The virtual course "Object Detection - Virtual Course - Coursera" is a course with different contents and offers video classes from . Explore its essential features, and click the orange button for detailed information on the Coursera e-Learning platform.
Are you interested in computer vision? Would you like to know what methods you can use to detect and recognize objects in an image? In this course you will be introduced to the basic principles of any automatic system for the detection and recognition of objects in images. Throughout the course we will analyze different representation and classification methods that will allow you to address increasingly complex application cases. The course content is structured from a basic object detection and recognition scheme that serves as a guide to introduce both the different methods of extracting features and image representation, as well as different alternatives to classify an image and locate all instances. of an object in the image. The syllabus includes basic concepts of image formation, convolution and its application to contour detection, region characteristics, image descriptors (Local Binary Pattern, Histogram of Oriented Gradients, Haar characteristics) and various classification methods (classifier linear, Support Vector Machine, Adaboost, Random Forest, Convolutional Neural Network). Completing the course will allow you to: • Design, from a common basic scheme, adapted solutions for different problems of detection and recognition of objects in an image, • Know the main techniques for the description and classification of an image, • Know the tools that allow the development of real object detection and recognition applications, so that you are able to develop your own object detection and recognition systems in multiple applications. The course is aimed at both university students in a degree related to computer science, engineering or mathematics, as well as other students with programming knowledge, interested in learning how to use computer vision techniques to extract information from images. START: December 1, 2015
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