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Techniques for violent scene detection and a↵ective impact prediction in videos can be deployed in many applications. In MediaEval 2015, we explore deep learning methods to tackle this challenging problem. Our system consists of several deep learning features. First, we train a Convolutional Neural Network (CNN) model with a subset of ImageNet classes(More)
The pattern of blood veins is unique, even in identical twins. The human palms have a broad and complicated vascular pattern and thus contain many kinds of features. The Vein Based Biometric (VBB) depends on measurement of the vascular pattern made by the blood vessels on the back of the hand. The VBB technology is the world's leading and most promising(More)
This paper proposes an algorithm which combines Particle Swarm Optimization (PSO) with Least Squares Support Vector Machines (LSSVM) to identify lithology by using well logging data. First of all, PSO is used for optimizing the main parameters of LSSVM, and then by using the optimized parameters to obtain a better PSO-LSSVM classification model which can be(More)
Owing to the large scale of multi-dimensional datasets in image processing, the standard Support Vector Machine (SVM) has a high time complexity in the training process for image segmentation. A new machine learning method, Ball Vector Machine (BVM) is used for image segmentation in order to reduce the training time in this paper. The experimental results(More)
This paper presents a novel low bit rate parametric stereo coding scheme which uses whole band inter-channel time difference (WITD) and whole band inter-channel phase difference (WIPD) together with a new effective downmixing method. The inter-channel level differences and inter-channel phase differences are also employed in the proposed stereo coding to(More)
In this paper we use L1 adaptive controller to augment a baseline for improved command tracking of the unmanned aerial vehicle. The procedure included baseline and adaptive augmentation controllers design. The eigenstructure assignment (EA) controller was designed for the nominal case, and then was augmented with a L1 adaptive controller. The L1 adaptive(More)
This paper presents the two new ITU-T Recommendations G.722 Annex D and G.711.1 Annex F, which are stereo extensions of the wideband codecs ITU-T G.722 and G.711.1 and their superwideband extensions (G.722 Annex B and G.711.1 Annex D). An embedded scalable structure is used to add stereo extension layers on top of the wideband or superwideband core coding.(More)
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