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Basics of Computer Vision using Python in Mumbai
A comprehensive course on Computer Vision using Python. This course is highly suited for beginners
12 weeks long course | 100% Online
Learn from leading experts in the industry
Project based learning with 2 industry level projects that learners can showcase on LinkedIn.
Learn Key Tools & Technologies Python, OpenCV, TensorFlow, Keras TensorFlow 2
Syllabus
This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects
Course Overview
- This course provides the parameters and processes that define computer vision and delve into details like image processing, edge detection and image segmentation.
- This course provides the basics of classical computer vision, 2d and 3d processing, camera projections, use of machine learning in vision systems
Course Syllabus in Mumbai
On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.
Week - 01 Introduction to Computer Vision
- General introduction,
- History of CV
- Formulating the field, why is it a hard topic?
- Definition of computer vision
- Required components
- What qualifies as a vision system
- Humans as a vision system: how good do we “see”?
- Useful applications
- Image acquisition using a camera
- Different types of cameras for different domain
- Stills, Video, DSLR, Bodycam, Drone
- Infrared, Ultrasonography, Magnetic resonance
- Physics of Color: color spectrum
- Human encoding of color: Rods and cons of eyes
- Color spaces:
- RGB, CMYK, HSV
- Color balance
- Camera specifications:
- Pinhole
- CMOS
- CCD
- Image specifications:
- Pixel (Picture element)
- Aspect ratio, HD, Interlacing
- Conversions
- Type of digital images:
- Binary, Grayscale, Color
- Conversion techniques
Week - 02 Image processing
- Noise Removal
- Pixel Neighborhood
- Salt and pepper noise
- Morphing to hide cracks in the image
- Applying filters to images
- Convolution of matrices
- Types of Filter:
- Mean or Box filtering
- Median Filter
- Mode, Mean, Pass
- Generic properties of smoothing
- Anisotropic filtering
- Gaussian: Isotropy condition, formulation, figure
- Weight influence of pixels by their distance to the center pixel
- Spread parameter
- Motivating examples
- Filter Separability
- Computation and Maths
- Gaussian Separability
Week - 03 Edge Detection
- Introduction to edges and gradients
- Intensity difference
- 1D versus 2D edge detection
- Edge detection in mammals
- 1D signals and 2D signals
- Difference and derivative mask
- Examples
- Image Gradient
- Image noise: Gaussian noise
- Smoothing + Edge detection
- Gaussian Derivative Signals
- 2D gradient operators
- Prewitt Masks
- Sobel Masks
- Steerable filters
- Laplacian filters
- Laplacian of Gaussian
- Zero Crossings
- Canny edge detection
- Hysteresis thresholding
- Non-maximal Suppression
Week - 04 Image Segmentation and features
- Thresholding based on histogram
- Otsu, Adaptive Otsu
- Formulation, Advancements, and effectiveness
- Examples
- Distance Metric: Norm functions
- Thresholding based on different metrics, covariance-based
- Different types of background subtraction
- Mean, Euclidean, Mahalanobis
- Covariance matrix, multidimensional mahalanobis
- Shadow modeling
- Transform to color spaces
- Multimodal background distribution
- Gaussian Mixture Model
- Foreground Assignment
- Clustering to Image Segmentation
- Agglomerative Clustering
- K Means, K Means ++
- Mean Shift Clustering
- Hierarchical Clustering
Week - 05 Binary Image Operation
- Morphology:
- Erosion, Dilation
- Open, Close
- Connected component
- Counting objects: Sequential count etc
- Recursive count
- Remove Small Features
- Hough Transformation Algorithm
- Radon Transformation Algorithm
- Image Pyramids: Gaussian Laplacian Coding Compression
Week - 06 Shape of Objects
- Largest component
- Medial axis
- Boundary coding
- Chain Coding
- Shape Numbering
- Quadtree Representation
- Bounding box
- Perimeter, Compactness, Circularity
- Centroid
- Spatial Moments
- Central
- Second third order
- Similitude Moment
- Dimensionality Reduction
- Linear basis set
- Principal Component Analysis
- Eigen Values and Vectors
- Finding Eigen sets
- Test on synthetic and real data
- Face Recognition using PCA: kernel trick
Week - 07 Motion
- Definition, simple motion
- Image differentiation
- Single constant threshold
- Weber's Law
- Optical flow
- Formulas, geometry, example
- Normal optic flow
- Weighted aggregate,
- Hierarchical Motion Estimation
- Motion: Use of linear Algebra
- 3D motion of a point
- Matrix operations for different motion in objects
- Pinhole Camera Model
- 2D matrix motion
- Translation Motion
- Similarity Motion
- Affine Motion
- Motion History Image
- Spatial Pattern of where motion occurred
- Progression of motion
- Motion Energy Image
- Silhouette Difference
Week - 08 Matching & Tracking
- Motivation, Example
- Feature-based tracking
- How to find good features to track
- Find Interest Points (General)
- Panoramic stitching
- Features from Accelerated Segment (FAST)
- Harris Detector
- Gradients
- Window weighing function
- Harris Corner Response Function
Week - 09 Interest Point
- SURF algorithm
- SIFT algorithm for automated feature selection
- Free alternative to SIFT and SURF in OpenCV
- Laplacian Of Gaussian
- Automated Feature selection
- Diff Of Gaussian
- Covariance tracking
- Descriptor Matrix
- Finding best match
- Rotation Invariance
- Kanade-Lucas-Tomasi (KLT) Tracker
- Tracking Features
- Formulations
- Reduction Pyramid
- Select “good” feature based on Eigen Value
- Mean shift tracking
- Weighted histograms using spatial kernels
- Evaluating similarity between distributions using Bhattacharyya coefficient
- Object tracking by target localization (in each frame) by maximizing the similarity function using mean shift
- Template Matching
- Sum-of-Absolute Differences
- Sum-of-Squared Differences
- Normalized Cross-Correlation
Week - 10 Image Registration
- Lens
- Thin Lens Model
- Focus, DoF, Aperture,
- Projective Camera Model
- Pinhole Camera
- Intrinsic and Extrinsic Camera Parameters
- Homogeneous Coordinate
- Projection
- Camera Projection
- Camera Matrices
- Estimating camera matrices
- Extracting parameter P
- Calibration
- Projection
- Perspective Effective
- Affine
- Orthographic
- Weak
- Transformation: Translation, Rotation, Skew, Reflection
- Planar homography/ Projective Transformation
- Solving homography matrix
- Normalized Direct Linear Transformation
- Example on real 3D data
- RANSAC Algorithm
- Gold Standard Algorithm
Week - 11 Lens & Camera projection
- 3D intro
- Motivation
- Ambiguity in single View
- Geometry for simple stereo system
- Depth and Calibration
- Epipolar Geometry
- Baseline, Epipole, Epipolar Line, Epipolar Plane
- Epipolar Constraint
- Converging camera
- Parallel Camera
- Camera Motion
- Fundamental Matrix
- Computation
- 8 point algorithm
- Depth Matrix
- Stereo Matching Algorithm
- Correspondence Search
- Estimate disparity by finding corresponding points
- Depth is inversely related to disparity
- Stereo Matching as Energy Minimization
- Graph cut algorithm
Week - 12 SOTA ML based CV Techniques
- LeNet
- AlexNet
- General detection techniques
- YOLO
- GAN
- Autonomous Vehicle Specific Networks
- Gaussian Neural Network
- Confidence in classification output: decide object confidence for autonomous vehicle
Our courses have been designed by industry experts to help students achieve their dream careers
Industry Projects in Mumbai
Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.
Real time traffic tracking using matching algorithms
Real time video segmentation using machine learning and feature extraction
Our courses have been designed by industry experts to help students achieve their dream careers
Ratings & Reviews by Learners
Skill-Lync has received honest feedback from our learners around the globe.
Instructors profiles
Our courses are designed by leading academicians and experienced industry professionals.
1 industry expert
Our instructors are industry experts along with a passion to teach.
7 years in the experience range
Instructors with 7 years extensive industry experience.
Areas of expertise
- Machine Learning
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