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Learning from data

Underwater Image Enhancement via a Fast yet Effective Traditional Method

Weidong Zhang, Peixian Zhuang, Hai-Han Sun, Guohou Li, Sam Kwong, Chongyi Li

Addressing underwater image challenges, our method MLLE enhances color, contrast, and details efficiently. Outperforming competitors, it processes 1024×1024×3 images in under 1s on a single CPU. Experiments show improved underwater image segmentation, keypoint detection, and saliency detection.

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An Echo in Time: Tracing the Evolution of Beamforming Algorithms

Ahmet M. Elbir, Kumar Vijay Mishra, Sergiy A. Vorobyov, and Robert W. Heath, Jr.

Beamforming is a widely used signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction.

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Estimation in Multi-Object State Space Model

Dr. Ba-Ngu Vo

A brief introduction to state estimation in multi-object system that arises from applications where the number of objects and their states are unknown and vary randomly with time. State space model (SSM) is a fundamental concept in system theory that permeated through many fields of study.

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Empirical Wavelets

Dr. Jérôme Gilles

We design a data-driven wavelet transform, called the empirical wavelet transform, which permits to extract very accurate time-frequency information from signals, or features from images.

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Facial Expression Analysis with Attention Mechanism

Dr. Jiabei Zeng

We develop algorithms to analyzing facial expression by learning from the data. Since local characters of muscle movements play an important role in distinguishing facial expression by machines, we explore the local characters of facial expressions by introducing the attention mechanism in both supervised and self-supervised supervised manners. Our methods is experimentally shown to be effective on facial expression recognition with occlusions and facial action unit detection.

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