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This paper introduces the sparse regularization for the convolutional neural network (CNN) with the rectified linear units (ReLU) in the hidden layers. By introducing the sparseness for the inputs of the ReLU, there is effect to push the inputs of the ReLU to zero in the learning process. Thus it is expected that the unnecessary increase of the outputs of the ReLU can be prevented. This is the similar...
It has long been considered a significant problem to improve the visual quality of lossy imageand video compression. Recent advances in computing power together with the availabilityof large training data sets has increased interest in the application of deep learning cnnsto address image recognition and image processing tasks. Here, we present a powerful cnntailored to the specific task of semantic...
Hierarchical feature learning based on convolutional neural networks (CNN) has recently shown significant potential in various computer vision tasks. While allowing high-quality discriminative feature learning, the downside of CNNs is the lack of explicit structure in features, which often leads to overfitting, absence of reconstruction from partial observations and limited generative abilities. Explicit...
The details of oriented visual stimuli are better resolved when they are horizontal or vertical rather than oblique. This "oblique effect" has been researched and confirmed in numerous research studies, including behavioral studies and neurophysiological and neuroimaging findings. Although the "oblique effect" has influence in many fields, little research integrated it into computational...
Visual object tracking is a challenging computer vision problem with numerous real-world applications. This paper investigates the impact of convolutional features for the visual tracking problem. We propose to use activations from the convolutional layer of a CNN in discriminative correlation filter based tracking frameworks. These activations have several advantages compared to the standard deep...
In this paper we evaluate the utility of sparseness as a criterion for selecting a sub-set of independent component filters (ICF). Four sparseness measures were presented more than a decade ago by Le Borgne et al., but have since been ignored for ICF selection. In this paper we present our evaluation in the context of texture retrieval. We compare the sparseness-based method with the dispersal-based...
This article addresses the problem of imagebased localization in indoor environments. The localization is achieved by querying a database of omnidirectional images that constitutes a detailed visual map of the building where the robot operates. Omnidirectional cameras have the advantage, when compared to standard perspectives, of capturing in a single frame the entire visual content of a room. This,...
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