Hough Transform on Reconfigurable Meshes

Chung, Kuo-Liang; Lin, Horn-Yi

doi:10.1006/cviu.1995.1020

Cited by 14 publications

(6 citation statements)

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“…The horizontal axis shows the size n of an 'n x n' square image and the vertical axis shows Log10 (Number of processors required). It is sure that the required numbers of processors are lesser compared to earlier works [14,15,18,19]. The computational complexity of each PE is also less in this method.…”

Section: Line Extractionmentioning

confidence: 78%

“…This work is focused on a line extraction technique for finding suitable method with minimum transistor count and maximum frames per second. By observing possibilities of different line alignments in a binary image, it is clear that number of line alignments in an image is less than the number of processors required for Parallel HT [14,15,18,19].…”

Section: Theory and Methodologymentioning

confidence: 99%

“…Since the computational complexity of HT is very large, many approaches are presented to speed up the computation process of HT. Parallel algorithms including Parallel HT are presented in several works in this area [13][14][15][16][17][18][19]. Recently some practical implementations of Parallel HT based on FPGA are carried out, but optimized results are not obtained to speed up the line extraction for some real time applications like robot navigation.…”

Section: = ø + ømentioning

confidence: 99%

“…Inter processor communication through reconfigurable mesh are required for the other methods. Also, the processors calculate cosine and sine values for different values of angles and perform arithmetic operations like addition and multiplication in the other methods [13][14][15][16][17][18][19][20][21][22]. The resolution of lines again can be increased by modifying edge detection method and increasing PEs.…”

Section: Line Extractionmentioning

confidence: 99%

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A Fast-Efficient parallel processing algorithm for straight line detection

Rasiq¹,

Jeevan²,

Krishnakumar³

2019

IJEECS

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<p>This work presents a novel method for detecting straight lines in an image at a very high speed with optimum number of processors and their functionalities. The method can be used to extract straight lines directly from an image without noise removal and pre-processing. First the square image is converted to a binary edge image using a parallel edge detection mechanism. The parallel edge detection mechanism used in this work is capable of producing edge image within a short time. Then the binary square image is transferred to a system having large number of Processing Elements (PEs). A PE has only limited jobs such as pixel scanning, compare line length with nearby PEs and transmit data to the Main Control Unit (MCU). The MCU collects data from all PEs and evaluates straight lines. Even if the number of PEs is high, it is comparatively very much less than the parallel Hough Transform method and practically implementable using recent ULSI technologies.</p>

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Section: Line Extractionmentioning

confidence: 78%

Section: Theory and Methodologymentioning

confidence: 99%

Section: = ø + ømentioning

confidence: 99%

Section: Line Extractionmentioning

confidence: 99%

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A Fast-Efficient parallel processing algorithm for straight line detection

Rasiq¹,

Jeevan²,

Krishnakumar³

2019

IJEECS

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show abstract

“…In order to accelerate the LHT by hardware, many systems have been developed to date [1][2][3][4][5][6][7][8][9][10][11]. They can be categorized into three groups; parallel systems with many processing elements, dedicated hardware systems with ASICs, and reconfigurable systems with Field Programmable Gate Arrays (FPGAs).…”

Section: Introductionmentioning

confidence: 99%

A Compact System for Real-Time Detection of Line Segments

Nagata

Matsumoto

2005

Image Analysis and Processing – ICIAP 2005

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In this paper, we describe a compact circuit for real-time detection of line segments using the Line Hough Transform (LHT). The LHT is a technique to find out lines in an image. The LHT is robust to noises, but requires long computation time. The circuit calculates (1) r and θ of lines (r is the distance from the origin to a line and θ is the angle of the line) by the LHT units in parallel, and (2) start and end points of the lines by the other units which are completely pipelined with the LHT units. With this parallel and pipeline processing, the circuit can detects line segments by π/512 angle steps in a standard size image (640 × 480) in real-time. This circuit was implemented on an off-the-shelf PCI board with one Field Programmable Gate Array (FPGA) chip. The size of the circuit is 45% of the chip, which makes it possible to implement other circuits for higher level processing of object recognition on the same chip, or the performance can be improved twice by using the rest of hardware resources. 2 Previous Works Many approaches by software to reduce the computational complexity have been proposed, and efficient algorithms to extract line segments have also been re

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Image Analysis and Computer Vision: 1995

Rosenfeld

1996

Computer Vision and Image Understanding

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Hough Transform on Reconfigurable Meshes

Cited by 14 publications

References 0 publications

A Fast-Efficient parallel processing algorithm for straight line detection

A Fast-Efficient parallel processing algorithm for straight line detection

A Compact System for Real-Time Detection of Line Segments

Image Analysis and Computer Vision: 1995

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