Results 271 to 280 of about 310,518 (314)
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Multi-Exposure Fusion with CNN Features
2018 25th IEEE International Conference on Image Processing (ICIP), 2018Multi-exposure fusion (MEF) is a widely used approach to high dynamic range imaging. The selection of features for fusion weight calculation is important to the performance of MEF. In this paper, we investigate the effectiveness of convolutional neural network (CNN) features for MEF.
Hui Li 0029, Lei Zhang 0006
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Feature Comparison and Feature Fusion for Traditional Dances Recognition
2013Traditional dances constitute a significant part of the cultural heritage around the world. The great variety of traditional dances along with the complexity of some dances increases the difficulty of identifying such dances, thus making the traditional dance recognition a challenging subset within the general field of activity recognition.
Ioannis Kapsouras +3 more
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Linear dependency modeling for feature fusion
2011 International Conference on Computer Vision, 2011This paper addresses the independent assumption issue in fusion process. In the last decade, dependency modeling techniques were developed under a specific distribution of classifiers. This paper proposes a new framework to model the dependency between features without any assumption on feature/classifier distribution.
Andy Jinhua Ma, Pong Chi Yuen
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A Fuzzy Vault Scheme for Feature Fusion
2011Widespread application of biometric authentication brings about new problem of privacy. Biometric template protection is becoming a hot research. Efficient feature fusion is deemed to have good performance possibly. In this paper we proposed a fuzzy vault scheme for feature fusion. In our scheme, two facial features Multi-Block Local Binary Pattern (MB-
Lifang Wu +3 more
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Hierarchical Feature Fusion for Visual Tracking
2007 IEEE International Conference on Image Processing, 2007A new method for object tracking in video sequences is presented. This method exploits the benefits of particle filters to tackle the multimodal distributions emerging from cluttered scenes. The tracked object is described by several models of different complexity, which are probabilistically linked together.
Alexandros Makris +3 more
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Multi-feature fusion deep networks
Neurocomputing, 2016In this paper, we propose a novel deep networks, multi-feature fusion deep networks (MFFDN), based on denoising autoencoder. MFFDN significantly reduces the classification error while giving the interpretability of the hidden-layer feature representation in learning process.
Gang Ma 0001 +3 more
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Feature fusion for lung nodule classification
International Journal of Computer Assisted Radiology and Surgery, 2017This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning.Three features based on (i) Gabor filter, (ii) multi-resolution local binary pattern (LBP) texture features and (iii) signed distance fused with LBP which generates a combinational shape and texture feature are utilized ...
Amal A. Farag +3 more
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Sequential Feature Fusion for Object Detection
2018In an image, the category and the location of an object are related to global, spatial and contextual visual information of the object, which are all extremely important for accurate and efficient object detection. In this paper, we propose a region-based detector named Sequential Feature Fusion Network (SFFN) which simultaneously utilizes global ...
Qiang Wang, Yahong Han
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Feature Selection and Fusion for Texture Classification
2005In this paper, a novel texture classification method using selected and combined features from wavelet frame and steerable pyramid decompositions has been proposed. Firstly, wavelet frame and steerable pyramid decompositions are used to extract complementary features from texture regions.
Shutao Li 0001, Yaonan Wang 0001
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