How effective are landmarks and their geometry for face recognition?
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: 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: 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: Normalized cuts and image segmentation
abstract: We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We have applied this approach to segmenting static images and found results very encouraging.

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: Perspectives on cognitive informatics
abstract: From a scientific perspective explaining how the brain thinks is a big goal. Cognitive informatics studies intelligent behavior from a computational point of view in terms of updated research efforts and processes of brain science and neuroscience. Cognitive informatics is the interdisciplinary study of cognition. Cognition includes mental states and processes, such as thinking, reasoning, remembering, language understanding and generation, visual and auditory perception, learning, consciousness, emotions, etc. In this paper we will point out basic research topics of learning, memory, thought, language, and neural computing which are active fields related to cognitive informatics.

title: Understanding popout through repulsion
abstract: Perceptual popout is defined by both feature similarity and local feature contrast. We identify these two measures with attraction and repulsion, and unify the dual processes of association by attraction and segregation by repulsion in a single grouping framework. We generalize normalized cuts to multi-way partitioning with these dual measures. We expand graph partitioning approaches to weight matrices with negative entries, and provide a theoretical basis for solution regularization in such algorithms. We show that attraction, repulsion and regularization each contributes in a unique way to popout. Their roles are demonstrated in various salience detection and visual search scenarios. This work opens up the possibilities of encoding negative correlations in constraint satisfaction problems, where solutions by simple and robust eigendecomposition become possible.

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.