- Retinal Image Analysis
- Road Sign Recognition
- Foveated Ray Tracing
- Set-Membership Filtering
- Reconstructing Background of DNA Microarray Imagery
- Dynamic Face Models: Construction and Applications
- Semantic Video Analysis
- Incremental and Robust Subspace Learning
- Robust Panoramic Scene Construction from MPEG Video
- Retinal Image Analysis
We perform the analysis of retinal images by detecting the eye structures such as the blood vessels and optic disc first. Then the retinal image is analyzed to detect the suspicious lesion regions if any. The segmentation of blood vessel is based on the Graph Cut technique. In opposed to the traditional formulation of the graph which is normally ineffective for long, thin structures like the blood vessel, we have included the flux vectors in the graph construction and achieved better results on a number of public datasets. We have addressed the problem of optic disc segmentation based on prior blood vessel segmentation. Two different methods have been developed and evaluated: a direct extension of the Graph Cut method with a compensation factor to eliminate the interference of blood vessels, and the background reconstruction on the blood vessels using Markov Random Field. Based on the results of blood vessels and optic disc, a further detection and classification of suspicious lesion areas in the retinal is performed.
- Dodo, B., Li, Y., Tucker, A., & Liu, X. Retinal OCT Segmentation Using Fuzzy Region Competition and Level Set Methods. In IEEE International Symposium on Computer-Based Medical Systems. Andalucia, Spain, 2019.
- Dodo, B., Li, Y., Liu, X., & Dodo M. Level Set Segmentation of Retinal OCT Images. In International Conference on Bioimaging. Czech Republic, 2019.
- Dodo, B., Li, Y., Eltayef, K., & Liu, X. Graph-Cut Segmentation of Retinal Layers from OCT Images. In International Conference on Bioimaging. Portugal, 2018. Best Student Paper Award.
- Wang, C., Wang, Y., & Li, Y. Automatic Choroidal Layer Segmentation Using Markov Random Field And Level Set Method. IEEE Journal of Biomedical and Health Informatics, volume 21, issue 6, pages 1694-1702, 2017.
- Djibril Kaba, Yaxing Wang, Chuang Wang, Xiaohui Liu, Haogang Zhu, Ana G. Salazar-Gonzalez and Yongmin Li. Retina Layer Segmentation Using Kernel Graph Cuts and Continuous Max-Flow. Optics Express, volume 23, issue 6, pages 7366-7384, 2015.
- Ana Salazar-Gonzalez, Djibril Kaba, Yongmin Li and Xiaohui Liu. Segmentation of blood vessels and optic disc in retinal images. IEEE Journal of Biomedical and Health Informatics, volume 18, number 6, page 1874-1886, 2014.
- A. Salazar-Gonzalez, D. Kaba and Y. Li. MRF Reconstruction of Retinal Images for the Optic Disc Segmentation. In Proc. International Conference on Health Information Science (HIS 2012), Beijing, China, 2012. Best Paper Award.
- A. Salazar-Gonzalez, Y. Li and X. Liu. Optic Disc Segmentation by Incorporating Blood Vessel Compensation. In Proc. International Workshop on Computational Intelligence in Medical Imaging, Paris, France, 2011.
- A. Salazar-Gonzalez, Y. Li and X. Liu. Retinal blood vessel segmentation via graph cut. In Proc. International Conference on Control, Automation, Robotics and Vision, Singapore, December 2010.
Road sign recognition is of great interest in Intelligent Transportation and Automatic Vehicles. However, this problem is non-trivial owing to limitations such as low resolution of input video, poor lighting condition, cluttered background, and changing scales/views while vehicles are moving. A comprehensive approach to online detection and recognition of traffic signs is presented in this work. The process is comprised of the typical three stages of detection, tracking and recognition, as commonly used in many object recognition problems. At the detection stage, a quad-tree operation is performed first on the densities of sign-specific colour gradients in order to locate the regions of interest. A regular polygon detector or a boosted classifier cascade is then used to detect the possible signs. We have also developed a Confidence-Weighted Mean Shift algorithm to refine the often redundant detection results. For the recognition, we have developed and evaluated several different approaches, including the model-based method of class specific discriminative features and data-driven methods of SimBoost and similarity-learning kernel regression trees. We have also demonstrated that these methods can be used for other general object recognition.
- A. Ruta, F. Porikli, S. Watanabe, and Y. Li. In-vehicle camera traffic sign detection and recognition. Machine Vision and Applications, volume 22, number 2, pages 359-375, 2011.
- A. Ruta, Y. Li and X. Liu. Robust Class Similarity Measure for Traffic Sign Recognition. IEEE Transactions on Intelligent Transportation Systems, volume 11, issue 4, pages 846-855, 2010.
- A. Ruta, Y. Li and X. Liu. Real-Time Traffic Sign Recognition from Video by Class-Specific Discriminative Features. Pattern Recognition, volume 43, issue 1, pages 416-430, 2010.
- A. Ruta, Y. Li, F. Porikli, S. Watanabe, H. Kage, K. Sumi. A New Approach for In-Vehicle Camera Traffic Sign Detection and Recognition. In Proc. IAPR Conference on Machine Vision Applications, Japan, 2009. MVA Most Influential Paper over the Decade Award (Awarded in 2019).
- A. Ruta, Y. Li, and X. Liu. Detection, Tracking and Recognition of Traffic Signs from Video Input. The 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, 2008.
- A. Ruta, Y. Li and X. Liu. Towards Real-Time Traffic Sign Recognition by Class-Specific Discriminative Features. In Proc. British Machine Vision Conference, Warwich, UK, September 2007. Best Poster Prize.
- A. Ruta, Y. Li and X. Liu. Traffic Sign Recognition Using Discriminative Local Features. In Advances in Intelligent Data Analysis VII, pages 355-366, 2007.
Ray tracing is capable of generating high degree of realistic images but suffers from real-time performance. We have presented an approach that significantly improves the real-time performance of ray tracing. This is done by combining foveated rendering based on eye tracking with reprojection rendering using previous frames in order to drastically reduce the number of new image samples per frame. To reproject samples a coarse geometry is reconstructed from a G-Buffer. Possible errors introduced by this reprojection as well as parts that are critical to the perception are scheduled for resampling. Additionally, a coarse colour buffer is used to provide an initial image, refined smoothly by more samples were needed.
Evaluations and user tests show that our method achieves real-time frame rates, while visual differences compared to fully rendered images are hardly perceivable. This method is well-suited for wide-FOV Head-Mounted Displays with eye tracking and can be used for many Virtual Reality applications.
- Roth, T., Weier, M., Hinkenjann, A., Slusallek, P., & Li, Y. A Quality-Centered Analysis of Eye Tracking Data in Foveated Rendering. Journal of Eye Movement Research, volume 10, number 5, pages 1-12, 2018.
- Thorsten Roth, Martin Weier, André Hinkenjann, Philipp Slusallek, Yongmin Li. An Analysis of Eye-Tracking Data in Foveated Ray Tracing. In Proc. the Second Workshop on Eye Tracking and Visualization. Baltimore, USA, 2016.
- Martin Weier, Thorsten Roth, Ernst Kruijff, André Hinkenjann, Philipp Slusallek, Yongmin Li. Foveated Real-Time Ray Tracing for Head-Mounted Displays. Computer Graphics Forum, volume 35, number 7, pages 289-298, 2016.
- Sebastian Szeracki, Thorsten Roth, Andre Hinkenjann and Yongmin Li. Boosting histogram-based denoising methods with gpu optimizations. In Proc. Workshop Virtual and Augmented Reality, Germany, 2015.
- Thorsten Roth, Andre Hinkenjann and Yongmin Li. Guided high-quality rendering. Advances in Visual Computing, pages 115-125, 2015.
- Andre Hinkenjann, Thorsten Roth, Jessica Millberg, Hojun Yun and Yongmin Li. Real-Time Simulation of Camera Errors and Their Effect on Some Basic Robotic Vision Algorithms. In Proc. Tenth Conference on Computer and Robot Vision, Canada, 2013.
In many real-world scenarios, tracking non-rigid objects (e.g. human faces) from video or image sequences, or in other words, estimating the ideal hidden state of a dynamic system with noisy visual observation, is a non-trivial task. Owing to the severe non-linearity from the intrinsic characteristics of the objects themselves, their dynamics, and the external complications from measurement environment, the traditional techniques (e.g. the Kalman Filter, the Extended Kalman Filter and the Unscented Kalman Filter) are inappropriate for this problem. Other methods such as Particle Filtering suffer from intensive computation and model degeneration. To overcome these limitations, we have developed methods of Set-Membership Filtering, where the state estimate is guaranteed within a set specified by an ellipsoid bound of the state vector. Recursive algorithms are also developed for fast computing.
- F. Yang and Y. Li. Set-Membership Fuzzy Filtering for Nonlinear Discrete-Time Systems. IEEE Transactions on Systems, Man, and Cybernetics: Part B, volume 40, issue 1, pages 116-124, 2010.
- F. Yang and Y. Li. Set-Membership Filtering with State Constraints. IEEE Transactions on Aerospace and Electronic Systems, volume 45, issue 4, pages 1619-1629, 2009.
- F. Yang and Y. Li. Set-Membership Filtering for Discrete-Time Systems with Nonlinear Equality Constraints. IEEE Transactions on Automatic Control, volume 54, issue 10, pages 2480-2486, 2009.
- F. Yang and Y. Li. Set-Membership Filtering for Systems with Sensor Saturation. Automatica, volume 45, issue 8, pages 1896-1902, 2009.
- F. Yang, W. Wang and Y. Li. An iterative LMI approach to H-infinity networked control with random communication delays. International Journal of Systems, Control and Communications, volume 1, number 3, pages 325-341, 2009.
- F. Yang, Y. Li and X. Liu. Robust Error Square Constrained Filter Design for Systems with Non-Gaussian Noises. IEEE Signal Processing Letters, volume 15, pages 930-933, 2008.
DNA microarray technology has enabled biologists to study all the genes within an entire organism to obtain a global view of gene interaction and regulation. However, the technology is still early in its development, and errors may be introduced at each of the main stages of the microarray process: spotting, hybridisation, and scanning. Consequently the microarray image data collected often contain errors and noise, which will then be propagated down through all later stages of processing and analysis. Therefore to realise the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. If this is not achieved many of the subtle and low level gene expression genes, which are often of biological significance, will not be analysed. Although there is recently much research on how to detect and eliminate these variations and errors, the progress has been slow. We have initiated research to develop a novel way of processing microarray image data by reconstructing background noise of the microarray chip, and this has shown much early promise in extracting high quality cDNA image data. Instead of using the standard approach of correcting anomalies in the signal, we focus on estimating the noise as accurately as possible, to the extent that we almost ignore the signal until the last stage of processing. The proposed project brings together expertise from the disparate fields of image processing, data mining and molecular biology to make an interdisciplinary attempt in advancing the state of art in this important area. It is particularly timely since there is an urgent need to have image analysis software that can save both time and labour as well as provide high-quality image data.
- K. Fraser, Z. Wang, Y. Li, P. Kellam, X. Liu. Can Graph-Cutting Improve Microarray Gene Expression Reconstructions? Pattern Recognition Letters, volume 29, number 16, pages 2129-2136, 2008.
- K. Fraser, Z. Wang, Y. Li, P. Kellam and X. Liu. Improving Microarray Expressions with Recalibration. In Proc. International Symposium on Computational Life Science, pages 3-15, Netherlands, October 2007.
- K. Fraser, Y. Li, Z. Wang, P. Kellam and X. Liu. Noise Filtering and Microarray Image Reconstruction via Chained Fouriers. In Advances in Intelligent Data Analysis VII, pages 308-319, 2007.
A comprehensive framework for face detection, head pose estimation, tracking, and recognition is presented in this work. Statistical learning methods and, in particular, Support Vector Machines (SVMs) are applied to multi-view face detection and 3D head pose estimation. A dynamic multi-view face model is designed to extract the identity and geometrical information of moving faces from video inputs. Kernel Discriminant Analysis (KDA), a non-linear method to maximise the between-class variance and minimise the within-class variance, is proposed to represent face patterns. The facial identity structures across views and over time, referred to as Identity Surfaces, are constructed for face recognition.
- Y. Li, S. Gong, J. Sherrah and H. Liddell. Support Vector Machine based multi-view face detection and recognition. Image and Vision Computing, 22(5), pages 413-427, 2004.
- Y. Li, S. Gong and H. Liddell. Constructing facial identity surfaces for recognition. International Journal of Computer Vision, 53(1), pages 71-92, 2003.
- Y. Li, S. Gong and H. Liddell. Recognising trajectories of facial identities using Kernel Discriminant Analysis. Image and Vision Computing, 21(13-14), Pages 1077-1086, December 2003.
- Y. Li, S. Gong and H. Liddell. Constructing facial identity surfaces in a nonlinear discriminating space . In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR2001), Kauai Marriott, Hawaii, December 2001.
- Y. Li, S. Gong and H. Liddell. Recognising trajectories of facial identities using Kernel Discriminant Analysis . In Proc. British Machine Vision Conference (BMVC2001), pages 613-622, Manchester, UK, September 2001. BMVA Best Scientific Paper Award 2001.
- Y. Li, S. Gong and H. Liddell. Constructing structures of facial identities on the view sphere using Kernel Discriminant Analysis . In Proc. International Workshop on Statistical and Computational Theories of Vision (SCTV2001), Vancouver, Canada, July 2001.
- Y. Li, S. Gong and H. Liddell. Video-based online face recognition using identity surfaces . In Proc. IEEE International Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems (RATFG-RTS 2001), pages 40-46, Vancouver, Canada, July 2001. Best Paper Prize.
- Y. Li, S. Gong and H. Liddell. Modelling faces dynamically across views and over time . In Proc. IEEE International Conference on Computer Vision (ICCV2001), volume 1, pages 554-559, Vancouver, Canada, July 2001.
- Y. Li, S. Gong and H. Liddell. Exploiting the dynamics of faces in spatial-temporal context . In Proc. International Conference on Control, Automation, Robotics and Vision (ICARCV2000), Singapore, December 2000.
- Y. Li, S. Gong and H. Liddell. Recognising the dynamics of faces across multiple views . In Proc. British Machine Vision Conference (BMVC2000), pages 242-251, Bristol, England, September 2000.
- Y. Li, S. Gong, J. Sherrah and H. Liddell. Multi-view face detection using Support Vector Machines and eigenspace modelling . In Proc. IEEE International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies (KES2000), pages 241-244, Brighton, England, August 2000.
- Y. Li, S. Gong and H. Liddell. Support vector regression and classification based multi-view face detection and recognition . In Proc. IEEE International Conference on Face and Gesture Recognition (FG2000), pages 300-305, Grenoble, France, March 2000.
With the rapidly growing mass of video data from media services, Internet and home digital cameras/camcorders, automatic methods for semantic video analysis become essential for parsing, indexing, retrieval, summarisation of these data. This task can take various forms depending on the granularity of semantics and application scenarios, e.g. from video genre classification, scene segmentation, to specific event/person detection and behaviour analysis. The aim of this research is to develop a multi-layered framework for semantic analysis of raw video. (1) At the lowest level, the acoustic and visual features, e.g. the mel-frequency cepstral coefficients (MFCC), colour, texture, shape and motion, are integrated together, which provide a fundamental description of video content. (2) At the intermediate layer, the low-level audio/visual features are processed and integrated to form the so-called "atomic semantic features" which seek to represent the semantic concepts over a minimal temporal period. (3) The user-oriented semantics analysis is performed at a higher level using statistic models such as the Bayesian Network and the Hidden Markov Model.
- L-Q. Xu and Y. Li. Video classification using spatial-temporal features and PCA. In Proc. IEEE International Conference on Multimedia and Expo (ICME2003), Baltimore, USA, July 2003.
Principal Component Analysis (PCA) has been of great interest in computer vision and pattern recognition. In particular, incrementally learning a PCA model, which is computationally efficient for large scale problems as well as adaptable to reflect the variable state of a dynamic system, is an attractive research topic with numerous applications such as adaptive background modelling and active object recognition. In addition, the conventional PCA, in the sense of least mean squared error minimisation, is susceptible to outlying measurements. Unfortunately the two issues have only been addressed separately in the previous studies. In this work, we have presented a novel algorithm of incremental PCA, and then extended it to robust PCA. In oppose to most previous studies where robust PCA is solved by intensive iterative algorithms, we use the current PCA model at each updating step to evaluate the likelihood of an element of a new observation to be an outlier, so that the robust analysis is efficiently embedded in the incremental updating framework. This is the key idea of our algorithm. Compared with the previous studies on robust PCA, our algorithm is computationally more efficient. We have applied this method to dynamic background modelling and multi-view face modelling, and obtained very encouraging results.
- Y. Li. On incremental and robust subspace learning. Pattern Recognition, 37(7), pages 1509-1518, 2004.
- Y. Li, L-Q. Xu, J. Morphett and R. Jacobs. An Integrated Algorithm of Incremental and Robust PCA. In Proc. IEEE International Conference on Image Processing (ICIP2003), Barcelona, Spain, September 2003.
Coarse macroblock motion vectors can be extracted from MPEG video with a minimal decompression. With a reasonable MPEG encoder, most of motion vectors usually reflect the real motion in a video scene although they are coded for compression purpose. Based on this observation, we developed a method of image mosaicking from MPEG video in this work. The main idea is that global motion estimation from MPEG motion vectors can be formulated as a robust parameter estimation problem which treats the "good" motion vectors as inliers and "bad" ones outliers. The bi-directional motion information in B-frames provides multiple routes to warp a frame to its previous anchor frame. A Least Median of Squares based algorithm is adopted for robust motion estimation. In the case of a large proportion of outliers, we detect possible algorithm failure and then perform re-estimation along a different route or interpolate the transform from neighbouring frames. We also developed a simplified method for constructing static background panorama and dynamic foreground panorama.
- Y. Li, L-Q. Xu, G. Morrison, C. Nightingale and J. Morphett. Robust panorama from MPEG video. In Proc. IEEE International Conference on Multimedia and Expo (ICME2003), Baltimore, USA, July 2003.