![]() OpenCV provides us with inbuilt functions to detect these contours in images. K-means Algorithm visualization ii) Contour DetectionĬontours can be simply defined as curves/polygons formed by joining the pixels that are grouped together according to intensity or color values. K-means algorithm can be used to find subgroups in the image and assign the image pixel to that subgroup which results in image segmentation. K-means is a clustering algorithm that is used to group data points into clusters such that data points lying in the same group are very similar to each other in characteristics. In this section, we will cover a few pre-requisite concepts in brief that will be useful to understand the techniques of image segmentation in Python in this article. A neural network is made up of a vast number of linked nodes, each with its own weight. This approach is widely used in segmenting medical images and separating them from the background. Neural network approach – For the goal of decision making, neural network-based segmentation algorithms replicate the learning techniques of the human brain.Example: Kmeans, Colour detection/classification. Based on established criteria, they divide the picture into a group of clusters with comparable features. All of them split the image into sections with comparable pixel counts. Thresholding, area expansion, and region splitting and merging are all included in this methodology. Similarity detection – A method of segmenting a picture into sections based on resemblance.Examples: Histogram filtering and contour detection. Discontinuity in edges generated due to intensity is recognized and used to establish area borders. Discontinuity detection – This is a method of segmenting a picture into areas based on discontinuity.Global segmentation – It is concerned with segmenting the entire image.Local segmentation – It is concerned with a specific area or region of the image.There are two forms of image segmentation: Image segmentation is an image processing task in which the image is segmented or partitioned into multiple regions such that the pixels in the same region share common characteristics. And then we will go through different techniques and implementations one by one. We will first explain what is image processing and cover some prerequisite concepts. In this article, we will show you how to do image segmentation in OpenCV Python by using multiple techniques. 8.2 iii) Create Mask by Detecting Color.7.2 ii) Apply Otsu Thresholding on Image.Image Segmentation using Otsu Thresholding 6.4 iii) Detecting Contours To Create Mask.Image Segmentation using Contour Detection 5.5 v) Image Segmentation Results for Different Values of K.5.4 iv) Applying K-Means for Image Segmentation.2.1 Types of Image Segmentation Approaches.
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