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Relevant bibliographies by topics / Modified Hough Transform

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Academic literature on the topic 'Modified Hough Transform'

Author: Grafiati

Published: 4 June 2025

Last updated: 23 June 2025

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Journal articles on the topic "Modified Hough Transform"

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Smereka, Marcin, and Ignacy Dulęba. "Circular Object Detection Using a Modified Hough Transform." International Journal of Applied Mathematics and Computer Science 18, no. 1 (2008): 85–91. http://dx.doi.org/10.2478/v10006-008-0008-9.

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Circular Object Detection Using a Modified Hough TransformA practical modification of the Hough transform is proposed that improves the detection of low-contrast circular objects. The original circular Hough transform and its numerous modifications are discussed and compared in order to improve both the efficiency and computational complexity of the algorithm. Medical images are selected to verify the algorithm. In particular, the algorithm is applied to localize cell nuclei of cytological smears visualized using a phase contrast microscope.

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Berger, Alan D., and Pradeep K. Khosla. "The modified adaptive Hough transform (MAHT)." Journal of Robotic Systems 7, no. 2 (1990): 277–90. http://dx.doi.org/10.1002/rob.4620070209.

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WANG, QIANG, HONGBO CHEN, XIAORONG XU, and HAIYAN LIU. "A NEWLY MODIFIED ALGORITHM OF HOUGH TRANSFORM FOR LINE DETECTION." International Journal of Image and Graphics 05, no. 04 (2005): 715–27. http://dx.doi.org/10.1142/s0219467805001975.

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The heavy burden of computational complexity and massive storage requirement is the drawback of the standard Hough transform (SHT). To overcome the weakness of SHT, many modified approaches, for example, the probabilistic Hough transform (PHT), have been presented. However, a very important fact, which is that a line has its own width in a real digital image and the width of the line is uniform, was ignored by all of these modified algorithms of Hough transform. This phenomenon influenced the result of line detection. In this paper a new modified algorithm of Hough transform for line detection is proposed. In our algorithm, the fact mentioned above is fully considered and a strip-shaped area corresponding to the accumulate cells of HT is proposed. Experimental results have shown that our approach is efficient and promising, and the effect of detection is far better than the popular modified approaches.

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Faruq, Md Omar, Md Almash Alam, and Md Muktar Hossain. "A Comparisonal Study on Circle Detection for Real-World Images." Bangladesh Journal of Multidisciplinary Scientific Research 1, no. 2 (2019): 19–25. http://dx.doi.org/10.46281/bjmsr.v1i2.364.

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Real-life objects have different characteristics such as form characteristics, texture characteristics, and color characteristics and so on. The circular objects are the most common shape in our day to day lives and industrial production. So circle detection algorithm is ever ending research today. The most common algorithm is Circular Hough Transform which is used to detect a circle in an image. It is not very robust to noise so a simple approach to modified Circular Hough Transform algorithm is applied to detect the circle from an image. The image is pre-processed by edge detection. A comparison between Circular Hough Transform and modified Circular Hough Transform algorithm is presented in this research.

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Jiankui, Zeng, He Zishu, Mathini Sellathurai, and Liu Hongming. "Modified Hough Transform for Searching Radar Detection." IEEE Geoscience and Remote Sensing Letters 5, no. 4 (2008): 683–86. http://dx.doi.org/10.1109/lgrs.2008.2002574.

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Shen, Fei, and Han Wang. "Corner detection based on modified Hough transform." Pattern Recognition Letters 23, no. 8 (2002): 1039–49. http://dx.doi.org/10.1016/s0167-8655(02)00035-1.

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Aldoshkin, Dmitry N., and Roman Y. Tsarev. "Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform." Cybernetics and Information Technologies 18, no. 2 (2018): 112–22. http://dx.doi.org/10.2478/cait-2018-0032.

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Abstract This paper proposes an algorithm that assesses the angular orientation of a mobile robot with respect to its referential position or a map of the surrounding space. In the framework of the suggested method, the orientation problem is converted to evaluating a dimensional rotation of the object that is abstracted as a polygon (or a closed polygonal chain). The method is based on Hough transform, which transforms the measurement space to a parametric space (in this case, a two-dimensional space [θ, r] of straight-line parameters). The Hough transform preserves the angles between the straight lines during rotation, translation, and isotropic scaling transformations. The problem of rotation assessment then becomes a one-dimensional optimization problem. The suggested algorithm inherits the Hough method’s robustness to noise.

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Zhdanov, I. N., A. S. Potapov, and O. V. Shcherbakov. "Erythrometry method based on a modified Hough transform." Journal of Optical Technology 80, no. 3 (2013): 201. http://dx.doi.org/10.1364/jot.80.000201.

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Dobeš, M., J. Martinek, D. Skoupil, Z. Dobešová, and J. Pospíšil. "Human eye localization using the modified Hough transform." Optik 117, no. 10 (2006): 468–73. http://dx.doi.org/10.1016/j.ijleo.2005.11.008.

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TIPWAI, P., and S. MADARASMI. "A Modified Generalized Hough Transform for Image Search." IEICE Transactions on Information and Systems E90-D, no. 1 (2007): 165–72. http://dx.doi.org/10.1093/ietisy/e90-1.1.165.

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Dissertations / Theses on the topic "Modified Hough Transform"

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Zahran, Mohamed. "Shape matching using a modified Generalized Hough Transform /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487947908401423.

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吳兆哲. "Application of Modified Hough Transform on Sign Recognition." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/04340856083663446868.

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碩士<br>國立臺灣海洋大學<br>機械與機電工程學系<br>100<br>This study uses a single webcam to capture the experimental field image. Then, the locations of signs are obtained by color segmentation in HIS representation. Recycling labeling is applied to label areas and minimum area size criterion is used to remove noise. The image of signs thus acquired is then processed to find the edges. The shapes of signs can be recognized by employing modified Hough transform technique. After the shapes are determined, the pattern inside the frame will be normalized to a standard size and processed by thinning algorithm. Finally, Hit and Miss Technique is applied to identify the sign pattern within the database in order to retrieve its meaning.

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Li, Hua-Xin, and 李華欣. "A New Modified Hough Transform Method with Merge Contraction." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/04288478443458599049.

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Liaw, Jiun-Jian, and 廖俊鑑. "Fast Circle/Circular arc Detection Methods Based on the Modified Hough Transform." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/63067811249004242080.

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博士<br>國立臺灣科技大學<br>高分子工程系<br>93<br>The drawbacks of the standard Hough transform (SHT) are the large computation and the large storage requirement. To improve the drawbacks of SHT, the randomized Hough transform (RHT) was proposed. But RHT isn't suitable for detecting the pattern with the complex image because the probability is too low. In this thesis, we first propose a modified RHT to detect circle/circular arc efficiently. We segment an image into sub-images based on edge information, then we use the proposed circular arc analysis and density check rule to modify RHT for the circle/circular arc detection. Since the voting method of SHT plays an important role to affect the pattern detection accuracy, we also propose a effective voting method to reduce the computation and the storage requirements of SHT for the circle detection. This method improves the efficiency of circle detection by letting each pixel only belong to one candidate of circle parameters. Then we describe a proposed fast randomized Hough transform for circle/circular arc detection. We first pick the seed point at random. Then we propose a checking rule to check the seed point is on the true circle or not. Comparing with the previous techniques, the fast randomized Hough transform is required less computation times and is more suitable for the complex image. At last, we use the proposed method to recognize the fiber patterns in the image of PET/Rayon composite yarn cross section. Our method consists of two voting techniques: the connected component voting (for obtaining single fiber locations) and the circle parameter voting (circle detection, for recognizing the fiber patterns). When comparing with the previous approach, the new method needs fewer parameters and is more flexible.

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Wang, Wei-Jun, and 王維鈞. "Face Detection with Modified Ellipse Hough Transform and Eye-Brow Block Searching." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/83541229330990159284.

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碩士<br>國立臺灣大學<br>生物產業機電工程學研究所<br>92<br>This study proposes a modified ellipse Hough transform(MEHT) as well as robust noise filtering and eye-brow block searching(RNFEBS) for face detection based on ellipticity, contrast and geometrical features of objects in an image. As for the face detection in a static gray level image with complex background, the most valuable information is the elliptical shapes of faces and the geometrical features of eyes. With MEHT, the position of the face center is easily estimated. And then, RNFEBS is applied afterwards to search for eye-brow block and locate eyes pair. The location and size of face could be decided accordingly. Different from the accumulator design of traditional Hough transform, the MEHT developing two types of accumulator which are related to both ellipticity and contrast of objects in an image. The factor KM and KA in the accumulators could be turned to suppress the influence of contrast on the accumulator and achieve the lower contribution than ellipticity. MEHT is very robust for the detection of nonstandard ellipse with complex background in an image if the factor KM and KA is carefully chosen based on noise margin. The positions of eye-brow block, eyes pair and face could be obtained accordingly based on eyes’ geometrical features by RNFEBS. This approach developed in the study is proved to be high efficient and accurate.

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Chuang, Chi-Han, and 莊季翰. "Visual object retrieval via perceptual grouping of image regions using modified generalized Hough transform." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/29077642751633191101.

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碩士<br>國立臺灣海洋大學<br>資訊工程學系<br>95<br>This paper presents an object-based image retrieval using a method based on perceptual grouping of image regions using the modified generalized Hough transform. The effectiveness of region-based representation for content-based image retrieval is extensively studied in the literature. One common weakness of the region-based approaches is that the homogeneous image regions have little correspondence to the semantic image concepts, thus, the retrieval results of region-based approaches in terms of regions’ low-level visual features are far from satisfactory. It is desirable and yet remains as a challenge for querying multimedia data by finding an object inside a target image. Given an object model, an added difficulty is that the object might be translated, rotated, and scaled inside a target image. Object segmentation and recognition is the primary step of computer vision for applying to image retrieval of higher-level image analysis. However, automatic segmentation and recognition of objects via object models is a difficult task without a priori knowledge about the shape of objects. Instead of segmentation and detailed object representation, the objective of this research is to develop and apply computer vision methods that explore the structure of an image object by perceptual grouping of image regions to retrieve images from a database. A voting scheme based on generalized Hough transform is proposed to provide object search method, which is invariant to the translation, rotation, scaling of image data, and hence, invariant to orientation and position. Computer simulation results show that the proposed method gives good performance in terms of retrieval accuracy and robustness.

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CHEN, MEI-YING, and 陳美瑛. "Two new approaches in handwritten Chinese character:a background thinning method for radicals extraction and modified hough transform for recognition." Thesis, 1986. http://ndltd.ncl.edu.tw/handle/14192161646511992797.

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Book chapters on the topic "Modified Hough Transform"

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Lee, Yun-Seok, Seung-Hun Yoo, and Chang-Sung Jeong. "Modified Hough Transform for Images Containing Many Textured Regions." In Rough Sets and Current Trends in Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11908029_85.

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Chhabra, Megha, and Ayush Goyal. "Accurate and Robust Iris Recognition Using Modified Classical Hough Transform." In Information and Communication Technology for Sustainable Development. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3920-1_50.

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Yadav, Virendra Kumar, Munesh Chandra Trivedi, Shyam Singh Rajput, and Saumya Batham. "Approach to Accurate Circle Detection: Multithreaded Implementation of Modified Circular Hough Transform." In Advances in Intelligent Systems and Computing. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0129-1_3.

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Zhonghua, Bao, Tian Shusen, and Lu Jianbin. "A Modified Hough Transform TBD Method for Radar Weak Targets Using Plot’s Quality." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_194.

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Potapov, A., O. Shcherbakov, and I. Zhdanov. "Fast reconstruction of Go board grids using the modified Hough transform." In Future Communication Technology and Engineering. CRC Press, 2015. http://dx.doi.org/10.1201/b18331-29.

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Mehta, Sonam, and Pragya Shukla. "An Efficient Technique for Passive Image Forgery Detection Using Computational Intelligence." In Advances in Digital Crime, Forensics, and Cyber Terrorism. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3942-5.ch003.

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This chapter proposes a scheme for the identification of copy-move forgery by inducing adaptive over-segmentation and matching of feature points with the help of discrete cosine transform (DCT). Copy-move forging is an image tampering technique that involves concealing undesired things or recreating desirable elements within the same image to create modified tampered images. Traditional methods added a large number of false matches. To conquer this problem, a new algorithm is proposed to incorporate an adaptive threshold method. So, the block feature matching mechanism is used, and the matching feature blocks classify the feature points using patch matching and Hough transform. Forged regions are detected with the help of the newly proposed algorithm. The results of the proposed method show that it can substantially reduce the number of false matches that lead to improvements in both performance and computational costs. This demonstrates the suggested algorithm's resistance against a variety of known attacks. Comparative results are presented for a better evaluation.

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Conference papers on the topic "Modified Hough Transform"

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Ferhat-taleb Alim, F., K. Messaoudi, S. Seddiki, and O. Kerdjidj. "Modified circular Hough transform using FPGA." In 2012 24th International Conference on Microelectronics (ICM). IEEE, 2012. http://dx.doi.org/10.1109/icm.2012.6471412.

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Guang-lin, He, Huang Ke-wei, and Yuan Ben-sheng. "A Method for Spherical Localization Using Modified Hough Transform." In 2008 Second International Symposium on Intelligent Information Technology Application (IITA). IEEE, 2008. http://dx.doi.org/10.1109/iita.2008.243.

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Yakimov, Pavel, and Vladimir Fursov. "Traffic Signs Detection and Tracking using Modified Hough Transform." In International Conference on Signal Processing and Multimedia Applications. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005543200220028.

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Popplewell, Khary, Kaushik Roy, Foysal Ahmad, and Joseph Shelton. "Multispectral iris recognition utilizing hough transform and modified LBP." In 2014 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2014. http://dx.doi.org/10.1109/smc.2014.6974110.

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Liu, Tianming, Jianfeng Lu, Jingxin Nie, et al. "Zebrafish Cell/Neuron Quantitation by Modified Circular Hough Transform." In 2006 IEEE/NLM Life Science Systems and Applications Workshop. IEEE, 2006. http://dx.doi.org/10.1109/lssa.2006.250413.

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Dong, Y. M., S. M. Tang, J. F. Yang, H. Zhang, and L. B. Xu. "A modified Hough transform algorithm for traffic light recognition." In International Conference on Civil, Urban and Environmental Engineering. WIT Press, 2015. http://dx.doi.org/10.2495/cuee140711.

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"A New Modified Hough Transform Method for Circle Detection." In International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004424600050012.

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Guang-lin, He, and Lao Li. "The Multi-vision Method for Localization Using Modified Hough Transform." In 2009 WRI World Congress on Computer Science and Information Engineering. IEEE, 2009. http://dx.doi.org/10.1109/csie.2009.1106.

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Forman, Jr., Arthur V. "A Modified Hough Transform For Detecting Lines In Digital Imagery." In 1986 Technical Symposium Southeast, edited by John F. Gilmore. SPIE, 1986. http://dx.doi.org/10.1117/12.964124.

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Han, Yunfeng, Cuie Zheng, and Dajun Sun. "Underwater Node Localization Using Modified Hough Transform and Least Square Method." In the 10th International Conference. ACM Press, 2015. http://dx.doi.org/10.1145/2831296.2831300.

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