Scattered Mosaic Rendering Using Unit Images

An image mosaic method that can be used when creating advertisements or posters is proposed in this study. Mosaic is a method that expresses an entire image using an arbitrary number of cells. Photomosaic generates new images using a combination of photos. In this paper, we propose a new mosaic algorithm that generates an abstract artistic mosaic image by filling a region that is divided by a boundary using a unit image, which is an image that only has a shape and no allocated color. A unit image can be changed diversely through rotation or shifting, and the corresponding region is filled by using the gradient direction and edge information of the input image. For this, we extract and use information from input image such as color, edge and gradient. In result we can generate various abstractive images which can be used in advertisement and multimedia contents market

Honeycomb Plots: Visual Enhancements for Hexagonal Maps

Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study.publishedVersio

On Martian Surface Exploration: Development of Automated 3D Reconstruction and Super-Resolution Restoration Techniques for Mars Orbital Images

Very high spatial resolution imaging and topographic (3D) data play an important role in modern Mars science research and engineering applications. This work describes a set of image processing and machine learning methods to produce the “best possible” high-resolution and high-quality 3D and imaging products from existing Mars orbital imaging datasets. The research work is described in nine chapters of which seven are based on separate published journal papers. These include a) a hybrid photogrammetric processing chain that combines the advantages of different stereo matching algorithms to compute stereo disparity with optimal completeness, fine-scale details, and minimised matching artefacts; b) image and 3D co-registration methods that correct a target image and/or 3D data to a reference image and/or 3D data to achieve robust cross-instrument multi-resolution 3D and image co-alignment; c) a deep learning network and processing chain to estimate pixel-scale surface topography from single-view imagery that outperforms traditional photogrammetric methods in terms of product quality and processing speed; d) a deep learning-based single-image super-resolution restoration (SRR) method to enhance the quality and effective resolution of Mars orbital imagery; e) a subpixel-scale 3D processing system using a combination of photogrammetric 3D reconstruction, SRR, and photoclinometric 3D refinement; and f) an optimised subpixel-scale 3D processing system using coupled deep learning based single-view SRR and deep learning based 3D estimation to derive the best possible (in terms of visual quality, effective resolution, and accuracy) 3D products out of present epoch Mars orbital images. The resultant 3D imaging products from the above listed new developments are qualitatively and quantitatively evaluated either in comparison with products from the official NASA planetary data system (PDS) and/or ESA planetary science archive (PSA) releases, and/or in comparison with products generated with different open-source systems. Examples of the scientific application of these novel 3D imaging products are discussed

Perceptual models for high-refresh-rate rendering

Rendering realistic images requires substantial computational power. With new high-refresh-rate displays as well as the renaissance of virtual reality (VR) and augmented reality (AR), one cannot expect that GPU performance will scale fast enough to meet the requirements of immersive photo-realistic rendering with current rendering techniques. In this dissertation, I follow the dual of the well-known computer vision approach: vision is inverse graphics: to improve graphical algorithms, I consider the operation of the human visual system. I propose to model and exploit the limitations of the visual system in the context of novel high-refresh-rate displays; specifically, I focus on spatio-temporal perception, a topic that has received remarkably less attention than spatial-only perception so far. I present three main contributions. First, I demonstrate the validity of the perceptual approach by presenting a conceptually simple rendering technique motivated by our eyes' limited sensitivity to high spatio-temporal change which reduces the rendering load and transmission requirement of current-generation VR headsets without introducing perceivable visual artefacts. Second, I present two visual models related to motion perception: (a) a metric for detecting flicker; and (b) a comprehensive visual model to predict perceived motion quality on monitors with arbitrary refresh rates and monitor resolutions. Third, I propose an adaptive rendering algorithm that utilises the proposed models. All algorithms operate on physical colorimetric units (instead of display-referenced pixel values), for which I provide the appropriate display measurements and models. All proposed algorithms and visual models are calibrated and validated with psychophysical experiments

Foveated Path Tracing with Fast Reconstruction and Efficient Sample Distribution

Polunseuranta on tietokonegrafiikan piirtotekniikka, jota on käytetty pääasiassa ei-reaaliaikaisen realistisen piirron tekemiseen. Polunseuranta tukee luonnostaan monia muilla tekniikoilla vaikeasti saavutettavia todellisen valon ilmiöitä kuten heijastuksia ja taittumista. Reaaliaikainen polunseuranta on hankalaa polunseurannan suuren laskentavaatimuksen takia. Siksi nykyiset reaaliaikaiset polunseurantasysteemi tuottavat erittäin kohinaisia kuvia, jotka tyypillisesti suodatetaan jälkikäsittelykohinanpoisto-suodattimilla. Erittäin immersiivisiä käyttäjäkokemuksia voitaisiin luoda polunseurannalla, joka täyttäisi laajennetun todellisuuden vaatimukset suuresta resoluutiosta riittävän matalassa vasteajassa. Yksi mahdollinen ratkaisu näiden vaatimusten täyttämiseen voisi olla katsekeskeinen polunseuranta, jossa piirron resoluutiota vähennetään katseen reunoilla. Tämän johdosta piirron laatu on katseen reunoilla sekä harvaa että kohinaista, mikä asettaa suuren roolin lopullisen kuvan koostavalle suodattimelle. Tässä työssä esitellään ensimmäinen reaaliajassa toimiva regressionsuodatin. Suodatin on suunniteltu kohinaisille kuville, joissa on yksi polunseurantanäyte pikseliä kohden. Nopea suoritus saavutetaan tiileissä käsittelemällä ja nopealla sovituksen toteutuksella. Lisäksi työssä esitellään Visual-Polar koordinaattiavaruus, joka jakaa polunseurantanäytteet siten, että niiden jakauma seuraa silmän herkkyysmallia. Visual-Polar-avaruuden etu muihin tekniikoiden nähden on että se vähentää työmäärää sekä polunseurannassa että suotimessa. Nämä tekniikat esittelevät toimivan prototyypin katsekeskeisestä polunseurannasta, ja saattavat toimia tienraivaajina laajamittaiselle realistisen reaaliaikaisen polunseurannan käyttöönotolle.Photo-realistic offline rendering is currently done with path tracing, because it naturally produces many real-life light effects such as reflections, refractions and caustics. These effects are hard to achieve with other rendering techniques. However, path tracing in real time is complicated due to its high computational demand. Therefore, current real-time path tracing systems can only generate very noisy estimate of the final frame, which is then denoised with a post-processing reconstruction filter. A path tracing-based rendering system capable of filling the high resolution in the low latency requirements of mixed reality devices would generate a very immersive user experience. One possible solution for fulfilling these requirements could be foveated path tracing, wherein the rendering resolution is reduced in the periphery of the human visual system. The key challenge is that the foveated path tracing in the periphery is both sparse and noisy, placing high demands on the reconstruction filter. This thesis proposes the first regression-based reconstruction filter for path tracing that runs in real time. The filter is designed for highly noisy one sample per pixel inputs. The fast execution is accomplished with blockwise processing and fast implementation of the regression. In addition, a novel Visual-Polar coordinate space which distributes the samples according to the contrast sensitivity model of the human visual system is proposed. The specialty of Visual-Polar space is that it reduces both path tracing and reconstruction work because both of them can be done with smaller resolution. These techniques enable a working prototype of a foveated path tracing system and may work as a stepping stone towards wider commercial adoption of photo-realistic real-time path tracing

Online Super-Resolution For Fibre-Bundle-Based Confocal Laser Endomicroscopy

Probe-based Confocal Laser Endomicroscopy (pCLE) produces microscopic images enabling real-time in vivo optical biopsy. However, the miniaturisation of the optical hardware, specifically the reliance on an optical fibre bundle as an imaging guide, fundamentally limits image quality by producing artefacts, noise, and relatively low contrast and resolution. The reconstruction approaches in clinical pCLE products do not fully alleviate these problems. Consequently, image quality remains a barrier that curbs the full potential of pCLE. Enhancing the image quality of pCLE in real-time remains a challenge. The research in this thesis is a response to this need. I have developed dedicated online super-resolution methods that account for the physics of the image acquisition process. These methods have the potential to replace existing reconstruction algorithms without interfering with the fibre design or the hardware of the device. In this thesis, novel processing pipelines are proposed for enhancing the image quality of pCLE. First, I explored a learning-based super-resolution method that relies on mapping from the low to the high-resolution space. Due to the lack of high-resolution pCLE, I proposed to simulate high-resolution data and use it as a ground truth model that is based on the pCLE acquisition physics. However, pCLE images are reconstructed from irregularly distributed fibre signals, and grid-based Convolutional Neural Networks are not designed to take irregular data as input. To alleviate this problem, I designed a new trainable layer that embeds Nadaraya- Watson regression. Finally, I proposed a novel blind super-resolution approach by deploying unsupervised zero-shot learning accompanied by a down-sampling kernel crafted for pCLE. I evaluated these new methods in two ways: a robust image quality assessment and a perceptual quality test assessed by clinical experts. The results demonstrate that the proposed super-resolution pipelines are superior to the current reconstruction algorithm in terms of image quality and clinician preference

Computer Vision for Fish Monitoring: Challenges and Possibilities

This master's thesis focuses on the evaluation and exploration of detection and tracking algorithms for fish in a dense underwater environment. The primary objectives were to achieve precise and accurate fish detection and to track fish over an extended period. The thesis explores the performance of two object detection algorithms, YOLOv4 and YOLOv8, as well as their integration with the DeepSORT tracking algorithm. The algorithms were trained and evaluated using a dataset collected from a densely populated underwater fish tank. The dataset was manually annotated using bounding box annotation techniques to accurately label the objects of interest. The results demonstrated the effectiveness of both YOLOv4 and YOLOv8 in detecting fish in densely populated environments. However, YOLOv8 achieved a significantly higher mAP50-95 score, indicating better localization and detection accuracy. It proved more adept at precisely locating the position of detected fish, leading to improved overall detection performance. In terms of fish tracking the combination of DeepSORT and YOLOv8 showed the best overall performance, as evidenced by higher MOTA and IDF1 scores, and lower MOTP scores. However, tracking individual fish over extended periods presented challenges due to occlusions and rapid trajectory changes, leading to a high number of identity switches. By evaluating and exploring the effectiveness of detection and tracking algorithms, this thesis contributes to the advancement of fish monitoring techniques in aquaculture. The findings provide valuable insights into the performance of YOLOv4 and YOLOv8 and the potential of DeepSORT for accurate and reliable fish detection and tracking. The results and methodologies presented in this study lay the groundwork for further research and development in the field, aiming to enhance fish welfare, optimize resource management, and improve efficiency in aquaculture practices