<p>Predmet doktorskog umetničkog rada &bdquo;Uloga sećanja u izgradnji identiteta&ldquo; je analiza procesa dramatizacije sećanja. Rad obuhvata teorijsko istraživanje sećanja, kao i načina za njegovu inscenaciju, s jedne strane, a sa druge umetničko istraživanje ličnog i kolektivnog sećanja povezanog s periodom odrastanja u specifičnom društvenom i istorijskom kontekstu Jugoslavije, prema kojem se, u datom trenutku, kroz pozorišnu predstavu, uspostavlja odnos.</p> / <p>The subject of doctoral art work &quot; Role of remembrance in identity formation&quot; is the analysis of the process of dramatization of memory. The thesis<br />includes theoretical research concerning memory, as well as the means for staging it, and, on the other hand - artistic research of the personal and collective memory connected to a period of growing up in a specific social and historical context of Yugoslavia towards which a relationship is established through a theater performance, at a specific given moment.</p>

In the current study, the four most common Particle Image Velocimetry (PIV) cross-correlation algorithms were evaluated by measuring the displacement of particles in computer generated images. The synthetic images were employed to compare the methods since the particle diameter, density, and intensity could be controlled, removing some of the uncertainty found in images collected during experiments, e. g. parallax, 3-D motion, etc. The most important parameter that was controlled in the synthetic images was the particle motion. Six different displacement functions were applied to move the particles between images: uniform translation, step, sawtooth, sinusoid, line source and line vortex. The four algorithms, which all use the fast Fourier transform (FFT) to perform the cross-correlation, were evaluated with four criteria; (1) spatial resolution, (2) dynamic range, (3) accuracy and (4) robustness. The uniform translation images determined the least error possible with each method, of which the deformed FFT proved to be the most accurate. The super resolution FFT and deformed FFT methods could not properly measure the infinite displacement gradient in the step images due to the interpolation of the displacement vector field used by each method around the step. However, the predictor corrector FFT scheme, which does not require interpolation when determining the interrogation area offset, successfully measured the infinite displacement gradient in the step images. The smaller interrogation areas used by the super resolution FFT scheme proved to be the best method to capture the high frequency finite displacement gradients in the sawtooth and sinusoid images. Also shown in the sawtooth and sinusoid images is the positional bias error introduced by assuming the measured particle displacement occurs at the centre of the interrogation area. The deformed FFT method produced the most accurate results for the source and vortex images, which both contained displacement gradients in multiple directions. Experimentally obtained images were also evaluated to verify the results derived using the synthetic images. The flow in a multiple grooved channel, using both water and air as the fluid medium in separate experiments, was measured and compared to DNS simulations reported by Yang. The mean velocity, average vorticity and turbulent fluctuations determined from both experiments using the deformed FFT method compared very well to the DNS calculations.

In the current study, the four most common Particle Image Velocimetry (PIV) cross-correlation algorithms were evaluated by measuring the displacement of particles in computer generated images. The synthetic images were employed to compare the methods since the particle diameter, density, and intensity could be controlled, removing some of the uncertainty found in images collected during experiments, e. g. parallax, 3-D motion, etc. The most important parameter that was controlled in the synthetic images was the particle motion. Six different displacement functions were applied to move the particles between images: uniform translation, step, sawtooth, sinusoid, line source and line vortex. The four algorithms, which all use the fast Fourier transform (FFT) to perform the cross-correlation, were evaluated with four criteria; (1) spatial resolution, (2) dynamic range, (3) accuracy and (4) robustness. The uniform translation images determined the least error possible with each method, of which the deformed FFT proved to be the most accurate. The super resolution FFT and deformed FFT methods could not properly measure the infinite displacement gradient in the step images due to the interpolation of the displacement vector field used by each method around the step. However, the predictor corrector FFT scheme, which does not require interpolation when determining the interrogation area offset, successfully measured the infinite displacement gradient in the step images. The smaller interrogation areas used by the super resolution FFT scheme proved to be the best method to capture the high frequency finite displacement gradients in the sawtooth and sinusoid images. Also shown in the sawtooth and sinusoid images is the positional bias error introduced by assuming the measured particle displacement occurs at the centre of the interrogation area. The deformed FFT method produced the most accurate results for the source and vortex images, which both contained displacement gradients in multiple directions. Experimentally obtained images were also evaluated to verify the results derived using the synthetic images. The flow in a multiple grooved channel, using both water and air as the fluid medium in separate experiments, was measured and compared to DNS simulations reported by Yang. The mean velocity, average vorticity and turbulent fluctuations determined from both experiments using the deformed FFT method compared very well to the DNS calculations.

Mastitis is the most common disease affecting Swedish milk cows. Automatic image classification can be useful for quickly classifying the bacteria causing this inflammation, in turn making it possible to start treatment more quickly. However, training an automatic classifier relies on the availability of data. Data collection can be a slow process, and GANs are a promising way to generate synthetic data to add plausible samples to an existing data set. The purpose of this thesis is to explore the usefulness of GANs for generating images of bacteria. This was done through researching existing literature on the subject, implementing a GAN, and evaluating the generated images. A cGAN capable of generating class-specific bacteria was implemented and improvements upon it made. The images generated by the cGAN were evaluated using visual examination, rapid scene categorization, and an expert interview regarding the generated images. While the cGAN was able to replicate certain features in the real images, it fails in crucial aspects such as symmetry and detail. It is possible that other GAN variants may be better suited to the task. Lastly, the results highlight the challenges of evaluating GANs with current evaluation methods.

<p>Plant phenotyping focuses on the measurement of plant characteristics throughout the growing season, typically with the goal of evaluating genotypes for plant breeding and management practices related to nutrient applications. Estimating plant characteristics is important for finding the relationship between the plant's genetic data and observable traits, which is also related to the environment and management practices. Recent machine learning approaches provide promising capabilities for high-throughput plant phenotyping using images. In this thesis, we focus on estimating plant traits for a field-based crop using images captured by Unmanned Aerial Vehicles (UAVs). We propose a method for estimating plant centers by transferring an existing model to a new scenario using limited ground truth data. We describe the use of transfer learning using a model fine-tuned for a single field or a single type of plant on a varied set of similar crops and fields. We introduce a method for rapidly counting panicles using images acquired by UAVs. We evaluate three different deep neural network structures for panicle counting and location. We propose a method for sorghum flowering time estimation using multi-temporal panicle counting. We present an approach that uses synthetic training images from generative adversarial networks for data augmentation to enhance the performance of sorghum panicle detection and counting. We reduce the amount of training data for sorghum panicle detection via semi-supervised learning. We create synthetic sorghum and maize images using diffusion models. We propose a method for tomato plant segmentation by color correction and color space conversion. We also introduce the methods for detecting and classifying bacterial tomato wilting from images.</p>

This study is dedicated to enhancing the effectiveness of Optical Character Recognition (OCR) systems, with a special emphasis on Arabic handwritten digit recognition. The choice to focus on Arabic handwritten digits is twofold: first, there has been relatively less research conducted in this area compared to its English counterparts; second, the recognition of Arabic handwritten digits presents more challenges due to the inherent similarities between different Arabic digits.OCR systems, engineered to decipher both printed and handwritten text, often face difficulties in accurately identifying low-quality or distorted handwritten text. The quality of the input image and the complexity of the text significantly influence their performance. However, data augmentation strategies can notably improve these systems' performance. These strategies generate new images that closely resemble the original ones, albeit with minor variations, thereby enriching the model's learning and enhancing its adaptability. The research found Conditional Variational Autoencoders (C-VAE) and Conditional Generative Adversarial Networks (C-GAN) to be particularly effective in this context. These two generative models stand out due to their superior image generation and feature extraction capabilities. A significant contribution of the study has been the formulation of the Synthetic Image Evaluation Procedure, a systematic approach designed to evaluate and amplify the generative models' image generation abilities. This procedure facilitates the extraction of meaningful features, computation of the Fréchet Inception Distance (LFID) score, and supports hyper-parameter optimization and model modifications.

Orientation estimation of objects plays a pivotal role in robotics, self-driving cars, and augmented reality. Beyond mere position, accurately determining the orientation of objects is essential for constructing precise models of the physical world. While 2D object detection has made significant strides, the field of orientation estimation still faces several challenges. Our research addresses these hurdles by proposing an efficient pipeline which facilitates rapid creation of labeled training data and enables direct regression of object orientation from a single image. We start by creating a digital twin of a physical object using an iPhone, followed by generating synthetic images using the Unity game engine and domain randomization. Our deep learning model, trained exclusively on these synthetic images, demonstrates promising results in estimating the orientations of common objects. Notably, our model achieves a median geodesic distance error of 3.9 degrees and operates at a brisk 15 frames per second.

<p>Recent advances in fluorescence  microscopy enable deeper cellular imaging in living tissues with near-infrared excitation light. </p> <p>High quality fluorescence microscopy images provide useful information for analyzing biological structures and diagnosing diseases.</p> <p>Nuclei detection and segmentation are two fundamental steps for quantitative analysis of microscopy images.</p> <p>However, existing machine learning-based approaches are hampered by three main challenges: (1) Hand annotated ground truth is difficult to obtain especially for 3D volumes, (2) Most of the object detection methods work only on 2D images and are difficult to extend to 3D volumes, (3) Segmentation-based approaches typically cannot distinguish different object instances without proper post-processing steps.</p> <p>In this thesis, we propose various new methods for microscopy image analysis including nuclei synthesis, detection, and segmentation. </p> <p>Due to the limitation of manually annotated ground truth masks, we first describe how we generate 2D/3D synthetic microscopy images using SpCycleGAN and use them as a data augmentation technique for our detection and segmentation networks.</p> <p>For nuclei detection, we describe our RCNN-SliceNet for nuclei counting and centroid detection using slice-and-cluster strategy. </p> <p>Then we introduce our 3D CentroidNet for nuclei centroid estimation using vector flow voting mechanism which does not require any post-processing steps.</p> <p>For nuclei segmentation, we first describe our EMR-CNN for nuclei instance segmentation using ensemble learning and slice fusion strategy.</p> <p>Then we present the 3D Nuclei Instance Segmentation Network (NISNet3D) for nuclei instance segmentation using gradient vector field array.</p> <p>Extensive experiments have been conducted on a variety of challenging microscopy volumes to demonstrate that our approach can accurately detect and segment the cell nuclei and outperforms other compared methods.</p> <p>Finally, we describe the Distributed and Networked Analysis of Volumetric Image Data (DINAVID) system we developed for biologists to remotely analyze large microscopy volumes using machine learning. </p>

A acolhida que as imagens eletrônicas e sintéticas vêm recebendo por parte da crítica é reveladora de um novo otimismo tecnológico e abre espaço para a discussão sobre as conseqüências das novas tecnologias sobre a sociedade. Fala-se, em relação a estas imagens, de um novo regime de visibilidade e de um novo imaginário – numérico – que transgride nossos códigos perceptivos e cognitivos e nossa relação simbólica com o mundo. Contudo, a desvinculação destes sistemas imagéticos de qualquer prática social mais ampla e da produção imagética que as antecedeu – especialmente a pintura, a fotografia e o cinema – é ilusória. Em muitos casos, os elementos apontados como marcas de ruptura são muito mais a potencialização de questões já presentes na história da arte do que a constituição de um novo sistema representativo. A potencialidade aberta por estes novos processos de enunciação da imagem deve, portanto, ser pensada a partir de uma perspectiva mais longa, inserida na lógica cultural do Capitalismo contemporâneo. Com isso, na esteira das colocações de Walter Benjamin e de Adorno, trataremos de ver o que as configurações sociais contemporâneas e o advento das novas tecnologias fazem com a arte, situando as imagens eletrônicas dentro das relações de produção de seu tempo. / The welcome that electronic and synthetic images have from the critique reveals a new technological optimism and gives room to the discussion of the consequences of the increasing interference of new technologies in society. Related to these images, is discussed a new regime of visibility and a new imaginary – run by numbers – that transgresses our perceptive and cognitive codes and our symbolic relation with the world. However, the well- known idea of an epistemic breach between the new images and the previous image production systems (in particular painting, photography and cinema) is illusory. Very often, the elements presented as marks of the rupture are in a great deal more the enhancement of questions already present in art history than the constitution of a new representational system. The open possibility created by these new processes of enunciation of image must, therefore, be interpreted from a wider perspective, rooted in the cultural logic of contemporary Capitalism. Thereby, following the path of Walter Benjamin and Adorno, we shall try to see what the contemporary social configurations and the advent of new technology do to art, placing the electronic images inside the productive relations of its time.

A acolhida que as imagens eletrônicas e sintéticas vêm recebendo por parte da crítica é reveladora de um novo otimismo tecnológico e abre espaço para a discussão sobre as conseqüências das novas tecnologias sobre a sociedade. Fala-se, em relação a estas imagens, de um novo regime de visibilidade e de um novo imaginário – numérico – que transgride nossos códigos perceptivos e cognitivos e nossa relação simbólica com o mundo. Contudo, a desvinculação destes sistemas imagéticos de qualquer prática social mais ampla e da produção imagética que as antecedeu – especialmente a pintura, a fotografia e o cinema – é ilusória. Em muitos casos, os elementos apontados como marcas de ruptura são muito mais a potencialização de questões já presentes na história da arte do que a constituição de um novo sistema representativo. A potencialidade aberta por estes novos processos de enunciação da imagem deve, portanto, ser pensada a partir de uma perspectiva mais longa, inserida na lógica cultural do Capitalismo contemporâneo. Com isso, na esteira das colocações de Walter Benjamin e de Adorno, trataremos de ver o que as configurações sociais contemporâneas e o advento das novas tecnologias fazem com a arte, situando as imagens eletrônicas dentro das relações de produção de seu tempo. / The welcome that electronic and synthetic images have from the critique reveals a new technological optimism and gives room to the discussion of the consequences of the increasing interference of new technologies in society. Related to these images, is discussed a new regime of visibility and a new imaginary – run by numbers – that transgresses our perceptive and cognitive codes and our symbolic relation with the world. However, the well- known idea of an epistemic breach between the new images and the previous image production systems (in particular painting, photography and cinema) is illusory. Very often, the elements presented as marks of the rupture are in a great deal more the enhancement of questions already present in art history than the constitution of a new representational system. The open possibility created by these new processes of enunciation of image must, therefore, be interpreted from a wider perspective, rooted in the cultural logic of contemporary Capitalism. Thereby, following the path of Walter Benjamin and Adorno, we shall try to see what the contemporary social configurations and the advent of new technology do to art, placing the electronic images inside the productive relations of its time.