Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

Deep learning has recently gained popularity achieving state-of-the-art performance in tasks
involving text, sound, or image processing. Due to its outstanding performance, there have …

Autoencoders have become a hot researched topic in unsupervised learning due to their
ability to learn data features and act as a dimensionality reduction method. With rapid …

Three-dimensional geometric data offer an excellent domain for studying representation
learning and generative modeling. In this paper, we look at geometric data represented as …

Fabric defect detection is a necessary and essential step of quality control in the textile
manufacturing industry. Traditional fabric inspections are usually performed by manual …

This letter introduces a robust representation of 3-D shapes, named DeepPano, learned with
deep convolutional neural networks (CNN). Firstly, each 3-D shape is converted into a …

3D point cloud presentation has been widely used in computer vision, automatic driving,
augmented reality, smart cities and virtual reality. 3D point cloud compression method with …

3D data is a valuable asset the computer vision filed as it provides rich information about the
full geometry of sensed objects and scenes. Recently, with the availability of both large 3D …

The objective of this research was to develop a deep learning method which consisted of
stacked auto-encoders (SAE) and fully-connected neural network (FNN) for predicting …

Visual inspection of texture surface defects is still a challenging task in the industrial
automation field due to the tremendous changes in the appearance of various surface …