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Detection of edge is a terminology in image processing and computer vision particularly in the areas of feature detection and extraction to refer to the algorithms which aims at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. Edge is a basic feature of image. The image edges include rich information that is very significant for obtaining the image characteristics by object recognition. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. So, edge detection is a vital step in image analysis and it is the key of solving many complex problems. This paper, describes edge detection algorithms for image segmentation using various computing approaches which have got great fruits. Experimental results prove that Canny operator is better than Prewitt and Sobel for the selected image. Subjective and Objective methods are used to evaluate the different edge operators. The performance of Canny, Sobel and Prewitt Edge Detection are evaluated for detection of edges in digital images.

The virtual classroom environment is created using virtual reality that enables multiple students to enter as if in a real class but with better learning environment. Conventional learning is currently limited in the current model of textbook teaching. An interactive and visual environment provided for learning enhances the rate at which the student grasps concepts. Even though many modern online teaching methods are available today, it is not possible to check whether a student is paying attention or not. Technology is evolving at a very fast rate, and this research is an apt integration of two modern technologies: machine learning and virtual reality, so as to increase the quality of education for students. A shared VR environment, optimised for learning, will be created. Students can wear a head-mounted display and select an avatar for themselves, which will be seen by other students and teachers. The VR environment is created using Unity3D software. Students will also have to wear an EEG scanner on their heads. The output of this scanner will be fed to the machine learning subpart. Neural networks are used to identify whether the student is paying attention or not. If a student is not paying attention, the teacher will be informed about it, with a message near the student's avatar. It has many advantages over traditional learning techniques, like usage of multiple senses and inclusivity for differently abled students.

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Medical imaging is the technique that is used to produce images of the human body or parts for clinical purpose. The CT image provides thorough information of structure of lungs, which could be used for better surgical preparation for treating Lung Cancer features. This work proposes a method for segmentation of lungs from the given CT images. Here a curve evolutional framework for segmentation is used. The flow fields driving the curves are based on the distributions of features in the inner and outer regions bounded by the curves. The segmentation method is automatic and shows good result.