Hough Transform Algorithm for Three-Dimensional Segment Extraction and its Parallel Hardware Implementation

This paper makes some improvements on Roberts representation for straight line in space and proposes a coarse-to-fine three-dimensional (3D) Randomized Hough Transform (RHT) for the detection of dim targets. Using range, bearing and elevation information of the received echoes, 3D RHT can detect constant velocity target in space. In addition, this paper applies a coarse-to-fine strategy to the 3D RHT, which aims to solve both the computational and memory complexity problems. The validity of the coarse-to-fine 3D RHT is verified by simulations. In comparison with the 2D case, which only uses the range-bearing information, the coarse-to-fine 3D RHT has a better practical value in dim target detection.

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HARDWARE IMPLEMENTATION OF EWA For Point-Based Three-Dimensional Graphics Rendering

Computational Imaging and Vision - Computer Vision and Graphics ◽

10.1007/1-4020-4179-9_85 ◽

2006 ◽

pp. 593-598

Author(s):

Adam Herout ◽

Pavel Zemčík ◽

Julien Reptin

Keyword(s):

Hardware Implementation ◽

Three Dimensional ◽

Graphics Rendering

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MEG–MRI CO-REGISTRATION USING 3D GENERALIZED HOUGH TRANSFORM

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237220500283 ◽

2020 ◽

Vol 32 (04) ◽

pp. 2050028

Author(s):

Sheng-Kai Lin ◽

Rong-Chin Lo ◽

Ren-Guey Lee

Keyword(s):

Human Brain ◽

Hough Transform ◽

Three Dimensional ◽

Measurement Tool ◽

High Temporal Resolution ◽

Medical Systems ◽

Registration Method ◽

Generalized Hough Transform ◽

Weak Magnetic Fields ◽

Magnetic Resonance Imaging Mri

In this paper, we propose a method to use the three-dimensional (3D) generalized Hough transform (GHT) to co-register magnetoencephalography (MEG) and magnetic resonance imaging (MRI) of a brain automatically, whose results can be used to align MRI images and MEG data accurately and efficiently. Recently, many medical devices have been developed to study the neuronal activity in the human brain. MEG is a high-temporal-resolution measurement tool to study the physiological functions of brain nerves noninvasively; whereas the MRI of the scalp, skull, and cortex of the human brain is a high-spatial-resolution tool. The proposed method combines two tools for investigating the cognitive neuroscience between the human brain structure and weak magnetic fields from two different medical systems. An accurate and automatic registration method is necessitated to improve the brain analysis processes by combining multimodal data. The conventional GHT is a well-known method for detecting two-dimensional (2D) images or locating transformed planar shapes in 2D image processes. To further improve the 2D GHT, we extended it to a 3D GHT, which can co-register MEG and MRI data automatically and accurately. Some experimental results are included to demonstrate and evaluate the error and applicability of MEG–MRI co-registration.

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A hardware implementation of Hough Transform based on parabolic duality

2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS) ◽

10.1109/mwscas.2014.6908373 ◽

2014 ◽

Author(s):

Naren Ramesh ◽

George Purdy ◽

Carla Purdy ◽

Justin Smith

Keyword(s):

Hough Transform ◽

Hardware Implementation

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Smooth Nonlinear Fitting Scheme for Analog Multiplierless Implementation of Hindmarsh-Rose Neuron Model

10.21203/rs.3.rs-242893/v1 ◽

2021 ◽

Author(s):

Jianming Cai ◽

Han Bao ◽

Quan Xu ◽

Zhongyun Hua ◽

Bocheng Bao

Keyword(s):

Large Scale ◽

Neuron Model ◽

Hardware Implementation ◽

Three Dimensional ◽

Two Dimensional ◽

Implementation Cost ◽

Hyperbolic Tangent ◽

Nonlinear Fitting ◽

Chaotic Bursting ◽

Electrical Activities

Abstract The Hindmarsh-Rose (HR) neuron model is built to describe the neuron electrical activities. Due to the polynomial nonlinearities, multipliers are required to implement the HR neuron model in analog. In order to avoid the multipliers, this brief presents a novel smooth nonlinear fitting scheme. We first construct two nonlinear fitting functions using the composite hyperbolic tangent functions and then implement an analog multiplierless circuit for the two-dimensional (2D) or three- dimensional (3D) HR neuron model. To exhibit the nonlinear fitting effects, numerical simulations and hardware experiments for the fitted HR neuron model are provided successively. The results show that the fitted HR neuron model with analog multiplierless circuit can display different operation patterns of resting, periodic spiking, and periodic/chaotic bursting, entirely behaving like the original HR neuron model. The analog multiplierless circuit has the advantage of low implementation cost and thereby it might be suitable for the hardware implementation of large-scale neural networks.

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Hardware Implementation for an Improved Full-Pixel Search Algorithm Based on Normalized Cross Correlation Method

Electronics ◽

10.3390/electronics7120428 ◽

2018 ◽

Vol 7 (12) ◽

pp. 428 ◽

Cited By ~ 3

Author(s):

Guohe Zhang ◽

Zejie Kuang ◽

Sufen Wei ◽

Kai Huang ◽

Feng Liang ◽

...

Keyword(s):

Cross Correlation ◽

Hardware Implementation ◽

Search Algorithm ◽

Single Point ◽

Three Dimensional ◽

Correlation Method ◽

Strong Stability ◽

Point Matching ◽

Digital Speckle Correlation Method ◽

Normalized Cross Correlation

Digital speckle correlation method is widely used in the areas of three-dimensional deformation and morphology measurement. It has the advantages of non-contact, high precision, and strong stability. However, it is very complex to be carried out with low speed software implementation. Here, an improved full pixel search algorithm based on the normalized cross correlation (NCC) method considering hardware implementation is proposed. According to the field programmable gate array (FPGA) simulation results, the speed of hardware design proposed in this paper is 2000 faster than that of software in single point matching, and 600 times faster than software in multi-point matching. The speed of the presented algorithm shows an increasing trend with the increase of the template size when performing multipoint matching.

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MAGNETOENCEPHALOGRAPHY–ELECTROENCEPHALOGRAPHY CO-REGISTRATION USING 3D GENERALIZED HOUGH TRANSFORM

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237220500246 ◽

2020 ◽

Vol 32 (03) ◽

pp. 2050024

Author(s):

Sheng-Kai Lin ◽

Rong-Chin Lo ◽

Ren-Guey Lee

Keyword(s):

Hough Transform ◽

Quantum Interference ◽

Three Dimensional ◽

Image Data ◽

Epileptic Activity ◽

High Temporal Resolution ◽

Accuracy Evaluation ◽

Registration Accuracy ◽

Registration Method ◽

Generalized Hough Transform

This study proposes an advanced co-registration method for an integrated high temporal resolution electroencephalography (EEG) and magnetoencephalography (MEG) data. The MEG has a higher accuracy for source localization techniques and spatial resolution by sensing magnetic fields generated by the entire brain using multichannel superconducting quantum interference devices, whereas EEG can record electrical activities from larger cortical surface to detect epilepsy. However, by integrating the two modality tools, we can accurately localize the epileptic activity compared to other non-invasive modalities. Integrating the two modality tools is challenging and important. This study proposes a new algorithm using an extended three-dimensional generalized Hough transform (3D GHT) to co-register the two modality data. The pre-process steps require the locations of EEG electrodes, MEG sensors, head-shape points of subjects and fiducial landmarks. The conventional GHT algorithm is a well-known method used for identifying or locating two 2D images. This study proposes a new co-registration method that extends the 2D GHT algorithm to a 3D GHT algorithm that can automatically co-register 3D image data. It is important to study the prospective brain source activity in bio-signal analysis. Furthermore, the study examines the registration accuracy evaluation by calculating the root mean square of the Euclidean distance of MEG–EEG co-registration data. Several experimental results are used to show that the proposed method for co-registering the two modality data is accurate and efficient. The results demonstrate that the proposed method is feasible, sufficiently automatic, and fast for investigating brain source images.

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