Analysis of Selected Methods Used in Forensic Paper-Based Document Examination

The examination of questioned documents is a very key part of forensics and the justice systems. This is because fraudsters and other criminals may temper with documents in their criminal activities. A number of forensic document examination methods and techniques have been developed to examine the authenticity of documents. These methods and techniques have advantages and disadvantages. This study thus sought to identify and analyse some of the methods used in forensic document examination. A literature review was applied in this study.  This study concluded that forensic document examination had advanced as it had a number of methods which could be used to determine the authenticity of documents. However, forensic document examination still needed to develop further as some methods being used were destructive and could lead to information loss or the deterioration of documents

Hyperspectral image analysis for questioned historical documents.

This thesis describes the application of spectroscopy and hyperspectral image processing to examine historical manuscripts and text. Major activities in palaeographic and manuscript studies include the recovery of illegible or deleted text, the minute analyses of scribal hands, the identification of inks and the segmentation and dating of text. This thesis describes how Hyperspectral Imaging (HSI), applied in a novel manner, can be used to perform quality text recovery, segmentation and dating of historical documents. The non-destructive optical imaging process of Spectroscopy is described in detail and how it can be used to assist historians and document experts in the exemption of aged manuscripts. This non-destructive optical method of analysis can distinguish subtle differences in the reflectance properties of the materials under study. Many historically significant documents from libraries such as the Royal Irish Academy and the Russell Library at the National University of Ireland, Maynooth, have been the selected for study using the hyperspectral imaging technique. Processing techniques have are described for the applications to the study of manuscripts in a poor state of conservation. The research provides a comprehensive overview of Hyperspectral Imaging (HSI) and associated statistical and analytical methods, and also an in-depth investigation of the practical implementation of such methods to aid document analysts. Specifically, we provide results from employing statistical analytical methods including principal component analysis (PCA), independent component analysis (ICA) and both supervised and automatic clustering methods to historically significant manuscripts and text VIII such as Leabhar na hUidhre, a 12th century Irish text which was subject to part-erasure and rewriting, a 16th Century pastedown cover, and a multi-ink example typical of that found in, for example, late medieval administrative texts such as Gttingen’s kundige bok. The purpose of which is to achieve an overall greater insight into the historical context of the document, which includes the recovery or enhancement of faded or illegible text or text lost through fading, staining, overwriting or other forms of erasure. In addition, we demonstrate prospect of distinguishing different ink-types, and furnishing us with details of the manuscript’s composition, all of which are refinements, which can be used to answer questions about date and provenance. This process marks a new departure for the study of manuscripts and may provide answer many long-standing questions posed by palaeographers and by scholars in a variety of disciplines. Furthermore, through text retrieval, it holds out the prospect of adding considerably to the existing corpus of texts and to providing very many new research opportunities for coming generations of scholars

Current research opportunities of image processing and computer vision

Image processing and computer vision is an important and essential area in today’s scenario. Several problems can be solved through computer vision techniques. There are a large number of challenges and opportunities which require skills in the field of computer vision to address them. Computer vision applications cover each band of the electromagnetic spectrum and there are numerous applications in every band. This article is targeted to the research students, scholars and researchers who are interested to solve the problems in the field of image processing and computer vision. It addresses the opportunities and current trends of computer vision applications in all emerging domains. The research needs are identified through available literature survey and classified in the corresponding domains. The possible exemplary images are collected from the different repositories available for research and shown in this paper. The opportunities mentioned in this paper are explained through the images so that a naive researcher can understand it well before proceeding to solve the corresponding problems. The databases mentioned in this article could be useful for researchers who are interested in further solving the problem. The motivation of the article is to expose the current opportunities in the field of image processing and computer vision along with corresponding repositories. Interested researchers who are working in the field can choose a problem through this article and can get the experimental images through the cited references for working further.

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques

Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen

A Survey on Hybrid Techniques Using SVM

Support Vector Machines (SVM) with linear or nonlinear kernels has become one of the most promising learning algorithms for classification as well as for regression. All the multilayer perceptron (MLP),Radial Basic Function(RBF) and Learning Polynomials are also worked efficiently with SVM. SVM is basically derived from statistical Learning Theory and it is very powerful statistical tool. The basic principal for the SVM is structural risk minimization and closely related to regularization theory. SVM is a group of supervised learning techniques or methods, which is used to do for classification or regression. In this paper discussed the importance of Support Vector Machines in various areas. This paper discussing the efficiency of SVM with the combination of other classification techniques

Spectral Band Selection for Ensemble Classification of Hyperspectral Images with Applications to Agriculture and Food Safety

In this dissertation, an ensemble non-uniform spectral feature selection and a kernel density decision fusion framework are proposed for the classification of hyperspectral data using a support vector machine classifier. Hyperspectral data has more number of bands and they are always highly correlated. To utilize the complete potential, a feature selection step is necessary. In an ensemble situation, there are mainly two challenges: (1) Creating diverse set of classifiers in order to achieve a higher classification accuracy when compared to a single classifier. This can either be achieved by having different classifiers or by having different subsets of features for each classifier in the ensemble. (2) Designing a robust decision fusion stage to fully utilize the decision produced by individual classifiers. This dissertation tests the efficacy of the proposed approach to classify hyperspectral data from different applications. Since these datasets have a small number of training samples with larger number of highly correlated features, conventional feature selection approaches such as random feature selection cannot utilize the variability in the correlation level between bands to achieve diverse subsets for classification. In contrast, the approach proposed in this dissertation utilizes the variability in the correlation between bands by dividing the spectrum into groups and selecting bands from each group according to its size. The intelligent decision fusion proposed in this approach uses the probability density of training classes to produce a final class label. The experimental results demonstrate the validity of the proposed framework that results in improvements in the overall, user, and producer accuracies compared to other state-of-the-art techniques. The experiments demonstrate the ability of the proposed approach to produce more diverse feature selection over conventional approaches

Context-dependent fusion with application to landmine detection.

Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods