The architecture of transit: photographing incidents of sublimity in the landscapes of motorway architecture between the Alps and Naples

The aesthetics of motorway architecture has not received attention within theoretical photographic discourse and has never been the subject of an academic photographic research project. This project begins from the understanding of the motorway as one continuous piece of architecture that crosses international boundaries on its route across Europe – an architecture so large that it cannot be perceived in its entirety. As a research-by-practice PhD, photography is used to identify and record incidents of the sublime in the route of the motorway. The photographs are produced with a large field study from the Swiss Alps to Naples, where numerous complex topographical and spatial conditions are found. This results in incidents of the sublime within its architecture when the motorway is forced to negotiate these conditions during its route. The research domain was chosen for its significance within the history of art and literature in European cultural history. Travelling in these regions was and is strongly related to the development of cultural concepts of the sublime. The questions that this research investigates are: Is it possible to make a depiction of architectural, spatial, topographical factors combined in a sublime incident? Can a methodology be defined to photograph these structures? How can photographs be made of large-scale architecture that cannot be seen or experienced in their entirety? The meaning of the term sublime has become diluted in contemporary usage, often being used inaccurately in description of something exquisite or delightful. This project revisits 18th-century formulations of this aesthetic categorisation, alongside historical travel literature, representations of landscape in painting and photography and contemporary architectural and photographic discourses. These references enable a thorough understanding of principles of aesthetic composition, resulting in the creation of a new understanding of the sublime and methodology for photographing large-scale motorway architecture. Employing a photographic aesthetic that embraces representation and post-production enhancement of Fine Art practice, the project culminates in the production of 29 photographs that form a narrative series exploring incidents of the sublime within motorway architecture between the Alps and Naples

Enabling viewpoint learning through dynamic label generation

Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpointqualities suffer from two major drawbacks: dependency on clean surface meshes, which are not always available, and the lack ofclosed-form expressions, which requires a costly search involving rendering. To overcome these limitations we propose to sepa-rate viewpoint selection from rendering through an end-to-end learning approach, whereby we reduce the in¿uence of the meshquality by predicting viewpoints from unstructured point clouds instead of polygonal meshes. While this makes our approachinsensitive to the mesh discretization during evaluation, it only becomes possible when resolving label ambiguities that arise inthis context. Therefore, we additionally propose to incorporate the label generation into the training procedure, making the labeldecision adaptive to the current network predictions. We show how our proposed approach allows for learning viewpoint pre-dictions for models from different object categories and for different viewpoint qualities. Additionally, we show that predictiontimes are reduced from several minutes to a fraction of a second, as compared to state-of-the-art (SOTA) viewpoint quality eval-uation. Code and training data is available at https://github.com/schellmi42/viewpoint_learning, whichis to our knowledge the biggest viewpoint quality dataset available.This work was supported in part by project TIN2017-88515-C2-1-R(GEN3DLIVE), from the Spanish Ministerio de Economía yCompetitividad, by 839 FEDER (EU) funds.Peer ReviewedPostprint (published version

Enabling Viewpoint Learning through Dynamic Label Generation

Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpoint qualities suffer from two major drawbacks: dependency on clean surface meshes, which are not always available, and the lack of closed-form expressions, which requires a costly search involving rendering. To overcome these limitations we propose to separate viewpoint selection from rendering through an end-to-end learning approach, whereby we reduce the influence of the mesh quality by predicting viewpoints from unstructured point clouds instead of polygonal meshes. While this makes our approach insensitive to the mesh discretization during evaluation, it only becomes possible when resolving label ambiguities that arise in this context. Therefore, we additionally propose to incorporate the label generation into the training procedure, making the label decision adaptive to the current network predictions. We show how our proposed approach allows for learning viewpoint predictions for models from different object categories and for different viewpoint qualities. Additionally, we show that prediction times are reduced from several minutes to a fraction of a second, as compared to state-of-the-art (SOTA) viewpoint quality evaluation. We will further release the code and training data, which will to our knowledge be the biggest viewpoint quality dataset available

(De)Constructing worlds: high Modernism, architecture and photography

Since the last decade of the twentieth century, there has been renewed interest in photographing high Modernist structures and architectures. A significant portion of these images has tended towards the autotelic or spectacle, with far fewer functioning as social commentary or critique. However, the need for an independent and critical photography of architecture remains. Such a practice furthers our understanding of the lasting legacy of architectural modernity and its ongoing impact/s. This dissertation investigates the critical representation of high Modernist structures, architectures, and urban planning in specific works by contemporary artists and photographers, Andreas Gursky, Filip Dujardin, David Goldblatt, and Beate Gütschow. However diverse their practice, each of these artists and photographers engages with the authoritarian impetus of high Modernism: a drive towards social order and control enacted through its structures and architectures. Through investigation of a range of photographic projects produced with a view to critique the social expression of high Modernism, I argue that contemporary photography which takes architecture as its subject has the ability to communicate wider notions about society. These artists and photographers reveal the degree to which humanity has been elided by high Modernist architectures and planning. By discussing these projects I contribute to a relatively under-researched area of study

Monitoring wild animal communities with arrays of motion sensitive camera traps

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans

Expanded Parts Model for Semantic Description of Humans in Still Images

We introduce an Expanded Parts Model (EPM) for recognizing human attributes (e.g. young, short hair, wearing suit) and actions (e.g. running, jumping) in still images. An EPM is a collection of part templates which are learnt discriminatively to explain specific scale-space regions in the images (in human centric coordinates). This is in contrast to current models which consist of a relatively few (i.e. a mixture of) 'average' templates. EPM uses only a subset of the parts to score an image and scores the image sparsely in space, i.e. it ignores redundant and random background in an image. To learn our model, we propose an algorithm which automatically mines parts and learns corresponding discriminative templates together with their respective locations from a large number of candidate parts. We validate our method on three recent challenging datasets of human attributes and actions. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.Comment: Accepted for publication in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI

The Application of Machine Learning to At-Risk Cultural Heritage Image Data

This project investigates the application of Convolutional Neural Network (CNN) methods and technologies to problems related to At-Risk cultural heritage object recognition. The primary aim for this work is the use of developmental software combining the disciplines of computer vision and artefact studies, developing applications in the field of heritage protection specifically related to the illegal antiquities market. To accomplish this digital image data provided by the Durham University Oriental Museum was used in conjunction with several different implementations of pre-trained CNN software models, for the purposes of artefact Classification and Identification. Testing focused on data capture using a variety of digital recording devices, guided by the developmental needs of a heritage programme seeking to create software solutions to heritage threats in the Middle East and North Africa (MENA) region. Quantitative data results using information retrieval metrics is reported for all model and test sets, and has been used to evaluate the models predictive results

Deep filter banks for texture recognition, description, and segmentation

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture representations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.Comment: 29 pages; 13 figures; 8 table