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This summer, Professor Kuo and his PhD students are providing MCL’s interns with 5-week summer internship courses. The courses, including image processing, web design, computer vision, computer graphic and entrepreneurship, were designed not only to improve students’ academic capability on media communication, but also to give them an edge in preparing business start-ups.

Students are required to demo their final projects by the end of next week. For the image processing class, they are required to show a face morphing video and explain related working details. For the web design class, they will introduce themselves using the website they designed. For the computer vision class, they will show how their own trained detectors work. For the computer graphic class, they are going to show their 3D model transformation video as well as explain how the codes work. Then on the last day, intern students, as well as other lab members, will present their case study results on various promising startups.

Finishing their own fantastic projects from the scratch is extremely meaningful for these interns, especially considering the fact that some of them just finished their freshman year and some are still applying for college entrance.  Through these courses, they obtained not only extracurricular knowledge but a better understanding of research methods and their appliance in business world.

We are so happy to welcome a new senior high school student – Matthew Tran to our lab in summer. Let us hear what he said about the intern in our group.

Q1: Could you briefly introduce yourself? (Previous research/project experience, research interest and expertise)

A1: I’m Matthew Tran and I will be a senior in high school in the Bay Area. I am interested in majoring in computer science. Although high school does not provide much research opportunities, I have done multiple projects that range from computer science and robotics to video production. I am interested to participate in research opportunities that involve web design and computer graphics.

Q2: What was your first impression of USC and MCL?

A2: USC is a great place to start my foundation in advanced engineering concepts. No matter what the major, it is an amazing place that provides opportunities to everyone. Everyone seems to have a drive to learn new things.

MCL has provided me with opportunities to learn concepts that my high school does not teach. It’s a great place to get a head start and see how what I learn can be applied into real life situations. The people at MCL are also very welcoming and help me feel comfortable in class, despite the age disparity.

Q3: What drove you to join MCL?

A3: I heard about MCL from Dr. Kuo, who provided me with my internship. I would like to thank him for giving me the opportunity to take classes that graduate students would take. The internship provides me with hands on experience, while also giving me a feel of how college life is like.

Q4: What is your further expectation of being an MCL member?

A4: I am very excited to learn new things during my five-week stay at USC. I [...]

Chun-Ting Huang, a member of MCL, attended CVPR 2015 hosted in Boston from June 8th to 12th to present his work in Biometric Workshop. The conference was held in Hynes Convention Center, where researchers from all over the world gathered to present their state-of-the-art results. There were six oral sessions, five poster sessions, and two plenary talks given by Yann LeCun and Jack L. Gallant. Unlike last year, CVPR newly added CNN Architectures into this year’s oral session, showing that CNN has been widely recognized and accepted by computer vision society.

According to Chun-Ting, “there were thousands of attendees presented in this large-scale event, and many interesting ideas were brought up and discussed with different aspects. It is an amazing and fruitful experience to me. I believe every researcher who attended this event surely has learned more than he/she originally expected.”

Chun-Ting presented his work in Biometric Workshop, and his paper’s title was “TAEF: A Cross-Distance/Environment Face Recognition Method”. This system provides a cross-distance face alignment, a cross-environment face enhancement, and a two-stage coarse-to-fine approach to handle long distance faces. The performance of this method achieves 97% first rank accuracy on 150 meter visible-light images in the LDHF database.

The Viterbi-India program is jointly funded by the Indo US Science, Technology Forum (IUSSTF) and the USC VSoE. Each student in the Viterbi-India program will receive stipend and travel reimbursement. These students will come to USC and join one of the labs under EE department during May 20 and July 18, 2015.

After a competitive review process, there will be 20 spots for the Indian students in the program. Among those strong Indian students, Vikranth Reddy is the one who will join MCL in 2015 summer as an intern. We conducted a short interview with him about his previous experiences and future expectations.

1. Could you briefly introduce yourself? (Previous research/project experience, research interest and expertise)

I am Vikranth Dwaracherla, a third year undergraduate from IIT Bombay, India. I am currently pursuing my major in Electrical Engineering and minors in Computer Science. My previous research projects include neural networks, device simulations and image processing. I am very interested in the fields of computer vision and robotics. I have been working on a project on autonomous fruit collecting robot.

2.What was your first impression of USC and MCL?

USC is a wonderful place and provides opportunities for all people invariant of their interests. It is a wonderful place where people can pursue their goals. The members in the lab are all very kind and willing to help others. I would like to thank all the members of MCLab for the hospitality shown towards me.

3.What is your further expectation of being an MCL member?

I am very happy to be selected into the Viterbi- India Program and come to USC, one of the leading institutions in the field of Engineering and Technology. I am also very pleased to join MCLab, and to have the opportunity to work with [...]

Five MCL members attended the Viterbi PhD hooding ceremony on Thursday, May 14, 2014, from 8:30-11:00 a.m. in the Bovard Auditorium. They were Sachin Chachada, Xiang Fu, Pang-Chang Lan, Sudeng Hu, and Joe Yuchieh Lin. Congratulations to them and their families for their accomplishments in completing their PhD program at USC.

Sachin Chachada received his B.E. degree in Electronics and Communication Engineering from S.R.K.N.E.C., RTM Nagpur University and an M.S. degree in Electrical Engineering from the University of Southern California (USC). Since 2009, he has been a member in the Media Communications Lab at USC, participating in the fields of statistical signal processing, and machine learning with applications to image and audio analysis. His dissertation, entitled “Environmental Sound Recognition: Classification and Retrieval,” discusses the algorithms that can be used to advance general audio understanding and management. His work demonstrates superior performance of ensemble learning algorithm for environmental audio classification, with use of classical, contemporary and a new set of time-frequency features. His work also includes novel algorithms for environmental sound retrieval for a large database, with promising applications for audio content management.

Xiang Fu received his B.S. degree in Electronic Engineering from Shanghai Jiao Tong University (SJTU), China in 2009, and his M.S. degrees in Electrical Engineering and in Computer Science, both from University of Southern California (USC), Los Angeles in 2011 and 2014, respectively. Since 2011, he has been pursuing his Ph.D. degree in Electrical Engineering with Media Communications Lab (MCL) at USC advised by Prof. C.-C. Jay Kuo, and has been working on various research areas including image/video segmentation, spectral clustering, visual tracking, object recognition, video surveillance, and machine learning. His dissertation, entitled “An Information Fusion Approach to Visual Data Segmentation”, discusses feature fusions [...]