Enroll Course: https://www.coursera.org/learn/deteccion-objetos
If you’re fascinated by computer vision and eager to learn how to detect and recognize objects in images, the ‘Detección de objetos’ course on Coursera is a fantastic opportunity. This course provides a comprehensive introduction to the fundamental principles of automatic object detection and recognition systems.
The course is structured into several weeks, each focusing on different aspects of object detection. In the first week, you will learn the basics of image formation and analysis, which are crucial for designing simple detectors based on pixel characteristics. Concepts like correlation and convolution are introduced, laying the groundwork for more complex detection systems.
Moving into the second week, the course delves into object classification. Here, you will explore the window classifier concept, which helps determine whether a candidate window contains the object of interest. The use of Local Binary Patterns (LBP) as an image descriptor and logistic regression for classification is particularly insightful.
The third week focuses on the detection phase, where potential candidates in the image are identified and analyzed using the classifier from the previous week. This segment emphasizes the importance of data preparation for training and evaluating the detector, along with objective performance evaluation methods.
In the fourth week, the course introduces a detector based on Histogram of Oriented Gradients (HOG) and Support Vector Machines (SVM). This week is particularly engaging as it showcases a practical application of the theoretical concepts learned earlier.
The fifth week covers a detector based on Haar features and Adaboost. This section is rich with information about integral images and how to train a classifier using Adaboost to select the best subset of Haar features. The concept of combining multiple classifiers in a cascade to implement a complete detection system is a highlight of this week.
Finally, the course wraps up with advanced techniques in the last week. It explores non-holistic models, domain adaptation methods, convolutional neural networks, and alternative candidate generation techniques. This week is essential for those looking to tackle more complex detection problems.
Overall, the ‘Detección de objetos’ course is well-structured, informative, and practical. It caters to both beginners and those with some prior knowledge of computer vision. The hands-on approach and clear explanations make it an excellent choice for anyone looking to deepen their understanding of object detection. I highly recommend this course to anyone interested in the field of computer vision, as it equips you with the necessary skills to embark on exciting projects in this rapidly evolving domain.
Enroll Course: https://www.coursera.org/learn/deteccion-objetos