This paper presents a scheme for feature extraction that can be applied for classification of
corals in submarine coral reef images. In coral reef image classification, texture features are …

Self-supervised learning aims to learn image feature representations without the usage of
manually annotated labels. It is often used as a precursor step to obtain useful initial network …

Background Fine-grained recognition of plants from images is a challenging computer vision
task, due to the diverse appearance and complex structure of plants, high intra-class …

Our goal is to recognize material categories using images and geometry information. In
many applications, such as construction management, coarse geometry information is …

The task of discriminating one object from another is almost trivial for a human being.
However, this task is computationally taxing for most modern machine learning methods; …

Due to our increasing reliance on computer systems, security incidents and their causes are
important problems that need to be addressed. To contribute to this objective, the paper …

In this paper, we propose a normalized difference vector (NDV) for texture representation.
Compared to local-binary-pattern-based descriptors, the proposed NDV takes full advantage …

The histograms of oriented gradients (HOG) and co-occurrence HOG (CoHOG) algorithms
are simple and intuitive descriptors. However, the HOG and CoHOG algorithms based on …

In this paper, we aim to recognize materials with combined use of auditory and visual
perception. To this end, we construct a new dataset named GLAudio that consists of both the …

Texture characterization is a key problem in image understanding and pattern recognition. In
this paper, we present a flexible shape-based texture representation using shape co …