Computer Vision : Chapter 1

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

Summary

This article introduces Computer Vision and Convolutional Neural Networks (CNNs), detailing the mathematical concept of convolution, its application in image processing for feature extraction, and the historical development of neural networks leading to modern CNNs. It covers early methods like smoothing/sharpening filters, edge detection (Roberts Cross, Sobel, Prewitt, Canny), corner/blob detection, and Gabor filters. The evolution of neural networks is traced from the Perceptron to Multilayer Perceptrons (MLPs) with backpropagation, and then to deep learning. Key milestones include Fukushima's Neocognitron, Yann LeCun's LeNet series (LeNet-1 to LeNet-5, achieving 98.76% accuracy on MNIST), and the pivotal AlexNet (2012) which achieved 15.3% Top-5 error on ImageNet LSVRC-2012. AlexNet's success was attributed to ReLU activation, multi-GPU training, Local Response Normalization, data augmentation, and Dropout.

Key takeaway

For Machine Learning Engineers developing computer vision systems, understanding the foundational principles of CNNs, from convolution to hierarchical feature learning, is crucial. You should prioritize architectures that leverage local receptive fields and weight sharing for efficiency and translation invariance. Consider modern techniques like ReLU and data augmentation to optimize training and generalization, especially for large datasets.

Key insights

Convolutional Neural Networks evolved from biological vision models and mathematical convolution to automatically learn hierarchical image features.

Principles

• Local receptive fields enable feature learning.
• Weight sharing ensures translation invariance.
• Hierarchical processing builds complex features.

Method

Image recognition can use Gabor filters and Sobel edge detection to generate feature maps. Statistical measurements (mean, std, energy, skewness, kurtosis) are computed from these maps, forming a feature vector for a machine learning model.

In practice

• Apply Gabor/Sobel filters for texture/edge features.
• Implement ReLU to accelerate CNN training.
• Employ data augmentation to reduce overfitting.

Topics

• Computer Vision
• Convolutional Neural Networks
• Deep Learning History
• Image Feature Extraction
• AlexNet Architecture
• ImageNet Dataset

Code references

• JiaenSuen/VisionLab

Best for: AI Scientist, Machine Learning Engineer, Computer Vision Engineer

Related on AIssential

• Computer Vision: Chapter 1 (Traditional Chinese) — CNNs leverage hierarchical feature extraction and convolution to enable machines to understand visual data, evolving from biological inspiration and early neural networks.
• Deep Model for Vision — Deep learning overcomes classical computer vision limitations, enabling scalable and adaptable solutions across diverse visual tasks.
• From Semantics to Pixels: Coarse-to-Fine Masked Autoencoders for Hierarchical Visual Understanding — C2FMAE learns hierarchical visual representations by integrating coarse-to-fine masking and cascaded decoding.
• How CNNs Replace Millions of Weights — Convolutional Neural Networks efficiently detect patterns across images by employing small, shared filters instead of numerous fixed-location weights.
• Python for Data Science & AI · Blog 18 of 20 — CNNs for Image Classification — Convolutional Neural Networks efficiently classify images by learning hierarchical features through local, reusable filters and weight sharing.
• the convolution operation #datascience #machinelearning #neuralnetworks #computervision — Convolution uses a sliding kernel to extract features from image patches via multiplication and summation.

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