Global ETD Search

1	Effects of glaucoma on detection and discrimination of image blur Bham, Habiba A., Denniss, Jonathan 09 December 2021 (has links) Yes / Blur is one of the most commonly reported visual symptoms of glaucoma, but it is not directly measured by current clinical tests. We aimed to investigate the effects of glaucoma on detection and discrimination of image blur. People with glaucoma, separated into two groups with (n=15) or without (n=17) central visual field defects measured by 10-2 perimetry, and an age-similar control group (n=18) participated. First, we measured contrast detection thresholds centrally using a 2-interval forced choice procedure. We then measured blur detection and discrimination thresholds for the same stimuli (reference blurs 0, 1 arcmin respectively) using a 2-alternative forced choice procedure under two contrast conditions; 4x individual detection threshold for the low contrast condition, 95% contrast for the high contrast condition. The stimulus was a horizontal edge bisecting a hard-edged circle of 4.5° diameter. Data were analysed by linear mixed modelling. Contrast detection thresholds for the glaucoma group with central visual field defects were raised by 0.014 ± 0.004 (mean ± SE, Michelson units) (p=0.002) and by 0.011 ± 0.004 (p=0.03) relative to control and glaucoma without central visual field defect groups respectively. Blur detection and discrimination thresholds were similar between groups, with small elevations in blur detection thresholds in the glaucoma groups not reaching statistical significance (detection p=0.29, discrimination p=0.91). The lower contrast level increased thresholds from the higher contrast level by 1.30 ± 0.10 arcmin (p<0.001) and 1.05 ± 0.096 arcmin (p<0.001) for blur detection and discrimination thresholds respectively. Early-moderate glaucoma resulted in only minimal elevations of blur detection thresholds that did not reach statistical significance in this study. Despite the prevalence of blur as a visual symptom of glaucoma, psychophysical measurements of blur detection or discrimination may not be good candidates for development as clinical tests for glaucoma / College of Optometrists PhD scholarship Glaucoma Image blur Detection
2	Image Blur Detection with Two-Dimensional Haar Wavelet Transform Andhavarapu, Sarat Kiran 01 August 2015 (has links) Efficient detection of image blur and its extent is an open research problem in computer vision. Image blur has a negative impact on image quality. Blur is introduced into images due to various factors including limited contrast, improper exposure time or unstable device handling. Toward this end, an algorithm is presented for image blur detection with the use of Two-Dimensional Haar Wavelet transform (2D HWT). The algorithm is experimentally compared with two other image blur detection algorithms frequently cited in the literature. When evaluated over a sample of images, the algorithm performed on par or better than the two other blur detection algorithms. Image Blur Detection Two-dimensional Haar Haar wavelet transform Computer Sciences Physical Sciences and Mathematics
3	Blur invariant pattern recognition and registration in the Fourier domain Ojansivu, V. (Ville) 13 October 2009 (has links) Abstract Pattern recognition and registration are integral elements of computer vision, which considers image patterns. This thesis presents novel blur, and combined blur and geometric invariant features for pattern recognition and registration related to images. These global or local features are based on the Fourier transform phase, and are invariant or insensitive to image blurring with a centrally symmetric point spread function which can result, for example, from linear motion or out of focus. The global features are based on the even powers of the phase-only discrete Fourier spectrum or bispectrum of an image and are invariant to centrally symmetric blur. These global features are used for object recognition and image registration. The features are extended for geometrical invariances up to similarity transformation: shift invariance is obtained using bispectrum, and rotation-scale invariance using log-polar mapping of bispectrum slices. Affine invariance can be achieved as well using rotated sets of the log-log mapped bispectrum slices. The novel invariants are shown to be more robust to additive noise than the earlier blur, and combined blur and geometric invariants based on image moments. The local features are computed using the short term Fourier transform in local windows around the points of interest. Only the lowest horizontal, vertical, and diagonal frequency coefficients are used, the phase of which is insensitive to centrally symmetric blur. The phases of these four frequency coefficients are quantized and used to form a descriptor code for the local region. When these local descriptors are used for texture classification, they are computed for every pixel, and added up to a histogram which describes the local pattern. There are no earlier textures features which have been claimed to be invariant to blur. The proposed descriptors were superior in the classification of blurred textures compared to a few non-blur invariant state of the art texture classification methods. bispectrum blur invariant features computer vision image alignment image blur image registration object recognition pattern classification texture analysis
4	Detekce a hodnocení zkreslených snímků v obrazových sekvencích / Detection and evaluation of distorted frames in retinal image data Vašíčková, Zuzana January 2020 (has links) Diplomová práca sa zaoberá detekciou a hodnotením skreslených snímok v retinálnych obrazových dátach. Teoretická časť obsahuje stručné zhrnutie anatómie oka a metód hodnotenia kvality obrazov všeobecne, ako aj konkrétne hodnotenie retinálnych obrazov. Praktická časť bola vypracovaná v programovacom jazyku Python. Obsahuje predspracovanie dostupných retinálnych obrazov za účelom vytvorenia vhodného datasetu. Ďalej je navrhnutá metóda hodnotenia troch typov šumu v skreslených retinálnych obrazoch, presnejšie pomocou Inception-ResNet-v2 modelu. Táto metóda nebola prijateľná a navrhnutá bola teda iná metóda pozostávajúca z dvoch krokov - klasifikácie typu šumu a následného hodnotenia úrovne daného šumu. Pre klasifikáciu typu šumu bolo využité filtrované Fourierove spektrum a na hodnotenie obrazu boli využité príznaky extrahované pomocou ResNet50, ktoré vstupovali do regresného modelu. Táto metóda bola ďalej rozšírená ešte o krok detekcie zašumených snímok v retinálnych sekvenciách.
5	The Effect of Cue and Target Similarity on Visual Search Response Times: Manipulation of Basic Stimulus Characteristics Fullenkamp, Steven Charles January 2013 (has links) No description available. Aerospace Engineering Behavioral Sciences Cognitive Psychology Communication Experimental Psychology Experiments Information Technology Information Science Information Systems Industrial Engineering Medical Imaging Operations Research Psychology Quantitative Psychology Scientific Imaging Systems Design cued visual search cue and target similarity image contrast image size image spatial resolution image blur cue effectiveness cue and target characteristics search time search efficiency cue target distractor visual search

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