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Computer Vision: Tools & Algorithms for Analyzing Images
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Sin Stock
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Peso con empaque
0.87 kg
0.87 kg
Devolución
No
No
Condición
Nuevo
Nuevo
Producto de
Amazon
Amazon
Sobre este producto
- Core Concepts: The book starts with essential topics such as digital image representation, pixel-level operations, image formats, and color spaces like RGB, HSV, and YUV.
- Image Processing Techniques: Readers are introduced to important image processing methods such as convolution, correlation, edge detection (Sobel, Canny), Gaussian smoothing, and thresholding.
- Feature Extraction: The book dives into keypoint detection methods, feature descriptors (like SIFT, ORB), and feature matching techniques, essential for tasks like image stitching, recognition, and object tracking.
- Segmentation and Object Detection: Different techniques for image segmentation (region-based, edge-based) and object detection, including traditional methods like HOG and Viola-Jones, as well as deep learning approaches like YOLO, SSD, and R-CNN, are covered in depth.
- Deep Learning in Computer Vision: A substantial portion is dedicated to convolutional neural networks (CNNs) and their application in computer vision tasks. Architectures like ResNet, DenseNet, and more advanced systems like Vision Transformers (ViT) are explained with examples.
- 3D Vision: The book explores 3D reconstruction techniques, depth estimation, and the use of point clouds for 3D object detection and tracking.
- Practical Applications: Real-world applications such as medical image analysis, autonomous driving, and augmented reality (AR/VR) are discussed, showing the impact of computer vision in various industries.
- Tools and Libraries: The book includes practical tutorials using popular libraries like OpenCV, TensorFlow, Keras, and PyTorch, guiding readers in building and training their own vision models.
- Emerging Trends: Topics like explainable AI, adversarial robustness, and continual learning are also introduced, giving readers insight into the future challenges and innovations in computer vision.
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