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Relevant bibliographies by topics / Algorytm Daugmana

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Academic literature on the topic 'Algorytm Daugmana'

Author: Grafiati

Published: 24 April 2022

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Journal articles on the topic "Algorytm Daugmana"

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Soltany, Milad, Saeid Toosi Zadeh, and Hamid Reza Pourreza. "Daugman’s Algorithm Enhancement for Iris Localization." Advanced Materials Research 403-408 (November 2011): 3959–64. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3959.

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Iris localization is considered the most difficult part in iris identification algorithms because it defines the inner and outer boundaries of iris region used for feature analysis. Several researches were taken in the subject of iris finding and segmentation. The main objective here is to remove any non-useful information, namely the pupil segment and the part outside the iris. Duda and Hart used Hough transforms to detect the contours and curves. Daugman proposed an integro-differential operator to find both the pupil and the iris contour. Daugman’s method is claimed to be the most efficient one. This paper proposes an implementation for Daugman's algorithm, which was found incompatible with visible light illuminated images. Then this paper proposes algorithm enhancement for solving this problem.

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Ren, Xinying, Zhiyong Peng, Qingning Zeng, Chaonan Peng, Jianhua Zhang, Shuicai Wu, and Yanjun Zeng. "An improved method for Daugman's iris localization algorithm." Computers in Biology and Medicine 38, no. 1 (January 2008): 111–15. http://dx.doi.org/10.1016/j.compbiomed.2007.07.007.

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Xu, Xiao Wen. "An Efficient Iris Segmentation Method for Non-Ideal Iris Images." Advanced Materials Research 658 (January 2013): 597–601. http://dx.doi.org/10.4028/www.scientific.net/amr.658.597.

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Segmenting the non-ideal iris images accurately is a main problem for iris recognition, due to the impact of the eyelids, eyelashes and deformation. The paper presents an iris segmentation method based on an improved level set. Firstly, we used gray projection algorithm to locate the pupil. Secondly, we applied the least square fitting algorithm to estimate the boundary between the pupil and the iris. Finally, we used the level set method to accurately segment the iris. Experimental results demonstrate the segmentation accuracy for outer boundary of the iris is 98.59%. The method presented in this paper is superior to Daugman method and Hough transform algorithm in iris segmentation, especially for non-ideal iris images.

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Violet, Oleka Chioma, and Ugwu Chukwuka Kennedy. "Implementation of Daugman’s Algorithm and Adaptive Noise Filtering Technique for Digital Recognition of Identical Twin using MATLAB." International Journal of Computer Science and Engineering 5, no. 9 (September 25, 2018): 12–20. http://dx.doi.org/10.14445/23488387/ijcse-v5i9p103.

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Jusman, Yessi, Siew Cheok Ng, and Khairunnisa Hasikin. "Performances of proposed normalization algorithm for iris recognition." International Journal of Advances in Intelligent Informatics 6, no. 2 (July 12, 2020): 161. http://dx.doi.org/10.26555/ijain.v6i2.397.

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Iris recognition has very high recognition accuracy in comparison with many other biometric features. The iris pattern is not the same even right and left eye of the same person. It is different and unique. This paper proposes an algorithm to recognize people based on iris images. The algorithm consists of three stages. In the first stage, the segmentation process is using circular Hough transforms to find the region of interest (ROI) of given eye images. After that, a proposed normalization algorithm is to generate the polar images than to enhance the polar images using a modified Daugman’s Rubber sheet model. The last step of the proposed algorithm is to divide the enhance the polar image to be 16 divisions of the iris region. The normalized image is 16 small constant dimensions. The Gray-Level Co-occurrence Matrices (GLCM) technique calculates and extracts the normalized image’s texture feature. Here, the features extracted are contrast, correlation, energy, and homogeneity of the iris. In the last stage, a classification technique, discriminant analysis (DA), is employed for analysis of the proposed normalization algorithm. We have compared the proposed normalization algorithm to the other nine normalization algorithms. The DA technique produces an excellent classification performance with 100% accuracy. We also compare our results with previous results and find out that the proposed iris recognition algorithm is an effective system to detect and recognize person digitally, thus it can be used for security in the building, airports, and other automation in many applications.

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Sierra-Vázquez, Vicente, and Ignacio Serrano-Pedraza. "Single-Band Amplitude Demodulation of Müller-Lyer Illusion Images." Spanish Journal of Psychology 10, no. 1 (May 2007): 3–19. http://dx.doi.org/10.1017/s1138741600006272.

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The perception of the Müller-Lyer illusion has previously been explained as a result of visual low band-pass spatial filtering, although, in fact, the illusion persists in band-pass and high-pass filtered images without visible low-spatial frequencies. A new theoretical framework suggests that our perceptual experience about the global spatial structure of an image corresponds to the amplitude modulation (AM) component (or its magnitude, also called envelope) of its AM-FM (alternatively, AM-PM) decomposition. Because demodulation is an ill-posed problem with a non-unique solution, two different AM-FM demodulation algorithms were applied here to estimate the envelope of images of Müller-Lyer illusion: the global and exact Daugman and Downing (1995) AMPM algorithm and the local and quasi-invertible Maragos and Bovik (1995) DESA. The images used in our analysis include the classic configuration of illusion in a variety of spatial and spatial frequency content conditions. In all cases, including those of images for which visual low-pass spatial filtering would be ineffective, the envelope estimated by single-band amplitude demodulation has physical distortions in the direction of perceived illusion. It is not plausible that either algorithm could be implemented by the human visual system. It is shown that the proposed second order visual model of pre-attentive segregation of textures (or “back-pocket” model) could recover the image envelope and, thus, explain the perception of this illusion even in Müller-Lyer images lacking low spatial frequencies.

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Jagadale, A. B., S. S. Sonavane, and D. V. Jadhav. "Development of Scale Invariant lens Opacity Estimation System using Hough Circle Detection Transform, Normalization and Entropy." International Journal of Innovative Technology and Exploring Engineering 10, no. 5 (March 30, 2021): 8–10. http://dx.doi.org/10.35940/ijitee.e8614.0210421.

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Clear eye lens is responsible for correct vision. Ageing effect acquires opacity at lens structure causing foggy or blurred vision. It is termed as cataract. This may become cause of permanent blindness if remain unidentified and untreated. Due to hazards change in environment and adoption of sluggish lifestyle many diseases like cataract are becoming universal challenge for health organization over the world. Lack of medication and diagnosis facility in developing countries makes cataract as savior vision problem. Proposed methodology suggests image processing based, low cost solution for lens opacity or cataract detection. In this system eye lens image from input image is acquired using Iterative Hough circle detection transform. It is normalized using Daugman’s rubber sheet normalization algorithm which makes system scale invariant. Structural variation in normalized lens image is estimated in terms of entropy or mean value. Comparison of right and left half entropies of normalized image is basis for estimation of lens opacity. It is used to detect and categorize lens opacity or cataract. This system easily categorize lens opacity based on structural features of opacity in one of three grades such as “No cataract”, “Cortical cataract” or “Nuclear cataract”.

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Jones, J. P., and L. A. Palmer. "An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex." Journal of Neurophysiology 58, no. 6 (December 1, 1987): 1233–58. http://dx.doi.org/10.1152/jn.1987.58.6.1233.

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1. Using the two-dimensional (2D) spatial and spectral response profiles described in the previous two reports, we test Daugman's generalization of Marcelja's hypothesis that simple receptive fields belong to a class of linear spatial filters analogous to those described by Gabor and referred to here as 2D Gabor filters. 2. In the space domain, we found 2D Gabor filters that fit the 2D spatial response profile of each simple cell in the least-squared error sense (with a simplex algorithm), and we show that the residual error is devoid of spatial structure and statistically indistinguishable from random error. 3. Although a rigorous statistical approach was not possible with our spectral data, we also found a Gabor function that fit the 2D spectral response profile of each simple cell and observed that the residual errors are everywhere small and unstructured. 4. As an assay of spatial linearity in two dimensions, on which the applicability of Gabor theory is dependent, we compare the filter parameters estimated from the independent 2D spatial and spectral measurements described above. Estimates of most parameters from the two domains are highly correlated, indicating that assumptions about spatial linearity are valid. 5. Finally, we show that the functional form of the 2D Gabor filter provides a concise mathematical expression, which incorporates the important spatial characteristics of simple receptive fields demonstrated in the previous two reports. Prominent here are 1) Cartesian separable spatial response profiles, 2) spatial receptive fields with staggered subregion placement, 3) Cartesian separable spectral response profiles, 4) spectral response profiles with axes of symmetry not including the origin, and 5) the uniform distribution of spatial phase angles. 6. We conclude that the Gabor function provides a useful and reasonably accurate description of most spatial aspects of simple receptive fields. Thus it seems that an optimal strategy has evolved for sampling images simultaneously in the 2D spatial and spatial frequency domains.

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"Iridian discards Daugman crutch with new algorithm." Biometric Technology Today 14, no. 5 (May 2006): 1. http://dx.doi.org/10.1016/s0969-4765(06)70516-1.

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"L-1 Identity Solutions releases Daugman 2007 algorithm." Biometric Technology Today 15, no. 9 (September 2007): 3–4. http://dx.doi.org/10.1016/s0969-4765(07)70146-7.

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Dissertations / Theses on the topic "Algorytm Daugmana"

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Semerád, Lukáš. "Generování kryptografického klíče z biometrických vlastností oka." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236038.

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The main topic of the thesis is creation of formulas for the amount of information entropy in biometric characteristics of iris and retina. This field of science in biometrics named above is unstudied yet, so the thesis tries to initiate research in this direction. The thesis also discusses the historical context of security and identification fields according to biometric characteristics of a human being with an overlap for potential usage in biometrics of iris and retina. The Daugman’s algorithm for converting iris image into the binary code which can be used as a cryptographic key is discussed in detail. An application implementing this conversion is also a part of the thesis.

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Jelen, Vilém. "Biometrická brána využívající kamer pro identifikaci osob." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403188.

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Biometric gateways are used to quickly and accurately identify people. Of the biometric characteristics, iris, face and fingerprints are commonly used. By combining them, better identification results can be achieved. The aim of this thesis is to create such a biometric gateway together with the control application. A combination of iris of both eyes and face is used, which is captured by cameras from three angles to increase accuracy. Neural networks are used to detect and extract face features. Iris recognition is realized using Daugman's algorithm.

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Maruniak, Lukáš. "Software pro biometrické rozpoznávání duhovky lidského oka." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-235000.

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In my thesis, I focus on the task of recognizing human iris from an image.In the beginning, the work deals with a question of biometrics, its importance and basic concepts, which are necessary for use in following text. Subsequently process of human Iris detection is described together with theory of evolution algorithms. In the implementation part, is described the design of implemented solution, which uses evolution algorithms, where is emphasis on correct pupil and iris boundary detection.

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Book chapters on the topic "Algorytm Daugmana"

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Peng, Zhi-yong, Hong-zhou Li, and Jian-ming Liu. "An Improvement Method for Daugman’s Iris Localization Algorithm." In Advances in Neural Networks – ISNN 2011, 364–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21090-7_43.

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"Daugman Algorithm." In Encyclopedia of Biometrics, 210. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_2112.

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Conference papers on the topic "Algorytm Daugmana"

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Cruz, Febus Reidj G., Carlos C. Hortinela, Benner E. Redosendo, Bianca Karla P. Asuncion, Christian Jay S. Leoncio, Noel B. Linsangan, and Wen-Yaw Chung. "Iris Recognition using Daugman algorithm on Raspberry Pi." In TENCON 2016 - 2016 IEEE Region 10 Conference. IEEE, 2016. http://dx.doi.org/10.1109/tencon.2016.7848401.

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Shengquan, Wang, Li Xianglong, Li Ang, and Jiang Shenlong. "Research on Iris Edge Detection Technology based on Daugman Algorithm." In 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2019. http://dx.doi.org/10.1109/icaica.2019.8873462.

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Shamsi, Mahboubeh, Puteh Bt Saad, Subariah Bt Ibrahim, and Abdolreza Rasouli Kenari. "Fast Algorithm for Iris Localization Using Daugman Circular Integro Differential Operator." In 2009 International Conference of Soft Computing and Pattern Recognition. IEEE, 2009. http://dx.doi.org/10.1109/socpar.2009.83.

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Minakova, Natalia N., and Ivan V. Petrov. "Modification of Daugman's Integrodifferential Operator Using Bresenham's Algorithm for Iris Localization." In 2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). IEEE, 2018. http://dx.doi.org/10.1109/apeie.2018.8545151.

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Vlachynska, Alzbeta, Zuzana Kominkova Oplatkova, and Martin Sramka. "The coordinate system of the eye in cataract surgery: Performance comparison of the circle Hough transform and Daugman’s algorithm." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4992259.

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