Texture analysis describes a variety of image analysis techniques that quantify the variation
in intensity and pattern. This paper provides an overview of several texture analysis …

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 …

This work focuses on a slice through the field-Computer Vision Metrics–from the view of
feature description metrics, or how to describe, compute and design the macro-features and …

Many algorithms for computer vision rely on locating interest points, or keypoints in each
image, and calculating a feature description from the pixel region surrounding the interest …

Scott Krig Textbook Edition Survey, Taxonomy and Analysis of Computer Vision, Visual
Neuroscience, and Deep Learning Page 1 Computer Vision Metrics Scott Krig Textbook Edition …

This chapter discusses several topics pertaining to ground truth data, the basis for computer
vision metric analysis. We look at examples to illustrate the importance of ground truth data …

This chapter describes the methods used to prepare images for further analysis, including
interest point and feature extraction. Some of these methods are also useful for global and …

Computer-aided diagnosis (CAD) based on histopathological imaging has progressed
rapidly in recent years with the rise of machine learning based methodologies. Traditional …

In this paper, a Fusion Feature-Level Face Recognition Model (FFLFRM) based on the
Laplacian Pyramid (LP) fusion technique is proposed. The proposed FFLFRM model …

LBP is known as one of the best performing local descriptor in texture representation. But
there are various shortcomings observed in LBP and these are finite spatial patch and large …