DOCSLIB.ORG

Dr. Longin Jan Latecki [email protected]

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

List of Publications of Longin Jan Latecki (h-index ≥ 54, i10-index ≥ 140, i100-index ≥ 20) Journal Articles (all peer reviewed) 2020 1. Daniel Pedronette and Longin Jan Latecki. Rank-based Self-Training for Graph Convolutional Networks. J. of Information Processing and Management. Accepted December 2020. 2. Quan Zhou, Yu Wang, Yawen Fan, Xiaofu Wu, Suofei Zhang, Bin Kang, Longin Jan Latecki. ALEDNet: Towards Real-time Semantic Segmentation of Self-driving Images via Attention- guided Lightweight Encoder-decoder Network. J. of Applied Soft Computing, accepted August 2020. pdf 3. Ilyass Abouelaziz, Aladine Chetouani, Mohammed El Hassouni, Longin Jan Latecki, and Hocine Cherifi. No-Reference Mesh Visual Quality Assessment via Ensemble of Convolutional Neural Networks and Compact Multi-Linear Pooling. Pattern Recognition, Vol. 100, April 2020. pdf 2019 4. Ilyass Abouelaziz, Aladine Chetouani, Mohammed El Hassouni, Longin Jan Latecki, and Hocine Cherifi. 3D Visual Saliency and Convolutional Neural Network for Blind Mesh Quality Assessment. Neural Computing and Applications, published online Oct 2019. pdf 5. Quan Zhou, Yu Wang, Jia Liu, Xin Jin, and Longin Jan Latecki. An open-source project for real- time image semantic segmentation. SCIENCE CHINA Information Sciences, 62(12), Nov 2019. pdf 6. Quan Zhou, Wenbing Yang, Guangwei Gao, Weihua Ou, Huimin Lu, Jie Chen, and Longin Jan Latecki. Multi-scale deep context convolutional neural networks for semantic segmentation. World Wide Web Journal. Special Issue on Deep vs. Shallow: Learning for Emerging Web-scale Data Computing and Applications, Vol. 22, Issue 2, pp 555–570, March 2019. pdf 7. Song Bai, Xiang Bai, Qi Tian, and Longin Jan Latecki. Regularized Diffusion Process on Bidirectional Context for Object Retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 41, Issue 5, pp. 1213-1226, May 2019. pdf code 8. Chao Li, Xinggang Wang, Wenyu Liu, Longin Jan Latecki, Bo Wang, Junzhou Huang. Weakly Supervised Mitosis Detection in Breast Histopathology Images using Concentric Loss. Medical Image Analysis, Vol. 53, pp. 165-178, April 2019. pdf 9. Zongxiao Zhu, Cong Rao, Song Bai, and Longin Jan Latecki. Training convolutional neural network from multi-domain contour images for 3D shape retrieval. Pattern Recognition Letters, Vol. 119, pp. 41-48, March 2019. pdf 10. Song Bai, Zhichao Zhou, Jingdong Wang, Xiang Bai, Longin Jan Latecki, and Qi Tian. Automatic Ensemble Diffusion for 3D Shape and Image Retrieval. IEEE Trans. Image Processing (IP), 28(1), pp. 88-101, January 2019. pdf code 2 Dr. Longin Jan Latecki [email protected] 2018 11. Quan Zhou, Jie Cheng, Huimin Lu, Yawen Fan, Suofei Zhan, Xiaofu Wu, Baoyu Zheng, Weihua Ou, and Longin Jan Latecki. Learning adaptive contrast combinations for visual saliency detection. Multimedia Tools and Applications, published online Nov 2018. pdf 12. Quan Zhou, Cheng Zhang, Wenbin Yu, Yawen Fan, Hu Zhu, Xiaofu Wu, Weihua Ou, Weiping Zhu, and Longin Jan Latecki. Face recognition via fast dense correspondence. Multimedia Tools and Applications, 77(9), pp. 10501-10519, May 2018. pdf 13. Chao Li, Xinggang Wang, Wenyu Liu, Longin Jan Latecki. DeepMitosis: Mitosis Detection via Deep Detection, Verification and Segmentation Networks. Medical Image Analysis, Vol. 45, pp. 121-133, April 2018. pdf 2017 14. Song Bai, Xiang Bai, Zhichao Zhou, Zhaoxiang Zhang, Qi Tian, Longin Jan Latecki. GIFT: Towards Scalable 3D Shape Retrieval. IEEE Trans. on Multimedia, Vol. 19, Issue 6, pp. 1257- 1271, June 2017. pdf 15. Zhuo Deng, Sinisa Todorovic, and Longin Jan Latecki. Unsupervised Object Region Proposals for RGB-D Indoor Scenes. Computer Vision and Image Understanding, Volume 154, Issue C, pp. 127-136, January 2017. pdf 2016 16. Quan Zhou, Baoyu Zheng, Weiping Zhu, and Longin Jan Latecki. Multi-scale Context for Scene Labeling via Flexible Segmentation Graph. Pattern Recognition, Vol. 59, pp. 312–324, September 2016. pdf 17. Yu Zhou, Xiang Bai, Wenyu Liu, and Longin Jan Latecki. Similarity Fusion for Visual Tracking. International Journal of Computer Vision (IJCV), 118(3), pp. 337-363, January 2016. pdf 2015 18. Xiang Bai, Song Bai, Zhuotun Zhu, and Longin Jan Latecki. 3D Shape Matching via Two Layer Coding. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 37(12), pp. 2361 – 2373, December 2015. pdf 19. Nagesh Adluru, Xingwei Yang, Longin Jan Latecki. Sequential Monte Carlo for Maximum Weight Subgraphs with Application to Solving Image Jigsaw Puzzles. International Journal of Computer Vision (IJCV), Vol. 112, Issue 3, pp. 319-341, April 2015. pdf 20. Hairong Liu, Longin Jan Latecki, and Shuicheng Yan. Dense Subgraph Partition of Positive Hypergraph. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 37, Issue 3, pp. 541-554, March 2015. pdf proofs 3 Dr. Longin Jan Latecki [email protected] 2014 21. Xinggang Wang, Bin Feng, Xiang Bai, Wenyu Liu, and Longin Jan Latecki. Bag of Contour Fragments for Robust Shape Classification. Pattern Recognition, Vol. 47, Issue 6, pp. 2116– 2125, June 2014. pdf 22. Yu Zhou, Yinfei Yang, Meng Yi, Xiang Bai, Wenyu Liu, Longin Jan Latecki. Online Multiple Targets Detection and Tracking from Mobile Robot in Cluttered Indoor Environments with Depth Camera. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), vol. 28, No. 1, March 2014. pdf 2013 23. Hairong Liu, Longin Jan Latecki, and Shuicheng Yan. Fast Detection of Dense Subgraphs with Iterative Shrinking and Expansion. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), vol. 35, no. 9, pp. 2131-2142, September 2013. pdf 24. Xingwei Yang, Xiang Bai, Suzan Koknar-Tezel, Longin Jan Latecki. Densifying Distance Spaces for Shape and Image Retrieval. Journal of Mathematical Imaging and Vision (JMIV), Vol. 46, Issue 1, pp 12-28, May 2013. pdf 25. Wei Shen, Xiang Bai, Xingwei Yang and Longin Jan Latecki. Skeleton pruning as trade-off between skeleton simplicity and reconstruction error. SCIENCE CHINA, April 2013, Vol. 56, No. 1, pp 1–18. pdf 26. Wei Shen, Yan Wang, Xiang Bai, Hongyuan Wang, Longin Jan Latecki. Shape Clustering: Common Structure Discovery. Pattern Recognition, 46(2), pp. 539 – 550, February 2013. pdf 27. Xingwei Yang, Lakshman Prasad, and Longin Jan Latecki. Affinity Learning with Diffusion on Tensor Product Graph. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 35, No. 1, pp. 28 – 38, January 2013. pdf 28. Wei Shen, Bo Wang, Xiang Bai, and Longin Jan Latecki. Face Identification Using Reference- based Features with Message Passing Model. Neurocomputing, 99, pp. 339-346, January 2013. pdf 2012 29. Marko Petkovic, David Porkajac, and Longin Jan Latecki. Spherical coverage verification. Applied Mathematics and Computation, 218(19), pp. 9699-9715, December 2012. pdf 30. Yunfeng Li, Tadamasa Sawada, Longin Jan Latecki, Robert Steinman, Zygmunt Pizlo. A tutorial explaining a machine vision model that emulates human performance when it recovers natural 3D scenes from 2D images. Journal of Mathematical Psychology, 56(4), pp. 217–231, August 2012. pdf 31. Jingting Zeng, Haibin Ling, Longin Jan Latecki, Shanon Fitzhugh, and Guodong Guo. Analysis of Facial Images across Age Progression by Humans. ISRN Machine Vision, Online January 2012. pdf 32. Hairong Liu, Xingwei Yang, Longin Jan Latecki, and Shuicheng Yan. Dense Neighborhoods on Affinity Graph. International Journal of Computer Vision (IJCV), 98, pp. 65-82 , May 2012. pdf 4 Dr. Longin Jan Latecki [email protected] 33. Xingwei Yang, Hairong Liu, and Longin Jan Latecki, Contour-Based Object Detection as Dominant Set Computation. Pattern Recognition (PR), 45(5), pp. 1927-1936, May 2012. pdf 34. Junwei Wang, Xiang Bai, Xinge You, Wenyu Liu, Longin Jan Latecki. Shape Matching and Classification Using Height Functions. Pattern Recognition Letters (PRL), Vol. 33(2), pp. 134- 143, January 2012. pdf 2011 35. Xingwei Yang, Daniel B. Szyld, and Longin Jan Latecki. Diffusion on a Tensor Product Graph for Semi-Supervised Learning and Interactive Image Segmentation. Advances in Imaging and Electron Physics, Vol. 169, pp. 147-172, December 2011. pdf 36. Suzan Koknar-Tezel and Longin Jan Latecki. Improving SVM Classification on Imbalanced Time Series Data Sets with Ghost Points. Knowledge and Information Systems. An International Journal, Vol. 28, Issue 1, pp. 1-23, July 2011. pdf 37. Wei Shen, Xiang Bai, Rong Hu, Hongyuan Wang, and Longin Jan Latecki. Skeleton Growing and Pruning with Bending Potential Ratio. Pattern Recognition (PR), 44, pp. 196-209, 2011. pdf software 2010 38. ChengEn Lu, Nagesh Adluru, Haibin Ling, Guangxi Zhu, Longin Jan Latecki. Contour Based Object Detection Using Part-Bundles. Computer Vision and Image Understanding (CVIU), Vol. 114, No. 7, pp. 827-834, July 2010. pdf 39. Xiang Bai, Xingwei Yang, Longin Jan Latecki, Wenyu Liu, Zhuowen Tu. Learning Context Sensitive Shape Similarity by Graph Transduction. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 32, No. 5, pp. 861-874, May 2010. pdf 40. Y. Tang, X. Bai, X. Yang, L. Lin, S. Liu, and L. J. Latecki. Skeletonization with Particle Filters. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), Vol. 24, No. 4, pp. 619-634, 2010. pdf 2009 41. Longin Jan Latecki, Marc Sobel, and Rolf Lakaemper. Piecewise Linear Models with Guaranteed Closeness to the Data. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), Vol. 31, No. 8, pp. 1525-1531, 2009. pdf 42. Nagesh Adluru and Longin Jan Latecki. Contour Grouping Based on Contour-Skeleton Duality. International Journal of Computer Vision (IJCV) 83, pp. 12-29, 2009. pdf 43. Wenfei Jiang, Longin Jan Latecki, Wenyu Liu, Hui Liang, Ken Gorman. A Video Coding Scheme Based on Joint Spatiotemporal and Adaptive Prediction. IEEE Trans. on Image Processing, Vol. 18, No. 5, 2009. pdf 5 Dr. Longin Jan Latecki [email protected] 2008 44. Hairong Liu, Longin Jan Latecki, and Wenyu Liu. A Unified Curvature Definition for Regular, Polygonal, and Digital Planar Curves. International Journal of Computer Vision (IJCV), Vol. 80, No. 1, pp. 104-124, 2008. pdf file 45. X. Bai, X. Yang, D.
Recommended publications
  • Volume Rendering

    Volume Rendering

    Volume Rendering 1.1. Introduction Rapid advances in hardware have been transforming revolutionary approaches in computer graphics into reality. One typical example is the raster graphics that took place in the seventies, when hardware innovations enabled the transition from vector graphics to raster graphics. Another example which has a similar potential is currently shaping up in the field of volume graphics. This trend is rooted in the extensive research and development effort in scientific visualization in general and in volume visualization in particular. Visualization is the usage of computer-supported, interactive, visual representations of data to amplify cognition. Scientific visualization is the visualization of physically based data. Volume visualization is a method of extracting meaningful information from volumetric datasets through the use of interactive graphics and imaging, and is concerned with the representation, manipulation, and rendering of volumetric datasets. Its objective is to provide mechanisms for peering inside volumetric datasets and to enhance the visual understanding. Traditional 3D graphics is based on surface representation. Most common form is polygon-based surfaces for which affordable special-purpose rendering hardware have been developed in the recent years. Volume graphics has the potential to greatly advance the field of 3D graphics by offering a comprehensive alternative to conventional surface representation methods. The object of this thesis is to examine the existing methods for volume visualization and to find a way of efficiently rendering scientific data with commercially available hardware, like PC’s, without requiring dedicated systems. 1.2. Volume Rendering Our display screens are composed of a two-dimensional array of pixels each representing a unit area.
  • Vector Median Filter with Directional Detector for Color Image Denoising

    Vector Median Filter with Directional Detector for Color Image Denoising

    Proceedings of the World Congress on Engineering 2011 Vol II WCE 2011, July 6 - 8, 2011, London, U.K. Vector Median Filter with Directional Detector for Color Image Denoising Vladimir V. Khryashchev, Member, IAENG, Denis K. Kuykin, and Alina A. Studenova Abstract – In this paper a new approach to the problem of There are many types of impulse noise. Let x be the impulse noise removal from color images is presented. The i proposed method is based on noise detection algorithm and vector, characterizing a pixel of a noisy image, vi – the weighted vector median filter. The results of its analysis compared to other known median-based vector filters are vector describing impulse noise model, zi is the noise-free presented. The analysis shows that the proposed algorithm is color vector, p – impulse noise probability, then more efficient in random-valued impulse noise removal from color digital images than other considered filters.1 vi , with probability p Index Terms – color image processing, impulse noise xi . removal, vector median filter, order statistic, genetic algorithm zi , with probability1 p optimization Depending on the type of vector vi researchers consider I. INTRODUCTION either fixed-valued or random-valued impulse noise models [4-6]. MAGE noise reduction without structure degradation is In the case of fixed-valued impulse noise v is I perhaps the most important low-level image processing i task [1, 2]. Faulty sensors, optic imperfectness, electronics characterized by the following expression: interference, and data transmission errors may introduce noise to digital images. In considering the signal-to-noise (,,),d zG zBT with probability p ratio over practical communication media, such as i i 1 R BT microwave or satellite links, there can be degradation in (,,),zi d zi with probability p2 vi , quality due to low received signal power.
  • Patterns Detection in SAED Images of Transmission Electron Microscopy*

    Patterns Detection in SAED Images of Transmission Electron Microscopy*

    Patterns Detection in SAED Images of Transmission Electron Microscopy* Stepan Nebaba 1[0000-0001-9203-2170], Dmitry Zavyalov 1[0000-0002-0128-2658], Alexander Pak 1[0000-0001-8447-1309] 1 Tomsk Polytechnic University, Tomsk, Russia {stepanlfx,zda,ayapak}@tpu.ru Abstract. Specialized software that supports existing approaches to processing images of the crystal structure of materials for analyzing transmission electron microscopy images have a lot of different digital image processing methods, but major part of it are weakly automated. Automatic algorithm is able to make the crystal structure analysis more fast and effective process. The paper considers the problem of automated processing of SAED images of transmission electron microscopy. Proposed automated image processing algorithm based on methods of adaptive binarization and Watershed segmentation allows one to determine the distances on the diffraction pattern of a material sample on the image of transmission electron microscopy. The proposed algorithm has been tested on several SAED images, distances were calculated in automatic mode and compared with the results of semi-automatic measurement in Digital Micrograph GMS 1.8 software. The analysis of the results showed high agreement in considered cases, which let us assume that proposed algorithm has good development prospects. Keywords: Computer vision, Image processing, Image analysis, Transmission electron microscopy, Crystalline diffraction pattern, Selected area electron diffraction. 1 Introduction Methods and algorithms of computer vision associated with the processing and analysis of raster images are widely used in various fields of science [1-5], including in the field of processing images obtained using electron microscopes. One of the fundamental problems in these areas is the automation of assessing the composition and structure of materials from their images.
  • Advanced Algorithms in Medical Computer Graphics

    Advanced Algorithms in Medical Computer Graphics

    EUROGRAPHICS2008/T.TheoharisandP.DutréSTAR–StateofTheArtReport Advanced Algorithms in Medical Computer Graphics Jan Klein1, Dirk Bartz2, Ola Friman1, Markus Hadwiger3, Bernhard Preim4, Felix Ritter1, Anna Vilanova5, Gabriel Zachmann6 1MeVis Research, Germany 2University of Leipzig, Visual Computing (ICCAS), Germany 3VRVis Research Center, Vienna, Austria 4Otto-von-Guericke-University, Institute for Simulation and Graphics, Germany 5Eindhoven University of Technology, Department of Biomedical Engineering, Netherlands 6TU Clausthal, Department of Informatics, Germany Abstract Advanced algorithms and efficient visualization techniques are of major importance in intra-operative imaging and image-guided surgery. The surgical environment is characterized by a high information flow and fast deci- sions, requiring efficient and intuitive presentation of complex medical data and precision in the visualization results. Regions or organs that are classified as risk structures are in this context of particular interest. This paper summarizes advanced algorithms for medical visualization with special focus on risk structures such as tumors, vascular systems and white matter fiber tracts. Algorithms and techniques employed in intra-operative situations or virtual and mixed reality simulations are discussed. Finally, the prototyping and software development process of medical visualization algorithms is addressed. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object-Modeling I.3.7 [Computer
  • Bedrich Benes

    Bedrich Benes

    Bedrich Benes July 11, 2020 Purdue University George W. McNelly Professor of Technology Department of Computer Graphics Technology and Computer Science West Lafeyette, In 47907-2021 Phone: (765)-496-2954 Email: [email protected] Research Interests Computer graphics, geometric modeling, procedural modeling, generative algorithms, simulation of natural phenomena, additive manufacturing. Education Ph.D. Computer Science, Czech Technical University in Prague, 1998. M.S. Computer Science, Czech Technical University in Prague, 1991. Professional Experience Apr. 2019 - present George McNelly Professor of Technology Aug. 2019 - present Professor of Department of Computer Science Purdue University, USA Dec. 2017 - 2019 Professor of Department of Computer Science (by courtesy) Purdue University, USA Aug. 2015 - present Professor of Department of Computer Graphics Technology Purdue University, USA Aug. 2010 - Aug. 2015 Associate Professor of Department of Computer Graphics Technology Purdue University, USA Aug. 2011 - Aug. 2012 Assistant Head of Department of Computer Graphics Technology Purdue University, USA Aug. 2005 - Aug. 2010 Assistant Professor of Department of Computer Graphics Technology Purdue University, USA Bedrich Benes 2 Aug. 2000 - Aug. 2005 Assistant Professor of Department of Computer Science Instituto Technol´ogicoy de Estudios Superiores de Monterrey (ITESM), Mexico Jul. 1999 - Jun. 2000 Visiting Professor of Department of Computer Science Instituto Technol´ogicoy de Estudios Superiores de Monterrey (ITESM), Mexico Jun. 1998 -
  • Dr. Andrew S. Glassner

    Dr. Andrew S. Glassner

    Dr. Andrew S. Glassner 726 N. 47th St. Seattle, WA 98103 USA [email protected] http://www.glassner.com (206) 632-7663 February 2014 Fields of Expertise Computer Graphics: Rendering, modeling, signal processing, texturing, shading, shape grammars Game Design: Creating and developing fun, participatory social games and entertainments Interactive Storytelling: Participatory fiction, multiplayer games Fiction: Novels, screenplays Non-fiction: Computer graphics, interactive story technique Education Ph.D., Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, July, 1988 M.S., Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 1987 B.S., Computer Engineering, Case Western Reserve University, Cleveland, OH, 1984 Employment President and Director of Education, The Imaginary Institute, LLC February 2013 – Present Novelist, Screenwriter-Director, Consultant, Coyote Wind, LLC May 2000 – Present Researcher, Microsoft Corporation, Microsoft Research, Redmond, WA July 1997 – May 2000 Research in 3D computer graphics and new media Writer-Director, The Microsoft Network, Redmond, WA October 1996 - July 1997 Created and developed Dead Air, an interactive web-based mystery show for The Microsoft Network. Wrote and directed the pilot episode. Researcher, Microsoft Corporation, Microsoft Research, Redmond, WA December 1994 - October 1996 Research in 3D computer graphics and media theory Member of the Research Staff, Xerox Corporation, Palo Alto Research Center (PARC), Palo Alto, CA September 1988 - September 1994 Research in computer graphics Visiting Scientist, Delft University of Technology, Delft, The Netherlands May 1987 - December 1987 Organized and taught advanced computer graphics course. Research in multidimensional ray tracing. Research Assistant University of North Carolina at Chapel Hill, Chapel Hill, NC September 1984 - May 1988 Research in computer graphics, assisted Dr.
  • Applying MATLAB to Computer Graphics and CAGD

    Applying MATLAB to Computer Graphics and CAGD

    Applying MATLAB to Computer Graphics and CAGD. Application to a Visualization Problem in the Automotive Industry. Akemi Gálvez1, Andrés Iglesias1,*, Flabio Gutiérrez2 1Department of Applied Mathematics and Computational Sciences, University of Cantabria Santander, Spain 2Department of Mathematics, National University of Piura, Piura, Peru * Corresponding author. used in this paper are also discussed in this section. Then, Abstract Section 3 introduces some additional commands we need to implement in order to solve some interesting problems Computer graphics are usually achieved by using some related to CAGD and computer graphics. This section also traditional programming languages (Fortran, Pascal, C, includes a wide description of the main MATLAB etc.). In this paper an extensive use of the general-purpose graphical commands, options and utilities that will be numerical computation programs (NCPs) in the computer useful for rendering surfaces. As an application, Section 4 graphics field is proposed instead of. The paper describes shows how they can be successfully applied to solve a the main advantages of this kind of programs, and several given visualization problem coming from the automotive examples of how they can be successfully applied to industry. Today, many industries are concerned about the computer graphics and visualization. Moreover, the paper possibility to transfer their information by Internet, briefly introduces several functions and commands avoiding other possible and slower transference ways. developed by the authors, which will be successfully Section 5 discusses such a possibility for the visual applied to solve a visualization problem coming from the information, through the MATLAB-VRML connection. automotive industry. Finally, the paper closes with the main conclusions and Keywords: Computer Graphics, MATLAB, CAGD.