1,721,448 research outputs found
Contributions of fractal structure and effects of Euclidean context on fractal perception
No full textdatafiles for 2 experiments measuring perception of 1. patterns with/without fractal arrangement and 2. fractal patterns imbedded in surrounding euclidean context (prototypical frame)
Memory for the Forest and the Trees: The Impact of Fractal Structure on Memory
No full textdata for 2 memory experiments. exp1: recognition memory for fractal and nonfractal images. exp2: source memory for regions of fractal and nonfractal images
Aesthetics and Psychological Effects of Fractal Based Design
No full textslider ratings for 3 experiments (2 unipolar and one bipolar) addressing visual judgements of fractal design patterns (both full "forest" design and local "tree" designs)
Fractal Preference across Development
No full text2-alternative force choice data indicating fractal pattern preference. Data collected from adult student sample at the University of Oregon and child sample gathered at Eugene Science Cente
Spectral dichromatic parameter recovery from two views via total variation hyper-priors
No full textIn this paper, we propose an approach for the recovery of the dichromatic model from two hyperspectral or multispectral images, i.e., the joint estimation of illuminant, reflectance, and shading of each pixel, as well as the optical flow between the two views. The approach is based on the minimization of an energy functional linking the dichromatic model to the image appearances and the flow between the images to the factorized reflectance component. In order to minimize the resulting under-constrained problem, we apply vectorial total variation regularizers both to the scene reflectance, and to the flow hyper-parameters. We do this by enforcing the physical priors for the reflectance of the materials in the scene and assuming the flow varies smoothly within rigid objects in the image. We show the effectiveness of the approach compared with single view model recovery both in terms of model constancy and of closeness to the ground truth
Enhanced kernel-based tracking for monochromatic and thermographic video
No full textIn this paper, we present an enhanced kernel-based tracker for monochromatic and thermographic video. The technique presented here employs the image intensity and the Local Binary Pattern (LBP) to construct a two dimensional histogram representative of the grayscale values and the texture of the target under study. With the histogram at hand, we proceed to compute its power density function. The new location of the object is then determined making use of a mean-shift optimisation approach. We illustrate the performance of our method in both, thermographic and monochromatic footages and compare our results to an alternative.Quang Anh Nguyen, Robles-Kelly, A. and Chunhua She
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
Get PDFThis paper describes work aimed at the unsupervised learning of shape-classes from shock trees. We commence by considering how to compute the edit distance between weighted trees. We show how to transform the tree edit distance problem into a series of maximum weight clique problems, and show how to use relaxation labeling to find an approximate solution. This allows us to compute a set of pairwise distances between graph-structures. We show how the edit distances can be used to compute a matrix of pairwise affinities using χ² statistics. We present a maximum likelihood method for clustering the graphs by iteratively updating the elements of the affinity matrix. This involves interleaved steps for updating the affinity matrix using an eigendecomposition method and updating the cluster membership indicators. We illustrate the new tree clustering framework on shock-graphs extracted from the silhouettes of 2D shapes
Kernel-based tracking from a probabilistic viewpoint
No full textIn this paper, we present a probabilistic formulation of kernel-based tracking methods based upon maximum likelihood estimation. To this end, we view the coordinates for the pixels in both, the target model and its candidate as random variables and make use of a generative model so as to cast the tracking task into a maximum likelihood framework. This, in turn, permits the use of the EM-algorithm to estimate a set of latent variables that can be used to update the target-center position. Once the latent variables have been estimated, we use the Kullback-Leibler divergence so as to minimise the mutual information between the target model and candidate distributions in order to develop a target-center update rule and a kernel bandwidth adjustment scheme. The method is very general in nature. We illustrate the utility of our approach for purposes of tracking on real-world video sequences using two alternative kernel functions.Quang Anh Nguyen, Robles-Kelly, A. and Chunhua She
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