
Learn how images are formed, camera models and sensors, 2D and 3D transformations, and how computer vision processes images to extract insights like face recognition and edge detection.
Explore how linear filtering, including convolution and correlation, reduces noise in grayscale images by applying kernels, revealing how box and gaussian filters create different blur effects.
Explore subsampling in computer vision, its effect on aliasing and image quality, and how low-pass Gaussian filtering prevents artifacts for reliable multi-resolution image pyramids.
Explore how edge detection identifies boundaries by analyzing intensity changes using first and second derivatives, Gaussian smoothing, and derivative filters, with hysteresis for robust localization.
Explore how the Gaussian function and its derivatives reveal image edges, from zero-order to first and higher orders, highlighting local maxima, minima, and zero crossings.
Learn how the laplacian of gaussian detects edges by using the Mexican hat shape and the second derivative of gaussian, and see how DoG approximates LoG with different sigma scales.
Learn Computer Vision (for Beginners) Part 2
Master essential image processing techniques and build a strong foundation in Computer Vision!
Are you ready to take your Computer Vision skills to the next level? This course is designed for beginners who want to dive deeper into image processing, feature extraction, and recognition techniques. Through interactive lessons and hands-on coding exercises, you will develop a solid understanding of key concepts and algorithms used in modern Computer Vision.
What You Will Learn:
Fundamentals of Image Processing
Understanding Linear Filtering and how it transforms images
Applying Convolution Kernels for edge detection and smoothing
Implementing Separable Filters for efficient computations
Exploring different Boundary Handling techniques
Noise Reduction & Image Transformations
Gaussian Pyramid & Aliasing: Multiscale image representation
Median Filter & Morphology: Techniques to remove noise and enhance structures
Edge Detection & Feature Extraction
Understanding and implementing the Canny Edge Detector
Non-Maximum Suppression for refined edges
Laplacian & Laplacian Pyramid for feature enhancement
Template-Based Recognition & Matching
Template Matching and its applications
SSD (Sum of Squared Differences) and Correlation techniques
Dimensionality Reduction & Subspaces
Introduction to PCA (Principal Component Analysis) and SVD (Singular Value Decomposition)
Understanding how reducing dimensions helps in feature extraction
Hands-on Coding Implementation
Code for Canny Edge Detection & Image Pyramids
Practical exercises to apply learned concepts
Who Is This Course For?
Students and professionals looking to expand their Computer Vision knowledge
Beginners who have a basic understanding of Python and want to implement image processing techniques
AI and Machine Learning enthusiasts curious about feature extraction and image recognition
By the end of this course, you will have built a strong intuition for image transformations, edge detection, and pattern recognition, giving you a solid foundation for advanced topics like deep learning in Computer Vision.
Enroll now and start your journey in Computer Vision today!