Global Control of Robotic Highway Safety Markers: A Real-time Solution
This paper presents the design and implementation of a real-time solution for the global control of robotic highway safety markers. Problems addressed in the system are: (1) poor scalability and predictability as the number of markers increases, (2) jerky movement of markers, and (3) misidentification of safety markers caused by objects in the environment.An extensive analysis of the system and two solutions are offered: a basic solution and an enhanced solution. They are built respectively upon two task models: the periodic task model and the variable rate execution (VRE) task model. The former is characterized by four static parameters: phase, period, worst case execution time and relative deadline. The latter has similar parameters, but the parameter values are allowed to change at arbitrary times.The use of real-time tasks and scheduling techniques solve the first two problems. The third problem is solved using a refined Hough transform algorithm and a horizon scanning window. The approach decreases the time complexity of traditional implementations of the Hough transform with only slightly increased storage requirements.

title: Image interpolation by two-dimensional parametric cubic convolution
abstract: Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2,2]/spl times/[-2,2] with constraints for biaxial symmetry, diagonal (or 90/spl deg/ rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-5PCC), relaxes the constraint of diagonal symmetry, based on the observation that many images have rotationally asymmetric statistical properties. This paper also develops a closed-form solution for determining the optimal parameter values for parametric cubic-convolution kernels with respect to ensembles of scenes characterized by autocorrelation (or power spectrum). This solution establishes a practical foundation for adaptive interpolation based on local autocorrelation estimates. Quantitative fidelity analyses and visual experiments indicate that these new methods can outperform several popular interpolation methods. An analysis of the error budgets for reconstruction error associated with blurring and aliasing illustrates that the methods improve interpolation fidelity for images with aliased components. For images with little or no aliasing, the methods yield results similar to other popular methods. Both 2D-3PCC and 2D-5PCC are low-order polynomials with small spatial support and so are easy to implement and efficient to apply.

title: How effective are landmarks and their geometry for face recognition?
abstract: This paper evaluates how biologically meaningful landmarks and their geometry extracted from face images can be used for face recognition. The traditional Procrustes distance is studied for the landmark-based face model. By using complex principal component analysis, we propose a refined Procrustes distance that incorporates statistical correlation of landmarks. Motivated by research results from art, anthropology, and aesthetic surgery where ratios play a significant role in human face descriptions, we also investigate how well-normalized Euclidean distances (special ratios) can be exploited for face recognition. Exploiting symmetry and using principal component analysis, we reduce the number of ratios to 20. In this investigation, we analyze the effectiveness of three well-known similarity measures including the typical l"1 norm, l"2 norm, and Mahalanobis distance. We also define a new similarity measure called the eigenvalue-weighted cosine (EWC) distance. We evaluate our approach using two standard face databases: the Purdue AR and the FERET database. The former has well-controlled face images and we use it to test the reliability of landmarks. The FERET database is used for performance evaluation of face recognition algorithms. This papers use a performance measure called the cumulative match score, which indicates the percentage of faces that can be eliminated from consideration. Experimental results show that our EWC distance outperforms the l"1 and l"2 norms and even the Mahalanobis distance. Our study also finds that landmarks and their geometry-based approach can account for variations of face expression and aging very well. Thus, they can be used either in stand-alone mode or in conjunction with other approaches to reduce the search space a priori.

title: A real-time model for the robotic highway safety marker system
abstract: We present the design and implementation of a real-time model for the global control of robotic highway safety markers. Problems addressed in the system are: 1) poor scalability and predictability as the number of markers increases, 2) jerky movement of markers, and 3) false-hits caused by environment objects. We extensively analyze the system and offer two solutions: a basic solution and an enhanced solution. They are built respectively upon two task models: the periodic task model and the variable rate execution task model. The former is characterized by four static parameters: phase, period, worst case execution time and relative deadline. The latter has similar parameters. Its parameter values, however, are allowed to change at arbitrary times. We then examine two typical real-time scheduling approaches: rate monotonic (RM) priority driven and earliest deadline first (EDF). Analysis of their sufficient conditions shows that our system is feasibly schedulable under either RM or EDF. This conclusion justifies that the path for each safety marker can be guaranteed to be smooth under the designed real-time system. For the scalability issue, we present a sufficient condition for the upper bound on the number of barrel robots that can be reliably controlled. One key technique integrated into our real-time system is the Hough transform. We refine its traditional implementation so that, for the task of detecting safety markers, the time complexity decreases and the reliability increases with only slight additional storage for search windows. The basic idea behind our improvements is to limit the search window for safety markers.

title: Interpolation by asymmetric, two-dimensional cubic convolution
abstract: This paper formulates a two-dimensional, piecewise-cubic interpolator that accounts for non-separable and rotationally asymmetric scene statistics. The resulting kernel has five parameters and is designated 2D-5PCC. As a low-order polynomial with small spatial support, the kernel is easy to implement and efficient to apply. A closed-form solution, developed in this paper, yields the optimal parameter values for scene ensembles characterized by autocorrelation or power spectrum. Experiments indicate that 2D-5PCC yields higher fidelity interpolations than several other popular methods.

title: Detecting unusual activity in video
abstract: We present an unsupervised technique for detecting unusual activity in a large video set using many simple features. No complex activity models and no supervised feature selections are used. We divide the video into equal length segments and classify the extracted features into prototypes, from which a prototype-segment co-occurrence matrix is computed. Motivated by a similar problem in document-keyword analysis, we seek a correspondence relationship between prototypes and video segments which satisfies the transitive closure constraint. We show that an important sub-family of correspondence functions can be reduced to co-embedding prototypes and segments to N-D Euclidean space. We prove that an efficient, globally optimal algorithm exists for the co-embedding problem. Experiments on various real-life videos have validated our approach.

title: Object recognition using boosted discriminants
abstract: We approach the task of object discrimination as that of learning efficient "codes" for each object class in terms of responses to a set of chosen discriminants. We formulate this approach in an energy minimization framework. The "code" is built incrementally by successively constructing discriminants that focus on pairs of training images of objects that are currently hard to classify. The particular discriminants that we use partition the set of objects of interest into two well-separated groups. We find the optimal discriminant as well as partition by formulating an objective criteria that measures the well-separateness of the partition. We derive an iterative solution that alternates between the solutions for two generalized eigenproblems, one for the discriminant parameters and the other for the indicator variables denoting the partition. We show how the optimization can easily be biased to focus on hard to classify pairs, which enables us to choose new discriminants one by one in a sequential manner We validate our approach on a challenging face discrimination task using parts as features and show that it compares favorably with the performance of an eigenspace method.

title: Filtered Gaussian Processes for Learning with Large Data-Sets
abstract: Kernel-based non-parametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those methods to problems with large data-sets. In this paper we develop a filtering approach based on a Gaussian process regression model. The idea is to generate a small-dimensional set of filtered data that keeps a high proportion of the information contained in the original large data-set. Model learning and prediction are based on the filtered data, thereby decreasing the computational burden dramatically.

title: Left cells containing a fully commutative element
abstract: Let W be a finite or an affine Coxeter group and Wc the set of all the fully commutative elements in W. For any left cell L of W containing some fully commutative element, our main result of the paper is to prove that there exists a unique element (say wL) in L ∩ Wc such that any z ∈ L has the form z = xwL with l(z) = l(x) + l(wL) for some x ∈ W. This implies that L is left connected, verifying a conjecture of Lusztig in our case.

title: Sign Types Associated to Posets
abstract: We start with a combinatorial definition of I-sign types which are a generalization of the sign types indexed by the root system of type Al (I?N finite). Then we study the set DIp of I-sign types associated to the partial orders on I. We establish a 1?1 correspondence between D[n]p and a certain set of polyhedral cones in a euclidean space by which we get a geometric distinction of the sign types in D[n]p from the other [n]-sign types. We give a graph-theoretical criterion for an Sn-orbit O of D[n]p to contain a dast and show that O contains at most one dast. Finally, we show the admirability of a poset associated to a dast.