Author: Suparno Roy
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Reducing Computer Vision Errors with Precision: The Role of Polygon Annotation
Introduction Computer vision is transforming the way industries operate — from autonomous vehicles navigating urban streets to AI-powered diagnostics revolutionizing healthcare. However, even the most advanced computer vision AI models face a persistent challenge- False Positive. False positives — instances where AI detects something that isn’t there — can have serious consequences, from triggering unnecessary…
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How Human-in-the-Loop Enhances Accuracy in Computer Vision Systems
In the race to build smarter AI, one truth remains: AI is only as good as the data that trains it. For computer vision (CV) systems, where perception fuels decision-making, data accuracy isn’t a bonus—it’s a baseline. And that’s where Human-in-the-Loop (HITL) becomes indispensable. While automation accelerates AI development, the strategic inclusion of human expertise…
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Enhancing AI with Depth Perception!!
In the rapidly evolving domain of artificial intelligence (AI), the pursuit of visual understanding is entering a new dimension. Traditional computer vision systems have long relied on 2D bounding boxes to identify and locate objects in imagery. However, these approaches are limited in their ability to capture the richness of our 3D world. Today, industries…
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HITL – The Secret Sauce for Your Computer Vision Models
Why the Future of Vision AI Belongs to Human-in-the-Loop Systems In the fast-evolving world of AI, computer vision models have become indispensable. From autonomous vehicles and medical diagnostics to retail analytics and industrial inspection, they’re reshaping how machines perceive and respond to the visual world. But as the stakes grow, so do the expectations for…
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Semantic Segmentation: The Cornerstone of Visual AI!!
In today’s AI-driven world, the ability to perceive and interpret complex visual scenes is no longer a futuristic ambition—it’s a foundational need. Whether it’s autonomous vehicles navigating chaotic city roads or medical systems pinpointing anomalies in scans, the demand for precise, contextual visual understanding is rapidly growing. At the heart of this capability lies a…
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Data Annotation and Labelling – How HITL Enhances Accuracy in AI Model Development
Artificial Intelligence (AI) is transforming industries at an unprecedented pace, but the accuracy of AI models heavily depends on the quality of data annotation and labelling. Poor data quality can severely compromise AI model performance, resulting in flawed predictions, biases, and operational failures. Recent Studies reveal that nearly 85% of AI projects fail due to…
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The Importance of Human-in-the-Loop (HITL) Annotation in AI Training
AI models are only as good as the data they learn from! constant—data quality matters. While automation is powerful, AI alone is not enough to ensure accurate and unbiased results. This is where Human-in-the-Loop (HITL) annotation comes in. By integrating human expertise with AI, HITL enhances accuracy, minimizes errors, ensuring ethical decision-making. Whether self-driving cars,…