Abstract Machine vision significantly improves the efficiency, quality, and reliability of defect
detection. In visual inspection, excellent optical illumination platforms and suitable image …

Abstract Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level
performance in edge detection with the rich and abstract edge representation capacities …

Texture analysis is used in a very broad range of fields and applications, from texture
classification (eg, for remote sensing) to segmentation (eg, in biomedical imaging), passing …

Automatic classification of brain tumor types is very important for accelerating the treatment
process, planning and increasing the patient's survival rate. Today, MR images are used to …

Feature extraction is vital for face recognition. In this paper, we focus on the general feature
extraction framework for robust face recognition. We collect about 300 papers regarding face …

Local binary patterns (LBP) are considered among the most computationally efficient high-
performance texture features. However, the LBP method is very sensitive to image noise and …

Abstract Local Binary Patterns (LBP) have emerged as one of the most prominent and
widely studied local texture descriptors. Truly a large number of LBP variants has been …

This paper presents a content-based image retrieval technique that focuses on extraction
and reduction in multiple features. To obtain multi-level decomposition of the image by …

Designing discriminative powerful texture features robust to realistic imaging conditions is a
challenging computer vision problem with many applications, including material recognition …

Deep-learning-based super-resolution (SR) methods have been extensively studied and
have achieved significant performance with deep convolutional neural networks. However …