36 citations · 48 across the 8 of their papers we have counts for
7 papers · 1 filter
Perceptual Color Difference Modeling Using Machine Learning and Human Similarity Judgments
Elnara Kadyrgali, Muragul Muratbekova, Adilet Yerkin +6
Accurate assessment of color differences is essential for applications ranging from digital design to quality control. While existing color difference metrics, such as CIEDE2000, a…
Semantic Context Matters: Analysis of Color Names Across Domains
Adilet Yerkin, Elnara Kadyrgali, Malika Ziyada +5
Color naming is influenced not only by physical color values but also by the semantic context in which colors are used. This paper investigates context-dependent color naming by ma…
Beyond Color Geometry: Evaluating Human-Like Color Representations in Vision Models
Ayan Igali, Pakizar Shamoi
Do vision models see colors the way humans do? Existing evaluations of color representations usually compare them with geometric spaces such as CIELAB or with discrete color labels…
Color Models in Image Processing: A Review and Experimental Comparison
Muragul Muratbekova, Nuray Toganas, Ayan Igali +4
Color representation is essential in computer vision and human-computer interaction. There are multiple color models available. The choice of a suitable color model is critical for…
Fuzzy Theory in Computer Vision: A Review
Adilet Yerkin, Ayan Igali, Elnara Kadyrgali +4
Computer vision applications are omnipresent nowadays. The current paper explores the use of fuzzy logic in computer vision, stressing its role in handling uncertainty, noise, and…
COLIBRI Fuzzy Model: Color Linguistic-Based Representation and Interpretation
Pakizar Shamoi, Nuray Toganas, Muragul Muratbekova +7
Colors are omnipresent in today's world and play a vital role in how humans perceive and interact with their surroundings. However, it is challenging for computers to imitate human…
Image Classification using Fuzzy Pooling in Convolutional Kolmogorov-Arnold Networks
Ayan Igali, Pakizar Shamoi
Nowadays, deep learning models are increasingly required to be both interpretable and highly accurate. We present an approach that integrates Kolmogorov-Arnold Network (KAN) classi…