1989, 9 (7) : 2432～2442.
[37] Levitt J B, Lund J S. Contrast dependence of contextual effects in primate visual cortex [J]. Nature, 1997, 387 (6628) : 73～76.
[38] Sillito A M, Grieve K L, Jones H E, et al. Visual cortical mechanisms detecting focal orientation discontinuities [J]. Nature,1995, 378 (6556) : 492～496.
[39] ZengerB, SagiD. Isolating excitatory and inhibitory nonlinear spatial interactions involved in contrast detection [J]. Vision Research,1996, 36 (16) : 2497～2513.
[40] Laurent Itti,Christ of KochFeature .Combination strategies for saliency-based visual attention systems Journal of Electronic Imaging 10(1), 161–169 (January 2001).
[41] Legge G.E., Pelli D.G et al. (1985). Psychophysics of reading. I. Normal vision. Vision Research 25 (2), 239–252.
[42] Legge, G.E. et al. (1989). Psychophysics of reading. VIII: The Minnesota Low-Vision Reading Test. Optometry and Vision Science 66(12), 843–853.
[43] Chai X et al. (2007). Recognition of pixelized Chinese character using simulated prosthetic vision. Artif Organs 31(3), 175-182.
[44] Cha K, Horch KW. (1992). Normann RA. Simulation of a phosphenebased visual field: visual acuity in a pixelized vision system. Ann Biomed Eng 20, 439–449.
[45] Cha K, Horch KW et al. (1992). Reading speed with a pixelized vision system. J Opt Soc Am 9, 673–7.
[46] Cha K et al. (1992). Mobility performance with a pixelized vision system. Vision Res 32, 1367–72.
[47] Hayes J. S. et al. (2003). Visually guided performance of simple tasks using simulated prosthetic vision. Artif. Organs 27, 1016–1028.
[48] Dagnelie, G. et al. (2007). Real and Virtual mobility performance in simulated prosthetic vision. Journeal of Neural Engineering 4, S92-S101.
[49] Boyle, J. R. et al.(2001). Challenges in Digital Imaging for Artificial Human Vision. Proceedings of SPIE 4299.
[50] Thompson R, et al.(2003). Facial recognition using simulated prosthetic pixelized vision. Investigative Ophthalmology& Vision Science, 44 (11), 5035-5042.
[51] Farag A, and Delp E. J. (1986). Some experiments with histogram-based segmentation. Proceedings of the 1986 Conference on Intelligent.Systems and Machines, Oakland University,Rochester, Michigan,.251-256.
[52] J.N. Kapur et al. (1985). A new method for gray level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, CVGIP 29, 273-285.
[53] Otsu N. (1979). A threshold selection method from gray-level histograms. IEEE Tkansactions on Systems, Man, and Cybernetics, SMC 9( 1)
[54] Nalwa V. S. (1993). A guided tour of computer vision. Addison-Wesley Longman Publishing Co., Inc., Boston, MA.
[55] Gilbert C D, Wiesel T N. (1989). Columnar specificity of intrinsic horizontal and cortical connections in cat visual cortex. Journal of Neuroscience 9 (7), 2432-2442.
[56] Levitt J B, Lund J S.(1997). Contrast dependence of contextual effects in primate visual cortex. Nature 387 (6628), 73-76.
[57] Sillito A M. et al. (1995). Visual cortical mechanisms detecting focal orientation discontinuities. Nature 378 (6556), 492-496.
[58] Zenger B, Sagi D. (1996). Isolating excitatory and inhibitory nonlinear spatial interactions involved in contrast detection. Vision Research 36 (16), 2497-2513.
[59] Dowling J et al. (2004). Mobility enhancement and assessment for a visual prosthesis. In: Proceedings of SPIE: Medical Imaging[ C ] , San Jose CA, USA, 5369, 780-791
[60] Snaith Martin et al. (1998). A low-cost system using spare vision for navigation in the urban environment. Image Vision and Computing 16,225-238
[61] 瑞泰 DSP,ICETEK-DM642-B 评估板使用手册
[62] Texas Instruments, TMS320DM642 Technical Overview, Sept.2002
[63] Texas Instruments, TMS320DM642 DIGITAL MEDIA PROCESSOR PRODUCT PREVIEW, 2002. 7
[64] Texas Instruments, TMS320DM642 Digital Media Processor Product Bulletin, 2002
[65] Texas Instrument, TM320DM642 Datasheet, Jul 2003
[66] TI, TMS320C64x DSP Video Port/VCXO Interpolated Control (VIC) Port Reference Guide, 2003
[67] TI, TMS320C6000 DSP Inter-Integrated Circuit (I2C) Module Reference Guide, 2002
[68] OminVision, OV6650 Datasheet
[69] Texas Instrument, DSP/BIOS Quick Start Reference Guide,l999
[70] TMS320C6000 Optimizing Compiler User's Guide. Texas Instruments Inc.,Oct. 2002
[71] TI, The TMS320DM642 Video Port Mini-Driver, Application Report, SPRA918A, August, 2003