A post-occupancy evaluation of a neighbourhood park: : Using PPGIS methods for mapping users’ experiences in Hyväntoivonpuisto Park

In densely built areas, neighbourhood parks have a close relationship with the residents and are an integral part of their everyday activity. However, the presence of the park itself does not ensure its use. Some of the major factors that affect park use are park facilities, proximity, location, environment quality, and park design. Despite all, the extent to which the park is used can only be determined once the park comes to use. Therefore, this thesis aims to research the factors that affect the park use and park activities based on users’ activity and their experiences in the park environment by conducting a post-occupancy evaluation in Hyväntoivonpuisto Park in Jätkäsaari. By analysing the questionnaire data collected in Jätkäsaari, Helsinki, Finland in 2022, through the PPGIS method, this thesis aims to examine what kind of activities take place in Hyväntoivonpuisto park, and how do the park location and design facilitate these activities. The types of activities in the park will be discussed in terms of Jan Gehl’s categories of activities. Additionally, this thesis aims to research the relation between users’ aesthetic experiences, their perception of safety, and their activity in the Hyväntoivonpuisto park. Furthermore, it aims to reveal the collective public image of Hyväntoivonpuisto park by operationalising Kevin Lynch’s theory of ‘the city and its elements’. The data for this study was collected using the PPGIS (Public participation Geographical Information Systems) method using Maptionnaire. The PPGIS study website consisted of 11 pages, with mapping tasks, open-ended questions, and general nonspatial questions. The data collection for the study was conducted between 17th March and 12th April 2022. There were 218 survey participants, among which responses from 200 participants were suitable for analysis. The survey participants marked a total of 934 locations. The data analysis was done using QGIS (Quantum GIS) and Microsoft Excel. This thesis found that the location and the design of the park do influence the type of activities that take place in the Hyväntoivonpuisto park. The aesthetic value of the park has a stronger influence on park activity in park areas that are left open for spontaneous activities and has a smaller impact on park facilities with specified uses. When people's perceptions of their safety are positive, they had a beneficial impact on park use, but when they were negative, they had little impact on park activities. Additionally, the design features strongly influence the public image of the park, and especially nodes and landmarks strongly define the identity of the park

One-for-All: Towards Universal Domain Translation with a Single StyleGAN

In this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences. The main idea behind our approach is leveraging the domain-neutral capabilities of CLIP as a bridging mechanism, while utilizing a separate module to extract abstract, domain-agnostic semantics from the embeddings of both the source and target realms. Fusing these abstract semantics with target-specific semantics results in a transformed embedding within the CLIP space. To bridge the gap between the disparate worlds of CLIP and StyleGAN, we introduce a new non-linear mapper, the CLIP2P mapper. Utilizing CLIP embeddings, this module is tailored to approximate the latent distribution in the P space, effectively acting as a connector between these two spaces. The proposed UniTranslator is versatile and capable of performing various tasks, including style mixing, stylization, and translations, even in visually challenging scenarios across different visual domains. Notably, UniTranslator generates high-quality translations that showcase domain relevance, diversity, and improved image quality. UniTranslator surpasses the performance of existing general-purpose models and performs well against specialized models in representative tasks. The source code and trained models will be released to the public

Perceptual modelling for 2D and 3D

Livrable D1.1 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D1.1 du projet

Camera positioning for 3D panoramic image rendering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Virtual camera realisation and the proposition of trapezoidal camera architecture are the two broad contributions of this thesis. Firstly, multiple camera and their arrangement constitute a critical component which affect the integrity of visual content acquisition for multi-view video. Currently, linear, convergence, and divergence arrays are the prominent camera topologies adopted. However, the large number of cameras required and their synchronisation are two of prominent challenges usually encountered. The use of virtual cameras can significantly reduce the number of physical cameras used with respect to any of the known camera structures, hence adequately reducing some of the other implementation issues. This thesis explores to use image-based rendering with and without geometry in the implementations leading to the realisation of virtual cameras. The virtual camera implementation was carried out from the perspective of depth map (geometry) and use of multiple image samples (no geometry). Prior to the virtual camera realisation, the generation of depth map was investigated using region match measures widely known for solving image point correspondence problem. The constructed depth maps have been compare with the ones generated using the dynamic programming approach. In both the geometry and no geometry approaches, the virtual cameras lead to the rendering of views from a textured depth map, construction of 3D panoramic image of a scene by stitching multiple image samples and performing superposition on them, and computation of virtual scene from a stereo pair of panoramic images. The quality of these rendered images were assessed through the use of either objective or subjective analysis in Imatest software. Further more, metric reconstruction of a scene was performed by re-projection of the pixel points from multiple image samples with a single centre of projection. This was done using sparse bundle adjustment algorithm. The statistical summary obtained after the application of this algorithm provides a gauge for the efficiency of the optimisation step. The optimised data was then visualised in Meshlab software environment, hence providing the reconstructed scene. Secondly, with any of the well-established camera arrangements, all cameras are usually constrained to the same horizontal plane. Therefore, occlusion becomes an extremely challenging problem, and a robust camera set-up is required in order to resolve strongly the hidden part of any scene objects. To adequately meet the visibility condition for scene objects and given that occlusion of the same scene objects can occur, a multi-plane camera structure is highly desirable. Therefore, this thesis also explore trapezoidal camera structure for image acquisition. The approach here is to assess the feasibility and potential of several physical cameras of the same model being sparsely arranged on the edge of an efficient trapezoid graph. This is implemented both Matlab and Maya. The quality of the depth maps rendered in Matlab are better in Quality

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs

A survey of DNN methods for blind image quality assessment

Blind image quality assessment (BIQA) methods aim to predict quality of images as perceived by humans without access to a reference image. Recently, deep learning methods have gained substantial attention in the research community and have proven useful for BIQA. Although previous study of deep neural networks (DNN) methods is presented, some novelty DNN methods, which are recently proposed, are not summarized for BIQA. In this paper, we provide a survey covering various DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods according to the role of DNN. Then, we compare the prediction performance of various DNN methods on the synthetic databases (LIVE, TID2013, CSIQ, LIVE multiply distorted) and authentic databases (LIVE challenge), providing important information that can help understand the underlying properties between different DNN methods for BIQA. Finally, we describe some emerging challenges in designing and training DNN-based BIQA, along with few directions that are worth further investigations in the future