Computer vision systems have become a key element of modern companies. However, its implantation not only requires specialized hardware components but also software to control the correct performance of all these components. The purpose of this article is to present the design and deployment of a software created to manage the different computer vision systems of any company. First, using our experience in mechanization and industrialization of company processes, a general company specification will be proposed as well as the main software requirements that have to be satisfied. Then, a three-level modular design composed of the core, a configuration module, and user interfaces with functionalities capable of satisfying the defined requirements will be presented. Special attention will be given to time restrictions and components’ synchronization. In addition, the different tests that have been carried out to control the correct performance of the software will be shown. The development process will end with a generic, modular, and scalable software able to fit different industrial scenarios by simply modifying a set of input parameters. To illustrate the correct performance of the proposal, the details of its installation in four real companies with different needs will be presented. The proposed work has a practical use in industry and it also provides a thorough description of the main components involved in computer vision systems of real company environments and how to manage them.Note to Practitioners—The aim of the article is to provide a software solution to manage the computer vision systems of a company. The proposed software is simple enough to be controlled by company operators with no need for great expertise on the technical aspects, but rather on the industrial workflow to be controlled. To develop such a software, it has been necessary to know about all the elements involved in the industrial scenarios controlled by computer vision systems as well as their relationships. This article presents all these elements providing a global and complete view of the problem with special attention to synchronization, which is fundamental to satisfy industrial time restriction requirements. The software is presented from a technical point of view, but also from a practical one, since different examples of its application in real cases are given. These examples illustrate the adaptability of the proposal to different scenarios and also exemplify how the different elements are modified to fit the different situations. In addition, this article provides a comprehensive overview of real industrial situations, focusing on the most important issues faced during real installations.

Estimation of lean meat percentage from computed tomography (CT) images of scanned carcasses is the basis for grading meat quality. Due to noise, artifacts and partial volume effects, the automatic classification of tissues and its posterior quantification is difficult. In this paper we present a new processing pipeline that integrates a partial volume model to classify the CT pork carcasses in three tissues: fat, lean, and bone. The approach has been tested on 10 CT pork carcasses and compared with manual dissection, thresholding and thresholding with bone filling techniques. We have also tested on simulated distorted images. In all the experiments our method outperforms the thresholding-based results in terms of accuracy and robustness.

Videoconferencing (VCF) is of key relevance for the development of many real-time eHealth applications and its adoption is increasing thanks to the advantages it provides. However, for a successful implementation, several issues need to be faced. In this paper we focus on VCF integration into existing or new eHealth applications. We analyze the main requirements for the use of VCF in eHealth, including legal and privacy issues, integration possibilities and available workflows. We also propose three new workflows devised for specific eHealth scenarios in order to demonstrate the importance of selecting a suitable VCF workflow in eHealth.

Abstract Different quality metrics have been proposed in the literature to evaluate how well a visualization represents the underlying data. In this paper, we present a new information‐theoretic framework that quantifies the information transfer between the source data set and the rendered image. This approach is based on the definition of an observation channel whose input and output are given by the intensity values of the volumetric data set and the pixel colors, respectively. From this channel, the mutual information, a measure of information transfer or correlation between the input and the output, is used as a metric to evaluate the visualization quality. The usefulness of the proposed observation channel is illustrated with three fundamental visualization applications: selection of informative viewpoints, transfer function design, and light positioning.

In this paper, we present a new framework for multimodal volume visualization that combines several information-theoretic strategies to define both colors and opacities of the multimodal transfer function. To the best of our knowledge, this is the first fully automatic scheme to visualize multimodal data. To define the fused color, we set an information channel between two registered input datasets, and afterward, we compute the informativeness associated with the respective intensity bins. This informativeness is used to weight the color contribution from both initial 1-D transfer functions. To obtain the opacity, we apply an optimization process that minimizes the informational divergence between the visibility distribution captured by a set of viewpoints and a target distribution proposed by the user. This distribution is defined either from the dataset features, from manually set importances, or from both. Other problems related to the multimodal visualization, such as the computation of the fused gradient and the histogram binning, have also been solved using new information-theoretic strategies. The quality and performance of our approach are evaluated on different datasets.

Multimodal visualization aims at fusing different data sets so that the resulting combination provides more information and understanding to the user. To achieve this aim, we propose a new information-theoretic approach that automatically selects the most informative voxels from two volume data sets. Our fusion criteria are based on the information channel created between the two input data sets that permit us to quantify the information associated with each intensity value. This specific information is obtained from three different ways of decomposing the mutual information of the channel. In addition, an assessment criterion based on the information content of the fused data set can be used to analyze and modify the initial selection of the voxels by weighting the contribution of each data set to the final result. The proposed approach has been integrated in a general framework that allows for the exploration of volumetric data models and the interactive change of some parameters of the fused data set. The proposed approach has been evaluated on different medical data sets with very promising results.

The majority of Computer Based Assessment (CBA) environments have been designed for fixed-response questions. This feature is not enough for the university context since not all the skills can be reduced to this typology of questions and free response exercises are required. In this paper, we present a web-based tool designed to automatically correct exercises that require a diagram or a graph to be solved. The main novelty of this tool is the strategy used for the correction. The tool starts without the knowledge about correct and incorrect solutions and then learns about the solutions provided by teachers and students. It automatically records the information of all entered solutions, the teacher correction and the corresponding feedback in a system database. When a new student solution is entered the information of the database is used for correction. The main components of the tool are the graph editor module, designed to support diagram drawing, and the correction module, which corrects the solutions entered by the students. These modules are integrated in a more general e-learning platform denoted ACME. ACME provides teachers all functionalities required for student work tracking, assessment, personalized attention, etc. The proposed tool has been evaluated on experimental group.

In this paper we present a framework to define transfer functions from a target distribution provided by the user. A target distribution can reflect the data importance, or highly relevant data value interval, or spatial segmentation. Our approach is based on a communication channel between a set of viewpoints and a set of bins of a volume data set, and it supports 1D as well as 2D transfer functions including the gradient information. The transfer functions are obtained by minimizing the informational divergence or Kullback-Leibler distance between the visibility distribution captured by the viewpoints and a target distribution selected by the user. The use of the derivative of the informational divergence allows for a fast optimization process. Different target distributions for 1D and 2D transfer functions are analyzed together with importance-driven and view-based techniques.

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms.

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Image registration is an important component of image analysis used to align two or more images. In this paper, we present a new framework for image registration based on compression. The basic idea underlying our approach is the conjecture that two images are correctly registered when we can maximally compress one image given the information in the other. The contribution of this paper is twofold. First, we show that the image registration process can be dealt with from the perspective of a compression problem. Second, we demonstrate that the similarity metric, introduced by Li et al., performs well in image registration. Two different versions of the similarity metric have been used: the Kolmogorov version, computed using standard real-world compressors, and the Shannon version, calculated from an estimation of the entropy rate of the images

One of the key aspects in 3D-image registration is the computation of the joint intensity histogram. We propose a new approach to compute this histogram using uniformly distributed random lines to sample stochastically the overlapping volume between two 3D-images. The intensity values are captured from the lines at evenly spaced positions, taking an initial random offset different for each line. This method provides us with an accurate, robust and fast mutual information-based registration. The interpolation effects are drastically reduced, due to the stochastic nature of the line generation, and the alignment process is also accelerated. The results obtained show a better performance of the introduced method than the classic computation of the joint histogram.