Publications
-
2024
PRAGO: Differentiable multi-view pose optimization from objectness detections
Matteo Taiana, Matteo Toso, Stuart James, Alessio Del Bue
International Conference on 3D Vision (3DV) | Davos, Swirzerland
2023
Inclusive Digital Storytelling: Artificial Intelligence and Augmented Reality to re-centre Stories from the Margins
Valentina Nisi, Stuart James, Paulo Bala, Alessio Del Bue, Nuno Jardim Nunes
International Conference on Interactive Digital Storytelling (ICIDS) | Kobe, Japan
As the concept of the Metaverse becomes a reality, storytelling tools sharpen their teeth to include Artificial Intelligence and Augmented Reality as prominent enabling features. While digitally savvy and privileged populations are well-positioned to use technology, marginalized groups risk being left behind and excluded from societal progress, deepening the digital divide. In this paper, we describe MEMEX, an interactive digital storytelling tool where Artificial Intelligence and Augmented Reality play enabling roles in support of the cultural integration of communities at risk of exclusion. The tool was developed in the context of 3 years EU-funded project, and in this paper, we focus on describing its final working prototype with its pilot study.
Connected to the people : Social Inclusion & Cohesion in Action through a Cultural Heritage Digital Tool
Valentina Nisi, Paulo Bala, Vanessa Cesário, Stuart James, Alessio Del Bue, and Nuno Jardim Nunes
ACM Conference On Computer-Supported Cooperative Work And Social Computing (CSCW) | Minneapolis, USA
Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models
Francesco Giuliari, Gianluca Scarpellini, Stuart James, Yiming Wang, Alessio Del Bue
arXiv | preprint
Positional reasoning is the process of ordering unsorted parts contained in a set into a consistent structure. We present Positional Diffusion, a plug-and-play graph formulation with Diffusion Probabilistic Models to address positional reasoning. We use the forward process to map elements' positions in a set to random positions in a continuous space. Positional Diffusion learns to reverse the noising process and recover the original positions through an Attention-based Graph Neural Network. We conduct extensive experiments with benchmark datasets including two puzzle datasets, three sentence ordering datasets, and one visual storytelling dataset, demonstrating that our method outperforms long-lasting research on puzzle solving with up to +18% compared to the second-best deep learning method, and performs on par against the state-of-the-art methods on sentence ordering and visual storytelling. Our work highlights the suitability of diffusion models for ordering problems and proposes a novel formulation and method for solving various ordering tasks. Project website at https://iit-pavis.github.io/Positional_Diffusion/
You are here! Finding position and orientation on a 2D map from a single image: The Flatlandia localization problem and dataset
Matteo Toso, Matteo Taiana, Stuart James, Alessio Del Bue
arXiv | preprint
We introduce Flatlandia, a novel problem for visual localization of an image from object detections composed of two specific tasks: i) Coarse Map Localization: localizing a single image observing a set of objects in respect to a 2D map of object landmarks; ii) Fine-grained 3DoF Localization: estimating latitude, longitude, and orientation of the image within a 2D map. Solutions for these new tasks exploit the wide availability of open urban maps annotated with GPS locations of common objects (\eg via surveying or crowd-sourced). Such maps are also more storage-friendly than standard large-scale 3D models often used in visual localization while additionally being privacy-preserving. As existing datasets are unsuited for the proposed problem, we provide the Flatlandia dataset, designed for 3DoF visual localization in multiple urban settings and based on crowd-sourced data from five European cities. We use the Flatlandia dataset to validate the complexity of the proposed tasks.
Locality-aware subgraphs for inductive link prediction in knowledge graphs
Hebatallah A. Mohamed, Diego Pilutti, Stuart James, Alessio Del Bue, Marcello Pelillo, Sebastiano Vascon
Pattern Recognition Letters (PR-L) | Journal
Recent methods of inductive reasoning on Knowledge Graphs (KGs) transform the link prediction problem into a graph classification task. They first extract a subgraph around each target link based on the -hop neighborhood of the target entities, encode the subgraphs using a Graph Neural Network (GNN), then learn a function that maps subgraph structural patterns to link existence. Although these methods have witnessed great successes, increasing often leads to an exponential expansion of the neighborhood, thereby degrading the GNN expressivity due to oversmoothing. In this paper, we formulate the subgraph extraction as a local clustering procedure that aims at sampling tightly-related subgraphs around the target links, based on a personalized PageRank (PPR) approach. Empirically, on three real-world KGs, we show that reasoning over subgraphs extracted by PPR-based local clustering can lead to a more accurate link prediction model than relying on neighbors within fixed hop distances. Furthermore, we investigate graph properties such as average clustering coefficient and node degree, and show that there is a relation between these and the performance of subgraph-based link prediction.2022
Writing with (Digital) Scissors: Designing a Text Editing Tool for Assisted Storytelling using Crowd-Generated Content
Paulo Bala, Stuart James, Alessio Del Bue, Valentina Nisi
International Conference on Interactive Digital Storytelling (ICIDS 2022) | Santa Cruz, USA
Digital Storytelling can exploit numerous technologies and sources of information to support the creation, refinement and enhancement of a narrative. Research on text editing tools has created novel interactions that support authors in different stages of the creative process, such as the inclusion of crowd-generated content for writing. While these interactions have the potential to change workflows, integration of these in a way that is useful and matches users’ needs is unclear. In order to investigate the space of Assisted Storytelling, we designed and conducted a study to analyze how users write and edit a story about Cultural Heritage using an auxiliary source like Wikipedia. Through a diffractive analysis of stories, creative processes, and social and cultural contexts, we reflect and derive implications for design. These were applied to develop an AI-supported text editing tool using crowd-sourced content from Wikipedia and Wikidata.
PoserNet: Refining Relative Camera Poses Exploiting Object Detections
Matteo Taiana, Matteo Toso, Stuart James, Alessio Del Bue
European Conference on Computer Vision (ECCV 2022) | Tal Aviv, Israel
The estimation of the camera poses associated with a set of images commonly relies on feature matches between the images. In contrast, we are the first to address this challenge by using objectness regions to guide the pose estimation problem rather than explicit semantic object detections. We propose Pose Refiner Network (PoserNet) a light-weight Graph Neural Network to refine the approximate pair-wise relative camera poses. PoserNet exploits associations between the objectness regions - concisely expressed as bounding boxes - across multiple views to globally refine sparsely connected view graphs. We evaluate on the 7-Scenes dataset across varied sizes of graphs and show how this process can be beneficial to optimisation-based Motion Averaging algorithms improving the median error on the rotation by 62 ◦ with respect to the initial estimates obtained based on bounding boxes. Code and data are available at github.com/IIT-PAVIS/PoserNet.@inproceedings{posernet_eccv2022,
Title = {PoserNet: Refining Relative Camera Poses Exploiting Object Detections},
Author = {Matteo Taiana and Matteo Toso and Stuart James and Alessio Del Bue},
booktitle = {Proceedings of the European Conference on Computer Vision ({ECCV})},
Year = {2022},
}
Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding
Hebatallah A. Mohamed, Sebastiano Vascon, Feliks Hibraj, Stuart James, Diego Pilutti, Alessio Del Bue, Marcello Pelillo
International Workshop on Pattern Recognition for Cultural Heritage (PatReCH 2022) | Montréal Québec
Knowledge Graphs (KGs) have proven to be a reliable way of structuring data. They can provide a rich source of contextual information about cultural heritage collections. However, cultural heritage KGs are far from being complete. They are often missing important attributes such as geographical location, especially for sculptures and mobile or indoor entities such as paintings. In this paper, we first present a framework for ingesting knowledge about tangible cultural heritage entities from various data sources and their connected multi-hop knowledge into a geolocalized KG. Secondly, we propose a multi-view learning model for estimating the relative distance between a given pair of cultural heritage entities, based on the geographical as well as the knowledge connections of the entities.
GANzzle: Reframing jigsaw puzzle solving as a retrieval task using a generative mental image
Davide Talon, Alessio Del Bue, Stuart James
IEEE International Conference on Image Processing (ICIP 2022) | Bordeaux, France`
Puzzle solving is a combinatorial challenge due to the difficulty of matching adjacent pieces. Instead, we infer a mental image from all pieces, which a given piece can then be matched against avoiding the combinatorial explosion. Exploiting advancements in Generative Adversarial methods, we learn how to reconstruct the image given a set of unordered pieces, allowing the model to learn a joint embedding space to match an encoding of each piece to the cropped layer of the generator. Therefore we frame the problem as a R@1 retrieval task, and then solve the linear assignment using differentiable Hungarian attention, making the process end-to-end. In doing so our model is puzzle size agnostic, in contrast to prior deep learning methods which are single size. We evaluate on two new large-scale datasets, where our model is on par with deep learning methods, while generalizing to multiple puzzle sizes.
Emerging Strategies in Asymmetric Sketch Interactions for Object Retrieval in Virtual Reality
Daniele Giunchi, Riccardo Bovo, Donald Degraen, Stuart James, and Anthony Steed
Interactive Media,Smart Systems and Emerging Technologiess (IMET 2022) | Cyprus
Multi-view 3D Objects Localization from Street-level Scenes
Javed Ahmad, Matteo Taiana, Matteo Toso, Stuart James, and Alessio Del Bue
International Conference on Image Analysis and Processing (ICIAP 2021) | Lecce, Italy
This paper presents a method to localize street-level objects in 3D from images of an urban area. Our method processes 3D sparse point clouds reconstructed from multi-view images and leverages 2D instance segmentation to find all objects within the scene and to generate for each object the corresponding cluster of 3D points and matched 2D detections. The proposed approach is robust to changes in image sizes, viewpoint changes, and changes in the object’s appearance across different views. We validate our approach on challenging street-level crowdsourced images from the Mapillary platform, showing a significant improvement in the mean average precision of object localization for the available Mapillary annotations. These results showcase our method’s effectiveness in localizing objects in 3D, which could potentially be used in applications such as high-definition map generation of urban environments.2021
Square peg, round hole: A case study on using Visual Question & Answering in Games
Paulo Bala, Valentina Nisi, Mara Dionı́sio, Nuno Jardim Nunes, Stuart James
CHI Play - WIP Track | Virtual
The discussion about what can Artificial Intelligence (AI) contribute to games has been running for a long time, however, recent advances in AI show promise of providing new kind of experiences for players and new tools for game developers. In contrast with the traditional Finite State Machine for interaction and response, we consider the scenario of Visual Question & Answering (VQA) - the automatic answering of a textual question about an image. VQA is a tool that can enrich possible answers by combining both visual and textual information. It is also trivial to extrapolate to a game setting without going away from the training domain. In this Work In Progress, we present two original prototypes designed to explore the potential of VQA in games and discuss preliminary findings originated through a Wizard of Oz (WOz) pilot study using VQA to investigate how people interact with such an AI algorithm.
Amnesia in the Atlantic: an AI Driven Serious Game on Marine Biodiversity
Mara Dionı́sio, Valentina Nisi, Jin Xin, Paulo Bala, Stuart James, Nuno Jardim Nunes
International Federation for Information Processing – International Conference on Entertainment Computing (IFIP-ICEC) - Work In Progress (WIP) Track | Coimbra, Portugal
The use of Conversational Interfaces has evolved rapidly in numerous fields; in particular, they are an interesting tool for Serious Games to leverage on. Conversational Interfaces can assist Serious Games' goals, namely in presenting knowledge through dialogue. With the global acknowledgment of the joint crisis in nature and climate change, it is essential to raise awareness to the fact that many ecosystems are being destroyed and that the biodiversity of our planet is at risk. Therefore in this paper, we present Amnesia in the Atlantic, a Serious Game enhanced with a Conversational Interface embracing the challenge of critically engaging players with marine biodiversity issues.
Artificial Intelligence and Art History: A Necessary Debate?
Mathieu Aubry, Lisandra Costiner, Stuart James
Histoire de l'art | Debate
Mixing Modalities of 3D Sketching and Speech for Interactive Model Retrieval in Virtual Reality
Daniele Giunchi, Alejandro Sztrajman, Stuart James, Anthony Steed
IMX'21 | New York
Sketch and speech are intuitive interaction methods that convey complementary information and have been independently used for 3D model retrieval in virtual environments. While sketch has been shown to be an effective retrieval method, not all collections are easily navigable using this modality alone. We design a new challenging database for sketch comprised of 3D chairs where each of the components (arms, legs, seat, back) are independently colored. To overcome this, we implement a multimodal interface for querying 3D model databases within a virtual environment. We base the sketch on the state-of-the-art for 3D Sketch Retrieval, and use a Wizard-of-Oz style experiment to process the voice input. In this way, we avoid the complexities of natural language processing which frequently requires fine-tuning to be robust. We conduct two user studies and show that hybrid search strategies emerge from the combination of interactions, fostering the advantages provided by both modalities.@inbook{10.1145/3452918.3458806,
author = {Giunchi, Daniele and Sztrajman, Alejandro and James, Stuart and Steed, Anthony},
title = {Mixing Modalities of 3D Sketching and Speech for Interactive Model Retrieval in Virtual Reality},
year = {2021},
isbn = {9781450383899},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3452918.3458806},
booktitle = {ACM International Conference on Interactive Media Experiences},
pages = {144–155},
numpages = {12}}
Consistent Mesh Colors for Multi-View Reconstructed 3D Scenes
Mohamed Dahy Elkhouly, Alessio Del Bue, Stuart James
arXiv | preprint
Specular highlights are commonplace in images, however, methods for detecting them and in turn removing the phenomenon are particularly challenging. A reason for this, is due to the difficulty of creating a dataset for training or evaluation, as in the real-world we lack the necessary control over the environment. Therefore, we propose a novel physically-based rendered LIGHT Specularity (LIGHTS) Dataset for the evaluation of the specular highlight detection task. Our dataset consists of 18 high quality architectural scenes, where each scene is rendered with multiple views. In total we have 2,603 views with an average of 145 views per scene. Additionally we propose a simple aggregation based method for specular highlight detection that outperforms prior work by 3.6% in two orders of magnitude less time on our dataset.
LIGHTS: LIGHT Specularity Dataset for specular detection in Multi-view
Mohamed Dahy Elkhouly, Theodore Tsesmelis, Alessio Del Bue, Stuart James
IEEE International Conference on Image Processing | Anchorage, Alaska
We address the issue of creating consistent mesh texture maps captured from scenes without color calibration. We find that the method for aggregation of the multiple views is crucial for creating spatially consistent meshes without the need to explicitly optimize for spatial consistency. We compute a color prior from the cross-correlation of observable view faces and the faces per view to identify an optimal per-face color. We then use this color in a re-weighting ratio for the best-view texture, which is identified by prior mesh texturing work, to create a spatial consistent texture map. Despite our method not explicitly handling spatial consistency, our results show qualitatively more consistent results than other state-of-the-art techniques while being computationally more efficient. We evaluate on prior datasets and additionally Matterport3D showing qualitative improvements.@inproceedings{ElkhoulyICIP21lights,
author={Elkhouly, Mohamed Dahy and Tsesmelis, Theodore and Bue, Alessio Del and James, Stuart},
booktitle={2021 IEEE International Conference on Image Processing (ICIP)},
title={Lights: Light Specularity Dataset For Specular Detection In Multi-View},
year={2021},
volume={},
number={},
pages={2908-2912},
doi={10.1109/ICIP42928.2021.9506354}}2020
Machine Learning for Cultural Heritage: A Survey
Marco Fiorucci, Marina Khoroshiltseva, Massimilano Pontil, Ariana Traviglia, Alessio Del Bue and Stuart James
Pattern Recognition Letters (PR-L) | Elsevier
The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the underlying algorithm versus using it within a ‘black box’ setting. We survey across ML and CH literature to identify the theoretical changes which contribute to the algorithm and in turn them suitable for CH applications. Alternatively, and most commonly, when there are no changes, we review the CH applications, features and pre/post-processing which make the algorithm suitable for its use. We analyse the dominant divides within ML, Supervised, Semi-supervised and Unsupervised, and reflect on a variety of algorithms that have been extensively used. From such an analysis, we give a critical look at the use of ML in CH and consider why CH has only limited adoption of ML.@article{FiorucciPRL20ml4ch, title = "Machine Learning for Cultural Heritage: A Survey",
journal = "Pattern Recognition Letters",
volume = "133",
pages = "102 - 108",
year = "2020",
issn = "0167-8655",
doi = "https://doi.org/10.1016/j.patrec.2020.02.017",
url = "http://www.sciencedirect.com/science/article/pii/S0167865520300532",
author = "Marco Fiorucci and Marina Khoroshiltseva and Massimiliano Pontil and Arianna Traviglia and Alessio [Del Bue] and Stuart James",
keywords = "Artificial Intelligence, Machine Learning, Cultural Heritage, Digital Humanities",
abstract = "The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the underlying algorithm versus using it within a ‘black box’ setting. We survey across ML and CH literature to identify the theoretical changes which contribute to the algorithm and in turn them suitable for CH applications. Alternatively, and most commonly, when there are no changes, we review the CH applications, features and pre/post-processing which make the algorithm suitable for its use. We analyse the dominant divides within ML, Supervised, Semi-supervised and Unsupervised, and reflect on a variety of algorithms that have been extensively used. From such an analysis, we give a critical look at the use of ML in CH and consider why CH has only limited adoption of ML."}2019
Mixing realities for sketch retrieval in Virtual Reality
Daniele Giunchi, Stuart James, Donald Degraen and Anthony Steed
VRCAI'19 | Brisbane, Austrailia
Users within a Virtual Environment often need support designing the environment around them with the need to find relevant content while remaining immersed. We, therefore, focus on the familiar sketch-based interaction to support the process of content placing and specifically investigate how interactions from a tablet or desktop translate into the virtual environment. To understand sketching interaction within a virtual environment, we compare different methods of sketch interaction, i.e., 3D mid-air sketching, 2D sketching on a virtual tablet, 2D sketching on a fixed virtual whiteboard, and 2D sketching on a real tablet. The user remains immersed within the environment and queries a database containing detailed 3D models and replace them into the virtual environment. Our results show that 3D mid-air sketching is considered to be a more intuitive method to search a collection of models; while the addition of physical devices creates confusion due to the complications of their inclusion within a virtual environment. While we pose our work as a retrieval problem for 3D models of chairs, our results are extendable to other sketching tasks for virtual environments.@inproceedings{GiunchiVRCAI19mixingReal,
author = {Giunchi, Daniele and James, Stuart and Degraen, Donald and Steed, Anthony},
title = {Mixing Realities for Sketch Retrieval in Virtual Reality},
year = {2019},
isbn = {9781450370028},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3359997.3365751},
doi = {10.1145/3359997.3365751},
booktitle = {The 17th International Conference on Virtual-Reality Continuum and Its Applications in Industry},
articleno = {Article 50},
numpages = {2},
keywords = {HCI, Sketch, CNN, Virtual Reality},
location = {Brisbane, QLD, Australia},
series = {VRCAI ’19}}
re-OBJ:Jointly learning the foreground and background for object instance re-identification
Viabhav Bansal, Stuart James and Alessio Del Bue
ICIAP'19 | Trento, Italy Best Student Paper Award
Conventional approaches to object instance re-identification rely on matching appearances of the target objects among a set of frames. However, learning appearances of the objects alone might fail when there are multiple objects with similar appearance or multiple instances of same object class present in the scene. This paper proposes that partial observations of the background can be utilized to aid in the object re-identification task for a rigid scene, especially a rigid environment with a lot of reoccurring identical models of objects. Using an extension to the Mask R-CNN architecture, we learn to encode the important and distinct information in the background jointly with the foreground relevant to rigid real-world scenarios such as an indoor environment where objects are static and the camera moves around the scene. We demonstrate the effectiveness of our joint visual feature in the re-identification of objects in the ScanNet dataset and show a relative improvement of around 28.25% in the rank-1 accuracy over the deepSort method.@inproceedings{BansalICIAP19reobj,
author = {Vaibhav Bansal and Stuart James and Alessio {Del Bue}},
editor = {Elisa Ricci and Samuel Rota Bul{\`{o}} and Cees Snoek and Oswald Lanz and Stefano Messelodi and Nicu Sebe},
title = {re-OBJ: Jointly Learning the Foreground and Background for Object Instance Re-identification},
booktitle = {Image Analysis and Processing - {ICIAP} 2019 - 20th International Conference,
Trento, Italy, September 9-13, 2019, Proceedings, Part{II}},
series = {Lecture Notes in Computer Science},
volume = {11752}, pages = {402--413},
publisher = {Springer},
year = {2019},
url = {https://doi.org/10.1007/978-3-030-30645-8\_37},
doi = {10.1007/978-3-030-30645-8\_37}}
Augmenting datasets for Visual Question and Answering for complex spatial reasoning
Stuart James and Alessio Del Bue
CVPR Workshop on VQA | California, USA
Autonomous 3D reconstruction, mapping and exploration of indoor environments with a robotic arm
Yiming Wang$, Stuart James,Elisavet Konstantina Stathopoulou, Carlos Beltran-Gonzalez, Yoshinori Konishi and Alessio Del Bue
IEEE Robotics and Automation Letters | Macau
We propose a novel information gain metric that combines hand-crafted and data-driven metrics to address the next best view problem for autonomous 3D mapping of unknown indoor environments. For the hand-crafted metric, we propose an entropy-based information gain that accounts for the previous view points to avoid the camera to revisit the same location and to promote the motion toward unexplored or occluded areas. Whereas for the learnt metric, we adopt a Convolutional Neural Network (CNN) architecture and formulate the problem as a classification problem. The CNN takes as input the current depth image and outputs the motion direction that suggests the largest unexplored surface. We train and test the CNN using a new synthetic dataset based on the SUNCG dataset. The learnt motion direction is then combined with the proposed hand-crafted metric to help handle situations where using only the hand-crafted metric tends to face ambiguities. We finally evaluate the autonomous paths over several real and synthetic indoor scenes including complex industrial and domestic settings and prove that our combined metric is able to further improve the exploration coverage compared to using only the proposed hand-crafted metric.@ARTICLE{WangRAL19explore, author={Y. {Wang} and S. {James} and E. K. {Stathopoulou} and C. {Beltrán-González} and Y. {Konishi} and A. {Del Bue}}, journal={IEEE Robotics and Automation Letters}, title={Autonomous 3-D Reconstruction, Mapping, and Exploration of Indoor Environments With a Robotic Arm}, year={2019}, volume={4}, number={4}, pages={3340-3347},}2018
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning
Paul Gay, Stuart James, Alessio Del Bue
ACCV'18 | Perth, Australia
Recent approaches on visual scene understanding attempt to build a scene graph -- a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single image, especially when considering complex spatial arrangements in the scene. Differently, an image sequence conveys useful information using the multi-view geometric relations arising from camera motion. Indeed, in such cases, object relationships are naturally related to the 3D scene structure. To this end, this paper proposes a system that first computes the geometrical location of objects in a generic scene and then efficiently constructs scene graphs from video by embedding such geometrical reasoning. Such compelling representation is obtained using a new model where geometric and visual features are merged using an RNN framework. We report results on a dataset we created for the task of 3D scene graph generation in multiple views.@InProceedings{GayACCV19vgfm,
author="Gay, Paul and Stuart, James and Del Bue, Alessio",
editor="Jawahar, C. V.and Li, Hongdong and Mori, Greg and Schindler, Konrad",
title="Visual Graphs from Motion (VGfM): Scene Understanding with Object Geometry Reasoning",
booktitle="Computer Vision -- ACCV 2018",
year="2019",
publisher="Springer International Publishing",address="Cham",
pages="330--346",
abstract="Recent approaches on visual scene understanding attempt to build a scene graph -- a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single image, especially when considering complex spatial arrangements in the scene. Differently, an image sequence conveys useful information using the multi-view geometric relations arising from camera motions. Indeed, object relationships are naturally related to the 3D scene structure. To this end, this paper proposes a system that first computes the geometrical location of objects in a generic scene and then efficiently constructs scene graphs from video by embedding such geometrical reasoning. Such compelling representation is obtained using a new model where geometric and visual features are merged using an RNN framework. We report results on a dataset we created for the task of 3D scene graph generation in multiple views.",
isbn="978-3-030-20893-6"}
Multi-view Aggregation for Color Naming with Shadow Detection and Removal
Mohamed Dahy Elkhouly, Stuart James, Alessio Del Bue
IPAS'18 | Nice, France Best Paper Award
This paper presents a set of methods for classifying the color attribute of objects when multiple images of the same objects are available. This problem is more complex than the single image estimation since varying environmental effects, such as, shadows or specularities from light sources, can result in poor accuracy. These depend primarily on the camera positions and the material type of the objects. Single image techniques focus on improving the discrimination of between colors, whereas in multi-view systems additional information is available but should be utilized wisely. To this end, we propose three methods to aggregate image pixel information in multi-view that boost the performance of color name classification. Moreover, we study the effect of shadows by employing automatic shadow detection and correction techniques on the color naming problem. We tested our proposals on a new multi-view color names dataset (M3DCN) which contain indoor and outdoor objects. The experimental evaluation shows that one out of the three presented aggregation methods is very efficient and it achieves the highest accuracy in term of classification results. Also, we experimentally show that addressing visual outliers like shadow in multi-view images improves the performance of the color attribute decision process.@INPROCEEDINGS{ElkhoulyIPAS18mvcolor, author={M. D. {Elkhouly} and S. {James} and A. {Del Bue}}, booktitle={2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS)}, title={Multi-view Aggregation for Color Naming with Shadow Detection and Removal}, year={2018}, volume={}, number={}, pages={115-120},}
3D Sketching for Interactive Model Retrieval in Virtual Reality
Daniele Giunchi, Stuart James, Anthony Steed
Expressive | Victoria, British Columbia, Canada
Users within a Virtual Environment often need support designing the environment around them with the need to find relevant content while remaining immersed. We, therefore, focus on the familiar sketch-based interaction to support the process of content placing and specifically investigate how interactions from a tablet or desktop translate into the virtual environment. To understand sketching interaction within a virtual environment, we compare different methods of sketch interaction, i.e., 3D mid-air sketching, 2D sketching on a virtual tablet, 2D sketching on a fixed virtual whiteboard, and 2D sketching on a real tablet. The user remains immersed within the environment and queries a database containing detailed 3D models and replace them into the virtual environment. Our results show that 3D mid-air sketching is considered to be a more intuitive method to search a collection of models; while the addition of physical devices creates confusion due to the complications of their inclusion within a virtual environment. While we pose our work as a retrieval problem for 3D models of chairs, our results are extendable to other sketching tasks for virtual environments.@inproceedings{10.1145/3229147.3229166,
author = {Giunchi, Daniele and James, Stuart and Steed, Anthony},
title = {3D Sketching for Interactive Model Retrieval in Virtual Reality},
year = {2018},
isbn = {9781450358927},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3229147.3229166},
doi = {10.1145/3229147.3229166},
booktitle = {Proceedings of the Joint Symposium on Computational Aesthetics and Sketch-Based Interfaces and Modeling and Non-Photorealistic Animation and Rendering},
articleno = {Article 1},
numpages = {12},
keywords = {HCI, sketch, virtual reality, CNN},
location = {Victoria, British Columbia, Canada},
series = {Expressive ’18}}
Model Retrieval by 3D Sketching in Immersive Virtual Reality
Daniele Giunchi, Stuart James, Anthony Steed
IEEE VR Poster | Reutlingen, Germany
We describe a novel method for searching 3D model collections using free-form sketches within a virtual environment as queries. As opposed to traditional Sketch Retrieval, our queries are drawn directly onto an example model. Using immersive virtual reality the user can express their query through a sketch that demonstrates the desired structure, color and texture. Unlike previous sketch-based retrieval methods, users remain immersed within the environment without relying on textual queries or 2D projections which can disconnect the user from the environment. We show how a convolutional neural network (CNN) can create multi-view representations of colored 3D sketches. Using such a descriptor representation, our system is able to rapidly retrieve models and in this way, we provide the user with an interactive method of navigating large object datasets. Through a preliminary user study we demonstrate that by using our VR 3D model retrieval system, users can perform quick and intuitive search. Using our system users can rapidly populate a virtual environment with specific models from a very large database,and thus the technique has the potential to be broadly applicable in immersive editing systems.2017
Texture Stationarization: Turning Photos into Tileable Textures
Joep Moritz, Stuart James, Tom S.F. Haines, Tobias Ritschel, Tim Weyrich
Computer Graphics Forum (Proc. Eurographics) | Lyon, France
Texture synthesis has grown into a mature field in computer graphics, allowing the synthesis of naturalistic textures and images from photographic exemplars. Surprisingly little work, however, has been dedicated to synthesizing tileable textures, that is, textures that when laid out in a regular grid of tiles form a homogeneous appearance suitable for use in memory-sensitive real-time graphics applications. One of the key challenges in doing so is that most natural input exemplars exhibit uneven spatial variations that, when tiled, show as repetitive patterns. We propose an approach to synthesize tileable textures while enforcing stationarity properties that effectively mask repetitions while maintaining the unique characteristics of the exemplar. We explore a number of alternative measures for texture stationarity and show how each measure can be integrated into a standard texture synthesis method (PatchMatch) to enforce stationarity at user-controlled scales. We demonstrate the efficacy of our approach using a database of 118 exemplar images, both from publicly available sources as well as new ones captured under uncontrolled conditions, and we quantitatively analyze alternative stationarity measures for their robustness across many test runs using different random seeds. In conclusion, we suggest a novel synthesis approach that employs local histogram matching to reliably turn input photographs of natural surfaces into tiles well suited for artifact-free tiling.
Digital Photographic Practices as Expressions of Personhood and Identity: Variations Across School Leavers and Recent Retirees
K Orzech, W Moncur, A Durrant, S James, J Collomosse
Journal of Visual Studies |
2016
Evolutionary Data Purification for Social Media Classification
Stuart James, John Collomosse
International Conference on Pattern Recognition (ICPR'16) | Cancun, Mexico
Towards Sketched Visual Narratives for Retrieval
Stuart James
SketchX - Human Sketch Analysis and its Applications | London, UK
2015
Visual Narratives: Free-hand Sketch for Visual Search and Navigation of Video
Stuart James
PhD Thesis | University of Surrey, Guildford, UK
2014
Enhanced Digital Literacy by Multi-modal Data Mining of the Digital Lifespan
John Collomosse, Stuart James, Abigail Durrant, Diego Trujillo-Pisanty, Wendy Moncur, Kathryn Orzech, Sarah Martindale, Mike Chantler.
DE2015 | London, UK
Interactive Video Asset Retrieval using Sketched Queries
Stuart James and John Collomosse
CVMP'14 | London
Particle Filtering approach to salient video object localization
C Gray, S James, J Collomosse and P Asente
ICIP'14 | Switzerland
ReEnact Sketch based Choreographic Design from Archival Dance Footage
S James, M Fonseca and J Collomosse
ACM International Conference on Multimedia Retrieval (ICMR'14) | Glasgow, UK
Admixed Portrait Design Intervention to Prompt Reflection on Being Online as a New Parent
D Trujillo-Pisanty, A Durrant, S Martindale, S James, J Collomosse
ACM DIS'14 |
2013
Markov Random Fields for Sketch based Video Retrieval
R Hu, S James, T Wang and J Collomosse
ACM International Conference on Multimedia Retrieval (ICMR'13) |
2012
Skeletons from Sketches of Dancing Poses
M Fonseca, S James and J Collomosse
IEEE VL/HCC'12 |
Annotated Free-hand Sketches for Video Retrieval using Object Semantics and Motion
R Hu, S James and J Collomosse
Springer ACM MultiMedia Modelling (MMM'12) |
2011
Annotated Sketches for Intuitive Video Retrieval
Stuart James and John Collomosse
BMVA / AVA Workshop on Biological and Machine Vision. Perception Journal | Cardiff, UK