133 research outputs found
Evaluating color texture descriptors under large variations of controlled lighting conditions
The recognition of color texture under varying lighting conditions is still
an open issue. Several features have been proposed for this purpose, ranging
from traditional statistical descriptors to features extracted with neural
networks. Still, it is not completely clear under what circumstances a feature
performs better than the others. In this paper we report an extensive
comparison of old and new texture features, with and without a color
normalization step, with a particular focus on how they are affected by small
and large variation in the lighting conditions. The evaluation is performed on
a new texture database including 68 samples of raw food acquired under 46
conditions that present single and combined variations of light color,
direction and intensity. The database allows to systematically investigate the
robustness of texture descriptors across a large range of variations of imaging
conditions.Comment: Submitted to the Journal of the Optical Society of America
Color Constancy Using CNNs
In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling, one fully connected layer and three output nodes. Within the network
structure, feature learning and regression are integrated into one optimization
process, which leads to a more effective model for estimating scene
illumination. This approach achieves state-of-the-art performance on a standard
dataset of RAW images. Preliminary experiments on images with spatially varying
illumination demonstrate the stability of the local illuminant estimation
ability of our CNN.Comment: Accepted at DeepVision: Deep Learning in Computer Vision 2015 (CVPR
2015 workshop
Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods
The underwater image processing area has received considerable attention within the last decades, showing important achievements. In this paper we review some of the most recent methods that have been specifically developed for the underwater environment. These techniques are capable of extending the range of underwater imaging, improving image contrast and resolution. After considering the basic physics of the light propagation in the water medium, we focus on the different algorithms available in the literature. The conditions for which each of them have been originally developed are highlighted as well as the quality assessment methods used to evaluate their performance
Automated Pruning for Deep Neural Network Compression
In this work we present a method to improve the pruning step of the current
state-of-the-art methodology to compress neural networks. The novelty of the
proposed pruning technique is in its differentiability, which allows pruning to
be performed during the backpropagation phase of the network training. This
enables an end-to-end learning and strongly reduces the training time. The
technique is based on a family of differentiable pruning functions and a new
regularizer specifically designed to enforce pruning. The experimental results
show that the joint optimization of both the thresholds and the network weights
permits to reach a higher compression rate, reducing the number of weights of
the pruned network by a further 14% to 33% compared to the current
state-of-the-art. Furthermore, we believe that this is the first study where
the generalization capabilities in transfer learning tasks of the features
extracted by a pruned network are analyzed. To achieve this goal, we show that
the representations learned using the proposed pruning methodology maintain the
same effectiveness and generality of those learned by the corresponding
non-compressed network on a set of different recognition tasks.Comment: 8 pages, 5 figures. Published as a conference paper at ICPR 201
Quasi Real-Time Apple Defect Segmentation Using Deep Learning
Defect segmentation of apples is an important task in the agriculture industry for quality control and food safety. In this paper, we propose a deep learning approach for the automated segmentation of apple defects using convolutional neural networks (CNNs) based on a U-shaped architecture with skip-connections only within the noise reduction block. An ad-hoc data synthesis technique has been designed to increase the number of samples and at the same time to reduce neural network overfitting. We evaluate our model on a dataset of multi-spectral apple images with pixel-wise annotations for several types of defects. In this paper, we show that our proposal outperforms in terms of segmentation accuracy general-purpose deep learning architectures commonly used for segmentation tasks. From the application point of view, we improve the previous methods for apple defect segmentation. A measure of the computational cost shows that our proposal can be employed in real-time (about 100 frame-per-second on GPU) and in quasi-real-time (about 7/8 frame-per-second on CPU) visual-based apple inspection. To further improve the applicability of the method, we investigate the potential of using only RGB images instead of multi-spectral images as input images. The results prove that the accuracy in this case is almost comparable with the multi-spectral case
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