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An Introduction to Computer Vision

In this free online course, learn the basics of image formation, image geometry and feature detection in computer vision

Publisher: NPTEL
As we open up our data to further types of inputs other than traditional text, computer vision provides machines with the ability to perceive what is being reviewed. This free online course facilitates the process of analysing image data, including object recognition and image classification, allowing us to examine our unstructured data more closely. Start learning about the various theoretical and practical aspects of computing with images.
An Introduction to Computer Vision
  • Duration

    3-4 Hours
  • Students

  • Accreditation






View course modules


This free online course illustrates Computer Vision’s significance in extracting higher-level abstractions or knowledge of the world from images. It begins with the historical review of computer vision to ascertain its emergence and evolution over the last few decades. You will explore the initial forays of computer vision and the affords that contributed to understanding images and the emergence of the deep learning era. In addition provenance of various designs and the essential enhancements in the field of computer vision over the decades are highlighted. You will be taught about the image formation process that produced a particular image given a set of lighting conditions, scene geometry, surface properties, and camera optics. Alongside this, how the digital camera model accounts for the essential effects such as exposure, nonlinear mappings, sampling and aliasing, and noise are also discussed.

Next, representing the image by breaking it down into tiny forms of pixels is explained. This will include the use of image processing techniques to preprocess the image and convert it into a form suitable for further analysis and the various image processing operators that map pixel values from one image to another. Further, you will discover the process of applying filters to an image for modifying or enhancing the features of the image. You will explore how the histogram equalization uses a collection of pixel values in the vicinity of a given pixel to determine its final output value for performing local tone adjustment of the image. Besides this, the image enhancement process in which the transfer function of the filter modifies part of the signal frequency spectrum, and determining the frequency characteristics of various filters using Fourier analysis, is also highlighted.

Finally, you will discover how using the Fast Fourier transform performs large-kernel convolutions in time that is independent of the kernel’s size. This includes the process of decomposing an image into its sine and cosine components using Fourier transforms. The significance of sampling and aliasing in the processing of images is explained. You will discover how the sampling rate determines the digitised image’s spatial resolution and the occurrence of aliasing when the sampling rate is not high enough to capture the amount of detail in the image. Lastly, the method of increasing and decreasing the resolution of an image using the interpolation and decimation filters are discussed. An Introduction to Computer Vision is an informative course that will interest those studying computer science or those interested in these topics. Why wait? Sign up today and start learning about this prominent computer vision field and alter your perception of how you see the world.

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