Convolutional neural networks (CNNs) have become a classic approach to solving
challenging computer vision problems. Much of its success is due to its ability to discover …

The article describes a system for image recognition using deep convolutional neural
networks. Modified network architecture is proposed that focuses on improving convergence …

Gabor filters have been recently integrated with deep convolutional neural networks to learn
better features with fewer model parameters. However, during feature learning the rotation …

In image segmentation and classification tasks, utilizing filters based on the target object
improves performance and requires less training data. We use the Gabor filter as …

In steerable filters, a filter of arbitrary orientation can be generated by a linear combination of
a set of “basis filters.” Steerable properties dominate the design of the traditional filters, eg …

Despite great effectiveness of very wide and deep convolutional neural networks (DCNNs)
in various computer vision tasks, the significant cost in terms of storage requirement of such …

Convolutional neural networks (CNNs) have achieved overwhelming success in image
recognition. For all the architectures of CNNs, the low-level features extracted from the input …

Deep-learning convolutional neural networks (CNNs) have proven to be successful in
various cognitive applications with a multilayer structure. The high computational energy …

Recent advances in self-attention and pure multi-layer perceptrons (MLP) models for vision
have shown great potential in achieving promising performance with fewer inductive biases …

Convolutional Neural Networks (CNN) are being increasingly used in computer vision for a
wide range of classification and recognition problems. However, training these large …