FPGA Implementation of the Generalized Hough Transform

Geninatti, Sergio R.; Benítez, José Ignacio Benavides; Calvino, M. Hernandez; Guil, Nicolás; Gómez-Luna, Juan

doi:10.1109/reconfig.2009.78

Cited by 17 publications

(8 citation statements)

References 8 publications

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“…An architecture for the implementation of the generalized HT on FPGA is introduced in [6]. The generalized HT is a modification to detect any shape with different scales and orientation.…”

Section: Detection Of Circlesmentioning

confidence: 99%

“…The generalized HT is a modification to detect any shape with different scales and orientation. However, in [6] it is used to find the similarity between two video frames. One of the frames is used as a template to be found in the image.…”

Section: Detection Of Circlesmentioning

confidence: 99%

“…This introduces limitations on the bandwidth of the data transfer which leads to limiting the size of the image window and frame rate. This is particularly the case when HT is extended to detect circles and other shapes [4,5,6]. In this paper we present an architecture to implement the HT for both straight lines and circles.…”

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confidence: 99%

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Memory efficient FPGA implementation of hough transform for line and circle detection

Elhossini

Moussa

2012

2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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Hough transform (HT) is a widely used algorithm in machine vision systems. In this paper, a memory efficient architecture for implementing HT on FPGAs is presented. The proposed architecture enables storing the HT space on the FPGA's memory blocks with no need for accessing external memory while processing large size images in real-time with high frame rate. It can be used for both line and circle detection. Results show very good accuracy with images processed at 30 fps frame rate and image size of 800 × 600. This compares favourably with other reported architectures in the literature. Index Terms-Hough Transform, FPGA, Computer vision 1. INTRODUCTIONIn the past decade, the use of reconfigurable hardware, and particularly FPGAs, has increased dramatically in the field of image processing and machine vision systems Hough Transform (HT) is commonly used for detecting straight lines in images [1]. Implementing this algorithm on FPGAs is challenging due to the high demand on memory storage and the complexity of the algorithm. Normally external memory is used to store the HT data [2,3,4]. This introduces limitations on the bandwidth of the data transfer which leads to limiting the size of the image window and frame rate. This is particularly the case when HT is extended to detect circles and other shapes [4,5,6]. In this paper we present an architecture to implement the HT for both straight lines and circles. It employs an efficient memory design that eliminates the need to use external memory and allows processing of large image sizes at a high frame rate. This paper is organized as follows: Section 2 covers background material about the HT while Section 3 surveys related work. Section 4 introduces the proposed architecture and FPGA implementation. Section 5 presents testing, results, and discussion. Finally section 6 concludes the paper. BACKGROUNDHT works on the contour points resulting from the edge detection process. Each contour point contributes in a voting process for several instances of the object being detected. For each instance a memory location is used to store its vote. The memory used to store the votes of all instances of the object is called HT space. The local minima in the HT space represents the object that received the maximum number of votes. A parameterized model of the object should be provided to relate contour pixels position to the object template. Straight line equation can be expressed in a parameterized form asThe HT space constructed using Equation 1 is a two dimensional space where each point in the space represents a line that corresponds to a single value of and . Each new pixel is used to compute the value of using the position of the pixel (x, y) for each angle . The vote for the line defined by and is then incremented. The HT space should be sampled to allow a limited memory size to store the voting results. Sampling the HT space affects the accuracy of the resulting transform, but is essential for efficient hardware implementation. HT can be modified to detect other shapes by mo...

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“…An architecture for the implementation of the generalized HT on FPGA is introduced in [6]. The generalized HT is a modification to detect any shape with different scales and orientation.…”

Section: Detection Of Circlesmentioning

confidence: 99%

Section: Detection Of Circlesmentioning

confidence: 99%

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Memory efficient FPGA implementation of hough transform for line and circle detection

Elhossini

Moussa

2012

2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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“…Implementation of image processing algorithms on reprogrammable hardware devices with an ability of parallel processing became a 'hot' research topic, and plenty of work has been reported in the literature [1][2][3][4][5]. As the requirement of computationally intensive image-processing algorithms for real-time industrial applications, such as robotic vision, imposes high-speed and highly parallel implementations, in recent years a trend has been witnessed towards utilizing field programmable gate arrays (FPGAs) or graphics processing units (GPUs) instead of using CPUs, which are inherently sequential processing devices.…”

Section: Introductionmentioning

confidence: 99%

Hardware implementation of a scale and rotation invariant object detection algorithm on FPGA for real-time applications

Peker¹,

Altun²,

Karakaya³

2016

Turk J Elec Eng & Comp Sci

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Abstract:A hardware implementation of a computationally light, scale, and rotation invariant method for shape detection on FPGA is devised. The method is based on histogram of oriented gradients (HOG) and average magnitude difference function (AMDF). AMDF is used as a decision module that measures the similarity/dissimilarity between HOG vectors of an image in order to classify the object. In addition, a simulation environment implemented on MATLAB is developed in order to overcome the time-consuming and tedious process of hardware verification on the FPGA platform.The simulation environment provides specific tools to quickly implement the proposed methods. It is shown that the simulator is able to produce exactly the same results as those obtained from FPGA implementation. The results indicate that the proposed approach leads to a shape detection method that is computationally light, scale, and rotation invariant, and, therefore, suitable for real-time industrial and robotic vision applications.

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“…Moreover, various hardware algorithms for circle detection have been proposed. These existing researches use the template matching [7], [8] and the Hough transform algorithms [9], [10], [11]. Shafer et al proposed an FPGA implementation to detect the iris position [7].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Implementation of the One-Dimensional Hough Transform Algorithm for Circle Detection on the FPGA

Zhou

Ito

Nakano

2014

2014 Second International Symposium on Computing and Networking

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The main contribution of this paper is to present an efficient implementation of the Hough transform algorithm that uses only one-dimensional parameter spaces for circles detection on a Xilinx Virtex-7 FPGA. We implemented the circuit using 398 DSP48E1 slices and 309 block RAMs with 18Kbits. The experimental results show that the architecture runs in 181.812MHz. For an edge image of size 400 × 400, our circuit can perform in at most 970434 clock cycles, i.e., 5337.568μs. Our implementation attains a speed-up factor of approximately 189 over the sequential implementation on the CPU.

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FPGA Implementation of the Generalized Hough Transform

Cited by 17 publications

References 8 publications

Memory efficient FPGA implementation of hough transform for line and circle detection

Memory efficient FPGA implementation of hough transform for line and circle detection

Hardware implementation of a scale and rotation invariant object detection algorithm on FPGA for real-time applications

An Efficient Implementation of the One-Dimensional Hough Transform Algorithm for Circle Detection on the FPGA

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