A concurrent modified algorithm for Generalized Hough Transform

Achalakul, Tiranee; Madarasmi, Suthep

doi:10.1109/icit.2002.1189300

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Several authors divided the original image into sub-images (Davies, 1986; Ser and Siu, 1992, 1995; Gatos et al, 1996; Olson, 1999; Achalakul and Madarasmi, 2002; Chau and Siu, 2004) and applied the divide and conquer technique. The proposed methods range from single level application of HT to the sub-images under the constraint of the curve passing through a set of pixels (Olson, 1999), or multi-level application of HT, firstly on sub-images, and then evaluating the contribution of each sub-image to the HT space of the original image (Gatos et al, 1996), or parallel running of matching sub-images of template and the image, using master-slave technique (Achalakul and Madarasmi, 2002). A performance boosting technique that can be applied to all the above sub-image-based techniques is to avoid processing of relatively empty blocks.…”

Section: Hough Transformmentioning

confidence: 99%

Hough Transform Implementation For Event-Based Systems: Concepts and Challenges

Seifozzakerini

Yau

Mao

et al. 2018

Front. Comput. Neurosci.

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Hough transform (HT) is one of the most well-known techniques in computer vision that has been the basis of many practical image processing algorithms. HT however is designed to work for frame-based systems such as conventional digital cameras. Recently, event-based systems such as Dynamic Vision Sensor (DVS) cameras, has become popular among researchers. Event-based cameras have a significantly high temporal resolution (1 μs), but each pixel can only detect change and not color. As such, the conventional image processing algorithms cannot be readily applied to event-based output streams. Therefore, it is necessary to adapt the conventional image processing algorithms for event-based cameras. This paper provides a systematic explanation, starting from extending conventional HT to 3D HT, adaptation to event-based systems, and the implementation of the 3D HT using Spiking Neural Networks (SNNs). Using SNN enables the proposed solution to be easily realized on hardware using FPGA, without requiring CPU or additional memory. In addition, we also discuss techniques for optimal SNN-based implementation using efficient number of neurons for the required accuracy and resolution along each dimension, without increasing the overall computational complexity. We hope that this will help to reduce the gap between event-based and frame-based systems.

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Section: Hough Transformmentioning

confidence: 99%

Hough Transform Implementation For Event-Based Systems: Concepts and Challenges

Seifozzakerini

Yau

Mao

et al. 2018

Front. Comput. Neurosci.

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“…Hough transform (HT) is an important and popular curve detection method which could be employed to recognize and detect analytically defined curves, including lines, circles, etc. [1,2], and the generalized Hough transform can be used to detect arbitrary curves under certain conditions [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A novel Hough transform based on eliminating particle swarm optimization and its applications

Cheng

Guo

Zhang

2009

Pattern Recognition

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“…In AHT [128], authors proposed a non-uniform space quantization, partitioning the space into four sub-spaces, called Analytic Hough Region (AHR). The core concept used in AHT is described originally in [130], that shows HT of a set of points will Several authors have used dividing the original image into sub-images [132][133][134][135][136][137][138] and applied the divide and conquer technique. The proposed methods range from single level application of HT to the sub-images under the constraint of the curve passing through a set of pixels [137], or multi-level application of HT, first on subimages, and then evaluating the contribution of each sub-image to the HT space of the original image [135], or parallel running of matching sub-images of template and the image, using master-slave technique [138].…”

Section: Digital Hough Transform (Dht)mentioning

confidence: 99%

Analysis of object and its motion in event-based videos

Seifozzakerini¹

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This thesis would not be possible if I did not have the help and advice of several people who contributed their valuable support in completion of this study. First of all, I would like to express my sincere appreciation and gratitude to my supervisor, Associate Professor Mao Kezhi for giving me an opportunity to pursue my interest in research work under his valuable guidance. He is not only a supervisor but also a warm hearted friend that I always can count on his support and help. It gives me immense pleasure to thank my mentor and co-supervisor, Doctor Yau Wei-Yun for his support, continuous guidance and astute criticism during this study. Nothing can gratify the invaluable time he generously dedicated to me despite his busy schedule. My special thanks go to my Thesis Advisory Committee members, Professor Huang Guangbin, Associate Professor Wang Han and Dr. Li Xiaoli for their invaluable guidance and encouragement throughout my research. Financial assistance provided by the Agency for Science, Technology and Research (A*STAR) in the form of Graduate Scholarship is thankfully acknowledgeable. I would like to express my gratitude to the staffs in Institute for Infocomm Research (I2R), Ms. Janice Lee Sze Min and Ms. Felicia Chew for their effort on my attachment matters. I cannot miss mentioning my home mates during last 4 years in Singapore, Amin Kianinejhad, Siavash Sakhavi, Hossein Dehghani Tafti and Ali Rajabipoor for making ii my life during Ph.D. a very memorable one. Also, I should mention my Iranian friends in Singapore, whom their moral supports were a great help during this candidature:

show abstract

A concurrent modified algorithm for Generalized Hough Transform

Cited by 5 publications

References 7 publications

Hough Transform Implementation For Event-Based Systems: Concepts and Challenges

Hough Transform Implementation For Event-Based Systems: Concepts and Challenges

A novel Hough transform based on eliminating particle swarm optimization and its applications

Analysis of object and its motion in event-based videos

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