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Mar 25, 2026
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AAIT 218 - Computer Vision
PREREQUISITES: AAIT 212 - Machine Learning
CREDIT HOURS MIN: 3
LECTURE HOURS MIN: 3
TOTAL CONTACT HOURS MIN: 48
This course introduces the foundational concepts and techniques that enable computers to interpret and analyze visual information from images and video. Students learn how visual data is represented and processed through operations such as color space conversion, filtering, and feature extraction. Core topics include image classification, object detection, segmentation, and the design and application of convolutional neural networks (CNNs). The course also explores transfer learning, data augmentation, and the use of pre-trained vision models in open-source and cloud-based environments. Students practice model evaluation using metrics such as accuracy, precision, and Intersection over Union (IoU), and examine the ethical implications of visual AI. Emphasis is placed on practical implementation, performance analysis, and responsible use of visual data in modern AI systems.
MAJOR COURSE LEARNING OBJECTIVES: Upon successful completion of this course the student will be expected to:
- Identify the core tasks and industry applications of computer vision.
- Describe how digital images are represented, transformed, and processed as data for analysis.
- Apply fundamental image processing techniques such as filtering, thresholding, and feature extraction to prepare visual data for model input.
- Explain the architecture and function of convolutional neural networks (CNNs) and their role in visual recognition tasks.
- Demonstrate object detection and image classification using pre-trained or transfer-learned vision models.
- Analyze model performance using metrics such as accuracy, precision, recall, and Intersection over Union (IoU).
- Evaluate methods for improving performance through data augmentation, fine-tuning, or model optimization.
- Assess ethical, legal, and privacy implications of computer vision technologies, including bias and responsible use of visual data.
- Summarize emerging trends in computer vision, such as vision transformers (ViT) and multimodal AI, and their impact on future computing.
COURSE CONTENT: Topical areas of study include -
- Computer Vision Fundamentals
- Image Representation and Processing
- Datasets and Feature Extraction
- Convolutional Neural Networks (CNNs)
- Image Classification and Object Detection
- Transfer Learning
- Model Evaluation
- Deployment and Integration
- Ethics of Visual Data
- Emerging Trends in Visual AI
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