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    Congratulations to Professor Kuo for Receiving the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award

Congratulations to Professor Kuo for Receiving the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award

MCL Director, Professor C.-C. Jay Kuo, received the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award on March 6 at the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) held in New Orleans, Louisiana, USA.

The IEEE Leon K. Kirchmayer Graduate Teaching Award is sponsored by the Leon K. Kirchmayer Memorial Fund and recognizes inspirational teaching of graduate students in the IEEE fields of interest.  Professor Kuo received this award for excellence in inspirational guidance of graduate students and curriculum development in the area of multimedia signal processing.

Professor Kuo gave the following short speech in the Award Ceremony. “It was a great honor to be recognized by the prestigious Lion K. Kirchmayer Graduate Teaching Award. I would like to use this opportunity to appreciate a few people who had great impacts on my teaching career. When I was a PhD student at MIT, I was fortunate to work with a few young faculty members. They were my PhD and Master thesis advisor Bernard Levy, my PhD thesis co-advisor, John Tsitsiklis, my MS thesis co-advisor, Bruce Musicus, my PhD thesis committee member, Nick Trefethen, and my postdoc mentor at UCLA, Tony Chan. They spent an enormous amount of time nurturing and advising me. I am obliged to them deeply. Furthermore, I would like to say thanks to my graduate students. They are not only my students but also my teachers. I learned many new topics together with them. Finally, I would like to give thanks to my wife and daughter. Their unconditional love and patience allow me to do whatever I want to pursue. I do owe them tremendously, and would to like to share my joy and honor with them.”

Congratulations to Professor [...]

By |March 12th, 2017|News|Comments Off on Congratulations to Professor Kuo for Receiving the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award|

Congratulations to Hao Xu for Passing His PhD Defense

Congratulations to Hao Xu for passing his defense on January 23, 2016. His Ph.D. thesis is entitled “Understanding Deep Learning from Its System Architectures, Feature Representations to Applications”.

Abstract of thesis:

Deep learning plays key roles in various aspects of the modern computer vision research. Our research focused on analyzing, adopting, and developing better CNN architectures which outperform the previous methods. To begin with, a car detection method using deformable part models consisting of composite feature sets (DPM/CF) is proposed. It recognizes cars of various types and from multiple viewing angles. The DPM/CF system consists of two stages. In the first stage, an HOG template is used to detect the bounding box of the entire car of a certain type and viewed from a certain angle (called a t/a pair), which yields a region of interest (ROI). In the second stage, we detect each salient part in a given t/a-specific ROI using either the HOG or the CNN feature. An optimization procedure based on latent logistic regression is adopted to choose the most discriminative location/size and the most suitable feature set for each part automatically. It is observed that the DPM/CF detector can strike a balance between detection performance and training complexity, through selecting the capable and simple feature from the composite feature set. Extensive experimental results are given to demonstrate the superior performance of the proposed DPM/CF method.

The CNN features used in DPM/CF demonstrate strong performance in detecting objects from images. To analyze the strength and weakness of the CNN feature representation, two quantitative metrics are proposed for the automatic evaluation of trained features at different convolution layers. The Gaussian confusion measure (GCM) is used to identify the discriminative ability of an individual feature, while [...]

By |January 29th, 2017|News|Comments Off on Congratulations to Hao Xu for Passing His PhD Defense|

Congratulations to Xiaqing Pan for Passing His PhD Defense

Congratulations to Xiaqing Pan for passing his defense on January 23, 2016. His Ph.D. thesis is entitled “Machine Learning Methods for 2D/3D Shape Retrieval and Classification”.

Abstract of thesis:

Shape classification and retrieval are two important problems in both computer vision and computer graphics. A robust shape analysis contributes to many applications such as manufacture components recognition and retrieval, sketch-based shape retrieval, medical image anaysis, 3D model repository management, etc. In this dissertation, we propose three methods to address three significant problems such as 2D shape retrieval, 3D shape retrieval and 3D shape classification, respectively.

First, in the 2D shape retrieval problem, most state-of-the-art shape retrieval methods are based on local features matching and ranking. Their retrieval performance is not robust since they may retrieve globally dissimilar shapes in high ranks. To overcome this challenge, we decompose the decision process into two stages. In the first irrelevant cluster filtering (ICF) stage, we consider both global and local features and use them to predict the relevance of gallery shapes with respect to the query. Irrelevant shapes are removed from the candidate shape set. After that, a local-features-based matching and ranking (LMR) method follows in the second stage.  We apply the proposed TSR system to three shape datasets: MPEG-7, Kimia99 and Tari1000. We show that TSR outperforms all other existing methods. The robustness of TSR is demonstrated by the retrieval performance.

Second, a novel solution for the content-based 3D shape retrieval problem using an unsupervised clustering approach, which does not need any label information of 3D shapes, is presented.  The proposed shape retrieval system consists of two modules in cascade: the irrelevance filtering (IF) module and the similarity ranking (SR) module. The IF module attempts to cluster gallery shapes that [...]

By |January 26th, 2017|News|Comments Off on Congratulations to Xiaqing Pan for Passing His PhD Defense|
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    Congratulations to Qin Huang for Passing His Qualifying Exam

Congratulations to Qin Huang for Passing His Qualifying Exam

Congratulations to Qin Huang for passing his Qualifying Exam on January 18, 2016. The title of his Ph.D. thesis proposal is “Machine Learning Techniques for Perceptual Quality Enhancement and Semantic Image Segmentation”. His qualifying exam committee consisted of Jay Kuo (Chair), Antonio Ortega, Justin Haldar, Sandy Sawchuk and Cyrus Shahabi (Outside Member).

Abstract of thesis proposal:

Researches on image processing and computer vision problems can be generally divided into two major steps: extracting powerful feature representations and designing efficient decision system. Traditional methods rely on hand-craft features, as well as pre-defined thresholds to generate a necessary condition required for the desired target. A more robust system could be designed taking advantage of machine learning techniques if enough training samples are provided. Thanks to the development of big data, millions of image and video contents are now available for training. To better utilize the information in the training, convolutional neural network based deep learning systems become popular in recent years. Specifically, the CNN based methods demonstrate better ability to acquire powerful feature representations in a simultaneous way. However, CNN based training has a high requirement of hardware and subtle process design. And therefore it should be carefully explored in order to obtain desired results.

In this proposal, we contribute to three works that gradually develop from the traditional method to deep learning based method. Based on the applications, the works can be divided into two major categories: perceptual quality enhancement and semantic image segmentation. In the first part, we focus on enhancing the quality of images and videos by considering related perceptual properties of human visual system. To begin with, we deal with a type of compression artifacts referred to as “false contour”. We then focus on the visual experience [...]

By |January 22nd, 2017|News|Comments Off on Congratulations to Qin Huang for Passing His Qualifying Exam|
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    Congratulations to Chun-Ting Huang for Passing His PhD Defense

Congratulations to Chun-Ting Huang for Passing His PhD Defense

Congratulations to Chun-Ting Huang for passing his defense on January 18, 2016. His Ph.D. thesis is entitled “Facial Identity Recognition and Attribute Classification Using Machine Learning Techniques”.

Abstract of thesis:

Robust face recognition plays a central role in biometric and surveillance applications.  Although the subject has been studied for about four decades, there still exist quite a few technical challenges and system design issues in deploying it in a real-world video surveillance environment.  Nowadays, the raw face images and their associated meta data are stored in a remote cloud storage system in a distributed face recognition platform.  One key challenge in the overall system design is to ensure the security of stored data. In this research, we first conduct a survey on this technology and then, study the problems of cross-distance/environment face recognition and facial attribute classification with machine learning techniques.

The problem of long distance face recognition and attribute classification arising from surveillance applications impose major challenges. The captured face from the surveillance system can be low resolution and quality, which is further degraded by an uncontrolled outdoor environment such as long distance during daytime or nighttime. In addition, human age/gender inferred by face images are fundamental attributes in our social interactions. This research has many applications such as demographics analysis, commercial user management, visual surveillance, and even aging progression. Despite the rapid development in automatic face recognition, there is far less work on automatic age/gender classification in an unconstrained environment.

Research in this dissertation provides effective solutions to three topics: 1) cross-distance/environment face recognition, 2) cross-distance/spectral face recognition and 3) age/gender classification.  For Topic 1, a two-stage alignment/enhancement filtering (TAEF) method is proposed to achieve the state-of-the-art performance.  For Topic 2, a locally linear embedding (LLE) [...]

By |January 19th, 2017|News|Comments Off on Congratulations to Chun-Ting Huang for Passing His PhD Defense|

Congratulations to Eddy Wu for Passing His Qualifying Exam

Congratulations to Eddy Wu for passing his Qualifying Exam on January 13, 2016. The title of his Ph.D. thesis proposal is “Deep Learning Techniques for Supervised and Semi-Supervised Pedestrian Detection”. His qualifying exam committee consisted of Jay Kuo (Chair), Sandy Sawchuk, Richard Leahy, Justin Haldar and Aiichiro Nakano (Outside Member).

Abstract of thesis proposal:

With the emergence of autonomous driving and the advanced driver assistance system (ADAS), the importance of pedestrian detection has increased significantly. A lot of research work has been conducted to tackle this problem with the availability of large-scale datasets. Methods based on the convolutional neural network (CNN) technology have achieved great success in pedestrian detection in recent years, which offers a giant step to the solution of this problem.  Although the performance of CNN-based solutions reaches a significantly higher level than traditional methods, it is still far from perfection. Further advancement in this field is still demanded. In this proposal, we conducted two research topics along this direction.

In the first topic, a boosted convolutional neural network (BCNN) system is proposed to enhance the pedestrian detection performance. Being inspired by the classic boosting idea, we develop a weighted loss function that emphasizes challenging samples in training a convolutional neural network (CNN). Two types of samples are considered challenging:

1) samples with detection scores falling in the decision boundary, and

2) temporally associated samples with inconsistent scores. A weighting scheme is designed for each of them. Finally, we train a boosted fusion layer to benefit from the integration of these two weighting schemes. We use the Fast-RCNN as the baseline and test the corresponding BCNN on the Caltech pedestrian dataset in the experiment and observe a significant performance gain of the BCNN over its baseline.

Data-driven pedestrian detection methods demand [...]

By |January 15th, 2017|News|Comments Off on Congratulations to Eddy Wu for Passing His Qualifying Exam|
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    Congratulations to Weihao Gan for Passing His Qualifying Exam

Congratulations to Weihao Gan for Passing His Qualifying Exam

Congratulations to Weihao Gan for passing his qualifying exam on January 11, 2016. The title of his Ph.D. thesis proposal is “Advanced Online Object Tracking Techniques by Exploiting Spatial and Temporal Information”. His qualifying exam committee consisted of Jay Kuo, Antonio Ortega, Keith Chugg, Panayiotis Georgiou and Ulrich Neumann.

Abstract of thesis proposal:

Online object tracking is one of the fundamental computer vision problems. It is commonly used in real world applications such as traffic control in video surveillance, autonomous vehicle, robotic navigation, medical imaging, etc. It is a very challenging problem due to multiple time-varying attributes in video sequences. In this research, we attempt to achieve online object tracking using both spatial and temporal cues with two novel methods.

First, we develop a new method, called the “temporal prediction and spatial refinement (TPSR)” tracker, to integrate spatial and temporal cues effectively. The TPSR tracking system consists of three cascaded modules: pre-processing (PP), temporal prediction (TP) and spatial refinement (SR). Illumination variation and shaking camera movement are two challenging factors in a tracking problem. They are compensated in the PP module. Then, a joint region-based template matching (TM) and pixel-wised optical flow (OF) scheme is adopted in the TP module, where the switch between TM and OF is conducted automatically. These two modes work in a complementary manner to handle different foreground and background situations. Finally, to overcome the drifting error arising from the TP module, the bounding box location and size are finetuned using the local spatial information of the new frame in the SR module.

Next, we apply the deep neural network architecture to the online object tracking problem. We have made several major improvements on the state-of-the-art multi- domain network (MDNet) tracker. The enhanced MDNet (EMDNet) tracker not [...]

By |January 12th, 2017|News|Comments Off on Congratulations to Weihao Gan for Passing His Qualifying Exam|

MCL Releases VideoSet in IEEE DataPort

The MCL joined a collaborative project to build a large-scale subjective video quality database. The database was proposed to boost a major breakthrough in video coding and processing. It consists of 220 5-second sequences in four resolutions (i.e., 1920×1080, 1280×720, 960×540 and 640×360). For each of the 880 video clips, we encoded it using the H.264 codec and conducted a large-scale subjective test on the perceptual quality. The dataset is called the “VideoSet”, which is an acronym for “Video Subject Evaluation Test (SET)”. The database are available to the public in the IEEE DataPort.

IEEE DataPort is a valuable and universally accessible repository of datasets serving the growing data needs in both research and industry. The repository is designed to accept all types of datasets, including Big Data datasets up to 2TB, and it provides both downloading capabilities and access to Cloud services to enable data analysis in the Cloud.

We appreciate the help from Dr. K. J. Ray Liu and Melissa Handa in hosting the VidoeSet database.

By |January 8th, 2017|News|Comments Off on MCL Releases VideoSet in IEEE DataPort|

Happy New Year – 2017

2016 has been a fruitful year for MCL. Some members graduated with impressive research work and began a new chapter of life. Some new students joined the MCL family and explored the joy of research. MCL members have made great efforts on their research and published quality research papers on top journals and conferences.

Wish all MCL members a happy new year.

Image credits: Photo 1: “Happy New Year” by maf04, used under CC BY-SA 2.0 / Resized with white padding on the borders; Photo 2: “New Years 2017” by maf04, used under CC BY-SA 2.0 / Resized with black padding on the borders.

By |January 1st, 2017|News|Comments Off on Happy New Year – 2017|

Congratulations to MCL Members for Passing Screening Exams

MCL members, Heming Zhang, Junting Zhang and Yueru Chen passed the screening exam in Fall 2016. The screening exam is an important examination during PhD study, which aims to estimate the research potential of the student. In the area of Signal and Image Processing, the screening exam includes four topics: applied linear algebra, digital signal processing, probability and random processes.

Congratulations to Heming, Junting and Yueru. We wish them all the best in their future research and PhD study.

By |December 25th, 2016|News|Comments Off on Congratulations to MCL Members for Passing Screening Exams|