Computer Vision
Lec1-CV.pptxLec 1 - CV vs CG vs IP
Lec 1 - CV vs CG vs IP
Lec2-CV.pptxLec 2 - Image Formation
Lec 2 - Image Formation
Lec3-CV.pptxLec 3 - Intensity Interpolation
Lec 3 - Intensity Interpolation
Lec4-CV.pptxLec 4 - Spatial Transformations
Lec 4 - Spatial Transformations
Lec5-CV.pptxLec 5 - Feature Based Registration
Lec 5 - Feature Based Registration
Lec6-CV.pptxLec 6 - Camera Model
Lec 6 - Camera Model
Lec5-DIP.pptxLec 7 - Intensity Transformation
Lec 7 - Intensity Transformation
Lec6-DIP.pptxLec 8 - Image Filtering
Lec 8 - Image Filtering
Lec11-DIP.pptxLec 9 - Feature Detection and Description
Lec 9 - Feature Detection and Description
Lec12-DIP.pptxLec 10 - Image Segmentation
Lec 10 - Image Segmentation
Lec14-DIP.pptxLec 11 - ML pipeline
Lec 11 - ML pipeline
Lec12-CV.pptxLec 11 - Optical Flow
Lec 11 - Optical Flow
Lec13-CV.pptxLec 12 - Intensity Based Registration
Lec 12 - Intensity Based Registration
Lec14-CV.pptxLec 13 - Neural Nets
Lec 13 - Neural Nets
Lec15-CV.pptxLec 14 - Convolutional Neural Nets
Lec 14 - Convolutional Neural Nets
Lec15-CV.pptxLec 15 - Back Prop in CNN layers
Lec 15 - Back Prop in CNN layers
Lec16-CV.pptxLec 16 - CNN Architectures
Lec 16 - CNN Architectures
Lec17-CV.pptxLec 17 - Recurrent Neural Nets
Lec 17 - Recurrent Neural Nets
Lec18-CV.pptxLec 18 - CNN Applications
Lec 18 - CNN Applications