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Showing posts from August, 2024

🚀 From Static Models to Living Systems: How Agentic AI is Redefining Enterprise Workflows

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For years, AI has been treated like a calculator with a very advanced brain: you give it input, it gives you output. Useful? Yes. Transformative? Not quite. What’s shifting today is the rise of Agentic AI — AI that doesn’t just respond but acts , remembers , adapts , and coordinates . Think less about “getting an answer” and more about “delegating a process.” And here’s the real unlock: agentic systems don’t replace humans, they reshape how work gets done by connecting intelligence with action. 🏢 The Enterprise Pain Points Agentic AI Can Solve Decision Bottlenecks : Reports are generated, but decisions still stall in inboxes. Tool Fragmentation : Finance in Excel, sales in Salesforce, ops in Jira — nothing “talks.” Knowledge Drain : Institutional know-how gets lost when people leave. Process Rigidity : Static rules can’t flex when markets shift overnight. ⚡ Where Agentic AI Shines Instead of simply suggesting, agentic systems execute : Finance : An AI agent d...
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Advanced Image Contrast Enhancement Techniques: Exploring HE, AHE, CLAHE, and LCCLAHE

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Histogram Equalization (HE) Histogram Equalization (HE) is a fundamental image processing technique used to improve the contrast of an image. The method works by redistributing the intensity values of pixels so that they cover the entire range of possible values more evenly. This redistribution results in a balanced distribution of intensity levels across the entire image histogram, thereby enhancing the contrast of the image. Let’s break down the steps involved in HE: Intensity Levels: Consider an image with L L  intensity levels, ranging from 0 0  to L − 1 L-1 . Probability Density Function (PDF): The probability of occurrence of each intensity level i i  is given by the probability density function (PDF): p ( i ) = n i n p(i) = \frac{n_i}{n} ​ ​ where n i n_i  is the number of pixels with intensity i i , and n n  is the total number of pixels. Cumulative Distribution Function (CDF): The cumulative distribution function (CDF) is calculated as: c ( i ) = ∑ j =...
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