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Back to course 'Computer Vision Basics'
  • 📚Chapter : 13-Aliasing

    This chapter series introduces Fourier Transform, explaining how signals are analyzed in the frequency domain, the role of convolution, and the efficiency of FFT. It also covers practical applications like smoothing, blurring, and filtering, along with key properties and common Fourier pairs to understand signal behavior.

      • > Table of Content

        1-Introduction |Video|Note|

        2 - Fourier Transform Sampling Pairs |Video|Note|

        3- Sampling and Reconstruction|Video|Note|medium|Code|

        4-Sampling in Digital Audio|Video|Note|

        5- Undersampling|Video 1|Note

        6-Aliasing|Video 1|Note

        7-Antialiasing|Video 1|Note

        8- Impulse Train and Bed of Nails|Video 1|Note

        9-Sampling Low Frequency|Video 1|Note

        10-Sampling High Frequency Signal|Video 1|Note

        11-Aliasing in Images|Video 1|Note

        12-Campbell-Robson Contrast Sensitivity|Video 1|Note

        13 - Image Compression|Video 1|Note

          • Notes

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