14318268
Learning and Feature Selection in Stereo Matching
We present a novel stereo matching algorithm which integrates learning, feature selection, and surface reconstruction. First, a new instance based learning (IBL) algorithm is used to generate an approximation to the optimal feature set for matching. In addition, the importance of two separate kinds of knowledge, image dependent knowledge and image independent knowledge, is discussed. Second, we develop an adaptive method for refining the feature set. This adaptive method analyzes the feature error to locate areas of the image that would lead to false matches. Then these areas are used to guide the search through feature space towards maximizing the class separation distance between the correct match and the false matches. Third, we introduce a self-diagnostic method for determining when apriori knowledge is necessary for finding the correct match. If the a priori knowledge is necessary then we use a surface reconstruction model to discriminate between match possibilities. Our algorithm is comprehensively tested against fixed feature set algorithms and against a traditional pyramid algorithm. Finally, we present and discuss extensive empirical results of our algorithm based on a large set of real images. >
11367095
New Devices for 3D Pose Estimation: Mantis Eyes, Agam Paintings, Sundials, and Other Space Fiducials
Several unconventional ideas for viewer/camera pose estimation are discussed. The methods proposed so far advocate the use of advanced image processing for identification and precise location of calibration objects in the images acquired, and base pose recovery on the identification of the viewing dependent deformations of these objects. We propose to more fully exploit the freedom in the design of “space fiducials” or calibration objects showing that we can build objects whose images directly encode, in easily identifiable gray-level/color or temporal patterns, the pose of their viewer. We also show how to construct high-precision fiducials, which can determine a viewing direction quite accurately when it is known to lie within a relatively narrow range.
15741182
Bias Term b in SVMs Again
The paper discusses and presents the use and calculation of the e x- plicit bias term b in the support vector machines (SVMs) within the Iterative Single t raining Data learning Algorithm (ISDA). The approach proposed can be used for both nonlinear classification and nonlinear regression tasks. Unlike the other iterative methods in solvin g the SVMs learning problems containing the huge data sets, such as sequential minimal optimization (SMO) and its variants that must use at least two training data pairs, the algorithms shown here use the single training data based iteration routine for so lving QP learning problem. In this way the various 2 nd order heuristics in choosing the data for an updating is avoided. This makes the proposed ISD learning method remarkably quick. The algorithm can also be thought off as an application of a classic Gauss-Seidel (GS) coordinate ascent procedure and its derivative known as the successive over-relaxation (SOR) algorithm in SVMs learning from huge data sets subject to both the box constraints and the equality ones. (The later coming from min i- mizing the primal objective function in respect to the bias term b). The final solution in a dual domain is not an approximate one, but it is the optimal set of dual variables which would have been obtained by using any of existing and proven QP problem solvers if they only could deal with huge data sets. Proceedings of 12
1127387
Automatic Symbolic Traffic Scene Analysis Using Belief Networks
Automatic symbolic traffic scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy periods, identify stalled vehicles and accidents, and aid the decision-making of an autonomous vehicle controller. Improvements in technologies for machine vision-based surveillance and high-level symbolic reasoning have enabled us to develop a system for detailed, reliable traffic scene analysis. The machine vision component of our system employs a contour tracker and an affine motion model based on Kalman filters to extract vehicle trajectories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about traffic events such as vehicle lane changes and stalls. In this paper, we discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype.
8693566
Facial expression recognition from video sequences: temporal and static modeling
The most expressive way humans display emotions is through facial expressions. In this work we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We introduce and test different Bayesian network classifiers for classifying expressions from video, focusing on changes in distribution assumptions, and feature dependency structures. In particular we use Naive-Bayes classifiers and change the distribution from Gaussian to Cauchy, and use Gaussian Tree-Augmented Naive Bayes (TAN) classifiers to learn the dependencies among different facial motion features. We also introduce a facial expression recognition from live video input using temporal cues. We exploit the existing methods and propose a new architecture of hidden Markov models (HMMs) for automatically segmenting and recognizing human facial expression from video sequences. The architecture performs both segmentation and recognition of the facial expressions automatically using a multi-level architecture composed of an HMM layer and a Markov model layer. We explore both person-dependent and person-independent recognition of expressions and compare the different methods.
13032855
Discovering Statistically Significant Clusters by Using Iterative Genetic Algorithms in Gene Expression Data
This study presents an iterative genetic algorithm (GA) approach to find significant clusters in gene expression data. Many heuristic algorithms have been developed to cluster genes on microarray data. However, their results may be dominated by some predefined criteria, such as the number of clusters and initial mean points of clusters. Moreover, most current algorithms assign each one of genes in the dataset into some cluster while some of them are irrelevant to the interested conditions. To automatically cluster informative genes together and discard the irrelevant genes, we consider the clustering problem as maximizing correlativity among genes in a cluster. An iterative GA approach is proposed to find the tightest clusters (no irrelevant genes) and the most appropriate number of clusters. The proposed approach is applied on Hepatocellular carcinoma (HCC) dataset, and 21 significant clusters derived while 336 genes are discarded. The derived clusters have the deserving characteristics of intracluster tightness and intercluster distinctness. Furthermore, our method compares with K-Means heuristics under several clustering validation criteria and performs better than K-Means.
30107270
ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement
In content-based image retrieval (CBIR), experimental (trial-and-error) query with relevance feedback is essential for successful retrieval. Unfortunately, the traditional user interfaces are not suitable for trying different combinations of query examples. This is because first, these systems assume query examples are always added incrementally. Second, the query and the result display are done on the same workspace. Once the user removes an image from the query examples, the image may disappear from the user interface. In addition, it is difficult to combine the result of different queries. 
 
In this paper, we propose a new interface for Content-based image retrieval named ImageGrouper. ImageGrouper is a Group-Oriented user interface in that all operations are done by creating groups of images. This approach has several advantages. First, the users can interactively compare different combinations of the query examples by dragging and grouping the images on the workspace (Query-by-Group). Because the query results are displayed on another pane, the user can quickly review the results and modify the query. Combining different queries is also easy. Furthermore, the concept of “Image Groups” is also applied for annotating and organizing many images. The Annotation-by-Groups method relieves the user of tedious task of annotating textual information on a large number of images. This method realizes a hierarchical annotation of the images as well as Bulk Annotation. The Organize-by-Group method lets the users manipulate the image groups as “Photo Albums” to organize the images. Finally, the usability of the system is compared with the traditional user interfaces. By incorporating the lessons from the experiments, the usability of ImageGrouper is further improved.