Professor: Marek A. Perkowski, Electrical Engineering.
ROBOT VISION AND PERCEPTION
This class starts from easy robot vision and continues with advanced topics such as Fast Fourier Transforms
and Radon Transforms.
All these theories are used in practical robot vision projects.
Goal of the class.
Students should learn elementary, medium and some advanced topics in robot vision and perception
to be used in software, hardware and integration/robotic projects.
This is the second class in robotics but can be taken with no prerequisities in case
that the students knows programming.
Students should get practical understanding how robot vision algorithms are used in robot systems.
Where is this knowledge useful?
Previous students who have done projects found this class useful to find industrial positions in the
following areas: robot vision, flexible automation systems with vision, medical image processing,
hardware design of image processors, bank image processing,
general software design, industrial board-level design, design automation.
Software.
Software used in class depends on every year projects. This quarter the projects
will use OpenCV, Visual Basic and C++.
Mandatory Lectures for year 2012.
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0001. Introduction to Morphological Operators. In PPT format.
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0002. Mathematics of Binary Morphology. ppt
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0002_A. Binary Morphology and Use in Robotic Path Planning. ppt
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0003.A. Big Example of morpology and measurements on image. ppt
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0004. Introduction to edge detection feature extraction. ppt
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0005. Labeling and sequential algorithms. Robot Soccer. ppt
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0006. Quad Trees and Octtrees. ppt
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0007. Partitioning and Thresholding. ppt
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0008. Shape Representation1. ppt
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0009. Image Processing for Melanoma Cancer. ppt
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0010. Histogramming socccer vision. ppt
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0011. Matlab Image Processing. ppt
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0012. Introduction to color image processing. ppt
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0014. Matlab Image Examples with color processing. ppt
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0015. Matlab hit and miss binary. ppt
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0020. Edge Detection Algorithm by Canny. pptx
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0021. Edge Detection with Sobel Algorithm. ppt
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0022. Edge Enhancement. ppt
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0024. Convolution low pass filtering. ppt
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1001. B. Introduction to Fourier Transform, 1D continuous. pptx
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1001. C. Sampling and frequencies. ppt
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1002. A. Continuous Fourier Transform Notations and theorems. pptx
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1002. B. Sampling Fourier Transform. ppt
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1003. B. Shift Invariant Linear Systems. ppt
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1004. A. Orthogonal, Separable and Fourier Mathematics. ppt
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1006. 2D spectral transforms. Examples. ppt
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1007. Combination of Classifiers for Indoor Room Recognition. pptx
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1008. Introduction to Generalized Hough Transform.ppt
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1009 Generalized Hough Transform. 1. ppt
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1009. A. Generalized Hough Transform 2. ppt
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1010. Radon Transform. Easy principles. ppt
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1011. Medical Imaging - acquisition and Radon Transform. ppt
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1012. Biometry and terrorists. pdf