We propose an e cient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.

Recently, machine learning models such as Generative Adversarial Networks and Autoencoders have received significant attention from the research community. In fact, researchers have produced novel ways for using this technology in the space of image manipulation for cross-domain image-to-image transformations, upsampling, style imprinting, human facial editing, and computed tomography correction. Previous work primarily focuses on transformations where the output inherits the same skeletal outline as the input image. This work proposes a novel framework, called BrandGAN, that tackles image correction for hand-drawn images. One of this problem’s novelties is that it requires the skeletal outline of the input image to be manipulated and adjusted to look more like a target reference while retaining key visual features that were included intentionally by its creator. GANs, when trained on a dataset, are capable of producing a large variety of novel images derived from a combination of visual features from the original dataset. StyleGAN is a model that iterated on the concept of GANs and was able to produce high-fidelity images such as human faces and cars. StyleGAN includes a process called projection that finds an encoding of an input image capable of producing a visually similar image. Projection in StyleGAN demonstrated the model’s ability to represent real images that were not a part of its training dataset. StyleGAN encodings are vectors that represent features of an image. Encodings can be combined to merge or manipulate features of distinct images. In BrandGAN, we tackle image correction by leveraging StyleGAN’s projection and encoding vector feature manipulation. We present a modified version of projection to find an encoding representation of hand-drawn images. We propose a novel GAN indexing technique, called GANdex, capable of finding encodings of novel images derived from the original dataset that share visual similarities with the input image. Finally, with vector feature manipulation, we combine the GANdex vector’s features with the input image’s projection to produce the final image-corrected output. Combining the vectors results in adjusting the input imperfections to resemble the original dataset’s structure while retaining novel features from the raw input image. We evaluate seventy-five hand-drawn images collected through a study with fifteen participants using objective and subjective measures. BrandGAN reduced the Fréchet inception distance from 193 to 161 and the Kernel-Inception distance from 0.048 to 0.026 when comparing the hand-drawn and BrandGAN output images to the reference design dataset. A blinded experiment showed that the average participant could identify 4.33 out of 5 images as their own when presented with a visually similar control image. We included a survey that collected opinion scores ranging from one or “strongly disagree” to five or “strongly agree.” The average participant answered 4.32 for the retention of detail, 4.25 for the output’s professionalism, and 4.57 for their preference of using the BrandGAN output over their own.

Diplomova praca sa zameriava na oblast zaostrovania obrazkov tvari. V teoretickej casti prace budu prezentovane moderne metody zaostrovania obrazkov pomocou jedineho obrazku a metody editacie obrazkov. Prakticka cast sa zameria na pristupy rekonstrukcie obrazkov zo sekvencie poskodenych obrazkov. Viacere modely neuronovych sieti so vstupom pre viacero obrazkov budu zhotovene a vyhodnotene. Alternativny pristup v podobe balika nastrojov na editaciu obrazkov bude taktiez predstaveny. Tieto nastroje budu vyuzivat najmodernejsie pristupy k editacii obrazkov s cielom spojit vizualne prvky tvari zo vstupnej sekvencie obrazkov do jedneho finalneho vystupu. V zavere prace budu vsetky metody navzajom porovnane.

Detecting failures in tricone drill bits is crucial in the mining industry due to their potential consequences, including operational losses, safety hazards, and delays in drilling operations. Timely identification of failures allows for proactive maintenance and necessary measures to ensure smooth drilling processes and minimize associated risks. Accurate failure detection helps mining operations avoid financial losses by preventing unplanned breakdowns, costly repairs, and extended downtime. Moreover, it optimizes operational efficiency by enabling timely maintenance interventions, extending the lifespan of drill bits, and minimizing disruptions. Failure detection also plays a critical role in ensuring the safety of personnel and equipment involved in drilling operations. Traditionally, failure detection in tricone drill bits relies on manual inspection, which can be time-consuming and labor-intensive. Incorporating artificial intelligence-based approaches can significantly enhance efficiency and accuracy. This thesis uses machine learning methods for failure inference in tricone drill bits. A classic Convolutional Neural Network (CNN) classification method was initially explored, but its performance was insufficient due to the small dataset size and imbalanced data. The problem was reformulated as an object detection task to overcome these limitations, and a post-processing operation was incorporated. Data augmentation techniques enhanced the training and evaluation datasets, improving failure detection accuracy. Experimental results highlighted the need for revising the initial CNN classification method, given the limitations of the small and imbalanced dataset. However, You Only Look Once (YOLO) algorithms such as YOLOv5 and YOLOv8 models exhibited improved performance. The post-processing operation further refined the results obtained from the YOLO algorithm, specifically YOLOv5 and YOLOv8 models. While YOLO provides bounding box coordinates and class labels, the post-processing step enhanced drill bit failure detection through various techniques such as confidence thresholding, etc. By effectively leveraging the YOLO-based models and incorporating post-processing, this research advances failure detection in tricone drill bits. These intelligent methods enable more precise and efficient detection, preventing operational losses and optimizing maintenance processes. The findings underscore the potential of machine learning techniques in the mining industry, particularly in mechanical drilling, driving progress and enhancing overall operational efficiency