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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...
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What is the difference between local descriptors and global descriptors?

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Descriptors  rely on Gradient-based or intensity variation approaches to detect Local Features (e.g., Edges, Blobs, and Regions). BLOB = Binary Large Object (i.e., the region of the image). Descriptors such as HOG, SIFT, SURF (rely on local gradient computation). Binary Descriptors such as BRISK, ORB or FREAK (rely on local intensity differences) Global Descriptor –  Describe the whole image, Local Descriptor  – Describe the patch within the image. (The best example of the global descriptor is SIFT). LDP = Local Directional Pattern. Local Directional Pattern is a  descriptor  i.e., used for Face Recognition. How to Calculate Local Directional Pattern (LDP) Code?|Kirsch Compass Mask| https://youtu.be/dY0KarfWTV4
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