Image Segmentation Techniques with Machine Learning

Image segmentation is the fundamental step to analyze images and extract data from them. This paper concentrates on the idea behind the basic methods used. Image segmentation is the process of partitioning a digital image into multiple segments(i. e. sets of pixels). The goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze. Segmentation is typically used to locate objects and boundaries(line, curve) in image. The Image segmentation is a set of segments that collectively cover the entire image. Each of pixels in a region are similar with respect to some characteristics colour, intensity or texture. The various segmentation techniques discussed in this paper.


I. INTRODUCTION
Image processing is one of the most important techniques which is used in many areas like Object detection, Diagnostic, Image analysis, Surgical Planning Robotics, Localization of tumors, Trace objects in satellite images (roads, forests, etc. ), Traffic control systems, face recognition, Image Understanding Model, etc. Image Segmentation is the process of assigning a label to every pixel in an image. Is a sub field of Artificial intelligence and computer vision. It is typically used to locate objects and boundaries in images. We might have wondered how fast and efficiently our brain is trained to identify and classify what our eyes perceive. Somehow our brain is trained in a way to analyze everything at a granular level. This helps us distinguish an apple from a bunch of oranges.
Computer vision is a field of computer science that enables computers to identify and process objects in videos and images just the way we humans do.
A. Object detection and image localization: When there is a single object present in an image, we use image localization technique to draw a bounding box around that object. In the case of object detection, it provides labels along with the bounding boxes; hence we can predict the location as well as the class to which each object belongs.

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B. Types of image segmentation methods: 1. Semantic Segmentation : Semantic Segmentation is the process of segmenting the image pixels into their respective classes and is given the same label/color value. 2. Instance Segmentation : Instance segmentation is more thorough and usually comes into picture when dealing with multiple objects. Every pixel of each object of a class is given a separate label/color value.

Segmentation by Edge Detection :
In the image segmentation process, the basic step is edge detection. It divides an image into an object and its background. Edge detection divides the image by observing the change in intensity or pixels of an image.

Segmentation by Thresholding :
One of the simplest approaches to segment an image is based on the intensity levels and is called as threshold based 3. Segmentation by Region based : In this technique pixels that are related to the same object are grouped for segmentation. The thresholding technique is bound with region based segmentation. The area that is detected for segmentation should be closed.

Segmentation by Feature based Clustering:
Clustering a process of organizing the groups based on its attributes. A cluster usually contains a group of similar pixels that belongs to a specific region and is different from other regions. Image Segmentation Applications: 1. Self-driving cars:ification: Image segmentation can be used in self-driving cars for giving easy distinctions between various objects. Be it traffic signals, signboards, humans, and cars. It can help the driving instruction algorithm to better assess the surroundings before generating the next instruction.

Circuit Board Defect Detection
A company has to bear the responsibility of defective devices. If a camera backed with an Image Segmentation model keeps scanning for defects produced in the final product, a lot of money and time can be saved in fixing a defective device.
3. Face detection: Nowadays, we have observed that the majority of cameras in phones support portrait mode. Portrait mode is technically an outcome of Image Segmentation. Apart from this, security surveillance will be much more effective when the faces are distinguishable from noisy objects.

III. CONCLUSION
Image processing is the general issue in today's era, when we work with computer vision. It is in itself, a broad view to be considered. In order to process the image, we need to segment it so that it would become easier for the computer to understand. Image segmentation is the process of segmenting the image into various segments that could be used for further applications such as: Image understanding model, Robotics, Image analysis, Medical diagnosis, etc. Image segmentation is the process of partitioning an image into multiple segments, so as to change the representation of an image into something that is more meaningful and easier to analyze.