image_segmentation's Introduction

image_segmentation

impimentation of different segmentation techinques like Region-Based Segmentation, Edge Detection Segmentation, Segmentation based on Clustering, etc in order to understand how image segmentation work.

Algorithm	Description	Advantages	Limitations
Region-Based Segmentation	Separates the objects into different regions based on some threshold value(s).	a. Simple calculations b. Fast operation speed c. When the object and background have high contrast, this method performs really well	When there is no significant grayscale difference or an overlap of the grayscale pixel values, it becomes very difficult to get accurate segments.
Edge Detection Segmentation	Makes use of discontinuous local features of an image to detect edges and hence define a boundary of the object.	It is good for images having better contrast between objects.	Not suitable when there are too many edges in the image and if there is less contrast between objects.
Segmentation based on Clustering	Divides the pixels of the image into homogeneous clusters.	Works really well on small datasets and generates excellent clusters.	a. Computation time is too large and expensive. b. k-means is a distance-based algorithm. It is not suitable for clustering non-convex clusters.
Mask R-CNN	Gives three outputs for each object in the image: its class, bounding box coordinates, and object mask	a. Simple, flexible and general approach b. It is also the current state-of-the-art for image segmentation	High training time

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