Computer Vision: Chapter 1 (Traditional Chinese)

2026-06-20 · Source: Deep Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Intermediate, extended

Summary

Convolutional Neural Networks (CNNs) are foundational to modern computer vision, enabling machines to "see" and understand images for applications like ADAS and OCR. The article details convolution's mathematical basis and its practical application as an image filter for feature extraction, covering techniques like smoothing, sharpening, and edge detection (e.g., Roberts Cross, Sobel, Gabor filters). Historically, CNNs evolved from Frank Rosenblatt's 1958 Perceptron, overcoming limitations with Backpropagation and drawing inspiration from biological vision (Hubel & Wiesel, Fukushima's Neocognitron). Yann LeCun's LeNet-5 (1998) for MNIST introduced local receptive fields and weight sharing. The resurgence of deep learning (Hinton, 2006) was propelled by GPUs, ReLU activation, and large datasets like ImageNet. AlexNet (2012) achieved a breakthrough on ImageNet with a 15.3% Top-5 error, utilizing multi-GPU training, data augmentation, and Dropout, marking the start of modern deep learning in computer vision.

Key takeaway

For Machine Learning Engineers designing computer vision systems, understanding the historical evolution and core principles of CNNs is crucial. You should recognize how foundational concepts like convolution, local receptive fields, and weight sharing enable robust image understanding. Leverage insights from early networks like LeNet and AlexNet regarding activation functions (ReLU), regularization (Dropout, data augmentation), and hardware utilization (GPUs) to build more efficient and accurate deep learning models for complex visual tasks.

Key insights

CNNs leverage hierarchical feature extraction and convolution to enable machines to understand visual data, evolving from biological inspiration and early neural networks.

Principles

• Local receptive fields enable focused feature detection.
• Weight sharing provides translation invariance.
• Hierarchical feature learning builds complex representations.

Method

To extract features for traditional image classification, resize and grayscale images, apply Gabor and Sobel filters, then compute statistical characteristics (mean, std, energy, skewness, kurtosis) from the resulting feature maps for input into a machine learning model.

In practice

• Apply Gabor and Sobel filters for low-level feature extraction.
• Calculate statistical metrics (mean, std, energy) from feature maps.
• Implement LeNet-5 with PyTorch for digit recognition.

Topics

• Convolutional Neural Networks
• Computer Vision History
• Image Feature Extraction
• Deep Learning Architectures
• AlexNet
• Backpropagation

Code references

• JiaenSuen/VisionLab

Best for: AI Scientist, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.