The Use of Hybrid Technique: Thresholding and Edge Detection for Identifying River from Aerial Photo

2012 ◽  
Author(s):  
Mohd. Shafry Mohd. Rahim ◽  
Nik Isrozaidi Nik Ismail ◽  
Mohd. Azuan Shah Idris
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Edge Detection ◽  
Key Words ◽  
Detection Technique ◽  
Hybrid Technique ◽  
Aerial Photo ◽  
Hybrid Techniques ◽  
Grey Scale ◽  
Sobel Edge Detection

Bidang pemprosesan imej merupakan satu bidang yang luas dengan pelbagai aplikasi terutama dalam bidang sains dan industri. Pemprosesan imej digunakan dalam manipulasi dan penambahbaikan imej untuk memudahkan proses seterusnya. Penyelidikan ini melibatkan penggunaan teknik hybrid yang menggabungkan teknik threshold dan teknik pengesanan sisi Sobel, untuk mengenal pasti sungai daripada imej berskala kelabu. Teknik thresholding digunakan untuk mengurangkan piksel sisi yang tak maksima, piksel sisi yang lemah dan mengurangkan kesan hingar, manakala edge detection digunakan untuk mengesan kehadiran piksel sisi. Hasil yang diperolehi daripada penggunaan teknik hybrid dibandingkan dengan teknik–teknik yang sedia ada seperti Sobel, Prewitt, Laplacian dan Robert Cross. Kata kunci: Pemprosesan imej, mengenal pasti ciri-ciri, pengesanan garis, foto udara The field of image processing is a broad field with many applications in science and industry. Image processing is used to manipulate and enhance an image, which ease the next process. This research involves the use of a hybrid techniques, which is a combination of thresholding and Sobel edge detection technique, to recognize a river from a grey scale image. Thresholding technique is used to reduce non-maxima pixels, weak edges and noise, whilst the edge detection technique is used to detect location of the edges. The output from this hybrid technique is compared to the existing techniques such as Sobel, Prewitt, Laplacian, and Robert Cross technique. Key words: Image processing, feature extraction, edge detection, aerial photo

scholarly journals Fractional-Order Colour Image Processing

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 457
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Paulo Gordo ◽  
Rui Melicio
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Fractional Derivative ◽  
Fractional Order ◽  
Fractional Derivatives ◽  
Grey Scale ◽  
Colour Image ◽  
Colour Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative.

An Algorithmic Approach Based on CMS Edge Detection Technique for the Processing of Digital Images

2020 ◽  
pp. 252-272
Author(s):  
Kalyan Kumar Jena ◽  
Sasmita Mishra ◽  
Sarojananda Mishra
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Images ◽  
Color Images ◽  
Experimental Results ◽  
Detection Technique ◽  
Algorithmic Approach ◽  
Current Scenario ◽  
Edge Detection Method

Research in the field of digital image processing (DIP) has increased in the current scenario. Edge detection of digital images is considered as an important area of research in DIP. Detecting edges in different digital images accurately is a challenging work in DIP. Different methods have been introduced by different researchers to detect the edges of images. However, no method works well under all conditions. In this chapter, an edge detection method is proposed to detect the edges of gray scale and color images. This method focuses on the combination of Canny, mathematical morphological, and Sobel (CMS) edge detection operators. The output of the proposed method is produced using matrix laboratory (MATLAB) R2015b and compared with Sobel, Prewitt, Roberts, Laplacian of Gaussian (LoG), Canny, and mathematical morphological edge detection operators. The experimental results show that the proposed method works better as compared to other existing methods in detecting the edges of images.

scholarly journals Hardware Implementation of Sobel Edge Detection Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03051
Author(s):  
Ankita Pujare ◽  
Priyanka Sawant ◽  
Hema Sharma ◽  
Khushboo Pichhode
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Feature Detection ◽  
Hardware Implementation ◽  
Software Tool ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Sobel Edge Detection ◽  
Image Edges

In the fields of image processing, feature detection, the edge detection is an important aspect. For detection of sharp changes in the properties of an image, edges are recognized as important factors which provides more information or data regarding the analysis of an image. In this work coding of various edge detection algorithms such as Sobel, Canny, etc. have been done on the MATLAB software, also this work is implemented on the FPGA Nexys 4 DDR board. The results are then displayed on a VGA screen. The implementation of this work using Verilog language of FPGA has been executed on Vivado 18.2 software tool.

Study on Edge Detection Technique in Material Bag Image

2010 ◽  
Vol 97-101 ◽  
pp. 4408-4411
Author(s):  
Tian Hou Zhang ◽  
Chang Chun Li ◽  
Shi Feng Wang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Theoretical Basis ◽  
Automatic Recognition ◽  
Detection Technique ◽  
Segmentation Method ◽  
Edge Information ◽  
Sobel Edge Detection ◽  
Iterative Threshold

According to the features of material bag image, the paper compares an analyzes the detection effects of different edge detection operators detecting material bag image. A new image segmentation method is proposed to combine Sobel edge detection operator and iterative threshold. The method can extract edge information of material bag image efficiently and provide a theoretical basis for the robot automatic recognition of material bags technique.

scholarly journals A High-Level Synthesis Implementation and Evaluation of an Image Processing Accelerator

Technologies ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 4
Author(s):  
Dimitris Tsiktsiris ◽  
Dimitris Ziouzios ◽  
Minas Dasygenis
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
High Level Synthesis ◽  
Bare Metal ◽  
Sobel Edge Detection ◽  
High Level ◽  
Graphics Processing ◽  
Better Than

Most frequently, an FPGA is used as an implementation platform in applications of graphics processing, as its structure can effectively exploit both spatial and temporal parallelism. Such parallelization techniques involve fundamental restrictions, namely being their dependence on both the processing model and the system’s hardware constraints, that can force the designer to restructure the architecture and the implementation. Predesigned accelerators can significantly assist the designer to solve this problem and meet his deadlines. In this paper, we present our accelerators for Grayscale and Sobel Edge Detection, two of the most fundamental algorithms used in digital image processing projects. We have implemented those algorithms with a “bare-metal” VHDL design, written purely by hand, as a portable USB accelerator device, as well as an HLS-based overlay of a similar implementation designed to be used by a Python interface. The comparisons of the two architectures showcase that the HLS generated design can perform equally to or even better than the handwritten HDL equivalent, especially when the correct compiler directives are provided.

scholarly journals Detection of Fake Currency using Image Processing

2021 ◽  
Vol 9 (VII) ◽  
pp. 419-423
Author(s):  
Ashik Shiby
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Programming Language ◽  
Money Laundering ◽  
National Currency ◽  
Ordinary People ◽  
Research Project ◽  
Legal Entity ◽  
Grey Scale ◽  
Image Separation

In its definition, the term 'currency' defines an agreed-upon exchange item, the national currency being the legal entity used by the selected controlling entity. Throughout history, issuers have faced 1 common threat: counterfeit. In recent years fake money note has been printed that has resulted in significant losses and damage to society. Therefore, it becomes necessary to build a tool for earning money. This research project proposes a way to look at the note of counterfeit money distributed in our country through their image. After selecting an image use pre-processing. In pre-processing, the acquired image is cropped, smooth, and adjust. Change the image to grey-scale. After conversion use image separation. Features are extracted and reduce. Finally, compare the picture to be real or fake. Duplicate money has been a major problem in the market. There are currency counting machines available in banks and other trading venues to check financial authenticity. Most people do not have access to such programs which is why there is a need for fake money laundering software, which can be used by ordinary people. This proposed framework uses Image Processing to determine whether the money is real or counterfeit. The research project program is built entirely using Python's programming language. It has the methods such as grayscale conversion, edge detection, segmentation, etc.

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