Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Mapping three-dimensional geological features from remotely-sensed images and digital elevation models.

Accurate mapping of geological structures is important in numerous applications, ranging from mineral exploration through to hydrogeological modelling. Remotely sensed data can provide synoptic views of study areas enabling mapping of geological units within the area. Structural information may be derived from such data using standard manual photo-geologic interpretation techniques, although these are often inaccurate and incomplete. The aim of this thesis is, therefore, to compile a suite of automated and interactive computer-based analysis routines, designed to help a the user map geological structure. These are examined and integrated in the context of an expert system. The data used in this study include Digital Elevation Model (DEM) and Airborne Thematic Mapper images, both with a spatial resolution of 5m, for a 5 x 5 km area surrounding Llyn Cow lyd, Snowdonia, North Wales. The geology of this area comprises folded and faulted Ordo vician sediments intruded throughout by dolerite sills, providing a stringent test for the automated and semi-automated procedures. The DEM is used to highlight geomorphological features which may represent surface expressions of the sub-surface geology. The DEM is created from digitized contours, for which kriging is found to provide the best interpolation routine, based on a number of quantitative measures. Lambertian shading and the creation of slope and change of slope datasets are shown to provide the most successful enhancement of DEMs, in terms of highlighting a range of key geomorphological features. The digital image data are used to identify rock outcrops as well as lithologically controlled features in the land cover. To this end, a series of standard spectral enhancements of the images is examined. In this respect, the least correlated 3 band composite and a principal component composite are shown to give the best visual discrimination of geological and vegetation cover types. Automatic edge detection (followed by line thinning and extraction) and manual interpretation techniques are used to identify a set of 'geological primitives' (linear or arc features representing lithological boundaries) within these data. Inclusion of the DEM data provides the three-dimensional co-ordinates of these primitives enabling a least-squares fit to be employed to calculate dip and strike values, based, initially, on the assumption of a simple, linearly dipping structural model. A very large number of scene 'primitives' is identified using these procedures, only some of which have geological significance. Knowledge-based rules are therefore used to identify the relevant. For example, rules are developed to identify lake edges, forest boundaries, forest tracks, rock-vegetation boundaries, and areas of geomorphological interest. Confidence in the geological significance of some of the geological primitives is increased where they are found independently in both the DEM and remotely sensed data. The dip and strike values derived in this way are compared to information taken from the published geological map for this area, as well as measurements taken in the field. Many results are shown to correspond closely to those taken from the map and in the field, with an error of < 1°. These data and rules are incorporated into an expert system which, initially, produces a simple model of the geological structure. The system also provides a graphical user interface for manual control and interpretation, where necessary. Although the system currently only allows a relatively simple structural model (linearly dipping with faulting), in the future it will be possible to extend the system to model more complex features, such as anticlines, synclines, thrusts, nappes, and igneous intrusions

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Towards an effective automated interpretation method for modern hydrocarbon borehole geophysical images

Borehole imaging is one of the fastest and most precise methods for collecting subsurface data that provides high resolution information on layering, texture and dips, permitting a core-like description of the subsurface. Although the range of information recoverable from this technology is widely acknowledged, image logs are still used in a strictly qualitative manner. Interpreting image logs manually is cumbersome, time consuming and is subjective based on the experience of the interpreter. This thesis outlines new methods that automate image log interpretation and extract subsurface lithofacies information in a quantitative manner. We developed two methodologies based on advanced image analysis techniques successfully employed in remote sensing and medical imaging. The first one is a pixelbased pattern recognition technique applying textural analysis to quantify image textural properties. These properties together with standard logs and core-derived lithofacies information are used to train a back propagation Neural Network. In principle the trained and tested Neural Network is applicable for automated borehole image interpretation from similar geological settings. However, this pixel-based approach fails to make use explicitly of the spatial characteristics of a high resolution image. TAT second methodology is introduced which groups identical neighbouring pixels into objects. The resultant spectrally and spatially consistent objects are then related to geologically meaningful groups such as lithofacies by employing fuzzy classifiers. This method showed better results and is applied to outcrop photos, core photos and image logs, including a ‘difficult’ data set from a deviated well. The latter image log did not distinguish some of the conductive and resistive regions, as observed from standard logs and core photos. This is overcome by marking bed boundaries using standard logs. Bed orientations were estimated using an automated sinusoid fitting algorithm within a formal uncertainty framework in order to distinguish dipping beds and horizontal stratification. Integration of these derived logs in the methodology yields a complete automated lithofacies identification, even from the difficult dataset. The results were validated through the interpretation of cored intervals by a geologist. This is a supervised classification method which incorporates the expertise of one or several geologists, and hence includes human logic, reasoning, and current knowledge of the field heterogeneity. By including multiple geologists in the training, the results become less dependent on each individual’s subjectivity and prior experience. The method is also easily adaptable to other geological settings. In addition, it is applicable to several kinds of borehole images, for example wireline electrical borehole wall images, core photographs, and logging-while-drilling (LWD) images. Thus, the theme of this dissertation is the development of methodologies which makes image log interpretation simpler, faster, less subjective, and efficient such that it can be applied to large quantities of data

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Evaluating accuracy issues in mapping benthic habitats: An investigation in the causes of misclassification and the importance of segmentation parameters

Benthic habitats are some of the most difficult habitats to map using remote sensing. In a study of six bays in the Texas Gulf Coast, maps were created from digital aerial imagery. Using an object-based image analysis (OBIA) approach, the image was classified with the Classification and Regression Tree (CART) technique to produce a draft map. The draft map was then extensively manually edited to produce a contractor map. Accuracy assessments of both maps revealed that the two were not significantly different. The objective of this study was to determine why the editing did not improve the draft map. Our analyses indicate that the small segmentation scale parameter chosen for the map over-segmented the image and reduced the effectiveness of the classification technique and the manual editing. When compared to a similar map with a larger scale parameter, the smaller initial polygons proved more difficult to accurately classify