Image Processing and its Purposes
Image processing is the process of manipulating images electronically or digitally. It involves adding, enhancing, extracting and filtering image data. Image processing can be done in a variety of ways using a computer system, including digital cameras and scanners. The most basic form of image processing is simply viewing an image on a screen or in print, but there are many more powerful processes that involve changing the properties of individual pixels in order to enhance certain features in an image. These changes include removing noise from images, detecting objects in scenes and removing them from scenes entirely by masking them out with another object.
Why is it important?
Image Processing Applications plays an important role in all aspects of daily life. For example, image processing is used for medical diagnostics, to monitor crops and equipment performance on farms, to inspect various products at factories, to look for flaws in printed materials such as documents and printed circuit boards (PCBs), to evaluate geological formations before blasting operations are started
Computer Vision is a branch of Artificial Intelligence that concerns how computers can be made to gain high-level understanding from digital images or videos. In computer vision, we try to create systems that can mimic human visual perception as closely as possible. This involves reasoning about the contents of an image and how it relates to other data, such as text labels or geometric maps.
Image processing deals with all the transformations we apply on images in order for them to be more sensibly analyzed by a computer vision system. It typically includes filtering, segmentation, shape recognition and much more. Image processing algorithms are designed in order to extract information contained in an image which is relevant for whatever task you would like your algorithm perform. They are built upon basic mathematical operations such as convolutions and transformations.
Some common image processing tasks include: Image filtering, edge detection, image segmentation, feature extraction, object recognition and others. Each of these techniques is designed for a particular purpose and they normally depend on each other in order to successfully perform the desired task. We may decide to use filter operations for classification purposes (e.g.: like key point recognition), or we might apply some statistical operations on edges in order to obtain higher level information about our objects (e.g.: we might want to classify certain objects based on their shape). It's up to us!
Image examples
Note that almost any algorithm can be used in either an image processing or computer vision context - the difference only lies in the intent and focus of the algorithm.
For example, we might be interested in designing an algorithm which classifies images by their edges. This is a basic image filtering step which does not give priority to object recognition - it just helps us find interesting areas of an image that we can then process more intelligently with other algorithms such as curvature-based detectors. In this case you would apply your algorithm on image data without processing or handling any spatial information (e.g.: what is "inside" and what is "outside" the edge). We will refer to these types of processing steps as "Image Filtering".
On the other hand, if we need our computer vision system to recognize circles within an image using histogram-based blob detection, we would apply our algorithm to image data which includes spatial information. In this case we would be doing "Image Segmentation".
We can also classify objects and perform object detection based on their statistical properties rather than their shape. For example, we might want to find all the points in an image that have a gray level above or equal to 200 (on a 0-255 scale). Note that this step would not give us any information about what kind of object these points belong to - so it is more helpful for the processing stages that come after this one. We will refer to such steps as "Feature Extraction" or "Morphological Processing". Visit Us
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