Tags
Language
Tags
July 2025
Su Mo Tu We Th Fr Sa
29 30 1 2 3 4 5
6 7 8 9 10 11 12
13 14 15 16 17 18 19
20 21 22 23 24 25 26
27 28 29 30 31 1 2
    Attention❗ To save your time, in order to download anything on this site, you must be registered 👉 HERE. If you do not have a registration yet, it is better to do it right away. ✌

    KoalaNames.com
    What’s in a name? More than you think.

    Your name isn’t just a label – it’s a vibe, a map, a story written in stars and numbers.
    At KoalaNames.com, we’ve cracked the code behind 17,000+ names to uncover the magic hiding in yours.

    ✨ Want to know what your name really says about you? You’ll get:

    🔮 Deep meaning and cultural roots
    ♈️ Zodiac-powered personality insights
    🔢 Your life path number (and what it means for your future)
    🌈 Daily affirmations based on your name’s unique energy

    Or flip the script – create a name from scratch using our wild Name Generator.
    Filter by star sign, numerology, origin, elements, and more. Go as woo-woo or chill as you like.

    💥 Ready to unlock your name’s power?

    👉 Tap in now at KoalaNames.com

    Visual Analysis of Behaviour: From Pixels to Semantics

    Posted By: hill0
    Visual Analysis of Behaviour: From Pixels to Semantics

    Visual Analysis of Behaviour: From Pixels to Semantics by Shaogang Gong
    English | 27 May 2011 | ISBN: 0857296698 | 378 Pages | PDF | 16.83 MB

    This book presents a comprehensive treatment of visual analysis of behaviour from computational-modelling and algorithm-design perspectives. Topics: covers learning-group activity models, unsupervised behaviour profiling, hierarchical behaviour discovery, learning behavioural context, modelling rare behaviours, and "man-in-the-loop" active learning; examines multi-camera behaviour correlation, person re-identification, and "connecting-the-dots" for abnormal behaviour detection; discusses Bayesian information criterion, Bayesian networks, "bag-of-words" representation, canonical correlation analysis, dynamic Bayesian networks, Gaussian mixtures, and Gibbs sampling; investigates hidden conditional random fields, hidden Markov models, human silhouette shapes, latent Dirichlet allocation, local binary patterns, locality preserving projection, and Markov processes; explores probabilistic graphical models, probabilistic topic models, space-time interest points, spectral clustering, and support vector machines.