Most digital cameras capture images through Color Filter Arrays (CFA), and reconstruct the full color image from the CFA image. Each CFA pixel only captures one primary color component at each pixel location; the other primary components will be estimated using information from neighboring pixels. During the demosaicking algorithm, the unknown color components will be estimated at each pixel location. Most of the demosaicking algorithms use the RGB Bayer CFA pattern with Red, Green and Blue filters. Some other CFAs contain four color filters. The additional filter is a panchromatic/white filter, and it usually receives the full light spectrum. In this research, we studied and compared different four channel CFAs with panchromatic/white filter, and compared them with three channel CFAs. An appropriate demosaicking algorithm has been developed for each CFA. The most well-known three-channel CFA is Bayer. The Fujifilm X-Trans pattern has been studied in this work as another three-channel CFA with a different structure. Three different four-channel CFAs have been discussed in this research: RGBW-Kodak, RGBW-Bayer and RGBW- $5 \times 5$. The structure and the number of filters for each color are different for these CFAs. Since the Least-Square Luma-Chroma Demultiplexing method is a state of the art demosaicking method for the Bayer CFA, we designed the Least-Square method for RGBW CFAs. The effect of noise on different CFA patterns will be discussed for four channel CFAs. The Kodak database has been used to evaluate our non-adaptive and adaptive demosaicking methods as well as the optimized algorithms with the least square method. The captured values of white (panchromatic/clear) filters in RGBW CFAs have been estimated using red, green and blue filter values. Sets of optimized coefficients have been proposed to estimate the white filter values accurately. The results have been validated using the actual white values of a hyperspectral image dataset. A new denoising-demosaicking method for RGBW-Bayer CFA has been presented in this research. The algorithm has been tested on the Kodak dataset using the estimated value of white filters and a hyperspectral image dataset using the actual value of white filters, and the results have been compared. The results in both cases have been compared with the previous works on RGB-Bayer CFA, and it shows that the proposed algorithm using RGBW-Bayer CFA is working better than RGB-Bayer CFA in presence of noise.

Les travaux menés dans cette thèse portent sur le dématriçage d'images intégré dans une caméra couleur mono capteur. Ce type de caméra acquiert une seule composante couleur en chaque pixel grâce à un filtre CFA (Colour Filter Array) spectralement sélectif. La procédure dite de dématriçage consiste à estimer les deux composantes couleur manquantes en chaque pixel pour obtenir une image couleur. Nous proposons donc de déterminer, parmi les méthodes de dématriçage, celles qui fournissent des images couleur les mieux adaptées à l’analyse automatique des images. Dans un premier temps, nous présentons les principes de l’acquisition d’images numériques couleur par les caméras mono capteur. Après avoir expliqué l’influence de la structure du CFA sur la performance du dématriçage, nous focalisons nos études sur le dématriçage d’image issue du CFA de Bayer. Une formalisation mathématique du dématriçage de l’image CFA en image couleur est ensuite proposée avant de présenter les nombreuses méthodes de dématriçage parues dans la littérature ainsi que les post-traitements qui corrigent les couleurs estimées par dématriçage. Puis, nous nous intéressons aux critères d'évaluation de la qualité des images estimées par dématriçage. En premier lieu, nous décrivons les différents artefacts pouvant être générés par le dématriçage ainsi que la formation de ces artefacts, ce qui permet de bien cerner les limites atteintes par les critères classiques d’évaluation de la qualité des images estimées. Nous proposons alors deux mesures originales qui quantifient la présence de chaque type d'artéfacts (fausse couleur et effet de fermeture éclair). Enfin, nous présentons de nouveaux critères basés sur l’analyse de primitives extraites des images, en mesurant la qualité des contours qui y sont détectés. / Our work deals with the quality of colour images provided by a mono-CCD colour camera, which acquires only one colour component at each pixel by means of the CFA (Colour Filter Array) which covers the CCD sensor. A procedure - called demosaicing - is necessary to estimate the other two missing colour components at each pixel, so as to obtain a colour image in this kind of cameras. We aim to determine which method of demosaicing provides the results that are best adapted to colour image analyses for the reconstruction of scene. First, we present the principles on how the mono-CCD cameras acquire digital colour images, as well as the different arrangements of CFA used in such cameras. Once the influence of the CFA arrangement on the performance of demosaicing has been presented, we focus our studies on the demosaicing methods based on the Bayer CFA. A mathematical formalization for demosaicing is proposed before we present the numerous demosaicing methods in the literature, as well as the post-processing algorithms to correct the demosaiced images. We then investigate the evaluation criteria for the quality of the colour images estimated by demosaicing. First are described the different possible artefacts generated by demosaicing and the reasons for their generation, which allow us to point out the limits of the classical measures used to evaluate the estimated images. We then propose two original measures to quantify the presence of the two main artefacts, namely false colour and zipper effect. At last, we present new criteria based on the analysis of features extracted from colour images, by measuring the quality of edge detection in the estimated images.

Standard Configural Frequency Analysis (CFA) is a one-step procedure that determines which cells of a cross-classification contradict a base model. Selecting these cells out does not guarantee that the base model fits. Therefore, the role played by these cells for the base model is unclear, and interpretation of types and antitypes can be problematic. In this paper, functional CFA is proposed. This model of CFA pursues two goals simultaneously. First, cells are selected out that constitute types and antitypes. Second, the base model is fit to the data. This is done using an iterative procedure that blanks out individual cells one at a time, until the base model fits or until there are no more cells that can be blanked out. In comparison to standard CFA, functional CFA is shown to be more parsimonious, that is, fewer types and antitypes need to be selected out. In comparison to Kieser and Victor's CFA which focuses exclusively on optimizing the fit of the base model, functional CFA needs, in most cases, more iteration steps, but the overall goodness-of-fit for the base model is better. The methods are illustrated and compared using data examples from the literature. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Using multi-group confirmatory factor analysis (MCFA) and multiple-indicator-multiple-cause (MIMIC) to investigate group difference in the context of the second-order factor model with either the unparceled or parceled data had never been thoroughly examined. The present study investigated (1) the difference of MCFA and MIMIC in terms of Type I error rate and power when testing the mean difference of the higher-order latent factor (delta kappa) in a second-order confirmatory factor analysis (CFA) model; and (2) the impact of data parceling on the test of (delta kappa) between groups by using the two approaches. The methods were introduced, including the design of the models, the design of Monte Carlo simulation, the calculation of empirical Type I Error and empirical power, the two parceling strategies, and the adjustment of the random error variance. The results suggested that MCFA should be favored when the compared groups were when the different group sizes were paired with the different generalized variances, and MIMIC should be favored when the groups were balanced (i.e., have equal group sizes) in social science and education disciplines. This study also provided the evidence that parceling could improve the power for both MCFA and MIMIC when the factor loadings were low without bringing bias into the solution when the first-order factors were collapsed. However, parceling strategies might not be necessary when the factor loadings were high. The results also indicated that the two approaches were equally favored when domain representative parceling strategy was applied.

The need for research instruments adaptable to culturally diverse populations has grown with globalization and digital connectivity. Ensuring measurement invariance (MI) is crucial for generating accurate and comparable scores, especially in comparative studies. Traditional approaches like Multi-Group Confirmatory Factor Analysis (MG-CFA) often involve intricate procedures and can become unwieldy when adjustments for partial invariance are needed. The Alignment-within-CFA (AwC) method emerged as a promising alternative, designed to approximate group-specific factors and produce latent variables with uniform metrics. This study rigorously compares the AwC method and traditional MG-CFA across moderate numbers of groups (3, 4, and 5) under various conditions of noninvariance and sample sizes. By employing Monte Carlo simulations, the study controls study variables and explores a wide range of hypothetical scenarios, enhancing the precision and reliability of MI testing. The findings indicate that the AwC method is similar to or superior to the step-wise partial invariance approach, offering accurate and consistent results in varied scenarios. Specifically, the study examines the conditions under which AwC outperforms traditional MG-CFA and investigates the impact of factors such as different types of invariance, number of groups, and sample size on bias and model fit. This research provides deeper insights into the strengths and limitations of each method, guiding researchers in selecting the most appropriate approach for their specific contexts. The results support the use of the AwC method in scenarios where minimizing bias and error in parameter estimates is critical, paving the way for more streamlined and effective research amidst increasing global diversity.

The existential psychology’s concern with the feeling of meaningfulness in life forms the basis for the present study aiming to investigate the relationship between perceived meaningfulness, and search for meaningfulness in life, and level of conformity.  An online survey was distributed to employees at a university in Sweden, and included two questionnaires; the Meaning in Life Questionnaire (MLQ) and the Concern for Appropriateness (CFA) questionnaire. One hundred and two respondents completed the survey. CFA was found to significantly correlate with MLQ-Presence (r = -.456 p = <.001) and MLQ-Search (r = .307 p = .002). The present study found that the feeling of present meaningfulness was significantly lower among those who cared more about fitting into others´ norms concerning how to behave.

No description available.

College student and Amazon's Mechanical TURK (MTURK) samples are regularly utilized in trauma research. Recent literature, however, has criticized these samples for not being generalizable to the general U.S. population. Measurement invariance (MI) using confirmatory factor analyses (CFA), is rarely utilized in trauma research, even though the analysis can determine whether groups are invariant across factor structure, factor loadings, item intercepts, and residual error variances on a given measure of PTSD symptom severity. The purpose of this study was to determine whether college student (n = 255) and MTURK (n = 316) samples are invariant on the PCL-5. Model fit indices indicated the 7-factor Hybrid model was the best fitting model, but the 6-factor anhedonia model was the most parsimonious model. Both models demonstrated equivalence in factor structures (configural invariance), factor loadings (metric invariance), intercepts (scalar invariance), and residuals (strict invariance), indicating MTURK and college student samples are similar in regards to PTSD symptom severity. These findings provide evidence that these groups can be combined in future studies to increase sample size for trauma research. Only the Anhedonia factor exhibited mean differences between groups, which may be related to true differences between college students and MTURK survey-takers. Thus, there is further evidence that the findings from trauma studies using these populations are generalizable to each other.

The adverse interaction between smoking and chronic pain has been known for decades. A variety of chronic pain conditions – ranging from headache to low back pain to fibromyalgia – markedly exacerbate smoking prevalence and intensity in packs per day among multiple patient populations. In patients seeking pain treatment, the prevalence of smoking approaches 50%, compared to less than 20% in the general population. Perhaps not surprisingly, the relationship is bidirectional: not only does persistent pain increase rates and intensity of smoking, but smoking also appears to exacerbate both the intensity and associated impairment of chronic pain. In fact, smoking appears to place individuals at risk for developing a chronic pain condition and may also facilitate the transition from acute to chronic pain. The growing body of literature documenting these associations has led to the proposition of a positive feedback loop: individuals smoke in part to cope with their pain, but smoking actually worsens the pain. Despite the strong evidence for the existence of this adverse interaction, the mechanisms responsible for it remain poorly understood. A number of preclinical and clinical studies have documented that nicotinic acetylcholine receptor (nAChR) agonists, e.g., nicotine, have analgesic efficacy in the acute pain setting, such as that produced experimentally in the research laboratory or experienced by patients postoperatively. In contrast, the role of nAChR activation in modulating chronic pain is less well characterized. The experiments described in this thesis determine whether persistent pain diminishes the antinociceptive (analgesic) efficacy of an α4β2 nAChR agonist in the rostral ventromedial medulla (RVM), a key brainstem pain modulatory nucleus, and subsequently begin to elucidate the mechanisms by which persistent pain elicits this plasticity. The complete Freund’s adjuvant (CFA) model of chronic pain was employed to test the hypothesis that persistent inflammatory injury diminishes the antinociceptive efficacy of the selective and potent α4β2 nAChR agonist epibatidine in key brainstem pain modulatory nuclei. Paw withdrawal latency to a noxious heat stimulus was used to evaluate the anti-hyperalgesic and antinociceptive effects of epibatidine microinjected in the RVM or periaqueductal gray (PAG) of male rats. The effects of epibatidine were assessed both in uninjured animals and in animals at different times after intraplantar CFA injection. Interestingly, pretreatment with an α4β2-selective antagonist demonstrated that the antinociceptive effects of epibatidine in naïve rats were mediated by α4β2 nAChRs in the RVM but not in the PAG. While the antinociceptive effects of epibatidine in the RVM were abolished after two weeks of inflammatory pain, the anti-hyperalgesic effects remained unchanged. Surprisingly, epibatidine no longer appeared to be acting primarily at α4β2 nAChRs as early as four hours after injury. Persistent inflammation did not alter the anti-hyperalgesic or antinociceptive effects of epibatidine in the PAG. Radioligand binding studies were conducted to test the most parsimonious hypothesis that a global reduction in α4β2 nAChR number or binding affinity during persistent injury was in part responsible for the decreased efficacy of epibatidine in the RVM after intraplantar CFA. Saturation binding using [3H]-epibatidine in membrane homogenates prepared from RVM and PAG tissue revealed no differences in receptors between saline- and CFA-treated rats at any time after injury, suggesting that a whole-nucleus reduction in nAChRs could not explain the observed behavioral phenomena. To query functional changes with greater resolution, whole-cell patch clamp electrophysiology was employed to begin assessing the consequences of nAChR activation by nicotine at the level of the neuron. Initial studies performed in the locus coeruleus demonstrated that all neurons responded to nicotine with an inward current that desensitized with continued exposure to the drug. Neurons in the RVM exhibited significantly more heterogeneity in their response to nicotine: desensitizing inward currents were seen in some; sustained outward currents in others; inward currents followed by outward currents in a third population; and still others had no response to nicotine exposure. The sustained outward currents persisted in the presence of the sodium channel blocker tetrodotoxin, were not blocked by an α4β2 nAChR-selective antagonist, and appeared to be mediated by G protein-coupled receptors and potassium channels. Taken together, the present results demonstrate that persistent inflammatory injury produces adaptive changes in nicotinic signaling in the RVM such that the antinociceptive effects of epibatidine activation are abolished in a time-dependent manner. These changes cannot be attributed to a whole-nucleus reduction in α4β2 nAChRs. However, nicotinic signaling in the RVM is complex, and small alterations in the pre- or postsynaptic actions of nicotine may have significant ramifications for the overall nociceptive sensitivity of an animal. The data presented here suggest that plasticity in nicotinic signaling within the bulbospinal pain modulatory pathways may in part explain the adverse interaction between smoking and chronic pain observed in humans.

Bachelor-Arbeit Univ. St. Gallen, 2004.