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Multimedia Signal Processing Lab. (MSP Lab.)
Research Focus:
Led by Professor Jing-Ming Guo, MSP Lab primarily focuses on various extensive data analysis, including digital video, images, music, speech, and text. In addition to conducting in-depth academic research, the lab collaborates closely with industry partners. With the recent maturity of artificial intelligence applications in hardware and algorithms, some AI systems have even outperformed human judgment in certain projects, demonstrating astonishing effects across different fields. MSP Lab tackles industry challenges and develops research topics based on industrial demand. Current research directions include biometric identification, object tracking in surveillance systems, behavior analysis, posture recognition, face recognition, industrial defect detection, vehicle image recognition analysis system, MRI brain tumor segmentation and classification, glaucoma identification in fundus images, weakly supervised image segmentation techniques, few-shot classification techniques, noise data recognition technology, unsupervised domain-adaptive classification, text super-resolution tasks, table structure analysis, invoice text layering tasks, cross-border (Japan) deep learning-assisted CMP parameter prediction and analysis technology, digital video compression, digital watermarking technology, digital halftoning technology, and more.
MSP Lab boasts abundant resources and actively develops image and visual extensive data analysis technology. The lab's hardware manufacturing technology and advantages complement its research topics, enabling it to solve industry technology bottlenecks and cultivate relevant talent familiar with industry technologies. MSP Lab aims to drive the pace of technological progress in society.
For more detailed research directions, please refer to the "Research" tag.
This forum is jointly written and edited by researchers and students of the laboratory. The content mainly covers five major categories: Linux, Python, Computer Vision, Machine Learning, and Deep Learning. The aim is to share related technical theories and practical details in a simple and easy-to-understand way with the public.
Contact Info:
Ariel Shang
Email: arielshang@mail.ntust.edu.tw
Tel: 02-27333141 #7713
EE602-2, No. 43, Sec. 4, Keelung Rd., Da’an Dist., Taipei City 106, Taiwan (R.O.C.)
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