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We are happy to welcome a MCL summer intern – Pranav Kulkarni! Here is an interview with Pranav:

1. Could you briefly introduce yourself and your research interests?

I am Pranav Kulkarni, a 3rd-year undergraduate student from Indian Institute of Technology Bombay, India with major in Electrical Engineering and minor in Computer Science. I am joining MCL as a summer intern for 2 months as a part of IUSSTF Viterbi-India Program. My research interest lies in the area of Signal and Image Processing. The major projects I have worked on include ‘Touchless Fingerprint Recognition’ and ‘Generalization of Fractional Fourier Transform and its application in Radar Imaging’. Apart from my studies, I like to play chess.

2. What is your impression about MCL and USC?

USC is an institute with its unique culture and people from diverse backgrounds. It has a wonderful campus and it’s going to be fun to explore it. The MCL is full of hardworking and enthusiastic masters and Ph.D. students. It is going to a great experience to get know them all and understand their perspectives.

3. What is your future expectation and plan in MCL?

I will be working on the project of ‘High-Resolution Face Image Synthesis using MSG’ with Jiali, Yao and Saksham, who is another intern. To start with, we are working on digit synthesis. Working under the guidance of Prof. Kuo is going to be a great learning experience. The research culture here at MCL is definitely a thing I would like to imbibe in myself.

A MCL Ph.D student, Siyang Li, graduated from USC on May 10, 2018.

Siyang received her Bachelor of Engineering degree from The University of Hong Kong, Hong Kong, China, in June 2014. After that, she started her Ph.D study at University of Southern California, with the Annenberg Fellowship and the WiSE Fellowship. She joined Media Communications Lab (MCL) and was supervised by Prof. C.-C. Jay Kuo.

Her research interest was computer vision, with specialties in object detection and segmentation. During her Ph.D study, she collaborated with Google Research on multiple projects and had several corresponding publications. On April 26, she successfully defended her thesis titled “Object Localization with Deep Learning Techniques”.

Congratulations to Siyang and her families for completing her Ph.D study at USC!

commentsDr. Kuo, Director of MCL, received the Education Award from the IEEE Signal Processing Society for his contributions to signal, image and multimedia education. The award ceremony was held at the International Conference on Acoustic, Speech and Signal Processing (ICASSP), Calgary, Canada, on April 17.

Furthermore, Dr. Kuo was honored at the USC annual Academic Honors Convocation on April 24 evening for his new appointment as Distinguished Professor of Electrical Engineering and Computer Science and recognition as the recipient of the Provost’s Mentoring Award.

The USC Distinguished Professor is a designation awarded very selectively to those whose accomplishments have brought special renown to USC. The Provost’s Mentoring Award honors an individual faculty member whose investment in and generosity toward the academic and professional success of other USC faculty, postdoctoral fellows, graduate students, or undergraduate students demonstrate exemplary mentoring.

Congratulations to Director Kuo for his great accomplishments and well-deserved honors and recognitions.

The first International Conference on Data Intelligence and Security (ICDIS-2018) was held at South Padre Island (SPI), Texas, USA, from April 9-10, 2018. The conference was organized by the University of Texas Rio Grande Valley (UTRGV). Key people in attendance were from the U.S. Department of Defense, the National Science Foundation, and other agencies focused on data intelligence and security.

Dr. Parwinder Grewal, UTRGV executive vice president for Research, Graduate Studies, and New Program Development, welcomed attendees to the conference in the open ceremony held on April 9 (Monday) morning. After his opening remarks, MCL Director, Professor Jay Kuo, gave the first keynote speech on the topic, “Why and Why Not Convolutional Neural Networks.” The abstract of his talk is given below.

“The superior performance of Convolutional Neural Networks (CNNs) has been demonstrated in many applications such as image classification, detection, and processing. Yet, the CNN solution has its own weaknesses such as robustness against perturbation, scalability against the class number and portability among different datasets. Furthermore, CNN’s working principle remains mysterious. In this talk, I will first explain the reasons behind the superior performance of CNNs. Then, I will present an alternative solution, which is motivated by CNNs yet allows rigorous and transparent mathematical treatment, based on a data-driven Saak (Subspace approximation with augmented kernels) transform. The kernels of the Saak transform are derived from the second-order statistics of inputs in a one-pass feedforward way. Neither data labels nor backpropagation is needed in kernel determination. The pros and cons of CNNs and multi-stage Saak transforms are compared.”

The South Padre Island (http://www.myspi.org/) is a tropical island located in the Mexico Gulf. It is famous for beautiful beaches, warm Gulf waters, fishing, boating, bird watching, and shopping. [...]

Biomedical image segmentation is a technique for automatic detection of organ boundaries within an image, used to obtain diagnostic insights in the field of medicine. Segmentation results are used in applications involving organ measurements, cell detection and blood flow simulations [1].

Zebrafish is a vertebrate that have similar organs and tissues as humans, making it a valuable model for studying human genetics and disease [2]. In this project, we use the zebrafish organs dataset for the analysis and evaluation of medical image segmentation methodologies.

Presently, we have trained a CNN based segmentation model using RefineNet [3] and a Saak [4] based model on the zebrafish data. Our goal is to compare and analyze the performance of CNN with Saak segmentation model. In future, we aim to improve these results by adopting content adaptive Saak with clustering techniques and statistical analysis algorithms.

-By Shilpashree Rao and Ruiyuan Lin

Reference:

[1]”Medical Image Segmentation”. [Online]. Available: https://www5.cs.fau.de/research/groups/medical-image-segmentation. [Accessed: 01- Apr- 2018].

[2]”Why use the zebrafish in research?”, 2018. [Online]. Available: https://www.yourgenome.org/facts/why-use-the-zebrafish-in-research. [Accessed: 01- Apr- 2018].

[3] G. Lin, A. Milan, C. Shen and I. Reid, “RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[4] C.-C. J. Kuo and Y. Chen, “On data-driven Saak transform,” Journal of Visual Communication and Image Representation, vol. 50, pp. 237–246, 2018.

Image credits:

Image showing the architecture of RefineNet is from [3].

Image showing the architecture of multi-stage Saak is from [4].