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In this paper, we will introduce a new method to investigate the best visualization of remote sensing imagery data sets on graphics processing units (GPU). The main shortcoming of classical dimension reduction methods is that their visualizations inevitably lose a significant amount of information in remote sensing imagery. Our proposed method (Enhancement Trustworthy (ET)) preserves the color distance as noticeably as possible for high dimension space by defining parallel-supervised extra step to enhance the quality of visualization. The ET classifies the visualization colors into false and true, and constructs a trustworthiness of CIE color space to improve the trustworthy value of false colors. The trustworthiness value of points inside CIE color space illustrate the locations of false and true projected colors in the visualization. Our method is supervised, where the user can select the interested points to enhance by ET. The proposed method is general enhancement step which can be used to enhance any visualization, for example, we used it to enhance the visualizations of remote sensing imagery data sets, which are carried by PCA, SPE and Isomap methods. The results showed the ability of ET to overcome the false colors and return the more reliable true colors. The colors of enhance visualizations showed more details which represent the real neighborhood relation in original data sets. HighlightsIn this paper, we will introduce a new method to investigate the best visualization of remote sensing imagery data sets on graphics processing units (GPU).Our enhanced method (ET) preserves the color distance for remote sensing imagery by defining parallel supervised extra step to enhance the quality of visualization.The ET classifies the visualization colors into false and true, and constructs a trustworthiness of CIE color space.The trustworthiness value of points inside CIE color space illustrate the locations of false and true projected colors in the visualization.The proposed method is general enhancement step which can be used to enhance any visualization.

Hyperspectral image visualization reduces color bands to three, but prevailing linear methods fail to address data characteristics, and nonlinear embeddings are computa- tionally demanding. Qualitative evaluation of embedding is also lacking. We propose faithful stochastic proximity embedding (FSPE), which is a scalable and nonlinear dimensionality re- duction method. FSPE considers the nonlinear characteristics of spectral signatures, yet it avoids the costly computation of geodesic distances that are often required by other nonlinear methods. Furthermore, we employ a pixelwise metric that measures the quality of hyperspectral image visualization at each pixel. FSPE outperforms the state-of-art methods by at least 12% on average and up to 25% in the qualitative measure. An implementation on graphics processing units is two orders of magnitude faster than the baseline. Our method opens the path to high-fidelity and real-time analysis of hyperspectral images.

We present a visualization tool aimed specifically at the development and optimization of photon map denoising methods. Our tool allows the rapid testing of hypotheses and algorithms through the use of parallel coordinates, domain-specific scripting, colour mapping and point plots. Interaction is carried out by brushing, adjusting parameters and focus-plus-context and yields interactive visual feedback and debugging information. We demonstrate the use of the tool to explore high-dimensional photon map data, facilitating the discovery of novel parameter spaces which can be used to dissociate complex caustic illumination. We then show how these new parametrizations may be used to improve upon pre-existing noise removal methods in the context of the photon relaxation framework.

A new nonlinear dimension reduction (DR) method which is called Trustworthy Stochastic Proximity Embedding (TSPE) is introduced in this paper to visualize different types of data sets. TSPE overcome the main shortcomings of the DR by sending the false neighbour points to the correct locations, and preserving the neighbourhood relation to the true neighbours, which are inside the local neighbourhood. The visualization of our proposed method displays the trustworthy, useful and meaningful colours, where the objects of the image can be easily distinguished. The performances of TSPE and 20 dimension reduction methods are compared, and the efficiency of the proposed method in both visualization accuracy and computational cost is shown. The results showed the ability of our method in preserving neighbourhood relation, where they revealed more interested information. In real data set, the efficiency of the visualization of tensor images data sets by TSPE might help the specialist to make a good decision about a patient’s treatment. The comparison with experimental data set, as three dimensions of curved cylinder, showed the ability of TSPE to unfold this complex data set efficiently whilst preserving most information of the original data set.

The aim of this work is to identify the depth cues that provide intuitive depth-ordering when used to visualize abstract data. In particular we focus on the depth cues that are effective on a high-dynamic-range (HDR) display: contrast and brightness. In an experiment participants were shown a visualization of the volume layers at different depths with a single isolated monocular cue as the only indication of depth. The observers were asked to identify which slice of the volume appears to be closer. The results show that brightness, contrast and relative size are the most effective monocular depth cues for providing an intuitive depth ordering.

This paper investigates the stability of an automatic system for classifying kerogen material from images of sieved rock samples. The system comprises four stages: image acquisition, background removal, segmentation, and classification of the segmented kerogen pieces as either inertinite or vitrinite. Depending upon a segmentation parameter d, called “overlap”, touching pieces of kerogen may be split differently. The aim of this study is to establish how robust the classification result is to variations of the segmentation parameter. There are two issues that pose difficulties in carrying out an experiment. First, even a trained professional may be uncertain when distinguishing between isolated pieces of inertinite and vitrinite, extracted from transmitted-light microscope images. Second, because manual labelling of large amount of data for training the system is an arduous task, we acquired the true labels (ground truth) only for the pieces obtained at overlap d=0.5. To construct ground truth for various values of d we propose here label-inheritance trees. With thus estimated ground truth, an experiment was carried out to evaluate the robustness of the system to changes in the segmentation through varying the overlap value d. The average system accuracy across values of d spanning the range from 0 to 1 was 86.5%, which is only slightly lower than the accuracy of the system at the design value of d=0.5 (89.07%).

Identification of fossil material under a microscope is the basis of micropalentology. Our task is to locate and count the pieces of inertinite and vitrinite in images of sieve sampled rock. The classical watershed algorithm oversegments the objects because of their irregular shapes. In this paper we propose a method for locating multiple objects in a black and white image while accounting for possible overlapping or touching. The method, called Centre Supported Segmentation (CSS), eliminates oversegmentation and is robust against differences in size and shape of the objects.

We develop the classification part of a system that analyses transmitted light microscope images of dispersed kerogen preparation. The system automatically extracts kerogen pieces from the image and labels each piece as either inertinite or vitrinite. The image pre-processing analysis consists of background removal, identification of kerogen material, object segmentation, object extraction (individual images of pieces of kerogen) and feature calculation for each object. An expert palynologist was asked to label the objects into categories inertinite and vitrinite, which provided the ground truth for the classification experiment. Ten state-of-the-art classifiers and classifier ensembles were compared: Naive Bayes, decision tree, nearest neighbour, the logistic classifier, multilayered perceptron (MLP), support vector machines (SVM), AdaBoost, Bagging, LogitBoost and Random Forest. The logistic classifier was singled out as the most accurate classifier, with an accuracy greater than 90. Using a 10 times 10-fold cross-validation provided within the Weka software, we found that the logistic classifier was significantly better than five classifiers (p

A forager in a patchy environment faces two types of uncertainty: ambiguity regarding the quality of the current patch and risk associated with the background opportunities. We argue that the order in which the forager deals with these uncertainties has an impact on the decision whether to stay at the current patch. The order effect is formalised with a context-dependent quantum probabilistic framework. Using Heisenberg's uncertainty principle, we demonstrate the two types of uncertainty cannot be simultaneously minimised, hence putting a formal limit on rationality in decision making. We show the applicability of the contextual decision function with agent-based modelling. The simulations reveal order-dependence. Given that foraging is a universal pattern that goes beyond animal behaviour, the findings help understand similar phenomena in other fields. Sponsorship:This work was partially supported by the European Commission Seventh Framework Programme under Grant Agreement Number FP7-601138 PERICLES. Xavier Rubio-Campillo is supported by the SimulPast Project (CSD2010-00034), funded by the CONSOLIDER-INGENIO2010 program of the Ministry of Science and Innovation -- Spain.

We present a context-preserving visualisation method for segmented volumetric medical images. A segmented volumetric image contains a number of anatomical objects which are important features to be visualised. Our context- preserving rendering utilises the curvature at the surfaces of the segmentation objects to modulate the opacity contribution during rendering. This results in the areas of high curvature, typically the most important features, being opaque and visible while everything else being transparent.

Classification of organic materials obtained from rock and drill cuttings involves finding multiple objects in the image. This task is usually approached by segmentation. The quality of segmentation is evaluated by matching the whole detected objects to a reference segmentation. We are interested in representing each object by a single reference point called the “centre”. This paper proposes an evaluation measure of image segmentation for such representation. We argue that measures based only on distance between obtained centres and a set of predefined centres are insufficient. The proposed measure is based on a list of desirable properties of the segmentation. The three components of the measure evaluate the under/over segmentation of the objects, the proportion of centres placed in the background rather than in objects, and the distance between the guessed and the true centres. The ability of the measure to distinguish between segmentation results of different quality is illustrated on three sets of examples including an image containing microfossils and pieces of inert material.

Recent progress in 3-D capture technology has made it possible to obtain much of realistic motion data of human subjects. Being captured in high frame rates, compression or extraction of key postures out of the motion data is useful for storage, transfer and browsing among them: this can serve as an Important pre-processing for applications such as rehabilitation, ergonomics and sports physiology. This paper addresses these problems by treating the motion date as trajectory curves In a high-dimensional space and doing a novel application of a curve simplification algorithm, typically used for planar curves, to human motion data.

We present a method for improving robustness in feature-based tracking of human motion. Motion flows of features estimated by a standard tracker are modified to be coherent with neighboring ones. This coherence constraint is computed based on a smooth approximation to initial motion flows computed by the tracker. With these tracking results, we demonstrate motion segmentation of different body parts in an image sequence.

This article describes an approach based on hierarchical radial basis functions for the reconstruction and modification of motion/image data, which are formulated as problems of scattered data interpolation. Assuming little about the data and choosing highly scalable/well adjustable basis functions, the approach can handle both the reconstruction and the manipulation in a unified and widely applicable manner: standard approaches are often applicable, but have limited scope due to their constrained assumptions about the number or location of the data points. Reconstruction of captured motion data and warping of face images are demonstrated.

Presentation of audio or video files for browsing is difficultdue to their serial and transitory nature whereas texts or pho-tos may be simply placed in the user’s visual field. This pa-per describes a simple technique for browsing and selectingamong temporal signals such as audio and video. In the pro-posed method, only two among a set of the signals are pre-sented to the user at a time and (s)he just needs to select a‘winner’ among the two. This process is repeated for manyrounds until a single winner remains as analogous tothe tour-nament match in many sport games. We applied this intu-itive technique to browsing and selecting among computer-animation clips for motion parameter setting. It would alsobe applicable to browsing/selecting among retrieved candi-dates in audio/video retrieval systems.Key W ords: browsing, retrieval, tournament, multimedia,audio, video, user interface. 1 Introduction Browsing in a visual user interface takes advantage of the fact that avariety of artifacts may be placed in the visual field and the user canrapidly scan them, as well as use peripheral vision to obtain somesense of the objects not in the immediate visual focus [8]. Presen-tation of a set of retrieved items is usually done this way in imageretrieval systems [5][6]: the user can rapidly scan them and choosethe best among them. However, presentation of temporal signalssuch as audio or video files for browsing is made difficult by theserial and transitory nature of them. As opposed to the static dis

Closed-form solutions for lowpass filtering of 3D digital geometry are presented. Conventional signal processing is difficult to apply since 3D digital geometry does not usually satisfy the prerequisites of regular sampling and global parameterisation. Representing digital geometry as a level set of a 3D scalar field fitted with Gaussians and polynomials, derived are closed-form solutions of convolution with Gaussian smoothing kernels and lowpass filtering without the pitfalls of other approaches are analytically achieved.

Convolutional radial basis function (RBF) networks are introduced for smoothing out irregularly sampled signals. The proposed technique involves training an RBF network and then convolving it with a Gaussian smoothing kernel in an analytical manner. Since the convolution results in an analytic form, the computation necessary for numerical convolution is avoided. Convolutional RBF networks need training only once, do not depend on any particular details of the training methods used, and different degrees of smoothing are immediately available.

This article describes a method of constructing parametric models out of captured motion and skeleton data. Casting the problem as scattered data interpolation, our work is based on a multi-step approximation for the interpolation function with motion data compressed by principal component analysis. This leads to smaller storage and faster computation than those of previous approaches based on classical methods of exact interpolation. As a result, motion models can be constructed out of a rich set of example data, but can be used for real-time applications. We demonstrate a motion model controllable by attributes including those invariant for each individual, such as age, gender, height and weight. A parametric skeleton model is also constructed and demonstrated.

This article addresses 2-dimensional layout of high-dimensional biomedical datasets, which is useful for browsing them efficiently. We employ the Isomap technique, which is based on classical MDS (multi-dimensional scaling) but seeks to preserve the intrinsic geometry of the data, as captured in the geodesic manifold distances between all pairs of data points while classical approaches can see just the Euclidean structure. According to first two of Isomap's coordinates, the high-dimensional data points are arranged in a plane. Experimental results with images of marine creatures' shapes and 3D bone renderings are presented.

The interactive evolution technique, a variation of genetic algorithms, is used in synthesizing various facial expressions from an input 2D image of a face. Smooth 2D transformations were used to compensate for changes in facial expressions. The large space of the transformations or facial expressions is searched through by interactive evolution, without tedious user specifications, design efforts, or knowledge of algorithmic details. Brief reviews on uses of genetic algorithms and interactive evolution in computer graphics are also included.

This article addresses visualization of deformation or shape differences between bones while conventional visualization techniques are often about a single bone such as its 3D reconstruction. Given a pair of bones with a set of corresponding anatomical landmarks, we compute displacement vectors describing the deformation from one bone to the other at the landmark points on one of the bones. Out of these prescribed ones, a displacement vector at each vertex on the bone surface is derived using a multi-level approximation technique of scattered data. Based on the value of inner product between a displacement vector and a surface normal at each vertex, color is mapped. Considering error-prone estimation of the landmark location in real applications, approximations at different levels are realized instead of exact interpolation as usually done in elastic image registration. Experimental results on a pair of femoral bones are presented.