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Color composition is an important cue for image retrieval and object classification. In this paper we address the problem of inferring the color composition of visual objects from the pixel-level color distribution over the basic color terms. We build a discriminative model to tag each region with a dominant color and an associate one. We learn the human preference and cooccurrance patterns of the...
We propose a novel unified approach for homography estimation from two or more correspondences of local elliptical features. The method finds a homography defined by first-order Taylor expansions at two (or more) points. The approximations are affine transformations that are constrained by the ellipse-to-ellipse correspondences. Unlike methods based on projective invariants of conics, the proposed...
In this paper, we introduce a puppet interface for the development of an intuitive animation system. Our puppet interface does not require any special devices and any type of puppet can be used. The puppet interface is developed by attaching ten visible markers onto a puppet. The user can manipulate the pose of the puppet interface to produce the desired motion in front of a camera. The puppet's joint...
We aim to match two hypergraphs via pairwise characterization of multiple relationships. To this end, we introduce a technique referred to as Marginalized Constrained Compatibility Estimation (MCCE), which transforms the compatibility tensor representing hyper-edge similarities into a compatibility matrix representing edge similarities. We then cluster graph vertices associated with the compatibility...
The paper discusses the issue of motion estimation by image assimilation in numerical models, based on Navier-Stokes equations. In such context, models' reduction is an attractive approach that is used to decrease cost in memory and computation time. A reduced model is obtained from a Galerkin projection on a subspace, defined by its orthogonal basis. Long temporal image sequences may then be processed...
Graph-based methods are an important category of semi-supervised learning techniques. However, in many situations the graph representation of relational patterns can lead to substantial loss of information. This is because in real-world problems objects and their features tend to exhibit multiple relationships rather than simple pairwise ones. In this paper, we develop a semi-supervised learning method...
Center-surround measurements are widely used for saliency detection but with some disadvantages: 1) Center-surround operation may cause inaccurate segmentation and even involve incorrect detection results; 2) In most situations, only using center-surround feature is not efficient to encode object saliency. To overcome these disadvantages, we describe a novel measurement, namely Corner-Surround Contrast...
We propose a new Iterative-Midpoint-Method (IMM) for video character gap filling based on end pixels and neighbor pixels in the extracted contour of a character. The method obtains the Enhanced Gradient Image (EGI) for the given gray character image to sharpen text pixels. Max-Min clustering and K-means clustering algorithm with K=2 are applied on the EGI to obtain text candidates. To clean up the...
Document clustering has become inevitable for applications that aim to extract information from huge corpuses. Such applications face two main challenges; one is the efficient representation of the documents, along with using an efficient similarity measure, and the second is dealing with the dynamic nature of the corpus. In this paper, an efficient document clustering model is introduced for incrementally...
To date the methods to create accuracy dense realistic 3D models of outdoors by using laser scanners are highly dependent on the on-site conditions in the very moment of the 3D data collection. Thus, researchers put in a lot of effort on eliminating colour incoherencies (sunny/shady, bright/dark, non sensed areas, etc.) or modelling the light of the scene to obtain free-illumination models. This paper...
This paper presents a new unsupervised statistical model for human activity discovery and recognition in pervasive environments. The activities are encoded in sequences recorded by non-intrusive sensors disseminated in the environment. Our model studies the relationship between the activities and the sequential patterns from the sequence analysis perspective. Activity discovery is formulated as an...
The problem of simultaneously estimating affine deformations between multiple objects occur in many applications. Herein, a direct method is proposed which provides the result as a solution of a linear system of equations without establishing correspondences between the objects. The key idea is to construct enough linearly independent equations using covariant functions, and then finding the solution...
Frequent itemset-based text clustering has emerged as a promising way to automatic organization of text documents, because it allows high clustering accuracy combined with understandable cluster descriptors. However, the clustering results may not be satisfactory because they do not reflect the user's point of view. In this context, active learning is an interesting approach to incorporate the user's...
Least square fitting of quadratic surfaces is a fundamental problem in pattern recognition, computer vision, graphics, and medical imaging analysis. This paper investigated in approaches to ellipsoid-specific fitting. In 2D case, Fitzgibbon's ellipse-specific fitting approach outperforms others since it is extremely robust, efficient, and easy to implement. This paper attempts to extend it from 2D...
Document image binarization is an important preprocessing technique for document image analysis that segments the text from the document image backgrounds. Many techniques have been proposed and successfully applied in different applications, such as document image retrieval. However, these techniques may perform poorly on degraded document images. In this paper, we propose a learning framework that...
Data has multi-view representations from various feature spaces in real world. Multi-view clustering algorithms allow leveraging information from multiple views of the data and this may substantially improve the clustering result obtained by using a single view. In this paper, we propose a novel algorithm called Collaborative PLSA (C-PLSA) for multi-view clustering, which works on the assumption that...
Non-negative matrix factorization [5](NMF) is a well known tool for unsupervised machine learning. It can be viewed as a generalization of the K-means clustering, Expectation Maximization based clustering and aspect modeling by Probabilistic Latent Semantic Analysis (PLSA). Specifically PLSA is related to NMF with KL-divergence objective function. Further it is shown that K-means clustering is a special...
Concentric circles (C2Tag's) are planar markers which offer great advantages for detection and tracking. As the circular point-pair (CPP) is the geometric information encoded by a C2Tag, this work is focused on factorization techniques for Structure-and-Motion from multiple CPP images. Gathering all of them in a measurement matrix, two issues are addressed: how to scale the existing entries and how...
In this paper we present a method to recover a spectra representation for reproduction and recognition on multispectral imagery. To do this, we commence by viewing the spectra in the image as a mixture which can be expressed in terms of the sample mean and a set of basis vectors and weights. This treatment leads to an MAP approach where the sample means is given by the centers yielded by the application...
We developed a motion blur restoration technique for surface orientation images using a correlation image sensor. This system consists of two components; one is ring-shaped modulation illumination for encoding surface orientation into the amplitude and phase of the reflected light intensity, and the other is the three-phase correlation image sensor (3PCIS) for demodulating the amplitude and phase...
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