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In this paper, we study and evaluate the application to image segmentation of a p-Laplacian based relaxation of the Cheeger Cut problem. Based on a l1 relaxation of the initial clustering problem, we show that these methods can outperform usual well-known graph based approaches, e.g., min-cut/max-flow algorithm or l2 spectral clustering, for unsupervised and very weakly supervised image segmentation...
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...
This paper proposes a novel approach named Compressed Submanifold Multifactor Analysis (CSMA) to concisely and precisely deal with multifactor analysis. Compared to the state-of-the-art MPCA method that loses the original local geometry structures of input factors due to the averaging process, our proposed approach can preserve their original geometry. In addition, the fast low-rank approximation...
In this paper, we propose a compact image signature based on VLAT. Our method integrates spatial information while significantly reducing the size of original VLAT by using two pojection steps. we carry out experiments showing our approach is competitive with state of the art signatures.
The underlying principle behind most optical flow algorithms is that the brightness of a pixel remains the same as it flows from one frame to the next. The first order Taylor approximation used in formulating this brightness constancy principle may not be accurate when intensity profiles change non-linearly. In this paper, we propose a method of alleviating the effect of this approximation. Instead...
In this paper, we present a theoretical analysis on learning anchors for local coordinate coding (LCC), which is a method to model functions for data lying on non-linear manifolds. In our analysis several local coding schemes, i.e., orthogonal coordinate coding (OC-C), local Gaussian coding (LGC), local Student coding (LSC), are theoretically compared, in terms of the upper-bound locality error on...
The large amount of digital data requests for scalable tools like efficient clustering algorithms. Many algorithms for large data sets request linear separability in an Euclidean space. Kernel approaches can capture the non-linear structure but do not scale well for large data sets. Alternatively, data are often represented implicitly by dissimilarities like for protein sequences, whose methods also...
Rule induction algorithms such as Ripper, solve a K > 2 class problem by converting it into a sequence of K — 1 two-class problems. As a usual heuristic, the classes are fed into the algorithm in the order of increasing prior probabilities. In this paper, we propose two algorithms to improve this heuristic. The first algorithm starts with the ordering the heuristic provides and searches for better...
Visual object tracking in video can be formulated as a time varying appearance-based binary classification problem. Tracking algorithms need to adapt to changes in both foreground object appearance as well as varying scene backgrounds. Fusing information from multimodal features (views or representations) typically enhances classification performance without increasing classifier complexity when image...
This paper describes a method of tracking multiple persons with occlusions using stereo. We previously developed an accurate and stable tracking method using overlapping silhouette templates which considers how persons overlap in the image. It realized a fast tracking by using an approximated likelihood map based on kernel density estimation. The method, however, treated only two overlapping persons...
In this paper we propose an original framework for the description and the subsequent recognition of objects of limited size. Although of general applicability, the framework is presented here as a way to trace different yet similar metal tools employed in the mechanical constructions industry. For the purpose of object description, time-varying silhouettes of the object are acquired under turntable...
Environment illumination is a key to achieving a realistic visualization of material appearance. One way to achieve such an illumination is an approximation by rendering of the material surface lit by a finite set of point light sources. In this paper we employed visual psychophysics to identify a minimal number of point light sources approximating realistic illumination. Furthermore, we analyzed...
In this paper, a computation-efficient adaptive support-window scheme is proposed to approximate the conventional bilateral filtering. The difference is that the pixel-wise weights in bilateral filter are thresholded to be only 0 or 1. This results in an adaptive support window, depending on the local image structure of the anchor pixel. A cross-based algorithm is devised to achieve adaptive support...
In this paper, we propose a new scoring method for local feature-based image retrieval. The proposed score is based on the ratio of the probability density function of an object model to that of background model, which is efficiently calculated via nearest neighbor density estimation. The proposed method has the following desirable properties: (1) a sound theoretical basis, (2) effectiveness than...
For many purposes such as rotatable feature extraction and noise reduction, the Gaussian filter is often used. Many present algorithms compute it using much time or compute a rectangle filter instead of it giving up rotation invariance. In this paper, we propose a method to shorten computational time of the Gaussian filter. The proposed method uses an nth-order spline, where n is higher than one....
Sparse coding is a widespread framework in signal and image processing. For instance, it has been employed in image/video classification to decompose visual feature vectors, such as local gradient descriptors into a linear combination of few elements of an over-complete basis, which is called dictionary. In order to learn such sparse representations, greedy algorithms like Orthogonal Matching Pursuit...
A linear-time algorithm is proposed for polygonal approximation of digital curves. The direction changes of the x- and y-coordinates are traced to generate a new, compact representation of curves. The algorithm, Direction Change-based Polygonal Approximation (DCPA), has two advantages: linear time complexity and insensitivity to parameter setting. Benchmark results demonstrate the competitive performance...
A large number of GPS trajectories, which include users' spatial and temporal information, are collected by geo-positioning mobile phones in recent years. The massive volumes of trajectory data bring about heavy burdens for both network transmission and data storage. To overcome these difficulties, GPS trajectory compression algorithm (GTC) was proposed recently that optimizes both the data reduction...
Local feature detectors and descriptors are widely used in many computer vision applications and various methods have been proposed during the past decade. There have been a number of evaluations focused on various aspects of local features, matching accuracy in particular, however there has been no comparisons considering the accuracy and speed trade-offs of recent extractors such as BRIEF, BRISK,...
One of most challenging and important tasks for electricity grid operators and utility companies is to predict and estimate the precise energy consumption and generation of individual households which have their own decentralized production system. This is a under-determined source separation problem since only the difference between energy production and consumption in the micro-generation system...
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