One of the main challenges in machine vision relates to the problem of obtaining robust
representation of visual features that remain unaffected by geometric transformations. This …

Many classes of images exhibit rotational symmetry. Convolutional neural networks are
sometimes trained using data augmentation to exploit this, but they are still required to learn …

Abstract Deep Convolution Neural Networks (DCNNs) are capable of learning
unprecedentedly effective image representations. However, their ability in handling …

Purpose Delineation of thyroid nodule boundaries from ultrasound images plays an
important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it …

We present a method for learning discriminative filters using a shallow Convolutional Neural
Network (CNN). We encode rotation invariance directly in the model by tying the weights of …

Purpose It is very important for calculation of clinical indices and diagnosis to detect thyroid
nodules from ultrasound images. However, this task is a challenge mainly due to …

This paper presents a novel approach to detect and classify the power quality disturbance
(PQD) signals based on singular spectrum analysis (SSA), curvelet transform (CT) and deep …

Methods, systems, and apparatus, including computer pro grams encoded on computer
storage media, for computing a layer output for a convolutional neural network layer, the …

Accurate neonatal brain MRI segmentation is valuable for investigating brain growth
patterns and tracking the progression of neurodevelopmental disorders. However, it is a …

Recently, learning equivariant representations has attracted considerable research
attention. Dieleman et al. introduce four operations which can be inserted into convolutional …