scholarly journals Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

2019 ◽  
Author(s):  
Mehdi Mafi
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Edge Detection ◽  
Noise Removal ◽  
Mixed Noise

scholarly journals Machine Learning-Based Technique for Image Classification and Identification of Similarity for Commonness of Source

2020 ◽  
Vol 9 (6) ◽  
pp. 544-548
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Edge Detection ◽  
Image Classification ◽  
Programming Language ◽  
Python Programming Language ◽  
Reserve Bank ◽  
Python Programming ◽  
Highlight Extraction ◽  
Reserve Bank Of India

In India Every year RBI (Reserve bank of India) faces the issue of fake currency. Fake Currency has consistently been an issue that has made a lot of chaos in the market. The expanding mechanical progressions have made the opportunities for making progressively fake currency which is circled in the market which decreases the general economy of the nation. There are machines present at banks and other business regions to check the validness of the monetary forms. Be that as it may, a typical man doesn't approach such frameworks and henceforth a requirement for a product to distinguish counterfeit cash emerges, which can be utilized by average folks. This proposed framework utilizes Image Processing to identify whether the currency is real or fake. The framework is structured utilizing Python programming language and OpenCV. It comprises of the means, for example, grayscale detection, edge detection, Highlight Extraction, and so forth which are performed utilizing reasonable strategies. And which will be further implemented in the Framework for Classification and Identification of Similarity for Commonness of Source

scholarly journals Dilated Filters for Edge-Detection Algorithms

2021 ◽  
Vol 11 (22) ◽  
pp. 10716
Author(s):  
Ciprian Orhei ◽  
Victor Bogdan ◽  
Cosmin Bonchis ◽  
Radu Vasiu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Edge Detection ◽  
Positive Impact ◽  
Image Resolution ◽  
Huge Number ◽  
Fundamental Feature ◽  
Detection Algorithms ◽  
Dilated Convolution ◽  
Processing Power

Edges are a basic and fundamental feature in image processing that is used directly or indirectly in huge number of applications. Inspired by the expansion of image resolution and processing power, dilated-convolution techniques appeared. Dilated convolutions have impressive results in machine learning, so naturally we discuss the idea of dilating the standard filters from several edge-detection algorithms. In this work, we investigated the research hypothesis that use dilated filters, rather than the extended or classical ones, and obtained better edge map results. To demonstrate this hypothesis, we compared the results of the edge-detection algorithms using the proposed dilation filters with original filters or custom variants. Experimental results confirm our statement that the dilation of filters have a positive impact for edge-detection algorithms from simple to rather complex algorithms.

scholarly journals MRI brain tumor segmentation: A forthright image processing approach

2020 ◽  
Vol 9 (3) ◽  
pp. 1024-1031
Author(s):  
Noor Elaiza Abd Khalid ◽  
Muhammad Firdaus Ismail ◽  
Muhammad Azri AB Manaf ◽  
Ahmad Firdaus Ahmad Fadzil ◽  
Shafaf Ibrahim
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Edge Detection ◽  
Confusion Matrix ◽  
Noise Removal ◽  
Tumor Segmentation ◽  
Morphological Operations ◽  
Mri Brain ◽  
Processing Approach ◽  
Depth Analysis

Brain tumor is a collection of cells that grow in an abnormal and uncontrollable way. It may affect the regular function of the brain since it grows inside the skull region. As a brain tumor can be possibly led to cancer, early detection in Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scanned images are crucial. Thus, this paper proposed a forthright image processing approach towards detection and localization of brain tumor region The approach consists of a few stages such as pre-processing, edge detection and segmentation. The pre-processing stage converts the original image into a greyscale image, and noise removal if necessary. Next, the image is enhanced using image enhancement techniques. It is then followed by edge detection using Sobel and Canny algorithms. Finally, the segmentation is applied to highlight the tumor with morphological operations towards the affected region in the MRI images. The in-depth analysis is measured using a confusion matrix. From the results, it signifies that the proposed approach is capable to provide decent segmentation of brain tumor from various MRI brain images.

scholarly journals Variable Splitting Based Method for Image Restoration with Impulse Plus Gaussian Noise

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Tingting Wu
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Fundamental Problem ◽  
Noise Removal ◽  
Phase Method ◽  
Two Phase ◽  
Variable Splitting ◽  
Mixed Noise ◽  
Mixed Noise Removal

Image denoising is a fundamental problem in realm of image processing. A large amount of literature is dedicated to restoring an image corrupted by a certain type of noise. However, little literature is concentrated on the scenario of mixed noise removal. In this paper, based on the model of two-phase method for image denoising proposed by Cai et al. (2008) and the idea of variable splitting, we are capable of decomposing the image denoising problem into subproblems with closed form. Numerical results illustrate the validity and robustness of the proposed algorithms, especially for restoring the images contaminated by impulse plus Gaussian noise.

Accurate Detection and Quantization of Leaf- Diseases through Soft Computing

2018 ◽  
Vol 1 (1) ◽  
pp. 236-247
Author(s):  
Divya Srivastava ◽  
Rajitha B. ◽  
Suneeta Agarwal
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agricultural Production ◽  
Bacterial Blight ◽  
Early Stage ◽  
Second Phase ◽  
Computationally Efficient ◽  
Stage Of Disease ◽  
Accurate Detection ◽  
Two Phases

Diseases in leaves can cause the significant reduction in both quality and quantity of agricultural production. If early and accurate detection of disease/diseases in leaves can be automated, then the proper remedy can be taken timely. A simple and computationally efficient approach is presented in this paper for disease/diseases detection on leaves. Only detecting the disease is not beneficial without knowing the stage of disease thus the paper also determine the stage of disease/diseases by quantizing the affected of the leaves by using digital image processing and machine learning. Though there exists a variety of diseases on leaves, but the bacterial and fungal spots (Early Scorch, Late Scorch, and Leaf Spot) are the most prominent diseases found on leaves. Keeping this in mind the paper deals with the detection of Bacterial Blight and Fungal Spot both at an early stage (Early Scorch) and late stage (Late Scorch) on the variety of leaves. The proposed approach is divided into two phases, in the first phase, it identifies one or more disease/diseases existing on leaves. In the second phase, amount of area affected by the disease/diseases is calculated. The experimental results obtained showed 97% accuracy using the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document