Price: $23.61
(as of Dec 24,2024 03:51:28 UTC – Details)
Publisher : O’Reilly Media; 1st edition (July 24, 2012)
Language : English
Paperback : 260 pages
ISBN-10 : 1449316549
ISBN-13 : 978-1449316549
Item Weight : 1.01 pounds
Dimensions : 7 x 0.68 x 9.19 inches
Customers say
Customers find the book provides useful code examples that explain concepts clearly. They find it easy to read and a great reference for those already familiar with Python.
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Computer vision is a rapidly growing field in the world of technology, with countless applications ranging from facial recognition to autonomous vehicles. If you’re interested in diving into the world of computer vision using Python, you’ve come to the right place. In this post, we’ll explore some essential tools and algorithms for analyzing images using Python.
1. OpenCV: OpenCV is an open-source computer vision library that provides a wide range of tools for image processing and analysis. With Python bindings, OpenCV allows you to perform tasks such as object detection, image segmentation, and feature extraction with ease.
2. scikit-image: scikit-image is another powerful library for image processing in Python. It provides a collection of algorithms for image analysis, including filters, feature detection, and image restoration. With scikit-image, you can easily manipulate and enhance images for your computer vision projects.
3. TensorFlow: TensorFlow is a popular machine learning library that includes tools for building deep learning models, including those for computer vision tasks. With TensorFlow, you can train neural networks to recognize objects in images, classify images, and perform other complex tasks.
4. Convolutional Neural Networks (CNNs): CNNs are a type of neural network that are particularly well-suited for image analysis tasks. By using layers of convolutional filters, CNNs can automatically learn features from images and make predictions based on those features. Implementing CNNs in Python with libraries like TensorFlow can help you achieve state-of-the-art results in image recognition tasks.
5. Feature extraction algorithms: In addition to deep learning techniques, there are many traditional algorithms for feature extraction in images. These include algorithms for edge detection, corner detection, and texture analysis. By combining these algorithms with machine learning models, you can create powerful image analysis pipelines in Python.
By mastering the tools and algorithms mentioned above, you’ll be well-equipped to start programming computer vision applications in Python. Whether you’re interested in building a face recognition system, developing a self-driving car, or analyzing satellite imagery, Python provides the tools you need to bring your vision to life. So grab your favorite IDE, fire up your Python interpreter, and start exploring the exciting world of computer vision.
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