Mr. Tian-Li Yu Profile

Tian-Li Yu

at Univ of Illinois

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Author

Publications (5)

Proceedings Article | 2 May 2007 Paper

Application of evolutionary algorithms and neural network concepts to the design of low-cost, wideband antenna arrays

Scott Santarelli, Robert Mailloux, Tian-Li Yu, Thomas Roberts, Michelle Champion, David Goldberg

Proceedings Volume 6563, 65630G (2007) https://doi.org/10.1117/12.724968

KEYWORDS: Antennas, Genetic algorithms, Chemical elements, Optimization (mathematics), Neural networks, Gallium, Evolutionary algorithms, Statistical analysis, Data modeling, Binary data

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Proceedings Article | 29 April 2005 Paper

Shape regularized active contour based on dynamic programming for anatomical structure segmentation

Tianli Yu, Jiebo Luo, Amit Singhal, Narendra Ahuja

Proceedings Volume 5747, (2005) https://doi.org/10.1117/12.594662

KEYWORDS: Image segmentation, Lung, Computer programming, Medical imaging, Principal component analysis, Radiography, Statistical analysis, Blood vessels, Chest, Algorithm development

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We present a method to incorporate nonlinear shape prior constraints into segmenting different anatomical structures in medical images. Kernel space density estimation (KSDE) is used to derive the nonlinear shape statistics and enable building a single model for a class of objects with nonlinearly varying shapes. The object contour is coerced by image-based energy into the correct shape sub-distribution (e.g., left or right lung), without the need for model selection. In contrast to an earlier algorithm that uses a local gradient-descent search (susceptible to local minima), we propose an algorithm that iterates between dynamic programming (DP) and shape regularization. DP is capable of finding an optimal contour in the search space that maximizes a cost function related to the difference between the interior and exterior of the object. To enforce the nonlinear shape prior, we propose two shape regularization methods, global and local regularization. Global regularization is applied after each DP search to move the entire shape vector in the shape space in a gradient descent fashion to the position of probable shapes learned from training. The regularized shape is used as the starting shape for the next iteration. Local regularization is accomplished through modifying the search space of the DP. The modified search space only allows a certain amount of deformation of the local shape from the starting shape. Both regularization methods ensure the consistency between the resulted shape with the training shapes, while still preserving DP’s ability to search over a large range and avoid local minima. Our algorithm was applied to two different segmentation tasks for radiographic images: lung field and clavicle segmentation. Both applications have shown that our method is effective and versatile in segmenting various anatomical structures under prior shape constraints; and it is robust to noise and local minima caused by clutter (e.g., blood vessels) and other similar structures (e.g., ribs). We believe that the proposed algorithm represents a major step in the paradigm shift to object segmentation under nonlinear shape constraints.

Proceedings Article | 31 July 2002 Paper

Combining video mosaic and edge overlap techniques for condensing information in a video clip to a single frame

Yujin Zhang, Tianli Yu

Proceedings Volume 4875, (2002) https://doi.org/10.1117/12.477074

KEYWORDS: Video, Cameras, Motion estimation, Edge detection, Visual system, Signal processing, Motion models, Distortion, Sensors, Electronics engineering

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Proceedings Article | 19 December 2001 Paper

Summarizing motion contents of the video clip using moving edge overlaid frame (MEOF)

Tianli Yu, Yujin Zhang

Proceedings Volume 4676, (2001) https://doi.org/10.1117/12.451111

KEYWORDS: Video, Motion estimation, Motion models, Image processing, Cameras, Affine motion model, Denoising, Video processing, Data modeling, Sensors

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Proceedings Article | 27 December 2000 Paper

Motion feature extraction for content-based video sequence retrieval

Tianli Yu, Yujin Zhang

Proceedings Volume 4311, (2000) https://doi.org/10.1117/12.411912

KEYWORDS: Motion models, Video, Optical flow, Affine motion model, Motion estimation, Feature extraction, Image segmentation, Vector spaces, Quantization, Motion analysis

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