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 objec...
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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 semanti...
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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 objec...
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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.
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 c...
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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 se...
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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.
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 ...
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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.
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 assu...
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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.
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 ...
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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 s...
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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.
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...
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