USING DATA MINING TO DETECT ANOMALOUS PRODUCER BEHAVIOR: AN ANALYSIS OF SOYBEAN PRODUCTION AND THE FEDERAL CROP INSURANCE PROGRAM

The analysis was conducted on the USDA's Risk Management Agency insurance data and NRCS Land Resource Regions from 1994 - 2001 to assist RMA in improving program integrity. The objective is to develop a data-mining algorithm that identifies anomalous producers and counties within LRRs based upon the percentage of acres harvested.Risk and Uncertainty,

Face and Object Recognition and Detection Using Colour Vector Quantisation

In this paper we present an approach to face and object detection and recognition based on an extension of the contentbased image retrieval method of Lu and Teng (1999). The method applies vector quantisation (VQ) compression to the image stream and uses Mahalonobis weighted Euclidean distance between VQ histograms as the measure of image similarity. This distance measure retains both colour and spatial feature information but has the useful property of being relatively insensitive to changes in scale and rotation. The method is applied to real images for face recognition and face detection applications. Tracking and object detection can be coded relatively efficiently due to the data reduction afforded by VQ compression of the data stream. Additional computational efficiency is obtained through a variation of the tree structured fast VQ algorithm also presented here

Homogenised Virtual Support Vector Machines

In many domains, reliable a priori knowledge exists that may be used to improve classifier performance. For example in handwritten digit recognition, such a priori knowledge may include classification invariance with respect to image translations and rotations. In this paper, we present a new generalisation of the Support Vector Machine (SVM) that aims to better incorporate this knowledge. The method is an extension of the Virtual SVM, and penalises an approximation of the variance of the decision function across each grouped set of "virtual examples", thus utilising the fact that these groups should ideally be assigned similar class membership probabilities. The method is shown to be an efficient approximation of the invariant SVM of Chapelle and Scholkopf, with the advantage that it can be solved by trivial modification to standard SVM optimization packages and negligible increase in computational complexity when compared with the Virtual SVM. The efficacy of the method is demonstrated on a simple problem

Measurement Function Design for Visual Tracking Applications

Extracting human postural information from video sequences has proved a difficult research question. The most successful approaches to date have been based on particle filtering, whereby the underlying probability distribution is approximated by a set of particles. The shape of the underlying observational probability distribution plays a significant role in determining the success, both accuracy and efficiency, of any visual tracker. In this paper we compare approaches used by other authors and present a cost path approach which is commonly used in image segmentation problems, however is currently not widely used in tracking applications

Visual tracking for sports applications

Visual tracking of the human body has attracted increasing attention due to the potential to perform high volume low cost analyses of motions in a wide range of applications, including sports training, rehabilitation and security. In this paper we present the development of a visual tracking module for a system aimed to be used as an autonomous instructional aid for amateur golfers. Postural information is captured visually and fused with information from a golf swing analyser mat and both visual and audio feedback given based on the golfer's mistakes. Results from the visual tracking module are presented

Autonomous Sports Training from Visual Cues

Computer driven biometric analysis of athlete's movements have proven themselves as effective sports training tools. Most current systems rely on the use of retro-reflective markers or magnetic sensors to capture the motion of the athlete, so the biometric analysis can be performed. Video based training tools have also proved to be valuable instructional aids, however most require significant human interaction for analysis to be performed. This paper outlines an ongoing project focussed on capturing posture without the use of any markers or sensors, while still capturing enough information for an automated analysis to be performed. The approach taken to solving this problem is presented, as well as the current state of development of a an instructional aid for golfers

Equivalent standard DEA models to provide super-efficiency scores

DEA super-efficiency models were introduced originally with the objective of providing a tie-breaking procedure for ranking units rated as efficient in conventional DEA models. This objective has been expanded to include sensitivity analysis, outlier identification and inter-temporal analysis. However, not all units rated as efficient in conventional DEA models have feasible solutions in DEA super-efficiency models. We propose a new super-efficiency model that (a) generates the same super-efficiency scores as conventional super-efficiency models for all units having a feasible solution under the latter, and (b) generates a feasible solution for all units not having a feasible solution under the latter. Empirical examples are provided to compare the two super-efficiency models

Robust Face Recognition in Rotated Eigen Space

Face recognition is a very complex classification problem due to nuisance variations in different conditions. Most face recognition approaches either assume constant lighting condition or standard facial expressions, thus cannot deal with both kinds of variations simultaneously. Principal Component Analysis (PCA) cannot handle complex pattern variations such as illumination and expression. Adaptive PCA rotates eigenspace to extract more representative features thus improving the performance. In this paper, we present a way to extract various sets of features by different eigenspace rotations and propose a method to fuse these features to generate nonorthogonal mappings for face recognition. The proposed method is tested on the Asian Face Database with 856 images from 107 subjects with 5 lighting conditions and 4 expressions. We register only one normally lit neutral face image and test on the remaining face images with variations. Experiments show a 95% classification accuracy and a 20% reduction in error rate. This illustrates that the fused features can provide significantly improved pattern classification

Visual Odometry for Quantitative Bronchoscopy Using Optical Flow

Optical Flow, the extraction of motion from a sequence of images or a video stream, has been extensively researched since the late 1970s, but has been applied to the solution of few practical problems. To date, the main applications have been within fields such as robotics, motion compensation in video, and 3D reconstruction. In this paper we present the initial stages of a project to extract valuable information on the size and structure of the lungs using only the visual information provided by a bronchoscope during a typical procedure. The initial implementation provides a realtime estimation of the motion of the bronchoscope through the patients airway, as well as a simple means for the estimation of the cross sectional area of the airway