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25th IEEE International Conference on Image processing, ICIP 2018

作者： Onur Ozyurt, Erdem Gunsel, Bilge Multimedia Signal Proc. and Pattern Recognition Lab. Istanbul Technical University Turkey

ISBN: (纸本)9781479970612

We propose an object tracking method for Wide Area Motion Imagery (WAMI) video sequences, which models the tracking as a regularization problem through sparse representation of aerial video content. The proposed object tracker, Ll Dpct, applies particle filter tracking, and unlike the existing methods, it integrates a deep-learning-based object detector into the regularization scheme to improve the tracking performance. In order to enhance robustness to occlusion and scale changes, Ll Dpct monitors the state propagation, the level of sparsity as well as the representation capability of the model and receives feedback from the detector to update the observation model of the particle filter. Ll Dpct incrementally updates the dictionary of the sparse representation that enables us to efficiently represent the appearance changes of the object arising from illumination changes and high motion. Numerical results obtained on commonly used VIVID and UAV123 datasets denote that Ll Dpct significantly improves the object tracking performance in terms of precision rate and success rate compared to the state-of-the-art trackers. © 2018 IEEE.

关键词： Antennas

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AIM 2020 Challenge on Image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European Conference on Computer Vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

This paper reviews the AIM 2020 challenge on extreme image inpainting. This report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. The goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. The challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. This report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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WAMI OBJECT TRACKING USING L_1 TRACKER INTEGRATED WITH A DEEP DETECTOR

WAMI OBJECT TRACKING USING L_1 TRACKER INTEGRATED WITH A DEE...

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IEEE International Conference on Image processing

作者： Erdem Onur Ozyurt Bilge Gunsel Multimedia Signal Proc. and Pattern Recognition Lab. Istanbul Technical University Turkey

We propose an object tracking method for Wide Area Motion Imagery (WAMI) video sequences, which models the tracking as a regularization problem through sparse representation of aerial video content. The proposed object tracker, L1Dpct, applies particle filter tracking, and unlike the existing methods, it integrates a deep-learning-based object detector into the regularization scheme to improve the tracking performance. In order to enhance robustness to occlusion and scale changes, L1Dpct monitors the state propagation, the level of sparsity as well as the representation capability of the model and receives feedback from the detector to update the observation model of the particle filter. L1Dpct incrementally updates the dictionary of the sparse representation that enables us to efficiently represent the appearance changes of the object arising from illumination changes and high motion. Numerical results obtained on commonly used VIVID and UAV123 datasets denote that L1Dpct significantly improves the object tracking performance in terms of precision rate and success rate compared to the state-of-the-art trackers.

关键词： Target tracking Detectors Dictionaries Object tracking Monitoring Minimization

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Benchmarking super-resolution algorithms on real data

arXiv

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arXiv 2017年

作者： Köhler, Thomas Bätz, Michel Naderi, Farzad Kaup, André Maier, Andreas K. Riess, Christian Pattern Recognition Lab Dept. of Computer Science Multimedia Communications and Signal Processing Dept. of Electrical Electronic and Communication Engineering Erlangen-Nürnberg Erlangen Germany

Over the past decades, various super-resolution (SR) techniques have been developed to enhance the spatial resolution of digital images. Despite the great number of methodical contributions, there is still a lack of comparative validations of SR under practical conditions, as capturing real ground truth data is a challenging task. Therefore, current studies are either evaluated 1) on simulated data or 2) on real data without a pixel-wise ground truth. To facilitate comprehensive studies, this paper introduces the publicly availab.e Super-Resolution Erlangen (SupER) database that includes real low-resolution images along with high-resolution ground truth data. Our database comprises image sequences with more than 20k images captured from 14 scenes under various types of motions and photometric conditions. The datasets cover four spatial resolution levels using camera hardware binning. With this database, we benchmark 15 single-image and multi-frame SR algorithms. Our experiments quantitatively analyze SR accuracy and robustness under realistic conditions including independent object and camera motion or photometric variations. Copyright © 2017, The Authors. All rights reserved.

关键词： Database systems

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Binary pattern flavored feature extractors for Facial Expression recognition: An overview

Binary pattern flavored feature extractors for Facial Expres...

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Proceedings of the International Convention MIPRO

作者： Rasmus Lyngby Kristensen Zheng-Hua Tan Zhanyu Ma Jun Guo Section of Image Analysis and Computer Graphics Technical University of Denmark Kgs. Lyngby Denmark Signal and Information Processing section (SIP) Aalborg University Aalborg Denmark Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781479981748

This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER terminology, describes open challenges, and provides recommendations to scientific evaluation of FER systems. Lastly, it studies the facial expression recognition accuracy and blur invariance of the Local Frequency Descriptor. The paper seeks to bring together disjointed studies, and the main contribution is to provide a solid overview for future research.

关键词： Feature extraction Face Face recognition Databases Three-dimensional displays Accuracy Gold

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SAR IMAGE CLASSIFICATION WITH NORMALIZED GAMMA PROCESS MIXTURES

SAR IMAGE CLASSIFICATION WITH NORMALIZED GAMMA PROCESS MIXTU...

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IEEE International Conference on Image processing

作者： Koray Kayabol Bilge Gunsel Multimedia Signal Processing and Pattern Recognition Lab. Istanbul Technical University

ISBN: (纸本)9781479923427

We propose a novel image prior for the non-parametric Bayesian mixture model based unsupervised classification of SAR images. We modified the Normalized Gamma Process prior that constitutes a more general form of the Dirichlet Process prior in order to enclose the contribution of the adjacent pixels into the classification scheme. This yields an image classification prior embedded in a mixture model that allows infinite number of clusters and enables reaching to smoothed classification maps. Based on the classification results obtained on synthetic and real TerraSAR-X images, it is shown that the proposed model is capable of accurately classifying the pixels. It applies a simple iterative update scheme at a single run without performing a hierarchical clustering strategy as used in the previously proposed methods. It is also demonstrated that the model order estimation accuracy of the proposed method outperforms the conventional finite mixture models.

关键词： Infinite mixture models Normalized gamma process mixtures Nonparametric Bayesian Image classification SAR images Image classification Mixture models images Radar polarimetry classification scheme pixel Rescue Synthetic aperture radar Specific absorption rate SAFETY ANALYSIS REPORTS mixtures

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Color constancy and non-uniform illumination: Can existing algorithms work?

Color constancy and non-uniform illumination: Can existing a...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Michael Bleier Christian Riess Shida Beigpour Eva Eibenberger Elli Angelopoulou Tobias Tröger André Kaup Pattern Recognition Lab University of Erlangen-Nuremberg Germany Computer Vision Center Universidad Autónoma de Barcelona Spain Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Germany

The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assume a uniformly illuminated scene. However, more often than not, this assumption is an insufficient approximation of real-world illumination conditions (multiple light sources, shadows, interreflections, etc.). Thus, illumination should be locally determined, taking under consideration that multiple illuminants may be present. In this paper we investigate the suitability of adapting 5 state-of-the-art color constancy methods so that they can be used for local illuminant estimation. Given an arbitrary image, we segment it into superpixels of approximately similar color. Each of the methods is applied independently on every superpixel. For improved accuracy, these independent estimates are combined into a single illuminant-color value per superpixel. We evaluated different fusion methodologies. Our experiments indicate that the best performance is obtained by fusion strategies that combine the outputs of the estimators using regression.

关键词： Image color analysis Lighting Databases Light sources Image segmentation Estimation Bayesian methods

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An ensemble based incremental learning framework for concept drift and class imbalance

An ensemble based incremental learning framework for concept...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Ditzler, Gregory Polikar, Robi ECE Department Rowan University Signal Processing and Pattern Recognition Lab. Glassboro NJ 08028 United States

ISBN: (纸本)9781424469178

We have recently introduced an incremental learning algorithm, Learn ++.NSE, designed to learn in nonstationary environments, and has been shown to provide an attractive solution to a number of concept drift problems under different drift scenarios. However, Learn++.NSE relies on error to weigh the classifiers in the ensemble on the most recent data. For balanced class distributions, this approach works very well, but when faced with imbalanced data, error is no longer an acceptable measure of performance. On the other hand, the well-established SMOTE algorithm can address the class imbalance issue, however, it cannot learn in nonstationary environments. While there is some literature availab.e for learning in nonstationary environments and imbalanced data separately, the combined problem of learning from imbalanced data coming from nonstationary environments is underexplored. Therefore, in this work we propose two modified frameworks for an algorithm that can be used to incrementally learn from imbalanced data coming from a nonstationary environment. © 2010 IEEE.

关键词： Learning algorithms

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A PERCEPTUALLY ENHANCED BLIND SINGLE-CHANNEL AUDIO SOURCE SEPARATION BY NON-NEGATIVE MATRIX FACTORIZATION

A PERCEPTUALLY ENHANCED BLIND SINGLE-CHANNEL AUDIO SOURCE SE...

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European signal processing Conference

作者： S. Kubiz B. Gunsel Multimedia Signal Processing and Pattern Recognition Lab. Istanbul Technical University Dept. of Electronics and Communications Engineering

This paper proposes a 2D Non-negative Matrix Factorization (NMF) based single-channel source separation algorithm that emphasizes perceptually important components of audio. Unlike the existing methods, the proposed scheme performs a psychoacoustic pre-processing on the mixture spectrogram in order to suppress audio components that are not critical to human hearing sensation while amplifying the perceptually important ones. This yields the auditory spectrogram referred as sonogram of the observed audio mixture and the individual sources are then extracted by 2D NMF. Test results reported in terms of signal-to-Distortion-Ratio (SDR), signal-to-Inference-Ratio (SIR) and signal-to-Artifact-Ratio (SAR) show that the proposed perceptually enhanced separation improves the quality of decomposed audio sources by 1.5-6.5 dB with a reduced computational complexity.

关键词： Audio matrix decomposition Sonogram factorization complexity classes Spectrogram NMF protocol

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Annealed SMC samplers for Dirichlet process mixture models

Annealed SMC samplers for Dirichlet process mixture models

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2010 20th International Conference on pattern recognition, ICPR 2010

作者： Ulker, Yener Gunsel, Bilge Cemgil, Ali Taylan Multimedia Signal Proc.and Pattern Recognition Lab. Dept. of Electronics and Communications Eng. Istanbul Technical University 34469 Maslak Istanbul Turkey Dept. of Computer Eng. Bogazici University 34342 Bebek Istanbul Turkey

ISBN: (纸本)9780769541099

In this work we propose a novel algorithm that approximates sequentially the Dirichlet Process Mixtures (DPM) model posterior. The proposed method takes advantage of the Sequential Monte Carlo (SMC) samplers framework to design an effective annealing procedure that prevents the algorithm to get trapped in a local mode. We evaluate the performance in a Bayesian density estimation problem with unknown number of components. The simulation results suggest that the proposed algorithm represents the target posterior much more accurately and provides significantly smaller Monte Carlo error when compared to particle filtering. © 2010 IEEE.

关键词： Mixtures

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