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 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 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.

A Real-Time Visual Tracking Approach Using Sparse Autoencoder and Extreme Learning Machine

2015 ◽  
Vol 03 (04) ◽  
pp. 267-275
Author(s):  
Liang Dai ◽  
Yuesheng Zhu ◽  
Guibo Luo ◽  
Chao He ◽  
Hanchi Lin
Keyword(s):  
Real Time ◽  
Extreme Learning Machine ◽  
Visual Tracking ◽  
Computational Cost ◽  
Real Time Tracking ◽  
Sparse Autoencoder ◽  
Selection For ◽  
Learning Machine ◽  
Hidden Layer ◽  
Online Process

Visual tracking algorithm based on deep learning is one of the state-of-the-art tracking approaches. However, its computational cost is high. To reduce the computational burden, in this paper, A real-time tracking approach is proposed by using three modules: a single hidden layer neural network based on sparse autoencoder, a feature selection for simplifying the network and an online process based on extreme learning machine. Our experimental results have demonstrated that the proposed algorithm has good performance of robust and real-time.

scholarly journals Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data

2009 ◽  
Vol 9 (2) ◽  
pp. 303-314 ◽  
Author(s):  
S. Martinis ◽  
A. Twele ◽  
S. Voigt
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Large Scale ◽  
Ground Truth ◽  
Training Data ◽  
Experimental Investigations ◽  
Inundation Area ◽  
Global Threshold ◽  
Flood Detection ◽  
Thresholding Algorithm

Abstract. In this paper, an automatic near-real time (NRT) flood detection approach is presented, which combines histogram thresholding and segmentation based classification, specifically oriented to the analysis of single-polarized very high resolution Synthetic Aperture Radar (SAR) satellite data. The challenge of SAR-based flood detection is addressed in a completely unsupervised way, which assumes no training data and therefore no prior information about the class statistics to be available concerning the area of investigation. This is usually the case in NRT-disaster management, where the collection of ground truth information is not feasible due to time-constraints. A simple thresholding algorithm can be used in the most of the cases to distinguish between "flood" and "non-flood" pixels in a high resolution SAR image to detect the largest part of an inundation area. Due to the fact that local gray-level changes may not be distinguished by global thresholding techniques in large satellite scenes the thresholding algorithm is integrated into a split-based approach for the derivation of a global threshold by the analysis and combination of the split inherent information. The derived global threshold is then integrated into a multi-scale segmentation step combining the advantages of small-, medium- and large-scale per parcel segmentation. Experimental investigations performed on a TerraSAR-X Stripmap scene from southwest England during large scale flooding in the summer 2007 show high classification accuracies of the proposed split-based approach in combination with image segmentation and optional integration of digital elevation models.

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