Teaching data science to artists. They have various artistic skills but are newbies to data science. Teaching them Data science is a tough task, let alone teaching them data analytics perspective of images.
2. binary representation of visual
information, such as drawings,
pictures, graphs, logos, or
individual video frames
Header + Data
Header – Meta data (Name, type
(extension), Color depth, color
Data – The pixels in colors as set
by the palette
5. Image enhancement: improving the visual quality of an image, such as
increasing contrast, reducing noise, and removing artifacts.
Image restoration: removing degradation from an image, such as
blurring, noise, and distortion.
Image segmentation: dividing an image into regions or segments, each
of which corresponds to a specific object or feature in the image.
Image representation and description: representing an image in a way
that can be analyzed and manipulated by a computer and describing the
features of an image in a compact and meaningful way.
Image analysis: using algorithms and mathematical models to extract
information from an image, such as recognizing objects, detecting
patterns, and quantifying features.
Image synthesis and compression: generating new images or
compressing existing images to reduce storage and transmission
Digital image processing is widely used in a variety of applications,
including medical imaging, remote sensing, computer vision, and