Abstract Vegetation plays a major role in the realistic display of outdoor scenes. However, manual plant placement can be tedious. For this reason this paper presents a new proposal in the field of procedural modeling of natural scenes. This method creates plant ecosystems that maximizes the covered space by optimizing an objective function subject to a series of constraints defined by a system of inequalities. This system includes the constraints of the environment taking into account characteristics of the terrain and the plant species involved. Once the inequality system has been defined, a solution will be obtained that tries to maximize the radius of the projected area of the trees and therefore the extension of the vegetation cover on the ground. The technique eliminates the trees that do not achieve a minimum growth radius, simulating the typical competitive process of nature. Results show the good performance and the high visual quality of the ecosystems obtained by the proposed technique. The use of this kind of optimization techniques could be used to solve other procedural modeling problems in other fields of application.

Abstract Augmented Reality techniques allow the user to visualize part of the real world through a display device by incorporating graphical information into the existing physical information. In this sense, it is important to know how the physical presence of the user in the augmented reality experience can affect the perception and evaluation of the product. To this end, this work presents a theoretical framework that explains how users perceive and evaluate the benefits and quality of augmentation with augmented reality through their physical presence, compared to visualizing the same experience through a video. The application was developed for the exhibition and sale of ceramic molds. Users viewed graphical information about the mold, placed between them and the screen while seeing themselves in the television as if it was a mirror. The experiments showed that the integration of the product into the environment and the spatial presence of the users had a positive effect on the perceived value in terms of usefulness and enjoyment, improved comfort in the purchase decision, and reinforced the overall opinion of the product.

Multidimensional optical imaging systems for information processing and visualization technologies have numerous applications in fields such as manufacturing, medical sciences, entertainment, robotics, surveillance, and defense. Among different three-dimensional (3-D) imaging methods, integral imaging is a promising multiperspective sensing and display technique. Compared with other 3-D imaging techniques, integral imaging can capture a scene using an incoherent light source and generate real 3-D images for observation without any special viewing devices. This review paper describes passive multidimensional imaging systems combined with different integral imaging configurations. One example is the integral-imaging-based multidimensional optical sensing and imaging systems (MOSIS), which can be used for 3-D visualization, seeing through obscurations, material inspection, and object recognition from microscales to long range imaging. This system utilizes many degrees of freedom such as time and space multiplexing, depth information, polarimetric, temporal, photon flux and multispectral information based on integral imaging to record and reconstruct the multidimensionally integrated scene. Image fusion may be used to integrate the multidimensional images obtained by polarimetric sensors, multispectral cameras, and various multiplexing techniques. The multidimensional images contain substantially more information compared with two-dimensional (2-D) images or conventional 3-D images. In addition, we present recent progress and applications of 3-D integral imaging including human gesture recognition in the time domain, depth estimation, mid-wave-infrared photon counting, 3-D polarimetric imaging for object shape and material identification, dynamic integral imaging implemented with liquid-crystal devices, and 3-D endoscopy for healthcare applications.

In this paper, the results of a semi-supervised approach based on the Expectation-Maximisation algorithm for model-based clustering are presented. We show in this work that, if the appropriate generative model is chosen, the classification accuracy on clustering for image segmentation can be significantly improved by the combination of a reduced set of labelled data and a large set of unlabelled data. This technique has been tested on real images as well as on medical images from a dermatology application. The preliminary results are quite promising. Not only the unsupervised accuracies have been improved as expected but the segmentation results obtained are considerably better than the results obtained by other powerful and well-known unsupervised image segmentation techniques.

In this contribution a feature selection method in semi-supervised problems is proposed. This method selects variables using a feature clustering strategy, using a combination of supervised and unsupervised feature distance measure, which is based on Conditional Mutual Information and Conditional Entropy. Real databases were analyzed with different ratios between labelled and unlabelled samples in the training set, showing the satisfactory behaviour of the proposed approach.

Hyperspectral imaging involves large amounts of information. This paper presents a technique for dimensionality reduction to deal with hyperspectral images. The proposed method is based on a hierarchical clustering structure to group bands to minimize the intracluster variance and maximize the intercluster variance. This aim is pursued using information measures, such as distances based on mutual information or Kullback–Leibler divergence, in order to reduce data redundancy and nonuseful information among image bands. Experimental results include a comparison among some relevant and recent methods for hyperspectral band selection using no labeled information, showing their performance with regard to pixel image classification tasks. The technique that is presented has a stable behavior for different image data sets and a noticeable accuracy, mainly when selecting small sets of bands.

In this paper, a band selection technique for hyperspectral image data is proposed. Supervised feature extraction techniques allow a reduction of the dimensionality to extract relevant features through a labeled training set. This implies an analysis of the existing class distributions, which usually means, in the case of hyperspectral imaging, a large number of samples, making the labeling process difficult. A possible alternative could be the use of information measures, which are the basis of the proposed method. The present approach basically behaves as an unsupervised feature selection criterion, to obtain the relevant spectral bands from a set of sample images. The relations of information content between spectral bands are analyzed, leading to the proposed technique based on the minimization of the dependent information between spectral bands, while trying to maximize the conditional entropies of the selected bands.