Advanced Statistical Methods in Biometric Research.

A major challenge in indexing unstructured hypertext databases is to automatically extract meta-data that enables structured search using topic taxonomies, circumvents keyword ambiguity, and improves the quality of search and profile-based routing and filtering. Therefore, an accurate classifier is an essential component of a hypertext database. Hyperlinks pose new problems not addressed in the extensive text classification literature. Links clearly contain high-quality semantic clues that are lost upon a purely term-based classifier, but exploiting link information is non-trivial because it is noisy. Naive use of terms in the link neighborhood of a document can even degrade accuracy. Our contribution is to propose robust statistical models and a relaxation labeling technique for better classification by exploiting link information in a small neighborhood around documents. Our technique also adapts gracefully to the fraction of neighboring documents having known topics. We experimented with pre-classified samples from Yahoo!1 and the US Patent Database2. In previous work, we developed a text classifier that misclassified only 13% of the documents in the well-known Reuters benchmark; this was comparable to the best results ever obtained. This classifier misclassified 36% of the patents, indicating that classifying hypertext can be more difficult than classifying text. Naively using terms in neighboring documents increased error to 38%; our hypertext classifier reduced it to 21%. Results with the Yahoo! sample were more dramatic: the text classifier showed 68% error, whereas our hypertext classifier reduced this to only 21%.

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Enhanced hypertext categorization using hyperlinks

In this study we use a technique referred to as Gaussian decomposition for processing and calibrating data acquired with a novel small-footprint airborne laser scanner that digitises the complete waveform of the laser pulses scattered back from the Earth's surface. This paper presents the theoretical basis for modelling the waveform as a series of Gaussian pulses. In this way the range, amplitude, and width are provided for each pulse. Using external reference targets it is also possible to calibrate the data. The calibration equation takes into account the range, the amplitude, and pulse width and provides estimates of the backscatter cross-section of each target. The applicability of this technique is demonstrated based on RIEGL LMS-Q560 data acquired over the city of Vienna.

Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner

With the fast development of remote sensor technologies, e.g. the appearance of Very High Resolution (VHR) optical sensors, SAR, LiDAR, etc., mounted on either airborne or spaceborne platforms, multi-source remote sensing data fusion techniques are emerging due to the demand for new methods and algorithms. The general fusion techniques have been well developed and applied in various fields ranging from satellite earth observation to computer vision, medical image processing, defence security and so on. Despite the fast development, the techniques remain challenging for multi-source data fusion within varying spatial and temporal resolutions. This article reviews current techniques of multi-source remote sensing data fusion and discusses their future trends and challenges through the concept of hierarchical classification, i.e., pixel/data level, feature level and decision level. This article concentrates on discussing optical panchromatic and multi-spectral data fusing methods. So far, the pixel level fus...

Multi-source remote sensing data fusion: status and trends

The investment in an automated fabric defect detection system is more than economical when reduction in labor cost and associated benefits are considered. The development of a fully automated web inspection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to the large number of fabric defect classes, which are characterized by their vagueness and ambiguity. Numerous techniques have been developed to detect fabric defects and the purpose of this paper is to categorize and/or describe these algorithms. This paper attempts to present the first survey on fabric defect detection techniques presented in about 160 references. Categorization of fabric defect detection techniques is useful in evaluating the qualities of identified features. The characterization of real fabric surfaces using their structure and primitive set has not yet been successful. Therefore, on the basis of the nature of features from the fabric surfaces, the proposed approaches have been characterized into three categories; statistical, spectral and model-based. In order to evaluate the state-of-the-art, the limitations of several promising techniques are identified and performances are analyzed in the context of their demonstrated results and intended application. The conclusions from this paper also suggest that the combination of statistical, spectral and model-based approaches can give better results than any single approach, and is suggested for further research.

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Computer-Vision-Based Fabric Defect Detection: A Survey

Using the Dempster–Shafer method for the fusion of LIDAR data and multi-spectral images for building detection

In this paper, we describe the evaluation of a method for building detection by the Dempster-Shafer fusion of airborne laser scanner (ALS) data and multi-spectral images. For this purpose, ground truth was digitised for two test sites with quite different characteristics. Using these data sets, the heuristic models for the probability mass assignments are validated and improved, and rules for tuning the parameters are discussed. The sensitivity of the results to the most important control parameters of the method is assessed. Further we evaluate the contributions of the individual cues used in the classification process to determine the quality of the results. Applying our method with a standard set of parameters on two different ALS data sets with a spacing of about 1 point/ in m(2), 95% of all buildings larger than 70 m(2) could be detected and 95% of all detected buildings larger than 70 m(2) were correct in both cases. Buildings smaller than 30 in m(2) could not be detected. The parameters used in the method have to be appropriately defined, but all except one (which must be determined in a training phase) can be determined from meaningful physical entities. Our research also shows that adding the multi-spectral images to the classification process improves the correctness of the results for small residential buildings by up to 20%. (c) 2007 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Building detection by fusion of airborne laser scanner data and multi-spectral images : Performance evaluation and sensitivity analysis

The paper discusses the bistatic radar parameters for the case when the transmitter is a satellite emitting communication signals. The model utilises signals from an Iridium-like low earth orbiting satellite system. The maximum detection range, when thermal noise-limited, is discussed at the theoretical level and these results are compared with experimentation. Satellite-radar signal levels and the power of ground reflections are evaluated.

Air target detection via bistatic radar based on LEOs communication signals

In this paper, we describe an algorithm for roof line delineation from LIDAR data which aims at achieving models of a high level of detail Roof planes are initially extracted by segmentation based on the local homogeneity of surface normal vectors of a digital surface model (DSM) A case analysis then reveals which of these roof planes intersect and which of them are separated by a step edge The positions of the step edges are determined precisely by a new algorithm taking into account domain specific information Finally, all step edges and intersection lines are combined to form consistent polyhedral models In all phases of this workflow, decision making is based upon statistical reasoning about geometrical relations between neighbouring entities in order to reduce the number of control parameters and to increase the robustness of the method

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Kurt Kubik

Papers

Using the Dempster–Shafer method for the fusion of LIDAR data and multi-spectral images for building detection

Building detection by fusion of airborne laser scanner data and multi-spectral images : Performance evaluation and sensitivity analysis

Air target detection via bistatic radar based on LEOs communication signals

Automated delineation of roof planes from LIDAR data

The estimation of the weights of measured quantities within the method of least squares