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
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...
A study of neural network-based LCD display characterization
About our Conference paper at London Imaging Meeting 2021 In the last London Imaging Meeting 2021 (LIM21) that took place between September 20 and 21, we were lucky to participate with our article "About our Conference paper at London Imaging Meeting 2021". Joan...
London Imaging Meeting 2021
"A study of neural network-based LCD display characterization" Our article "A study of neural network-based LCD display characterization" (OpenConf ID 3) has been accepted for oral presentation at London Imaging Meeting 2021: Imaging for Deep Learning.The conference...
Webinar: Divisive Normalization models and Image Statistics
by Prof. Dr. Jesús Malo At the cortical level, spatial texture, motion and color is encoded as a set of responses of wavelet sensors tuned to certain spatio-temporal frequencies and certain directions of the color space. These sensors have different linear gain and...