71 research outputs found
Non-Negative Local Sparse Coding for Subspace Clustering
Subspace sparse coding (SSC) algorithms have proven to be beneficial to
clustering problems. They provide an alternative data representation in which
the underlying structure of the clusters can be better captured. However, most
of the research in this area is mainly focused on enhancing the sparse coding
part of the problem. In contrast, we introduce a novel objective term in our
proposed SSC framework which focuses on the separability of data points in the
coding space. We also provide mathematical insights into how this
local-separability term improves the clustering result of the SSC framework.
Our proposed non-linear local SSC algorithm (NLSSC) also benefits from the
efficient choice of its sparsity terms and constraints. The NLSSC algorithm is
also formulated in the kernel-based framework (NLKSSC) which can represent the
nonlinear structure of data. In addition, we address the possibility of having
redundancies in sparse coding results and its negative effect on graph-based
clustering problems. We introduce the link-restore post-processing step to
improve the representation graph of non-negative SSC algorithms such as ours.
Empirical evaluations on well-known clustering benchmarks show that our
proposed NLSSC framework results in better clusterings compared to the
state-of-the-art baselines and demonstrate the effectiveness of the
link-restore post-processing in improving the clustering accuracy via
correcting the broken links of the representation graph.Comment: 15 pages, IDA 2018 conferenc
Solving Uncalibrated Photometric Stereo using Total Variation
International audienceEstimating the shape and appearance of an object, given one or several images, is still an open and challenging research problem called 3D-reconstruction. Among the different techniques available, photometric stereo (PS) produces highly accurate results when the lighting conditions have been identified. When these conditions are unknown, the problem becomes the so-called uncalibrated PS problem, which is ill-posed. In this paper, we will show how total variation can be used to reduce the ambiguities of uncalibrated PS, and we will study two methods for estimating the parameters of the generalized bas-relief ambiguity. These methods will be evaluated through the 3D-reconstruction of real-world objects
1/f2 Characteristics and Isotropy in the Fourier Power Spectra of Visual Art, Cartoons, Comics, Mangas, and Different Categories of Photographs
Art images and natural scenes have in common that their radially averaged (1D) Fourier spectral power falls according to a power-law with increasing spatial frequency (1/f2 characteristics), which implies that the power spectra have scale-invariant properties. In the present study, we show that other categories of man-made images, cartoons and graphic novels (comics and mangas), have similar properties. Further on, we extend our investigations to 2D power spectra. In order to determine whether the Fourier power spectra of man-made images differed from those of other categories of images (photographs of natural scenes, objects, faces and plants and scientific illustrations), we analyzed their 2D power spectra by principal component analysis. Results indicated that the first fifteen principal components allowed a partial separation of the different image categories. The differences between the image categories were studied in more detail by analyzing whether the mean power and the slope of the power gradients from low to high spatial frequencies varied across orientations in the power spectra. Mean power was generally higher in cardinal orientations both in real-world photographs and artworks, with no systematic difference between the two types of images. However, the slope of the power gradients showed a lower degree of mean variability across spectral orientations (i.e., more isotropy) in art images, cartoons and graphic novels than in photographs of comparable subject matters. Taken together, these results indicate that art images, cartoons and graphic novels possess relatively uniform 1/f2 characteristics across all orientations. In conclusion, the man-made stimuli studied, which were presumably produced to evoke pleasant and/or enjoyable visual perception in human observers, form a subset of all images and share statistical properties in their Fourier power spectra. Whether these properties are necessary or sufficient to induce aesthetic perception remains to be investigated
Face Recognition Human–Machine Comparison Under Heavy Lighting
We demonstrate the performance of the Fisherface method for face recognition compared to human eye and simple Eigenface method. These methods do not involve many adjustable parameters. Images undergo the principal component analysis (PCA) and linear discriminant analysis (LDA). The goal of the work is a detailed comparison of the rates of false recognition between the computer vision methods and human perception. We find that humans show more flexibility and perform perfectly on easy tasks, whereas on tasks that are impossible to humans, Fisherface method also fails
Context-aware textures
Interesting textures form on the surfaces of objects as the result of external chemical, mechanical, and biological agents. Simulating these textures is necessary to generate models for realistic image synthesis. The textures formed are progressively variant, with the variations depending on the global and local geometric context. We present a method for capturing progressively varying textures and the relevant context parameters that control them. By relating textures and context parameters, we are able to transfer the textures to novel synthetic objects. We present examples of capturing chemical effects, such as rusting; mechanical effects, such as paint cracking; and biological effects, such as the growth of mold on a surface. We demonstrate a user interface that provides a method for specifying where an object is exposed to external agents. We show the results of complex, geometry-dependent textures evolving on synthetic objects. © 2007 ACM.link_to_subscribed_fulltex
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