Image processing using matlab
Internship 2024
Learn MATLAB image processing, including GUI, manipulation, segmentation, feature extraction, denoising, compression, and encryption. Apply methods like K-means, watershed, GLCM, and neural networks for pattern recognition.
1/ 2 Months
Online
8+ Live Projects
Dual Certification
Ultimate Step towards your Career Goals: Expert in Image processing MATLAB
Master MATLAB image processing: GUI, segmentation, feature extraction, denoising, compression, encryption, and neural network-based pattern recognition.
Internship Benifits
Mentorship
Receive guidance and insights from industry experts.
Hands-on Experience
Gain practical skills in a real-world cutting-edge projects.
Networking
Connect with professionals and peers in your field.
Skill Development
Enhance your technical and soft skills.
Career Advancement
Boost your resume with valuable experience.
Certificate
Get a certification to showcase your achievements.
MATLAB Internship Overview
Introduction to Image Processing & Its Applications
- Overview of image processing, including applications in fields like medical imaging, robotics, and remote sensing.
MATLAB Fundamentals & Toolbox
- Introduction to MATLAB’s interface, fundamental commands, and toolbox usage for image processing and data analysis.
GUI, Graphs & Plots in MATLAB
- Basics of creating GUI, plotting graphs, and visualizing data using MATLAB for better analysis and interactivity.
Graphical User Interface (GUI – I & II) in MATLAB
- Step-by-step creation of GUIs in MATLAB, with elements like buttons, sliders, and input fields to make applications interactive.
Commands, Control Statements & Loops in MATLAB
- Using control statements and loops for decision-making, iteration, and flow control in MATLAB scripts.
Basic Image Manipulation
- Techniques for resizing, rotating, cropping, and color adjustments, as well as basic pixel-wise operations.
Morphological Image Pre-processing Techniques – Blurring & Deblurring
- Applying blurring and deblurring methods for noise reduction and sharpening images.
Morphological Image Pre-processing Techniques – Erosion, Dilation & Fusion
- Morphological operations like erosion, dilation, and image fusion for enhancing or combining image regions.
De-noising Images | Filters in Images
- Techniques to reduce noise in images using filters such as median, Gaussian, and bilateral filters.
Image Compression – DWT & SWT Based
- Image compression using Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) to reduce file size while preserving quality.
Image Compression – SWT & Watermarking
- Combining SWT for image compression with watermarking techniques for secure data embedding in images.
Feature Extraction in Images
- Extracting important features from images, like edges, textures, and shapes, for image recognition and classification.
Feature Extraction Using GLCM
- Using Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction to analyze spatial relationships of pixels.
Image Segmentation – K-Means
- Segmenting images using the K-Means clustering algorithm, often used in medical and object-based segmentation tasks.
Image Segmentation – APP
- Active Pixel Processing (APP) based segmentation for enhanced object boundary detection in images.
Image Segmentation – Otsu Thresholding
- Thresholding using Otsu’s method for automatic foreground and background separation in images.
Clustering of Images – Region Based Segmentation
- Region-based clustering methods for dividing images into distinct regions based on similarities in pixel characteristics.
Image Segmentation – Watershed
- Watershed algorithm for separating overlapping objects, commonly used in complex segmentation tasks.
Texture Segmentation Using Gabor Filter
- Segmenting textures in images with Gabor filters, useful for applications like fingerprint and material analysis.
Edge Detection in Images | Face Detection Using HAAR Cascade
- Edge detection techniques for finding object boundaries, with HAAR cascades specifically for face detection.
Image Encryption – AES Algorithm
- Securing images by applying the AES encryption algorithm to protect data from unauthorized access.
RSA Algorithm-Based Image Encryption
- Using RSA encryption for image data security, which employs public and private keys for encryption and decryption.
Image Pattern Recognition | Training Datasets in Images
- Techniques for recognizing patterns in images, training models on datasets for tasks like object and scene recognition.
Neural Networks in Images
- Applying neural networks to image data for applications like classification, recognition, and image-based predictions.
Looking for in-depth Syllabus Information? Explore your endless possibilities in MATLAB with our Brochure!
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Project Submission: Example Output Screenshots from Our Clients
Take a look at these sample outputs crafted by our clients. These screenshots showcase the impressive results achieved through our courses and projects. Be inspired by their work and visualize what you can create!
Dual Certification: Internship Completion & Participation
Earn prestigious Dual Certification upon successful completion of our internship program. This recognition validates both your participation and the skills you have honed during the internship, providing a competitive edge in your professional journey. Show off your expertise with pride!
How does this Internship Program Work?
Step 1 Enroll in the Program
✅ Get a Mentor Assigned
✅Presentations & Practice Codes
✅ Learn at your Flexible Time
✅ Apprehend the concepts
Step 2 Project Development
✅ Implement Skills Learn
✅ Develop Projects with assistance
✅ Get Codes for Reference
✅ Visualise the Concepts
Step 3 Certification
✅ Get Certified
✅ Certificate of Internship
✅ Project Completion Certificate
✅ Share on social media
✅ Get Job Notifications
Internship Letter (LoR)
Internship Reports
Flexible Schedules
1 Month
₹1999/-
1 Month
- Internship Acceptance Letter
- 90 Days from the date of payment
- 4 LIVE intractive Mastermind Sessions
- 4+ Capstone Projects & Codes
- Full Roadmap
- Internship Report
- 1 Month Internship Certificate
₹3299/-
2 Month
- Internship Acceptance Letter
- 180 Days from the date of payment
- 4 LIVE interactive Mastermind Sessions
- 12+ Capstone Projects & Codes
- Full Roadmap
- Internship Report
- Participation Certificate
- 2 Month Internship Certificate
Our Alumni Employers
Curious where our graduates make their mark? Our students go on to excel in leading tech companies, innovative startups, and prestigious research institutions. Their advanced skills and hands-on experience make them highly sought-after professionals in the industry.
FAQ
What is image processing, and where is it applied?
Image processing involves manipulating images to enhance or extract useful information. Applications include medical imaging, facial recognition, autonomous vehicles, satellite imaging, and multimedia.
How do I get started with MATLAB for image processing?
MATLAB is equipped with specialized toolboxes for image processing. You can start by loading images with imread, displaying them with imshow and experimenting with functions from the Image Processing Toolbox for filtering, segmentation, and feature extraction.
What is MATLAB’s GUI, and how is it useful for image processing?
MATLAB’s GUI enables interactive applications, where users can visualize, modify, and analyze images with custom controls like sliders, buttons, and text boxes. This is especially useful in developing user-friendly tools for processing and analyzing images.
What are the basics of creating a GUI in MATLAB?
MATLAB provides the App Designer and GUIDE tools for creating GUIs. You can add graphical components and program their behavior using callbacks to perform specific actions based on user input.
What is image encryption, and how are AES and RSA used?
Image encryption secures images by encoding them. AES (symmetric encryption) and RSA (asymmetric encryption) transform pixel values to prevent unauthorized access.
How does GLCM help in feature extraction?
GLCM (Gray Level Co-occurrence Matrix) is a statistical method for analyzing texture. It captures spatial relationships between pixel intensities, aiding in texture-based classification tasks.
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