Real-time reprogrammable low-level image processing: edge detection and edge tracking accelerator

1993 ◽  
Author(s):  
M. Meribout ◽  
Kun M. Hou
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Real Time ◽  
Low Level ◽  
Edge Tracking

scholarly journals Early warning flood detector adopting camera by Sobel Canny edge detection algorithm method

2021 ◽  
Vol 22 (3) ◽  
pp. 1796
Author(s):  
Satryo B. Utomo ◽  
Januar Fery Irawan ◽  
Rizqi Renafasih Alinra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Detection System ◽  
Water Levels ◽  
Level Gauge ◽  
Canny Edge Detection ◽  
Canny Edge

Early warning of floods is an essential part of disaster management. Various automatic detectors have been developed in flood mitigation, including cameras. But reliability and accuracy have not been improved. Besides, the use of monitoring devices has been employed to monitor water levels in various water building facilities. The early warning flood detector was carried out with a sensor camera using an orange ball that floats near the water level gauge in a bounding box. This approach uses the integration of computer vision and image processing, namely digital image processing techniques, with Sobel Canny edge detection (SCED) algorithms to detect quickly and accurately water levels in real-time. After the water level is measured, a flood detection process is carried out based on the specified water level. According to the results of experiments in the laboratory, it has been shown that the proposed approach can detect objects accurately and fast in real-time. Besides, from the water level detection experiment, good results were obtained. Therefore, the object detection system and water level can be used as an efficient and accurate early detection system for flood disasters.

scholarly journals An Application based Efficient Thread Level Parallelism Scheme on Heterogeneous Multicore Embedded System for Real Time Image Processing.

2020 ◽  
Vol 21 (1) ◽  
pp. 47-56
Author(s):  
K Indragandhi ◽  
Jawahar P K
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Embedded System ◽  
Real Time ◽  
Execution Time ◽  
Image Size ◽  
Multicore Processor ◽  
Detection Scheme ◽  
Thread Level Parallelism ◽  
Level Parallelism

The recent advent of the embedded devices is equipped with multicore processor as it significantly improves the system performance. In order to utilize all the core in multicore processor in an efficient manner, application programs need to be parallelized. An efficient thread level parallelism (ETLP) scheme is proposed in this paper and uses computationally intensive edge detection algorithm for evaluation. Edge detection is the important process in various real time applications namely vehicle detection in traffic control, medical image processing etc. The main objective of ETLP scheme is to reduce the execution time and increase the CPU core utilization. The performance of ETLP scheme is evaluated with basic edge detection scheme (BEDS) for different image size. The experimental results reveal that the proposed ETLP scheme achieves efficiency of 49% and 72% for the image size 300 x 256 and 1024 x 1024 respectively. Furthermore an ETLP scheme reducing 66% execution time for image size 1024 x 1024 when compared with BEDS.

scholarly journals New Edge Detection Method Using Elisabeth Method: Case Study Javanese Batiks

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
Elisabeth Denis Setiani ◽  
Suyoto Suyoto
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Method ◽  
New Method ◽  
Unique Pattern ◽  
Low Level ◽  
Edge Detection Method

Abstract. This article will introduce a new edge detection method called Elisabeth method to analyze image. The case study here is Javanese Batik’s motif. Edges are basic low level primitives for image processing. It helps to identify pictures. Methods used are the combination between Sobel and Prewitt. This method is completely new to analyze Javanese Batik’s motif. Every batik motif has unique pattern. The purpose of this research is to improving edge detection method that already known now. The result is a new method in edge detection problems. Batik is one of the Indonesian Heritage that avowed as a Heritage World Cultures. With this research it hoped can help our country to classify and identify Batik’s motif items in Indonesia. Keywords: Prewitt, Sobel, Elisabeth, Javanese Batik, Parang, Kawung Abstrak. Metode Baru Deteksi Tepi Menggunakan Metode Elisabeth: Studi Kasus Batik Jawa. Artikel ini akan memperkenalkan sebuah metode baru deteksi tepi yang disebut dengan metode Elisabeth untuk menganalisis citra. Studi kasus yang digunakan disini adalah motif Batik Jawa. Tepi adalah primitif level dasar untuk pemrosesan citra. Ini membantu mengidentifikasi gambar. Metode yang digunakan adalah kombinasi antara Sobel dan Prewitt. Metode ini benar-benar baru untuk menganalisis motif Batik Jawa. Setiap motif batik memiliki pola yang unik. Tujuan dari penelitian ini adalah untuk meningkatkan metode deteksi tepi yang sudah dikenal sekarang. Hasilnya adalah metode baru dalam masalah deteksi tepi. Batik adalah salah satu Warisan Indonesia yang diakui sebagai Warisan Budaya Dunia. Dengan penelitian ini diharapkan dapat membantu negara kita untuk mengklasifikasikan dan mengidentifikasi motif Batik di Indonesia. Kata Kunci: Prewitt, Sobel, Elisabeth, Batik Jawa, Parang, Kawung

Embedded Realization of a Adaptive Threshold Edge Detection Base on Canny Operator

2014 ◽  
Vol 543-547 ◽  
pp. 2766-2769 ◽  
Author(s):  
Cheng Po Mu ◽  
Qing Xian Dong ◽  
Jie Lian ◽  
Ming Song Peng
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Data Processing ◽  
Embedded System ◽  
Real Time ◽  
Adaptive Threshold ◽  
Processing Technology ◽  
Embedded Image Processing ◽  
Image Processing Technology ◽  
Embedded Image

Edge detection that is an important means to realize image segmentation has important application significance in image processing, industrial detection, artificial intelligence and the target recognition field. As the demand for real-time and rapidity in image processing, the embedded image processing technology has been widely applied. But the realization of real-time edge detection for image requires a large amount of data processing, limited system resources of embedded system is the main reason of the embedded image processing technology development. In order to shorten time embedded systems edge detection processing large amounts of data, based on adaptive threshold Canny algorithm, this paper as the FPGA data processing DSP chips and made a FPGA + DSP hardware architecture, effectively improve the system real-time, get a good edge detection results.

scholarly journals Traffic Light Controller using Image Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 405-411 ◽  
Author(s):  
Lakshmanan M, Et. al.
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Signals ◽  
Daily Basis ◽  
Traffic Density ◽  
Traffic Light ◽  
Traffic Lights ◽  
Traffic Systems

Traffic congestion at junctions is a serious issue on a daily basis. The prevailing traffic light controllers are unable to manage the different traffic flows. Most of the current systems operate on a timing mechanism that changes the signal after a particular interval of time. This may cause frustration and result in motorist's time waste. Traffic congestion is a major problem in the currently existing systems. Delays, safety, parking, and environmental problems are the main issues of current traffic systems that emit smoke and contribute to increasing Global Warming. Sensor-based systems reduce the waiting time and maximize the total number of vehicles that can cross an intersection. Our proposed system can control the traffic lights based on image processing without the need for traffic police. This can reduce congestion, delay, road accidents, need for manpower. Under image processing, we use sub techniques like RGB to Gray conversion, Image resizing, Image Enhancement, Edge detection, Image matching, and Timing allocation. A real-time image is captured for every 1 second. After edge detection procedure for both reference and real-time images, these images are compared using SURF Algorithm. Then the amount of traffic is detected and the details are stored in the server. Arduino is used for a traffic signal in the hardware part. It consists of a Wi-Fi module. The micro-controller used in the system Arduino. Four cameras are placed on respective roads and these cameras are used to capture images to analyze traffic density. Then the traffic signals are decided according to the density of traffic. Our technique can be effective to combat traffic on Indian Roads. A lot of time can be saved by deploying this system and also it conserves a lot of resources as well as the economy

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