News

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].

MCL member, Ye Wang presented Qin Huang’s paper at Winter Conference on Applications of Computer Vision (WACV) 2018, Lake Tahoe, NV/CA

The title of Qin’s paper is “Unsupervised Clustering Guided Semantic Segmentation”, with Chunyang Xia, Siyang Li, Ye Wang, Yuhang Song and C.-C. Jay Kuo as the co-authors. Here is a brief summary:

“With the development of Fully Convolutional Neural Network (FCN), there have been progressive advances in the field of semantic segmentation in recent years. The FCN-based solutions are able to summarize features across training images and generate matching templates for the desired object classes, yet they overlook intra-class difference (ICD) among multiple instances in the same class. In this work, we present a novel fine-to-coarse learning (FCL) procedure, which first guides the network with designed ‘finer’ sub-class labels, whose decisions are mapped to the original ‘coarse’ object category through end-to-end learning. A sub-class labeling strategy is designed with unsupervised clustering upon deep convolutional features, and the proposed FCL procedure enables a balance between the fine-scale (i.e. sub-class) and the coarse-scale (i.e. class) knowledge. We conduct extensive experiments on several popular datasets, including PASCAL VOC, Context, Person-Part and NYUDepth-v2 to demonstrate the advantage of learning finer sub-classes and the potential to guide the learning of deep networks with unsupervised clustering.”

Congratulations to Qin Huang for his successful presentation at WACV!

We are so happy to welcome a new Master’s member of MCL, Charan Shettyhalli Guruswamy. Here is an interview with Charan :
1. Could you briefly introduce yourself and your research interests?
I am Charan, a new grad student who joined EE dept in spring 2018. I love to work hard and think more. I got attracted to image processing and machine learning during my undergrad. Starting then I have pursued computer vision and the math behind it. My previous research included face & object detection and recognition, biometrics, NLP, optimization, ML and deep learning. I have a passion to play with numbers, and I see images as just matrices. I am not a big fan of neural networks, I like convolutions more than deep learning. I have interest in designing new transforms for data, I hope to research on scene understanding and video completion.

2. What is your impression about MCL and USC?
MCL is a group filled with image processing passionates. Every one of them has their own beautiful perspectives in analyzing the images, and their unique works enthrall me. There is a lot of math, analysis, and visualization here, that’s what I like in MCL. Weekly sessions by doctoral students help in understanding the new topics in the IP field, thus triggering motivation in all. The rich MCL alumni and community guiding the current MCL candidates is ideal for career growth. MCL helps in breaking our shell and surpass our limits.

3. What is your future expectation and plan in MCL?
My goal is to design a transforms which can extract and select features from any data. I want to learn how to model this transforms. I want to join the peer community of [...]