Applied Computer Vision: A Strategic Introduction
Description
An applied introductory workshop designed to provide a comprehensive understanding of Computer Vision (CV) capabilities, limitations, and core applications within modern products and organizational processes.
This workshop focuses on conceptual mastery, use-case analysis, integration with existing systems, requirements characterization, and informed decision-making regarding CV implementation.
Intended audience
• Product and Project Managers
• Innovation and Applied AI Specialists
• UX / UI Designers
• Technical Decision-Makers
Topics
Introduction to Computer Vision
• Defining CV: Scope and boundaries.
• The distinction between Human Vision, Computer Vision, and Generative AI.
• Common enterprise and product-driven use cases.
Core Problem Domains in CV
• Image Classification
• Object Detection
• Segmentation
• Image Reconstruction (High-level overview).
Applied Object Detection
• System Inputs and Outputs: Bounding Boxes, Confidence Scores, and Thresholds.
• Common pitfalls and their impact on User Experience (UX).
• Real-world constraints and environmental limitations
Leveraging Existing Ecosystems
• Working with Pre-trained Models and APIs.
• Trade-offs in Cost, Accuracy, and Scalability.
• On-device vs. Cloud processing.
• Output analysis and business implications
Advanced Techniques & Implementation Strategy
Segmentation – Precision vs. Usability
• Semantic vs. Instance Segmentation.
• Masks vs. Bounding Boxes.
• Common applications and UX considerations
Understanding Image Reconstruction
• Super-Resolution, Denoising, and Deblurring.
• Inpainting techniques.
• Distinguishing between enhancement, completion, and data hallucination
Risks, Ethics, and Regulation
• Data Bias and algorithmic fairness.
• Evaluating False Positives vs. False Negatives in a business context.
• Privacy concerns and visual identification ethics.
Capstone Exercise
• Real-world Use-Case Analysis.
• Evaluating CV suitability for specific problem statements.
• Identifying project risks and defining Key Performance Indicators (KPIs).
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