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Hidden Markov Model (HMM)

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Hidden Markov Model (HMM)  VIDEO LINK:  https://youtu.be/YIGCWNG8BIA A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. Video Chapters: HMM in Artificial Intelligence 00:00 Introduction 00:31 Statistical Model 00:54 HMM Examples 02:30 HMM 03:10 HMM Components 05:23 Viterbi Algorithm 06:23 HMM Applications 06:38 HMM Problems 07:28 HMM in Handwriting Recognition 11:20 Conclusion  HMM COMPONENTS A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. An HMM consists of states, observations, transition probabilities, emission probabilities, and initial probabilities. It is commonly used in a...

Nicholl - Lee Nicholl (NLN) Line Clipping in Computer Graphics

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 In the Cohen-Sutherland method, multiple intersections may be calculated along the path of a single line before an intersection on the clipping rectangle or line is completely rejected.  In Nicholl - Lee Nicholl Line Clipping, Extra intersection calculations are eliminated.  ADVANTAGE OF Nicholl - Lee Nicholl (NLN) Line Clipping 1.) The  number of clipping points is reduced to one, compared to other algorithms that may require two or more clipping.  2.)  Nicholl - Lee Nicholl (NLN) Line Clipping is FAST. Clipping algorithms for Circle, Curved boundary clipping regions are SLOWER because intersections calculations involve nonlinear curve equations.  3.)    Nicholl - Lee Nicholl (NLN) Line Clipping avoids multiple clipping of Line segments.  4.) As Compare to Cohen Sutherland and Liang-Barsy  Nicholl - Lee Nicholl Line Clipping performed fewer comparisons.    How Nicholl - Lee Nicholl Line Clipping works?  Nicholl...
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