scholarly journals Fully Parallel Implementation of Otsu Automatic Image Thresholding Algorithm on FPGA

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4151
Author(s):  
Wysterlânya K. P. Barros ◽  
Leonardo A. Dias ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Real Time ◽  
Parallel Implementation ◽  
Computational Cost ◽  
Image Thresholding ◽  
Otsu Method ◽  
Field Programmable ◽  
High Resolution Images ◽  
Area Occupation ◽  
Thresholding Algorithm ◽  
High Computational Cost

This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two classes. However, this technique has a high computational cost, making it difficult to use in real-time applications. Thus, this paper proposes a hardware design exploiting parallelization to optimize the system’s processing time. The implementation details and an analysis of the synthesis results concerning the hardware area occupation, throughput, and dynamic power consumption, are presented. Results have shown that the proposed hardware achieved a high speedup compared to similar works in the literature.

scholarly journals Proposta de Implementação em Hardware do Algoritmo de Otsu Aplicado ao Rastreamento em Tempo Real de Vermes

2021 ◽  
Author(s):  
Wysterlânya Kyury Pereira Barros ◽  
Marcelo Fernandes
Keyword(s):  
Rna Interference ◽  
High Resolution ◽  
Real Time ◽  
Computational Cost ◽  
Field Programmable ◽  
Otsu’S Method ◽  
Real Time Tracking ◽  
Global Thresholding ◽  
Thresholding Algorithm ◽  
High Computational Cost

This work proposes an implementation in Field Programmable GateArray (FPGA) of the Otsu’s method applied to real-time trackingof worms called Caenorhabditis elegans. Real-time tracking is necessaryto measure changes in the worm’s behavior in response totreatment with Ribonucleic Acid (RNA) interference. Otsu’s methodis a global thresholding algorithm used to define an optimal thresholdbetween two classes. However, this technique in real-time applicationsassociated with the processing of high-resolution videoshas a high computational cost because of the massive amount ofdata generated. Otsu’s algorithm needs to identify the worms ineach frame captured by a high-resolution camera in a real-timeanalysis of the worm’s behavior. Thus, this work proposes a highperformanceimplementation of Otsu’s algorithm in FPGA. Theresults show it was possible to achieve a speedup up to 5 timeshigher than similar works in the literature.

scholarly journals HyP-DESPOT: A hybrid parallel algorithm for online planning under uncertainty

2020 ◽  
pp. 027836492093707
Author(s):  
Panpan Cai ◽  
Yuanfu Luo ◽  
David Hsu ◽  
Wee Sun Lee
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Search Tree ◽  
Planning Under Uncertainty ◽  
State Action ◽  
Online Planning ◽  
Planning Algorithm ◽  
One Step ◽  
Robotic Tasks ◽  
High Computational Cost

Robust planning under uncertainty is critical for robots in uncertain, dynamic environments, but incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, Hybrid Parallel DESPOT (HyP-DESPOT) is a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs; it performs parallel Monte Carlo simulations at the leaf nodes of the search tree using GPUs. HyP-DESPOT provably converges in finite time under moderate conditions and guarantees near-optimality of the solution. Experimental results show that HyP-DESPOT speeds up online planning by up to a factor of several hundred in several challenging robotic tasks in simulation, compared with the original DESPOT algorithm. It also exhibits real-time performance on a robot vehicle navigating among many pedestrians.

scholarly journals A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Kanjana Charansiriphaisan ◽  
Sirapat Chiewchanwattana ◽  
Khamron Sunat
Keyword(s):  
High Performance ◽  
Artificial Bee Colony ◽  
Computational Cost ◽  
Multilevel Thresholding ◽  
Image Thresholding ◽  
Bee Colony ◽  
Function Calls ◽  
Optimal Thresholds ◽  
Otsu’S Method ◽  
High Computational Cost

Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu’s method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various meta-heuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an onlooker bee to select an employee bee was modified to serve our purposes. The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance. The ranking was performed by Friedman ranks. The experimental results showed that IABC/best/1 outperformed the other techniques when all of them were applied to multilevel image thresholding. Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm.

scholarly journals Real-Time Haze Removal Using Normalised Pixel-Wise Dark-Channel Prior and Robust Atmospheric-Light Estimation

2020 ◽  
Vol 10 (3) ◽  
pp. 1165 ◽  
Author(s):  
Yutaro Iwamoto ◽  
Naoaki Hashimoto ◽  
Yen-Wei Chen
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Dark Channel Prior ◽  
Detailed Structure ◽  
Haze Removal ◽  
Acceptable Quality ◽  
Colour Channel ◽  
Dark Channel ◽  
High Computational Cost ◽  
Atmospheric Light

This study proposes real-time haze removal from a single image using normalised pixel-wise dark-channel prior (DCP). DCP assumes that at least one RGB colour channel within most local patches in a haze-free image has a low-intensity value. Since the spatial resolution of the transmission map depends on the patch size and it loses the detailed structure with large patch sizes, original work refines the transmission map using an image-matting technique. However, it requires high computational cost and is not adequate for real-time application. To solve these problems, we use normalised pixel-wise haze estimation without losing the detailed structure of the transmission map. This study also proposes robust atmospheric-light estimation using a coarse-to-fine search strategy and down-sampled haze estimation for acceleration. Experiments with actual and simulated haze images showed that the proposed method achieves real-time results of visually and quantitatively acceptable quality compared with other conventional methods of haze removal.

scholarly journals A Framework for Real-Time 3D Freeform Manipulation of Facial Video

2019 ◽  
Vol 9 (21) ◽  
pp. 4707
Author(s):  
Jungsik Park ◽  
Byung-Kuk Seo ◽  
Jong-Il Park
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Video Frame ◽  
Face Model ◽  
Live Video ◽  
Time Performance ◽  
Face Region ◽  
3D Manipulation ◽  
Comparison Results ◽  
High Computational Cost

This paper proposes a framework that allows 3D freeform manipulation of a face in live video. Unlike existing approaches, the proposed framework provides natural 3D manipulation of a face without background distortion and interactive face editing by a user’s input, which leads to freeform manipulation without any limitation of range or shape. To achieve these features, a 3D morphable face model is fitted to a face region in a video frame and is deformed by the user’s input. The video frame is then mapped as a texture to the deformed model, and the model is rendered on the video frame. Because of the high computational cost, parallelization and acceleration schemes are also adopted for real-time performance. Performance evaluation and comparison results show that the proposed framework is promising for 3D face editing in live video.

scholarly journals Real-Time Collision-Free Navigation of Multiple UAVs Based on Bounding Boxes

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1632
Author(s):  
Paloma Sánchez ◽  
Rafael Casado ◽  
Aurelio Bermúdez
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Computational Cost ◽  
Multiple Uavs ◽  
Navigation Algorithms ◽  
Excellent Candidate ◽  
Bounding Boxes ◽  
Velocity Obstacle ◽  
High Computational Cost ◽  
Multi Uav

Predictably, future urban airspaces will be crowded with autonomous unmanned aerial vehicles (UAVs) offering different services to the population. One of the main challenges in this new scenario is the design of collision-free navigation algorithms to avoid conflicts between flying UAVs. The most appropriate collision avoidance strategies for this scenario are non-centralized ones that are dynamically executed (in real time). Existing collision avoidance methods usually entail a high computational cost. In this work, we present Bounding Box Collision Avoidance (BBCA) algorithm, a simplified velocity obstacle-based technique that achieves a balance between efficiency and cost. The performance of the proposal is analyzed in detail in different airspace configurations. Simulation results show that the method is able to avoid all the conflicts in two UAV scenarios and most of them in multi-UAV ones. At the same time, we have found that the penalty of using the BBCA collision avoidance technique on the flying time and the distance covered by the UAVs involved in the conflict is reasonably acceptable. Therefore, we consider that BBCA may be an excellent candidate for the design of collision-free navigation algorithms for UAVs.

scholarly journals Single Image Haze Removal from Image Enhancement Perspective for Real-Time Vision-Based Systems

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5170 ◽  
Author(s):  
Dat Ngo ◽  
Seungmin Lee ◽  
Quoc-Hieu Nguyen ◽  
Tri Minh Ngo ◽  
Gi-Dong Lee ◽  
...  
Keyword(s):  
Real Time ◽  
Hardware Implementation ◽  
Computational Cost ◽  
Weather Conditions ◽  
Single Image ◽  
Computationally Efficient ◽  
Real Time Processing ◽  
Haze Removal ◽  
Field Programmable ◽  
Processing Techniques

Vision-based systems operating outdoors are significantly affected by weather conditions, notably those related to atmospheric turbidity. Accordingly, haze removal algorithms, actively being researched over the last decade, have come into use as a pre-processing step. Although numerous approaches have existed previously, an efficient method coupled with fast implementation is still in great demand. This paper proposes a single image haze removal algorithm with a corresponding hardware implementation for facilitating real-time processing. Contrary to methods that invert the physical model describing the formation of hazy images, the proposed approach mainly exploits computationally efficient image processing techniques such as detail enhancement, multiple-exposure image fusion, and adaptive tone remapping. Therefore, it possesses low computational complexity while achieving good performance compared to other state-of-the-art methods. Moreover, the low computational cost also brings about a compact hardware implementation capable of handling high-quality videos at an acceptable rate, that is, greater than 25 frames per second, as verified with a Field Programmable Gate Array chip. The software source code and datasets are available online for public use.

scholarly journals Parallelization of ARACNe, an Algorithm for the Reconstruction of Gene Regulatory Networks

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 25
Author(s):  
Uxía Casal ◽  
Jorge González-Domínguez ◽  
María J. Martín
Keyword(s):  
Cellular Networks ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Parallel Implementation ◽  
Computational Cost ◽  
Spearman Correlation ◽  
Mathematical Methods ◽  
Other Substances ◽  
Gene Regulatory ◽  
High Computational Cost

Gene regulatory networks are graphical representations of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression. There are different computational approaches for the reverse engineering of these networks. Most of them require all gene-gene evaluations using different mathematical methods such as Pearson/Spearman correlation, Mutual Information or topology patterns, among others. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) is one of the most effective and widely used tools to reconstruct gene regulatory networks. However, the high computational cost of ARACNe prevents its use over large biologic datasets. In this work, we present a hybrid MPI/OpenMP parallel implementation of ARACNe to accelerate its execution on multi-core clusters, obtaining a speedup of 430.46 using as input a dataset with 41,100 genes and 108 samples and 32 nodes (each of them with 24 cores).

scholarly journals Hardware in the loop co-simulation of finite set-model predictive control using FPGA for a three level CHB inverter

2020 ◽  
Vol 11 (4) ◽  
pp. 1719
Author(s):  
Mai Van Chung ◽  
Do Tuan Anh ◽  
Phuong Vu ◽  
Linh Manh Nguyen
Keyword(s):  
Control Algorithm ◽  
Computational Cost ◽  
Current Control ◽  
Computational Time ◽  
Hardware In The Loop ◽  
Field Programmable ◽  
Power Switches ◽  
Finite Set ◽  
Predictive Controller ◽  
High Computational Cost

Along with the development of powerful microprocessors and microcontrollers, the applications of the model predictive controller, which requires high computational cost, to fast dynamical systems such as power converters and electric drives have become a tendency recently. In this paper, two solutions are offered to quickly develop the finite set predictive current control for induction motor fed by 3-level H-Bridge cascaded inverter. First, the field programmable gate array (FPGA) with capability of parallel computation is employed to minimize the computational time. Second, the hardware in the loop (HIL) co-simulation is used to quickly verify the developed control algorithm without burden of time on hardware design since the motor and the power switches are emulated on a real-time platform with high-fidelity mathematical models. The implementation procedure and HIL co-simulation results of the developed control algorithm shows the effectiveness of the proposed solution.

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