This lecture overviews Linear Algebra that has many applications in Machine Learning, Computer Vision and Scientific Computing. It covers the following topics in detail: Vectors, matrices, System of linear equations, Eigenanalysis, Singular value Decomposition, Other matrix decompositions, Tensors Fundamentals, Tensor decompositions, BLAS.
This lecture overviews Geometric Spaces that has many applications in Machine Learning and Digital Signal Processing and Analysis. It covers the following topics in detail: Vector Spaces, Affine Spaces, Metric Spaces.
This lecture overviews Geometry that has many applications in Computer Vision and Machine Learning. It covers the following topics in detail: Vector calculus (inner/cross vector products, coplanarity), 3D geometric transformations (rotation, translation,quarternions), Projective geometry: homogenous coordinates, Perspective (or central) projections, Vanishing points, Cross-ratio, Conic sections.
This lecture overviews Mathematical Analysis that has many applications in Computer Vision, Machine Learning and Autonomous Systems. It covers the following topics in detail: 1D/2D/3D functions with applications in signal, image and video processing. Analytical and numerical differentiation of 1D functions. Analytical and numerical integration of 1D functions. Analytical and numerical partial differentiation of 2D/3D/spatiotemporal… Continue reading Mathematical Analysis
This lecture overviews Soccer Video Analysis that has many applications in Human-centered Computing, Image and Video Analysis and Sports Analytics/coaching. It covers the following topics in detail: Playfield Detection. Ball Detection and Tracking. Player Detection and Tracking. Referee Assistance. Tactics Analysis.
This lecture overviews Athlete Motion Analysis that has many applications in Human-centered Computing, Image and Video Analysis and Sports coaching. It covers the following topics in detail: Motion Caption. Body pose. Rigid object motion. Articulated object motion. Motion Analysis in Sports.