IMaLeVICS: New VIPLab project
Interpretable Machine Learning for Vision, Imaging, and Color Sciences
The objective of IMaLeVICS is to use interpretable machine learning methods to address various issues related to vision, imaging, and color sciences. We have two goals. Firstly, we aim to obtain systems that can solve specific problems in each of these fields. Secondly, we aim to analyze what these systems have learned to interpret the knowledge extracted from the data and determine whether this knowledge can be incorporated into current scientific models that address the corresponding problems.
VISUAL IMAGE PROCESSING LABORATORY
A research project on computational vision science
The main objective of the interdisciplinary group that makes up VIP Lab (Visual Image Processing Laboratory) is to contribute to a better understanding of how digital images are perceived by the human visual system. To begin, contributing with data from psychophysics on how images are perceived by the visual system, secondly, addressing how to represent this new knowledge with computational models, to finish using these models to approach the improvement/optimization of the visual quality in different applications and image processing tasks.
Recent availability of image capturing and displaying devices in increasing image importance for both home and scientific use. Particularly important is image perceptual quality, a matter on which an intensive active research is being carried out in many centers around the globe. The VIP Lab project is devoted to develop research on image perception and, in the long term, establish a reference research center in the matter.
The research group consists of professionals that combine experience in vision science, color science, image processing, psychophysics, and mathematical modeling.
LATESTS RESEARCH AND PUBLICATIONS
A comparative analysis of machine learning methods for display characterization
Methodology and Study Approach Khleef Almutairi, Samuel Morillas Gómez, Pedro Latorre Carmona, Makan Dansoko and María José Gacto. Displays, 85, 2024, 102849. We are thrilled to announce that the IMaLeVICS team has published a new article in Displays titled "A...
IMaLeVICS at the Colour and Visual Computing Symposium 2024 in Norway
Colour and Visual Computing Symposium 2024 (CVCS 2024) Last week, members of the IMaLeVICS research group, including Samuel Morillas Gómez, Rafael Huertas, and Marius Pedersen, gathered in Norway to participate in the twelfth edition of the Colour and Visual Computing...
IMaLeVICS at the XIV National Optics Meeting in Murcia
XIV National Optics Meeting From July 3 to 5, the city of Murcia becomes the epicenter of research in optics and photonics, hosting the XIV National Optics Meeting, organized by the Optics Laboratory of the University of Murcia. This event brings together more than...
Fuzzy Inference Systems to Fine-Tune a Local Eigenvector Image Smoothing Method
Fuzzy Eigenvector Image Smoothing Method Khleef Almutairi, Samuel Morillas and Pedro Latorre-Carmona. "Fuzzy Inference Systems to Fine-Tune a Local Eigenvector Image Smoothing Method", published in Electronics 2024, 13, 1150. This paper introduces a new method for...
We have received the grant for the IMaLeVICS project!
IMaLeVICS: a new project within VIP Lab In 2020, the Visual Image Processing Laboratory (VIP Lab) was launched with the mission to contribute to a better understanding of how the human visual system perceives digital images. In 2023, this work will continue through...
Interpretable Machine Learning for Vision, Imaging, and Color Sciences (IMaLeVICS)
IMaLeVICS: New VIPLab project We propose this "Interpretable Machine Learning for Vision, Imaging, and Color Sciences (IMaLeVICS)" project to obtain systems able to solve specific problems in each of the fields and also to analyze what the systems have learned to...
A Fuzzy Logic Inference System for Display Characterization
"A Fuzzy Logic Inference System for Display Characterization" at IbPRIA 2023 Khleef Almutairi, Samuel Morillas, Pedro Latorre-Carmona and and Makan Dansoko. A Fuzzy Logic Inference System for Display Characterization. Khleef Almutairi, a PhD student in VIPLab under...
Fuzzy inference system in a local eigenvector based colour image smoothing framework
IMPROVE 2023 Khleef Almutairi, Samuel Morillas, and Pedro Latorre-Carmona. Fuzzy inference system in a local eigenvector based colour image smoothing framework. Khleef Almutairi, a Ph.D. student in VIPLab under the supervision of doctoral advisor Professor Samuel...
New fidelity measure between computed image quality and observers quality scores
Our proposal of a new fidelity measure between computed image quality and observers quality scores Pedro Latorre-Carmona, Rafael Huertas, Marius Pedersen and Samuel Morillas. Proposal of a new fidelity measure between computed image quality and observers quality...
Image Noise Reduction by bootstrap resampling
Bootstrapping-Based Fuzzy Numbers Reza Ghasemi, Samuel Morillas, Ahmad Nezakati and Mohammadreza Rabiei. Image Noise Reduction by Means of Bootstrapping-Based Fuzzy Numbers. Appl. Sci. 2022, 12, 9445. As you know, nonlinear methods significantly reduce image noise and...
On Principal Fuzzy Metric Spaces
The notion of fuzzy metric space (X,M,∗) Valentín Gregori, Juan-José Miñana, Samuel Morillas and Almanzor Sapena. In this paper, we deal with the notion of fuzzy metric space (X ,M, ∗), or simply X , due toGeorge and Veeramani. It is well known that such fuzzy metric...
Fuzzy method for detection of inconsistent data in experimental datasets of perceptual color differences
The color difference formula used was CIEDE2000 In this post, we share the two articles published in which we explain the fuzzy method for the detection of inconsistent data in experimental datasets of perceptual color differences. The results have been obtained using...