Computer vision is becoming an increasingly trendy word in the area of image processing.
With the emergence of computer vision applications, there is a significant demand to …

Convolutional neural networks (CNNs) have made resounding success in many computer
vision tasks such as image classification and object detection. However, their performance …

Abstract Deep Convolutional Neural Network (CNN) is a special type of Neural Networks,
which has shown exemplary performance on several competitions related to Computer …

Deep domain adaptation has emerged as a new learning technique to address the lack of
massive amounts of labeled data. Compared to conventional methods, which learn shared …

State-of-the-art algorithms for many semantic visual tasks are based on the use of
convolutional neural networks. These networks are commonly trained, and evaluated, on …

Data classification in presence of noise can lead to much worse results than expected for
pure patterns. In this paper we investigate this problem in the case of deep convolutional …

Deep learning grew in importance in recent years due to its versatility and excellent
performance on supervised classification tasks. A core assumption for such supervised …

Due to the advances made in recent years, methods based on deep neural networks have
been able to achieve a state-of-the-art performance in various computer vision problems. In …

While high resolution images contain semantically more useful information than their lower
resolution counterparts, processing them is computationally more expensive, and in some …

Convolutional neural networks rely on image texture and structure to serve as discriminative
features to classify the image content. Image enhancement techniques can be used as …