Patient-specific anisotropic model of human trunk based on MR data

There are many ways to generate geometrical models for numerical simulation, and most of them start with a segmentation step to extract the boundaries of the regions of interest. This paper presents an algorithm to generate a patient-specific three-dimensional geometric model, based on a tetrahedral mesh, without an initial extraction of contours from the volumetric data. Using the information directly available in the data, such as gray levels, we built a metric to drive a mesh adaptation process. The metric is used to specify the size and orientation of the tetrahedral elements everywhere in the mesh. Our method, which produces anisotropic meshes, gives good results with synthetic and real MRI data. The resulting model quality has been evaluated qualitatively and quantitatively by comparing it with an analytical solution and with a segmentation made by an expert. Results show that our method gives, in 90% of the cases, as good or better meshes as a similar isotropic method, based on the accuracy of the volume reconstruction for a given mesh size. Moreover, a comparison of the Hausdorff distances between adapted meshes of both methods and ground-truth volumes shows that our method decreases reconstruction errors faster. Copyright © 2015 John Wiley & Sons, Ltd.

PSWT Based Linear Predictive Coding and Development of a Parallel Multiple Subsequence Structure for DWT Computation

During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.

Influence of personality factors on the consumption of personal care products

The study is significant from both an application perspective of marketing management as well as from an academic angle. The market for personal care products is a highly fragmented one, with intense competition for specific niche segments. It is well known in marketing literature that the bulk of the volume of sale is accounted for by the minority who are the heavy users. This study will help the marketers to identify the personality profile of such a group and understand how the interaction of personality factors at least partially explains differences in consumption. This knowledge might be useful for better segmentation using psychographic variables as well as for designing specific advertisement campaigns to target the vulnerable groups of customers. From a theoretical perspective, the research may contribute to understanding how specific personality variables and their interaction lead to differences in consumption. The knowledge corresponding to self theory, social comparison theory, persuasibility, evidence from psychology of eating disorders: these all may be integrated into a common frame work for explaining consumption of products having a social function.

Customer perceptions of Banking Products and Services in Kerala with reference to Specific Customer Segments in the Urban Area

As of 1999. the state of Kerala has 3210 offices of scheduled commercial banks (SCBS). In all, there are 48 commercial banks operating in Kerala, which includes PSBs, OPBs, NPBS. FBs, and Gramin Banks. The urban areas give a complete picture of the competition in the present day banking scenario with the presence of all bank groups. Semi-urban areas of Kerala have 2196 and urban areas have 593 as on March 1995.“ The study focuses on the selected segments ofthe urban customers in Kerala which is capable of giving the finer aspects of variation in customer behaviour in the purchase of banking products and services. Considering the exhaustive nature of such an exercise, all the districts in the state have not been brought under the purview of the study. Instead. three districts with largest volume of business in terms of deposits, advances, and number of offices have been short listed as representative regions for a focused study. The study focuses on the retail customer segment and their perceptions on the various products or services offered to them. Non Resident Indians (NRIs), and Traders and Small—ScaIe Industries segments have also been included in the study with a view to obtain a comparative picture with respect to perception on customer satisfaction and service quality dimensions and bank choice behaviour. The research is hence confined to customer behaviour and the implications for possible strategies for segmentation within the retail segment customers

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATOR

Biometrics has become important in security applications. In comparison with many other biometric features, iris recognition has very high recognition accuracy because it depends on iris which is located in a place that still stable throughout human life and the probability to find two identical iris's is close to zero. The identification system consists of several stages including segmentation stage which is the most serious and critical one. The current segmentation methods still have limitation in localizing the iris due to circular shape consideration of the pupil. In this research, Daugman method is done to investigate the segmentation techniques. Eyelid detection is another step that has been included in this study as a part of segmentation stage to localize the iris accurately and remove unwanted area that might be included. The obtained iris region is encoded using haar wavelets to construct the iris code, which contains the most discriminating feature in the iris pattern. Hamming distance is used for comparison of iris templates in the recognition stage. The dataset which is used for the study is UBIRIS database. A comparative study of different edge detector operator is performed. It is observed that canny operator is best suited to extract most of the edges to generate the iris code for comparison. Recognition rate of 89% and rejection rate of 95% is achieved

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing

Automatic Optic Disc Boundary Extraction from Color Fundus Images

Efficient optic disc segmentation is an important task in automated retinal screening. For the same reason optic disc detection is fundamental for medical references and is important for the retinal image analysis application. The most difficult problem of optic disc extraction is to locate the region of interest. Moreover it is a time consuming task. This paper tries to overcome this barrier by presenting an automated method for optic disc boundary extraction using Fuzzy C Means combined with thresholding. The discs determined by the new method agree relatively well with those determined by the experts. The present method has been validated on a data set of 110 colour fundus images from DRION database, and has obtained promising results. The performance of the system is evaluated using the difference in horizontal and vertical diameters of the obtained disc boundary and that of the ground truth obtained from two expert ophthalmologists. For the 25 test images selected from the 110 colour fundus images, the Pearson correlation of the ground truth diameters with the detected diameters by the new method are 0.946 and 0.958 and, 0.94 and 0.974 respectively. From the scatter plot, it is shown that the ground truth and detected diameters have a high positive correlation. This computerized analysis of optic disc is very useful for the diagnosis of retinal diseases

Development of 2-D MRT based Image Compression Techniques

The thesis explores the area of still image compression. The image compression techniques can be broadly classified into lossless and lossy compression. The most common lossy compression techniques are based on Transform coding, Vector Quantization and Fractals. Transform coding is the simplest of the above and generally employs reversible transforms like, DCT, DWT, etc. Mapped Real Transform (MRT) is an evolving integer transform, based on real additions alone. The present research work aims at developing new image compression techniques based on MRT. Most of the transform coding techniques employ fixed block size image segmentation, usually 8×8. Hence, a fixed block size transform coding is implemented using MRT and the merits and demerits are analyzed for both 8×8 and 4×4 blocks. The N2 unique MRT coefficients, for each block, are computed using templates. Considering the merits and demerits of fixed block size transform coding techniques, a hybrid form of these techniques is implemented to improve the performance of compression. The performance of the hybrid coder is found to be better compared to the fixed block size coders. Thus, if the block size is made adaptive, the performance can be further improved. In adaptive block size coding, the block size may vary from the size of the image to 2×2. Hence, the computation of MRT using templates is impractical due to memory requirements. So, an adaptive transform coder based on Unique MRT (UMRT), a compact form of MRT, is implemented to get better performance in terms of PSNR and HVS The suitability of MRT in vector quantization of images is then experimented. The UMRT based Classified Vector Quantization (CVQ) is implemented subsequently. The edges in the images are identified and classified by employing a UMRT based criteria. Based on the above experiments, a new technique named “MRT based Adaptive Transform Coder with Classified Vector Quantization (MATC-CVQ)”is developed. Its performance is evaluated and compared against existing techniques. A comparison with standard JPEG & the well-known Shapiro’s Embedded Zero-tree Wavelet (EZW) is done and found that the proposed technique gives better performance for majority of images

The role of Ken&Barbie in JAK/STAT signalling during development of Drosophila melanogaster

Cell-cell interactions during embryonic development are crucial in the co-ordination of growth, differentiation and maintenance of many different cell types. To achieve this co-ordination each cell must properly translate signals received from neighbouring cells, into spatially and temporally appropriate developmental responses. A surprisingly limited number of signal pathways are responsible for the differentiation of enormous variety of cell types. As a result, pathways are frequently 'reused' during development. Thus, in mammals the JAK/STAT pathway is required during early embryogenesis, mammary gland formation, hematopoiesis and, finally, plays a pivotal role in immune response. In the canonical way, the JAK/STAT pathway is represented by a transmembrane receptor associated with a Janus kinase (JAK), which upon stimulation by an extra-cellular ligand, phosphorylates itself, the receptor and, finally, the signal transducer and activator of transcription (STAT) molecules. Phosphorylated STATs dimerise and translocate to the nucleus where they activate transcription of target genes. The JAK/STAT pathway has been conserved throughout evolution, and all known components are present in the genome of Drosophila melanogaster. Besides hematopoietic and immunity functions, the pathway is also required during development for processes including embryonic segmentation, tracheal morphogenesis, posterior spiracle formation etc. This study describes Drosophila Ken&Barbie (Ken) as a selective regulator of JAK/STAT signalling. ken mutations identified in a screen for modulators of an eye overgrowth phenotype, caused by over-expression of the pathway ligand unpaired, also interact genetically with the pathway receptor domeless (dome) and the transcription factor stat92E. Over-expression of Ken can phenocopy developmental defects known to be caused by the loss of JAK/STAT signalling. These genetic interactions suggest that Ken may function as a negative regulator of the pathway. Ken has C-terminal Zn-finger domain, presumably for DNA binding, and N-terminal BTB/POZ domain, often found in transcriptional repressors. Using EGFP-fused construct expressed in vivo revealed nuclear accumulation of Ken. Therefore, it is proposed that Ken may act as a suppresser of STAT92E target genes. An in vitro assay, termed SELEX, determined that Ken specifically binds to a DNA sequence, with the essential for DNA recognition core overlapping that of STAT92E. This interesting observation suggests that not all STAT92E sites may also allow Ken binding. Strikingly, when effects of ectopic Ken on the expression of putative JAK/STAT pathway target genes were examined, only a subset of the genes tested, namely vvl, trh and kni, were down-regulated by Ken, whereas some others, such as eve and fj, appeared to be unresponsive. Further analysis of vvl, one of the genes susceptible to ectopic Ken, was undertaken. In the developing hindgut, expression of vvl is JAK/STAT pathway dependent, but remains repressed in the posterior spiracles, despite the stimulation of STAT92E by Upd in their primordia. Importantly, ken is also expressed in the developing posterior spiracles. Strikingly, up-regulation of vvl is observed in these tissues in ken mutant embryos. These imply that while ectopic Ken is sufficient to repress the expression of vvl in the hindgut, endogenous Ken is also necessary to prevent its activation in the posterior spiracles. It is therefore conceivable that ectopic vvl expression in the posterior spiracles of the ken mutants may be the result of de-repression of endogenous STAT92E activity. Another consequence of these observations is a fine balance that must exist between STAT92E and Ken activities. Apparently, endogenous level of Ken is sufficient to repress vvl, but not other, as yet unidentified, JAK/STAT pathway targets, whose presumable activation by STAT92E is required for posterior spiracle development as the embryos mutant for dome, the receptor of the pathway, show severe spiracle defects. These defects are also observed in the embryos mis-expressing Ken. Though it is possible that the posterior spiracle phenotype caused by higher levels of Ken results from a JAK/STAT pathway independent activity, it seems to be more likely that Ken acts in a dosage dependent manner, and extra Ken is able to further antagonise JAK/STAT pathway target genes. While STAT92E binding sites required for target gene expression have been poorly characterised, the existence of genome data allows the prediction of candidate STAT92E sites present in target genes promoters to be attempted. When a 6kb region containing the putative regulatory domains flanking the vvl locus are examined, only a single potential STAT92E binding site located 825bp upstream of the translational start can be detected. Strikingly, this site also includes a perfect Ken binding sequence. Such an in silico observation, though consistent with both Ken DNA binding assay in vitro and regulation of STAT92E target genes in vivo, however, requires further analysis. The JAK/STAT pathway is implicated in a variety of processes during embryonic and larval development as well as in imago. In each case, stimulation of the same transcription factor results in different developmental outcomes. While many potential mechanisms have been proposed and demonstrated to explain such pleiotropy, the present study indicates that Ken may represent another mechanism, with which signal transduction pathways are controlled. Ken selectively down-regulates a subset of potential target genes and so modifies the transcriptional profile generated by activated STAT92E - a mechanism, which may be partially responsible for differences in the morphogenetic processes elicited by JAK/STAT signalling during development.

Digital image analysis as a tool to estimate legume contributions in legume-grass swards

Summary: Productivity and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. For these objectives the botanical composition of the swards is of particular importance, especially, the content of legumes due to their ability to fix airborne nitrogen. As it can vary considerably within a field, a non-destructive detection method while doing other tasks would facilitate a more targeted sward management and could predict the nitrogen supply of the soil for the subsequent crop. This study was undertaken to explore the potential of digital image analysis (DIA) for a non destructive prediction of legume dry matter (DM) contribution of legume-grass mixtures. For this purpose an experiment was conducted in a greenhouse, comprising a sample size of 64 experimental swards such as pure swards of red clover (Trifolium pratense L.), white clover (Trifolium repens L.) and lucerne (Medicago sativa L.) as well as binary mixtures of each legume with perennial ryegrass (Lolium perenne L.). Growth stages ranged from tillering to heading and the proportion of legumes from 0 to 80 %. Based on digital sward images three steps were considered in order to estimate the legume contribution (% of DM): i) The development of a digital image analysis (DIA) procedure in order to estimate legume coverage (% of area). ii) The description of the relationship between legume coverage (% area) and legume contribution (% of DM) derived from digital analysis of legume coverage related to the green area in a digital image. iii) The estimation of the legume DM contribution with the findings of i) and ii). i) In order to evaluate the most suitable approach for the estimation of legume coverage by means of DIA different tools were tested. Morphological operators such as erode and dilate support the differentiation of objects of different shape by shrinking and dilating objects (Soille, 1999). When applied to digital images of legume-grass mixtures thin grass leaves were removed whereas rounder clover leaves were left. After this process legume leaves were identified by threshold segmentation. The segmentation of greyscale images turned out to be not applicable since the segmentation between legumes and bare soil failed. The advanced procedure comprising morphological operators and HSL colour information could determine bare soil areas in young and open swards very accurately. Also legume specific HSL thresholds allowed for precise estimations of legume coverage across a wide range from 11.8 - 72.4 %. Based on this legume specific DIA procedure estimated legume coverage showed good correlations with the measured values across the whole range of sward ages (R2 0.96, SE 4.7 %). A wide range of form parameters (i.e. size, breadth, rectangularity, and circularity of areas) was tested across all sward types, but none did improve prediction accuracy of legume coverage significantly. ii) Using measured reference data of legume coverage and contribution, in a first approach a common relationship based on all three legumes and sward ages of 35, 49 and 63 days was found with R2 0.90. This relationship was improved by a legume-specific approach of only 49- and 63-d old swards (R2 0.94, 0.96 and 0.97 for red clover, white clover, and lucerne, respectively) since differing structural attributes of the legume species influence the relationship between these two parameters. In a second approach biomass was included in the model in order to allow for different structures of swards of different ages. Hence, a model was developed, providing a close look on the relationship between legume coverage in binary legume-ryegrass communities and the legume contribution: At the same level of legume coverage, legume contribution decreased with increased total biomass. This phenomenon may be caused by more non-leguminous biomass covered by legume leaves at high levels of total biomass. Additionally, values of legume contribution and coverage were transformed to the logit-scale in order to avoid problems with heteroscedasticity and negative predictions. The resulting relationships between the measured legume contribution and the calculated legume contribution indicated a high model accuracy for all legume species (R2 0.93, 0.97, 0.98 with SE 4.81, 3.22, 3.07 % of DM for red clover, white clover, and lucerne swards, respectively). The validation of the model by using digital images collected over field grown swards with biomass ranges considering the scope of the model shows, that the model is able to predict legume contribution for most common legume-grass swards (Frame, 1992; Ledgard and Steele, 1992; Loges, 1998). iii) An advanced procedure for the determination of legume DM contribution by DIA is suggested, which comprises the inclusion of morphological operators and HSL colour information in the analysis of images and which applies an advanced function to predict legume DM contribution from legume coverage by considering total sward biomass. Low residuals between measured and calculated values of legume dry matter contribution were found for the separate legume species (R2 0.90, 0.94, 0.93 with SE 5.89, 4.31, 5.52 % of DM for red clover, white clover, and lucerne swards, respectively). The introduced DIA procedure provides a rapid and precise estimation of legume DM contribution for different legume species across a wide range of sward ages. Further research is needed in order to adapt the procedure to field scale, dealing with differing light effects and potentially higher swards. The integration of total biomass into the model for determining legume contribution does not necessarily reduce its applicability in practice as a combined estimation of total biomass and legume coverage by field spectroscopy (Biewer et al. 2009) and DIA, respectively, may allow for an accurate prediction of the legume contribution in legume-grass mixtures.

Konzeption von Entwicklungspfaden für Zulieferparks in der Automobilindustrie

Die Automobilindustrie reagiert mit Modularisierungsstrategien auf die zunehmende Produktkomplexität, getrieben durch die wachsenden Individualisierungsanforde-rungen auf der Kundenseite und der Modellpolitik mit neuen Fahrzeuganläufen. Die Hersteller verlagern die Materialbereitstellungskomplexität durch Outsourcing an die nächste Zulieferebene, den First Tier Suppliern, die seit Beginn der 90er Jahre zunehmend in Zulieferparks in unmittelbarer Werknähe integriert werden. Typische Merkmale eines klassischen Zulieferparks sind: Bereitstellung einer Halleninfrastruktur mit Infrastrukturdienstleistungen, Anlieferung der Teileumfänge im JIS-Verfahren (Just-in-Sequence = reihenfolgegenaue Synchronisation), lokale Wertschöpfung (Vormontagen, Sequenzierung) des Zulieferers, Vertragsbindung der First Tier Zulieferer für die Dauer eines Produktlebenszyklus und Einbindung eines Logistikdienstleisters. Teilweise werden zur Finanzierung Förderprojekte des öffent-lichen Sektors initiiert. Bisher fehlte eine wissenschaftliche Bearbeitung dieses Themas "Zulieferpark". In der Arbeit werden die in Europa entstandenen Zulieferparks näher untersucht, um Vor- und Nachteile dieses Logistikkonzeptes zu dokumentieren und Entwicklungs-trends aufzuzeigen. Abgeleitet aus diesen Erkenntnissen werden Optimierungs-ansätze aufgezeigt und konkrete Entwicklungspfade zur Verbesserung der Chancen-Risikoposition der Hauptakteure Automobilhersteller, Zulieferer und Logistikdienst-leister beschrieben. Die Arbeit gliedert sich in vier Haupteile, einer differenzierten Beschreibung der Ausgangssituation und den Entwicklungstrends in der Automobilindustrie, dem Vorgehensmodell, der Dokumentation der Analyseergebnisse und der Bewertung von Zulieferparkmodellen. Im Rahmen der Ergebnisdokumentation des Analyseteils werden vier Zulieferparkmodelle in detaillierten Fallstudien anschaulich dargestellt. Zur Erarbeitung der Analyseergebnisse wurde eine Befragung der Hauptakteure mittels strukturierten Fragebögen durchgeführt. Zur Erhebung von Branchentrends und zur relativen Bewertung der Parkmodelle wurden zusätzlich Experten befragt. Zur Segmentierung der Zulieferparklandschaft wurde die Methode der Netzwerk-analyse eingesetzt. Die relative Bewertung der Nutzenposition basiert auf der Nutzwertanalyse. Als Ergebnisse der Arbeit liegen vor: · Umfassende Analyse der Zulieferparklandschaft in Europa, · Segmentierung der Parks in Zulieferparkmodelle, Optimierungsansätze zur Verbesserung einer Win-Win-Situation der beteiligten Hauptakteure, · Relative Nutzenbewertung der Zulieferparkmodelle, · Entwicklungspfade für klassische Zulieferparks.

The Computation of Color

This thesis takes an interdisciplinary approach to the study of color vision, focussing on the phenomenon of color constancy formulated as a computational problem. The primary contributions of the thesis are (1) the demonstration of a formal framework for lightness algorithms; (2) the derivation of a new lightness algorithm based on regularization theory; (3) the synthesis of an adaptive lightness algorithm using "learning" techniques; (4) the development of an image segmentation algorithm that uses luminance and color information to mark material boundaries; and (5) an experimental investigation into the cues that human observers use to judge the color of the illuminant. Other computational approaches to color are reviewed and some of their links to psychophysics and physiology are explored.