Deus vult: Images of crusader violence c. 1095-1100.

This study sets out to provide new information about the interaction between abstract religious ideas and actual acts of violence in the early crusading movement. The sources are asked, whether such a concept as religious violence can be sorted out as an independent or distinguishable source of aggression at the moment of actual bloodshed. The analysis concentrates on the practitioners of sacred violence, crusaders and their mental processing of the use of violence, the concept of the violent act, and the set of values and attitudes defining this concept. The scope of the study, the early crusade movement, covers the period from late 1080 s to the crusader conquest of Jerusalem in 15 July 1099. The research has been carried out by contextual reading of relevant sources. Eyewitness reports will be compared with texts that were produced by ecclesiastics in Europe. Critical reading of the texts reveals both connecting ideas and interesting differences between them. The sources share a positive attitude towards crusading, and have principally been written to propagate the crusade institution and find new recruits. The emphasis of the study is on the interpretation of images: the sources are not asked what really happened in chronological order, but what the crusader understanding of the reality was like. Fictional material can be even more crucial for the understanding of the crusading mentality. Crusader sources from around the turn of the twelfth century accept violent encounters with non-Christians on the grounds of external hostility directed towards the Christian community. The enemies of Christendom can be identified with either non-Christians living outside the Christian society (Muslims), non-Christians living within the Christian society (Jews) or Christian heretics. Western Christians are described as both victims and avengers of the surrounding forces of diabolical evil. Although the ideal of universal Christianity and gradual eradication of the non-Christian is present, the practical means of achieving a united Christendom are not discussed. The objective of crusader violence was thus entirely Christian: the punishment of the wicked and the restoration of Christian morals and the divine order. Meanwhile, the means used to achieve these objectives were not. Given the scarcity of written regulations concerning the use of force in bello, perceptions concerning the practical use of violence were drawn from a multitude of notions comprising an adaptable network of secular and ecclesiastical, pre-Christian and Christian traditions. Though essentially ideological and often religious in character, the early crusader concept of the practise of violence was not exclusively rooted in Christian thought. The main conclusion of the study is that there existed a definable crusader ideology of the use of force by 1100. The crusader image of violence involved several levels of thought. Predominantly, violence indicates a means of achieving higher spiritual rewards; eternal salvation and immortal glory.

Mediated music makers : Constructing author images in popular music

The subject of the thesis is the mediated construction of author images in popular music. In the study, the construction of images is treated as a process in which artists, the media and the members of the audience participate. The notions of presented, mediated and compiled author images are used in explaining the mediation process and the various authorial roles of the agents involved. In order to explore the issue more closely, I analyse the author images of a group of popular music artists representing the genres of rock, pop and electronic dance music. The analysed material consists mostly of written media texts through which the artists authorial roles and creative responsibilities are discussed. Theoretically speaking, the starting points for the examination lie in cultural studies and discourse analysis. Even though author images may be conceived as intertextual constructions, the artist is usually presented as a recognizable figure whose purpose is to give the music its public face. This study does not, then, deal with musical authors as such, but rather with their public images and mediated constructions. Because of the author-based functioning of popular music culture and the idea of the artist s individual creative power, the collective and social processes involved in the making of popular music are often superseded by the belief in a single, originating authorship. In addition to the collective practices of music making, the roles of the media and the marketing machinery complicate attempts to clarify the sharing of authorial contributions. As the case studies demonstrate, the differences between the examined author images are connected with a number of themes ranging from issues of auteurism and stardom to the use of masked imagery and the blending of authorial voices. Also the emergence of new music technologies has affected not only the ways in which music is made, but also how the artist s authorial status and artistic identity is understood. In the study at hand, the author images of auteurs, stars, DJs and sampling artists are discussed alongside such varied topics as collective authorship, evaluative hierarchies, visual promotion and generic conventions. Taken altogether, the examined case studies shed light on the functioning of popular music culture and the ways in which musical authorship is (re)defined.

Assessing the relationship between shire winter crop yield and seasonal variability of the MODIS NDVI and EVI images

Australian researchers have been developing robust yield estimation models, based mainly on the crop growth response to water availability during the crop season. However, knowledge of spatial distribution of yields within and across the production regions can be improved by the use of remote sensing techniques. Images of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, available since 1999, have the potential to contribute to crop yield estimation. The objective of this study was to analyse the relationship between winter crop yields and the spectral information available in MODIS vegetation index images at the shire level. The study was carried out in the Jondaryan and Pittsworth shires, Queensland , Australia . Five years (2000 to 2004) of 250m resolution, 16-day composite of MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images were used during the winter crop season (April to November). Seasonal variability of the profiles of the vegetation index images for each crop season using different regions of interest (cropping mask) were displayed and analysed. Correlation analysis between wheat and barley yield data and MODIS image values were also conducted. The results showed high seasonal variability in the NDVI and EVI profiles, and the EVI values were consistently lower than those of the NDVI. The highest image values were observed in 2003 (in contrast to 2004), and were associated with rainfall amount and distribution. The seasonal variability of the profiles was similar in both shires, with minimum values in June and maximum values at the end of August. NDVI and EVI images showed sensitivity to seasonal variability of the vegetation and exhibited good association (e.g. r = 0.84, r = 0.77) with winter crop yields.

Constructing skilled images

When experts construct mental images, they do not rely only on perceptual features; they also access domain-specific knowledge and skills in long-term memory, which enables them to exceed the capacity limitations of the short-term working memory system. The central question of the present dissertation was whether the facilitating effect of long-term memory knowledge on working memory imagery tasks is primarily based on perceptual chunking or whether it relies on higher-level conceptual knowledge. Three domains of expertise were studied: chess, music, and taxi driving. The effects of skill level, stimulus surface features, and the stimulus structure on incremental construction of mental images were investigated. A method was developed to capture the chunking mechanisms that experts use in constructing images: chess pieces, street names, and visual notes were presented in a piecemeal fashion for later recall. Over 150 experts and non-experts participated in a total of 13 experiments, as reported in five publications. The results showed skill effects in all of the studied domains when experts performed memory and problem solving tasks that required mental imagery. Furthermore, only experts' construction of mental images benefited from meaningful stimuli. Manipulation of the stimulus surface features, such as replacing chess pieces with dots, did not significantly affect experts' performance in the imagery tasks. In contrast, the structure of the stimuli had a significant effect on experts' performance in every task domain. For example, taxi drivers recalled more street names from lists that formed a spatially continuous route than from alphabetically organised lists. The results suggest that the mechanisms of conceptual chunking rather than automatic perceptual pattern matching underlie expert performance, even though the tasks of the present studies required perception-like mental representations. The results show that experts are able to construct skilled images that surpass working memory capacity, and that their images are conceptually organised and interpreted rather than merely depictive.

Spectral analysis of pair-correlation bandwidth: Application to cell biology images

Images from cell biology experiments often indicate the presence of cell clustering, which can provide insight into the mechanisms driving the collective cell behaviour. Pair-correlation functions provide quantitative information about the presence, or absence, of clustering in a spatial distribution of cells. This is because the pair-correlation function describes the ratio of the abundance of pairs of cells, separated by a particular distance, relative to a randomly distributed reference population. Pair-correlation functions are often presented as a kernel density estimate where the frequency of pairs of objects are grouped using a particular bandwidth (or bin width), Δ>0. The choice of bandwidth has a dramatic impact: choosing Δ too large produces a pair-correlation function that contains insufficient information, whereas choosing Δ too small produces a pair-correlation signal dominated by fluctuations. Presently, there is little guidance available regarding how to make an objective choice of Δ. We present a new technique to choose Δ by analysing the power spectrum of the discrete Fourier transform of the pair-correlation function. Using synthetic simulation data, we confirm that our approach allows us to objectively choose Δ such that the appropriately binned pair-correlation function captures known features in uniform and clustered synthetic images. We also apply our technique to images from two different cell biology assays. The first assay corresponds to an approximately uniform distribution of cells, while the second assay involves a time series of images of a cell population which forms aggregates over time. The appropriately binned pair-correlation function allows us to make quantitative inferences about the average aggregate size, as well as quantifying how the average aggregate size changes with time.

Simple technique for noise reduction in images retrieved from crystalline storage media

Now that crystals are being considered suitable for high density optical information storage, it is important to reduce the noise levels of retrieved images. The paper describes a simple technique to bring this about.

Identifying landscape meanings: Images and interactions at Gas Works Park

This thesis contributes a substantial new theoretical understanding of what 'landscape meanings' are, and what constitutes the specific meanings of particular landscapes to individuals. Further, it proposes how landscape architects may identify these meanings to inform critical and ethical research, theory, professional practice and education. What emerges from this representative case study of the landscape of Richard Haag's Gas Works Park in Seattle is the understanding that a person's expressions of their 'cognitive landscape images' of a particular landscape, coupled with their expressions of their 'interactions' with that landscape, constitute the specific 'meaning-narrative' they attach to it.

A new methodology for realizing magnification of holographic images

The article describes a new method for obtaining a holographic image of desired magnification, consistent with the stipulated criteria for its resolution and aberrations.

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.

An algebraic approach to the restoration of images

Two different matrix algorithms are described for the restoration of blurred pictures. These are illustrated by numerical examples.

Decision fusion for reliable flood mapping using remote sensing images

Flood extent mapping is a basic tool for flood damage assessment, which can be done by digital classification techniques using satellite imageries, including the data recorded by radar and optical sensors. However, converting the data into the information we need is not a straightforward task. One of the great challenges involved in the data interpretation is to separate the permanent water bodies and flooding regions, including both the fully inundated areas and the wet areas where trees and houses are partly covered with water. This paper adopts the decision fusion technique to combine the mapping results from radar data and the NDVI data derived from optical data. An improved capacity in terms of identifying the permanent or semi-permanent water bodies from flood inundated areas has been achieved. Computer software tools Multispec and Matlab were used.

Block-based feature detection and matching for mosaicing of camera-captured document images

In this paper, we present a new feature-based approach for mosaicing of camera-captured document images. A novel block-based scheme is employed to ensure that corners can be reliably detected over a wide range of images. 2-D discrete cosine transform is computed for image blocks defined around each of the detected corners and a small subset of the coefficients is used as a feature vector A 2-pass feature matching is performed to establish point correspondences from which the homography relating the input images could be computed. The algorithm is tested on a number of complex document images casually taken from a hand-held camera yielding convincing results.

Edge-based connected component approach for skew correction of complex document images

Skew correction of complex document images is a difficult task. We propose an edge-based connected component approach for robust skew correction of documents with complex layout and content. The algorithm essentially consists of two steps - an 'initialization' step to determine the image orientation from the centroids of the connected components and a 'search' step to find the actual skew of the image. During initialization, we choose two different sets of points regularly spaced across the the image, one from the left to right and the other from top to bottom. The image orientation is determined from the slope between the two succesive nearest neighbors of each of the points in the chosen set. The search step finds succesive nearest neighbors that satisfy the parameters obtained in the initialization step. The final skew is determined from the slopes obtained in the 'search' step. Unlike other connected component based methods, the proposed method does not require any binarization step that generally precedes connected component analysis. The method works well for scanned documents with complex layout of any skew with a precision of 0.5 degrees.

Entropy based Skew correction of document images

The document images that are fed into an Optical Character Recognition system, might be skewed. This could be due to improper feeding of the document into the scanner or may be due to a faulty scanner. In this paper, we propose a skew detection and correction method for document images. We make use of the inherent randomness in the Horizontal Projection profiles of a text block image, as the skew of the image varies. The proposed algorithm has proved to be very robust and time efficient. The entire process takes less than a second on a 2.4 GHz Pentium IV PC.

GAP-RBF based NR image quality measurement for JPEG coded images

In this paper, we present a growing and pruning radial basis function based no-reference (NR) image quality model for JPEG-coded images. The quality of the images are estimated without referring to their original images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the problem of quality estimation is transformed to a function approximation problem and solved using GAP-RBF network. GAP-RBF network uses sequential learning algorithm to approximate the functional relationship. The computational complexity and memory requirement are less in GAP-RBF algorithm compared to other batch learning algorithms. Also, the GAP-RBF algorithm finds a compact image quality model and does not require retraining when the new image samples are presented. Experimental results prove that the GAP-RBF image quality model does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.