A study and evaluation of image analysis techniques applied to remotely sensed data

An analysis of phenomena causing nonlinearities in the transformation from Landsat multispectral scanner coordinates to ground coordinates is presented. Experimental results comparing rms errors at ground control points indicated a slight improvement when a nonlinear (8-parameter) transformation was used instead of an affine (6-parameter) transformation. Using a preliminary ground truth map of a test site in Alabama covering the Mobile Bay area and six Landsat images of the same scene, several classification methods were assessed. A methodology was developed for automatic change detection using classification/cluster maps. A coding scheme was employed for generation of change depiction maps indicating specific types of changes. Inter- and intraseasonal data of the Mobile Bay test area were compared to illustrate the method. A beginning was made in the study of data compression by applying a Karhunen-Loeve transform technique to a small section of the test data set. The second part of the report provides a formal documentation of the several programs developed for the analysis and assessments presented

Evaluation of registration, compression, and classification algorithms. Volume 2: Documentation

There are no author-identified significant results in this report

Interactive Transcription of Old Text Documents

Nowadays, there are huge collections of handwritten text documents in libraries all over the world. The high demand for these resources has led to the creation of digital libraries in order to facilitate the preservation and provide electronic access to these documents. However text transcription of these documents im- ages are not always available to allow users to quickly search information, or computers to process the information, search patterns or draw out statistics. The problem is that manual transcription of these documents is an expensive task from both economical and time viewpoints. This thesis presents a novel ap- proach for e cient Computer Assisted Transcription (CAT) of handwritten text documents using state-of-the-art Handwriting Text Recognition (HTR) systems. The objective of CAT approaches is to e ciently complete a transcription task through human-machine collaboration, as the e ort required to generate a manual transcription is high, and automatically generated transcriptions from state-of-the-art systems still do not reach the accuracy required. This thesis is centered on a special application of CAT, that is, the transcription of old text document when the quantity of user e ort available is limited, and thus, the entire document cannot be revised. In this approach, the objective is to generate the best possible transcription by means of the user e ort available. This thesis provides a comprehensive view of the CAT process from feature extraction to user interaction. First, a statistical approach to generalise interactive transcription is pro- posed. As its direct application is unfeasible, some assumptions are made to apply it to two di erent tasks. First, on the interactive transcription of hand- written text documents, and next, on the interactive detection of the document layout. Next, the digitisation and annotation process of two real old text documents is described. This process was carried out because of the scarcity of similar resources and the need of annotated data to thoroughly test all the developed tools and techniques in this thesis. These two documents were carefully selected to represent the general di culties that are encountered when dealing with HTR. Baseline results are presented on these two documents to settle down a benchmark with a standard HTR system. Finally, these annotated documents were made freely available to the community. It must be noted that, all the techniques and methods developed in this thesis have been assessed on these two real old text documents. Then, a CAT approach for HTR when user e ort is limited is studied and extensively tested. The ultimate goal of applying CAT is achieved by putting together three processes. Given a recognised transcription from an HTR system. The rst process consists in locating (possibly) incorrect words and employs the user e ort available to supervise them (if necessary). As most words are not expected to be supervised due to the limited user e ort available, only a few are selected to be revised. The system presents to the user a small subset of these words according to an estimation of their correctness, or to be more precise, according to their con dence level. Next, the second process starts once these low con dence words have been supervised. This process updates the recogni- tion of the document taking user corrections into consideration, which improves the quality of those words that were not revised by the user. Finally, the last process adapts the system from the partially revised (and possibly not perfect) transcription obtained so far. In this adaptation, the system intelligently selects the correct words of the transcription. As results, the adapted system will bet- ter recognise future transcriptions. Transcription experiments using this CAT approach show that this approach is mostly e ective when user e ort is low. The last contribution of this thesis is a method for balancing the nal tran- scription quality and the supervision e ort applied using our previously de- scribed CAT approach. In other words, this method allows the user to control the amount of errors in the transcriptions obtained from a CAT approach. The motivation of this method is to let users decide on the nal quality of the desired documents, as partially erroneous transcriptions can be su cient to convey the meaning, and the user e ort required to transcribe them might be signi cantly lower when compared to obtaining a totally manual transcription. Consequently, the system estimates the minimum user e ort required to reach the amount of error de ned by the user. Error estimation is performed by computing sepa- rately the error produced by each recognised word, and thus, asking the user to only revise the ones in which most errors occur. Additionally, an interactive prototype is presented, which integrates most of the interactive techniques presented in this thesis. This prototype has been developed to be used by palaeographic expert, who do not have any background in HTR technologies. After a slight ne tuning by a HTR expert, the prototype lets the transcribers to manually annotate the document or employ the CAT ap- proach presented. All automatic operations, such as recognition, are performed in background, detaching the transcriber from the details of the system. The prototype was assessed by an expert transcriber and showed to be adequate and e cient for its purpose. The prototype is freely available under a GNU Public Licence (GPL).Serrano Martínez-Santos, N. (2014). Interactive Transcription of Old Text Documents [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37979TESI

Algorithms for the automated correction of vertical drift in eye-tracking data

A common problem in eye tracking research is vertical drift\u2014the progressive displacement of fixation registrations on the vertical axis that results from a gradual loss of eye tracker calibration over time. This is particularly problematic in experiments that involve the reading of multiline passages, where it is critical that fixations on one line are not erroneously recorded on an adjacent line. Correction is often performed manually by the researcher, but this process is tedious, time-consuming, and prone to error and inconsistency. Various methods have previously been proposed for the automated, post-hoc correction of vertical drift in reading data, but these methods vary greatly, not just in terms of the algorithmic principles on which they are based, but also in terms of their availability, documentation, implementation languages, and so forth. Furthermore, these methods have largely been developed in isolation with little attempt to systematically evaluate them, meaning that drift correction techniques are moving forward blindly. We document ten major algorithms, including two that are novel to this paper, and evaluate them using both simulated and natural eye tracking data. Our results suggest that a method based on dynamic time warping offers great promise, but we also find that some algorithms are better suited than others to particular types of drift phenomena and reading behavior, allowing us to offer evidence-based advice on algorithm selection

EU accession and Poland's external trade policy

No description supplie

Recognition of off-line handwritten cursive text

The author presents novel algorithms to design unconstrained handwriting recognition systems organized in three parts: In Part One, novel algorithms are presented for processing of Arabic text prior to recognition. Algorithms are described to convert a thinned image of a stroke to a straight line approximation. Novel heuristic algorithms and novel theorems are presented to determine start and end vertices of an off-line image of a stroke. A straight line approximation of an off-line stroke is converted to a one-dimensional representation by a novel algorithm which aims to recover the original sequence of writing. The resulting ordering of the stroke segments is a suitable preprocessed representation for subsequent handwriting recognition algorithms as it helps to segment the stroke. The algorithm was tested against one data set of isolated handwritten characters and another data set of cursive handwriting, each provided by 20 subjects, and has been 91.9% and 91.8% successful for these two data sets, respectively. In Part Two, an entirely novel fuzzy set-sequential machine character recognition system is presented. Fuzzy sequential machines are defined to work as recognizers of handwritten strokes. An algorithm to obtain a deterministic fuzzy sequential machine from a stroke representation, that is capable of recognizing that stroke and its variants, is presented. An algorithm is developed to merge two fuzzy machines into one machine. The learning algorithm is a combination of many described algorithms. The system was tested against isolated handwritten characters provided by 20 subjects resulting in 95.8% recognition rate which is encouraging and shows that the system is highly flexible in dealing with shape and size variations. In Part Three, also an entirely novel text recognition system, capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented. This system is an extension of the above recognition system. Tokens are extracted from a onedimensional representation of a stroke. Fuzzy sequential machines are defined to work as recognizers of tokens. It is shown how to obtain a deterministic fuzzy sequential machine from a token representation that is capable'of recognizing that token and its variants. An algorithm for token learning is presented. The tokens of a stroke are re-combined to meaningful strings of tokens. Algorithms to recognize and learn token strings are described. The. recognition stage uses algorithms of the learning stage. The process of extracting the best set of basic shapes which represent the best set of token strings that constitute an unknown stroke is described. A method is developed to extract lines from pages of handwritten text, arrange main strokes of extracted lines in the same order as they were written, and present secondary strokes to main strokes. Presented secondary strokes are combined with basic shapes to obtain the final characters by formulating and solving assignment problems for this purpose. Some secondary strokes which remain unassigned are individually manipulated. The system was tested against the handwritings of 20 subjects yielding overall subword and character recognition rates of 55.4% and 51.1%, respectively

Sequential Detection of Linear Features in Two-Dimensional Random Fields

The detection of edges, lines, and other linear features in two-dimensional discrete images is a low level processing step of fundamental importance in the automatic processing of such data. Many subsequent tasks in computer vision, pattern recognition, and image processing depend on the successful execution of this step. In this thesis, we will address one class of techniques for performing this task: sequential detection. Our aims are fourfold. First, we would like to discuss the use of sequential techniques as an attractive alternative to the somewhat better known methods of approaching this problem. Although several researchers have obtained significant results with sequential type algorithms, the inherent benefits of a sequential approach would appear to have gone largely unappreciated. Secondly, the sequential techniques reported to date appear somewhat lacking with respect to a theoretical foundation. Furthermore, the theory that is advanced incorporates rather severe restrictions on the types of images to which it applies, thus imposing a significant limitation to the generality of the method(s). We seek to advance a more general theory with minimal assumptions regarding the input image. A third goal is to utilize this newly developed theory to obtain quantitative assessments of the performance of the method. This important step, which depends on a computational theory, can answer such vital questions as: Are assumptions about the qualitative behavior of the method justified? How does signal-to-noise ratio impact its behavior? How fast is it? How accurate? The state of theoretical development of present techniques does not allow for this type of analysis. Finally, a fourth aim is to\u27 extend the earlier results to include correlated image data. Present sequential methods as well as many non-sequential methods assume that the image data is uncorrelated and cannot therefore make use of the mutual information between pixels in real-world images. We would like to extend the theory to incorporate correlated images and demonstrate the advantages incurred by the use of the existing mutual information. The topics to be discussed are organized in the following manner. We will first provide a rather general discussion of the problem of detecting intensity edges in images. The edge detection problem will serve as the prototypical problem of linear feature extraction for much of this thesis. It will later be shown that the detection of lines, ramp edges, texture edges, etc. can be handled in similar fashion to intensity edges, the only difference being the nature of the preprocessing operator used. The class of sequential techniques will then be introduced, with a view to emphasize the particular advantages and disadvantages exhibited by the class. This Chapter will conclude with a more detailed treatment of the various sequential algorithms proposed in the literature. Chapter 2 then develops the algorithm proposed by the author, Sequential Edge Linking or SEL. It begins with some definitions, follows with a derivation of the critical path branch metric and some of its properties, and concludes with a discussion of algorithms. The third Chapter is devoted exclusively to an analysis of the dynamical behavior and performance of the method. \u27 Chapter 4 then deals with the case of correlated random fields. In that Chapter, a model is proposed for which paths searched by the SEL algorithm are shown to possess a well-known autocorrelation function. This allows the use of a simple linear filter to decorrelate the raw image data. Finally, Chapter 5 presents a number of experimental results and corroboration of the theoretical conclusions of earlier Chapters. Some concluding remarks are also included in Chapter 5

GRAVITY SURVEY OF A BURIED TRIASSIC RIFT BASIN, BERTIE COUNTY, NORTH CAROLINA

The North American rift margin includes of a series of Triassic rift basins along the eastern seaboard of the United States and Canada. This continent-scale rift basin system is comprised of complex and variable geometries that can be generalized into regions with similar structural, deformational, and sedimentary characteristics. Rift basins provided accommodation space for organic-rich Triassic age sediments that may be source rocks for natural gas and petroleum. Most of the known basins are exposed at the surface and relatively easy to access, but a few buried basins have been identified beneath coastal plain strata. I used primarily geophysical methods to study a buried Triassic rift basin in Bertie County, North Carolina, recently discovered from a deep core sample that documented Triassic sedimentary rocks buried underneath approximately 300 meters of Cretaceous and younger, sediments and sedimentary rock. Approximately 30 meters of Triassic strata were recovered from the well, but basement rock was not reached leaving the overall thickness of the basin undetermined. I used a gravity survey to constrain the dimensions and geometry of the basin and surrounding rock bodies at depth. Data processing, modeling, and integration with preexisting data was accomplished using Oasis:Montaj software. The buried basin creates a maximum gravity anomaly of approximately 7 mGal. Modeling of the data suggests the basin is generally elongate, SW to NE, and has maximum dimensions of approximately 15 km wide, 50 km long, and as much as 2.5 km deep (basin infill). In cross section, the basin is asymmetrical and wedge-shaped, with a NW margin that dips steeply SE and a SE margin that dips more shallowly NW. The Bertie basin is deepest to the south and was likely hydrogeologically open in that direction. Previous datasets have been derived from analysis of the cores at the North Carolina Geologic Survey and include whole rock geochemical analysis, thin sections, and magnetic susceptibility. Interpretation of the geochemical data suggests the Triassic strata are derived from a continental island arc, and thin section analysis suggests a provenance of recycled orogenic material. The rocks classified as Triassic tend to have lower magnetic susceptibility than the overlying Cretaceous rock. One interpretation of these data is a change in sediment provenance from late-stage Triassic basin infill to the overlying Cretaceous strata. The Bertie Basin is located in the Southern Segment of the North American rift margin, suggesting that its geometry and stratigraphy should reflect regional trends and exhibit characteristics similar to other southern rift basins. The characteristic geometry of basins in the Southern Segment generally includes narrow to medium size (10 to 25 km across), fault-bounded, half-grabens with no or very subtle growth structures. The Bertie Basin may be part of a series of basins or a sub-basin within a larger basin due to sequential, domino-style faulting during rift migration. Higher extensional rates and faster rift migration within the Coastal Plain province may be related to its reduced dimensions. Burial underneath Coastal Plain strata may also have helped to preserve the Bertie Basin's original geometry and size which allows for improved constraints on initial tectonic conditions and structures, sedimentary deposition, paleo-environments, and processes related to supercontinent breakup