[ad_1] Introduction Stereo matching is a significant area of research in computer vision and image processing, which aims to estimate the disparity map between a pair of stereo images. Disparity estimation plays a crucial role…
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[ad_1] Thesis Overview: Title: Optical flow estimation for motion analysis Introduction: In the field of computer vision, optical flow estimation is a critical task that involves tracking the movement of objects in a sequence of…
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[ad_1] Introduction Instance segmentation is a computer vision task that aims to detect individual object instances in an image and classify each pixel as belonging to a particular object category. This process is crucial for…
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[ad_1] Introduction Action recognition is a crucial task in video understanding and has gained significant attention in recent years due to its wide range of applications in various fields such as surveillance, human-computer interaction, sports…
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[ad_1] Introduction: Gaze estimation for attention tracking has become a prominent research area in recent years, with applications in various fields such as human-computer interaction, psychology, and medical diagnosis. The ability to accurately estimate where…
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[ad_1] Introduction: Pose estimation for human body analysis is a rapidly growing field in computer vision and artificial intelligence. It involves the process of detecting and tracking the body joints and limbs of a human…
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[ad_1] Introduction In recent years, the explosion of online video content has created a need for automated methods of video summarization for highlight extraction. Video summarization involves condensing the content of a video into a…
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[ad_1] Introduction 1.1 Introduction 1.2 Background of study 1.3 Problem Statement 1.4 Objective of study 1.5 Limitation of study 1.6 Scope of study 1.7 Significance of study 1.8 Structure of the Thesis 1.9 Definition of…
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[ad_1] Introduction Recurrent neural networks (RNNs) have gained significant attention in recent years for their ability to model sequential data and make predictions based on this data. In particular, recurrent neural network language models have…
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[ad_1] Introduction Hopfield networks are a type of recurrent neural network that have been widely used for associative memory tasks. First introduced by John Hopfield in 1982, these networks are capable of storing and recalling…
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