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To avoid the introduction of false information during the fusion progress, a novel multi-focus image fusion method is proposed in quaternion wavelet transform domain. To obtain the dependency in different high frequency subbands, a quaternion wavelet contextual hidden Markov model (Q-CHMM) is established for modeling quaternion wavelet coefficients. And for better image representations, several features...
The ever increasing data generation confronts both practitioners and researchers on handling massive and sequentially generated amounts of information, the so-called data streams. In this context, a lot of effort has been put on the extraction of useful patterns from streaming scenarios. Learning from data streams embeds a variety of problems, and by far, the most challenging is concept drift, i.e...
We present an algorithm for learning a feature representation for video segmentation. Standard video segmentation algorithms utilize similarity measurements in order to group related pixels. The contribution of our paper is an unsupervised method for learning the feature representation used for this similarity. The feature representation is defined over video supervoxels. An embedding framework learns...
Automatic and spontaneous speech emotion recognition is an important part of a human-computer interactive system. However, emotion identification in spontaneous speech is difficult because most often the emotion expressed by the speaker are not necessarily as prominent as in acted speech. In this paper, we propose a spontaneous speech emotion recognition framework that makes use of the associated...
Land cover classification is a task that requires methods capable of learning high-level features while dealing with high volume of data. Overcoming these challenges, Convolutional Networks (ConvNets) can learn specific and adaptable features depending on the data while, at the same time, learn classifiers. In this work, we propose a novel technique to automatically perform pixel-wise land cover classification...
In this paper, we focus on the text/non-text classification problem: distinguishing images that contain text from a lot of natural images. To this end, we propose a novel neural network architecture, termed Convolutional Multi-Dimensional Recurrent Neural Network (CMDRNN), which distinguishes text/non-text images by classifying local image blocks, taking both region pixels and dependencies among blocks...
In this paper, with the help of controllable active near-infrared (NIR) lights, we construct near-infrared differential (NIRD) images. Based on reflection model, NIRD image is believed to contain the lighting difference between images with and without active NIR lights. Two main characteristics based on NIRD images are exploited to conduct spoofing detection. Firstly, there exist obviously spoofing...
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