If you are looking to master computer vision and image processing with Python, then the Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD is the course for you. This course offers a comprehensive guide to the fundamental concepts of image processing, focusing on both face and object detection. With over 20 years of experience, you can trust that the course review content writer has curated accurate information for you in this review.
What to Expect in the Course
During the course, you will learn about computer vision theory, pixel intensity values, convolution, kernels (filters), and edge detection in computer vision. Further, you will delve into computer vision approaches for lane detection, including Canny's algorithm and Hough transform.
You will also learn about the Viola-Jones approach and the sliding-windows approach for face detection. The course will also cover advanced approaches such as the Histogram of Oriented Gradients (HOG) algorithm, Convolution Neural Networks (CNNs), and Region based Convolutional Neural Networks (C-RNNs) among others.
In addition to these foundational topics, this course will teach you more advanced techniques, including the popular object detection algorithms: You Only Look Once (YOLO) and Single Shot MultiBox Detector(SDD). The course will further explore the main idea behind the SSD algorithm and its implementation on VGG16 and MobileNet architectures for single-shot object detection.
The Many Applications of Computer Vision
Computer vision has various applications across different fields including software engineering, crime investigation, and autonomous vehicles. For instance, self-driving cars rely heavily on computer vision techniques to detect obstacles and pedestrians.
With the increasing availability of graphical processing units (GPUs) and deep learning in recent years, it has become possible to run these algorithms in real-time videos. Thus, the course will not only teach you the theory behind face and object detection, but you will learn the practical application of these techniques for real-time videos.
Coverage of Course Topics
The Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD course covers a range of topics, starting with the fundamental concepts of image processing and leading up to advanced object detection algorithms. The course is structured in seven sections:
Section 1 - Image Processing Fundamentals
This section lays the foundation for the rest of the course. Here, you will learn about the theoretical background of computer vision, including pixel intensity values, convolution, kernels (filters), and edge detection. With these concepts, you can then move onto more advanced techniques for the detection of lanes, faces, and objects.
Section 2 - Serf-Driving Cars and Lane Detection
In this section, you will explore how computer vision techniques can be applied in lane detection. You will learn about Canny's algorithm and the Hough transform as well as how to use these methods to find lines based on pixel intensities.
Section 3 - Face Detection with Viola-Jones Algorithm
This section covers the Viola-Jones approach and uses the sliding-windows approach for face detection. You will learn how to detect faces in images and videos.
Section 4 - Histogram of Oriented Gradients (HOG) Algorithm
Here, you will learn how to outperform the Viola-Jones algorithm with better techniques. You will learn how to detect gradients and edges in an image, construct histograms of oriented gradients, and use support vector machines (SVMs) as underlying machine learning algorithms.
Section 5 - Convolution Neural Networks (CNNs) Based Approaches
In this section, you will explore problems with the sliding-windows approach and how region proposals and selective search algorithms help mitigate those problems. You will learn about Region based Convolutional Neural Networks (C-RNNs), Fast C-RNNs, and Faster C-RNNs.
Section 6 - You Only Look Once (YOLO) Object Detection Algorithm
This section provides an in-depth look at the YOLO algorithm, including how to construct bounding boxes. You will learn how to detect objects in an image with a single look and use intersection of union (IOU) algorithm, and non-max suppression for the most relevant bounding box.
Section 7 - Single Shot MultiBox Detector (SSD) Object Detection Algorithm
In the final section, you will explore the main idea behind the SSD algorithm, including constructing anchor boxes. VGG16 and MobileNet architectures will also be explored, and you will learn how to implement SSD with real-time videos.
Course Reviews
Learners who have taken this course have praised it for its comprehensive content and easy-to-follow approach. The course rating aggregate currently stands at 3.96231 with 187 course reviews available.
One reviewer commented, "This course is great for beginners in computer vision. The instructor explains everything step-by-step, and I felt confident in applying the concepts I learned in this course."
Another reviewer noted, "I appreciated how the course covered both the theoretical and practical aspects of computer vision, making it easier for me to understand the materials."
If you are looking to master computer vision and image processing with Python, then the Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD course is well worth considering. The course offers a broad range of topics, including both fundamental and advanced approaches, to provide learners with a comprehensive guide in computer vision.
