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Open	Shreyasha Joshi is a 2nd Year Student at the RV College of Engineering, Bangalore, India.

Poster Abstract

One of the important objectives of image processing is to interpret the content of image efficiently and finds the meaningful and significant information from it. The much awareness has been received from various researchers in the field of image interpretation. One of the most severe steps in image interpretation is to mine the edges information from the image appropriately. Edges are the fundamental features of the image and can be formed from the outlines of the object. Edge detection is generally used in image analysis and processing. There are several types of algorithms to detect the edges. The several edge detection techniques are Prewitt, Sobel, Canny, Roberts and Laplacian of Gaussian. It is experimentally observed that Canny edge detector is working well than others.

HPCC and Edge Detection

Edge detection is very important process where the accuracy must be maintained high. Because of that it needs to be trained with lot of images and Data. So Big data computations is just in case of edge detection. Therefore, this model comes under big data category and hence an efficient way of processing is inevitable here. So, the services and features offered by HPCC can help to create and the image processing process will be much smoother and hence can be applied for real life applications.

Why Edge Detection

Edge detection allows users to observe the features of an image for a significant change in the grey level. This texture indicating the end of one region in the image and the beginning of another.

Edge detection is a common process in the treatment of medical images, and it is a very useful task for object recognition of human organs. Edge detection also show where shadows fall in an image or any other distinct change in the intensity.

Presentation

In this Video Recording, Shreyasha provides a tour and explanation of his poster content.

Edge Detection in Image Processing

Click on the poster for a larger image. 

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