Abstract: In this work we present a comprehensive computational framework to help in the clinical assessment of cancer response from a pair of time consecutive oncological PET-CT scans. In this scenario, the design and implementation of a supervised machine learning system to predict and quantify cancer progression or response conditions by introducing a novel feature set that models the underlying clinical context is described. Performance results in 100 clinical cases (corresponding to 200 whole body PET-CT scans) in comparing expert-based visual analysis and classifier decision making show up to 70% accuracy within a completely automatic pipeline and 90% accuracy when providing the system with expert-guided PET tumor segmentation masks.

Abstract: This paper presents a patient-sensitive simulation strategy capable of using the most efficient way the high-performance computational resources. The proposed strategy directly involves three different players: Computational Mechanics Scientists (CMS), Image Processing Scientists and Cardiologists, each one mastering its own expertise area within the project. This paper describes the general integrative scheme but focusing on the CMS side presents a massively parallel implementation of computational electrophysiology applied to cardiac tissue simulation. The paper covers different angles of the computational problem: equations, numerical issues, the algorithm and parallel implementation. The proposed methodology is illustrated with numerical simulations testing all the different possibilities, ranging from small domains up to very large ones. A key issue is the almost ideal scalability not only for large and complex problems but also for medium-size meshes. The explicit formulation is particularly well suited for solving this highly transient problems, with very short time-scale.

Abstract: Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.

Abstract: The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations.

Abstract: Several studies have recorded color emotions in subjects viewing uniform color (UC) samples. We conduct an experiment to measure and model how these color emotions change when texture is added to the color samples. Using a computer monitor, our subjects arrange samples along four scales: warm–cool, masculine–feminine, hard–soft, and heavy–light. Three sample types of increasing visual complexity are used: UC, grayscale textures, and color textures (CTs). To assess the intraobserver variability, the experiment is repeated after 1 week. Our results show that texture fully determines the responses on the Hard-Soft scale, and plays a role of decreasing weight for the masculine–feminine, heavy–light, and warm–cool scales. Using some 25,000 observer responses, we derive color emotion functions that predict the group-averaged scale responses from the samples' color and texture parameters. For UC samples, the accuracy of our functions is significantly higher (average R2 = 0.88) than that of previously reported functions applied to our data. The functions derived for CT samples have an accuracy of R2 = 0.80. We conclude that when textured samples are used in color emotion studies, the psychological responses may be strongly affected by texture. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010

Abstract: Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images.

Abstract: In this article we provide an overview of color name applications in computer vision. Color names are linguistic labels which humans use to communicate color. Computational color naming learns a mapping from pixels values to color names. In recent years color names have been applied to a wide variety of computer vision applications, including image classification, object recognition, texture classification, visual tracking and action recognition. Here we provide an overview of these results which show that in general color names outperform photometric invariants as a color representation.

Abstract: Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.