Statistical Learning for Signal Processing Lab. (SLSP) 

(formally Multimedia Processing Lab.) was established under the guidance of Professor Yoo Changdong in 1999, the year he arrived at KAIST. Using various machine learning theories and novel signal processing techniques, signals such as image, text, speech, audio, video, EEG and financial data are processed for longstanding and emerging applications.  


For direction to our lab, click here.


 [Lab. Introduction]

SLSP Lab. News

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Doctoral candidate Youngjoo Seo presented a lab seminar on the 25th, Wednesday, featuring "Discovering What is a moving object in video?: Application to action recognition...
Doctoral candidate Sanghyuk Park presented a lab seminar on the 12th, Thursday, featuring "Discovering Representative Behavior Pattern based on Kernelized Collaborative Pa...
Doctoral candidate Jinho Choi successfully finished his dissertation defense in the recent presentation, held on the 11th, before the committee. His thesis was "Under...
Two Master's degree candidates of SLSP had their graduation ceremony on the 13th, February. One of the graduates, Tae-ho Kim, has decided to continue his research as ...

Recently Accepted Papers

paper

cvpr2015.jpgDonghoon Lee, Hyunsin Park, and Chang D. Yoo, "Facial Landmark Estimation using Cascade Gaussian Process Regression Trees", in Proceedings of International Conference on Computer Vision and Pattern Recognition,  Boston, USA,  2015.

Interspeech.jpgJanghoon Cho and Chang D. Yoo, "Underdetermined Convolutive BSS : Bayes Risk Minimization Based on a Mixture of Super-Gaussian Posterior Approximation", accepted for publication in IEEE Transactions on Audio, Speech and Language Processing, 2015.

Interspeech.jpgChulhee Yun, Donghoon Lee, and Chang D. Yoo, "Face Detection using Local Hybrid Patterns," in Proceedings of International Conference on Acoustics, Speech and Signal Processing, Brisbane, Australia, 2015.

Interspeech.jpgJinho Choi and Chang D. Yoo, “Underdetermined High-Resolution DOA Estimation: A 2pth-Order Source-Signal/Noise Subspace Constrained Optimization ”, accepted for publication in IEEE Transactions on Signal Processing, 2014.