| Records |
| Author |
Eduard Vazquez |
Title  |
Unsupervised image segmentation based on material reflectance description and saliency |
Type |
Book Whole |
| Year |
2011 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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| Abstract |
Image segmentations aims to partition an image into a set of non-overlapped regions, called segments. Despite the simplicity of the definition, image segmentation raises as a very complex problem in all its stages. The definition of segment is still unclear. When asking to a human to perform a segmentation, this person segments at different levels of abstraction. Some segments might be a single, well-defined texture whereas some others correspond with an object in the scene which might including multiple textures and colors. For this reason, segmentation is divided in bottom-up segmentation and top-down segmentation. Bottom up-segmentation is problem independent, that is, focused on general properties of the images such as textures or illumination. Top-down segmentation is a problem-dependent approach which looks for specific entities in the scene, such as known objects. This work is focused on bottom-up segmentation. Beginning from the analysis of the lacks of current methods, we propose an approach called RAD. Our approach overcomes the main shortcomings of those methods which use the physics of the light to perform the segmentation. RAD is a topological approach which describes a single-material reflectance. Afterwards, we cope with one of the main problems in image segmentation: non supervised adaptability to image content. To yield a non-supervised method, we use a model of saliency yet presented in this thesis. It computes the saliency of the chromatic transitions of an image by means of a statistical analysis of the images derivatives. This method of saliency is used to build our final approach of segmentation: spRAD. This method is a non-supervised segmentation approach. Our saliency approach has been validated with a psychophysical experiment as well as computationally, overcoming a state-of-the-art saliency method. spRAD also outperforms state-of-the-art segmentation techniques as results obtained with a widely-used segmentation dataset show |
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Thesis |
Ph.D. thesis |
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Editor |
Ramon Baldrich |
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CIC |
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no |
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Admin @ si @ Vaz2011b |
Serial |
1835 |
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| Author |
David Guillamet; Jordi Vitria |
| Title |
Unsupervised Learning of Part-Based Representations |
Type |
Miscellaneous |
| Year |
2001 |
Publication |
Proceedings 9th International Conference CAIP 2001,700–708, W. Skarbek (Ed.): Computer Analysis of Images and Patterns, LNCS 2059, Springer Verlag, 123–134. |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ GVi2001b |
Serial |
106 |
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| Author |
David Guillamet; Jordi Vitria |
| Title |
Unsupervised Learning of Structural Object Representations |
Type |
Miscellaneous |
| Year |
2001 |
Publication |
Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 2:73–78 |
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OR;MV |
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BCNPCL @ bcnpcl @ GuV2001b |
Serial |
102 |
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| Author |
Miguel Oliveira; Angel Sappa; V.Santos |
| Title |
Unsupervised Local Color Correction for Coarsely Registered Images |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
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Issue |
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Pages |
201-208 |
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| Abstract |
The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time. |
| Address |
Colorado Springs |
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| ISSN |
1063-6919 |
ISBN |
978-1-4577-0394-2 |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ OSS2011; ADAS @ adas @ |
Serial |
1766 |
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| Author |
Angel Sappa; Niki Aifanti; Sotiris Malassiotis; Michael G. Strintzis |
| Title |
Unsupervised Motion Classification by means of Efficient Feature Selection and Tracking |
Type |
Miscellaneous |
| Year |
2004 |
Publication |
IEEE Int. Symp. on 3D Data Processing, Visualization and Transmission |
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Thessaloniki (Greece) |
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no |
| Call Number |
ADAS @ adas @ SAM2004a |
Serial |
456 |
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| Author |
Daniel Ponsa; Xavier Roca |
| Title |
Unsupervised Parameterisation of Gaussian Mixture Models |
Type |
Miscellaneous |
| Year |
2002 |
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ADAS;ISE |
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no |
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ADAS @ adas @ PoR2002c |
Serial |
313 |
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| Author |
Estefania Talavera; Nicolai Petkov; Petia Radeva |
| Title |
Unsupervised Routine Discovery in Egocentric Photo-Streams |
Type |
Conference Article |
| Year |
2019 |
Publication |
18th International Conference on Computer Analysis of Images and Patterns |
Abbreviated Journal |
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| Volume |
11678 |
Issue |
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Pages |
576-588 |
| Keywords |
Routine discovery; Lifestyle; Egocentric vision; Behaviour analysis |
| Abstract |
The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people. |
| Address |
Salermo; Italy; September 2019 |
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LNCS |
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CAIP |
| Notes |
MILAB; no proj |
Approved |
no |
| Call Number |
Admin @ si @ TPR2019a |
Serial |
3367 |
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| Author |
Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew Bagdanov; Alberto del Bimbo |
| Title |
Unsupervised scene adaptation for faster multi- scale pedestrian detection |
Type |
Conference Article |
| Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
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Pages |
3534 - 3539 |
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| Address |
Stockholm; Sweden; August 2014 |
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Conference |
ICPR |
| Notes |
LAMP; 600.079 |
Approved |
no |
| Call Number |
Admin @ si @ BLK2014 |
Serial |
2519 |
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| Author |
Lluis Pere de las Heras; David Fernandez; Ernest Valveny; Josep Llados; Gemma Sanchez |
| Title |
Unsupervised wall detector in architectural floor plan |
Type |
Conference Article |
| Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
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Pages |
1245-1249 |
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| Abstract |
Wall detection in floor plans is a crucial step in a complete floor plan recognition system. Walls define the main structure of buildings and convey essential information for the detection of other structural elements. Nevertheless, wall segmentation is a difficult task, mainly because of the lack of a standard graphical notation. The existing approaches are restricted to small group of similar notations or require the existence of pre-annotated corpus of input images to learn each new notation. In this paper we present an automatic wall segmentation system, with the ability to handle completely different notations without the need of any annotated dataset. It only takes advantage of the general knowledge that walls are a repetitive element, naturally distributed within the plan and commonly modeled by straight parallel lines. The method has been tested on four datasets of real floor plans with different notations, and compared with the state-of-the-art. The results show its suitability for different graphical notations, achieving higher recall rates than the rest of the methods while keeping a high average precision. |
| Address |
Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
| Notes |
DAG; 600.061; 600.056; 600.045 |
Approved |
no |
| Call Number |
Admin @ si @ HFV2013 |
Serial |
2319 |
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| Author |
Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
| Title |
Unsupervised writer adaptation of whole-word HMMs with application to word-spotting |
Type |
Journal Article |
| Year |
2010 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
31 |
Issue |
8 |
Pages |
742–749 |
| Keywords |
Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis |
| Abstract |
In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.
Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition. |
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Elsevier |
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DAG |
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no |
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DAG @ dag @ RPS2010 |
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1290 |
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| Author |
Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
| Title |
Unsupervised writer style adaptation for handwritten word spotting |
Type |
Conference Article |
| Year |
2008 |
Publication |
Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award. |
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Tampa, USA |
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ICPR |
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DAG |
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no |
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DAG @ dag @ RPS2008 |
Serial |
1077 |
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| Author |
Mohamed Ramzy Ibrahim; Robert Benavente; Daniel Ponsa; Felipe Lumbreras |
| Title |
Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification |
Type |
Conference Article |
| Year |
2023 |
Publication |
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications |
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14469 |
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Pages |
214–228 |
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Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications. |
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CIARP |
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MSIAU |
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no |
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Admin @ si @ IBP2023 |
Serial |
4008 |
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| Author |
Fernando Vilariño |
| Title |
Unveiling the Social Impact of AI |
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Conference Article |
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2020 |
Publication |
Workshop at Digital Living Lab Days Conference |
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September 2020 |
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MV; DAG; 600.121; 600.140;SIAI |
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no |
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Admin @ si @ Vil2020 |
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3459 |
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Mickael Cormier; Andreas Specker; Julio C. S. Jacques; Lucas Florin; Jurgen Metzler; Thomas B. Moeslund; Kamal Nasrollahi; Sergio Escalera; Jurgen Beyerer |
| Title |
UPAR Challenge: Pedestrian Attribute Recognition and Attribute-based Person Retrieval – Dataset, Design, and Results |
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Conference Article |
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2023 |
Publication |
2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops |
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166-175 |
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In civilian video security monitoring, retrieving and tracking a person of interest often rely on witness testimony and their appearance description. Deployed systems rely on a large amount of annotated training data and are expected to show consistent performance in diverse areas and gen-eralize well between diverse settings w.r.t. different view-points, illumination, resolution, occlusions, and poses for indoor and outdoor scenes. However, for such generalization, the system would require a large amount of various an-notated data for training and evaluation. The WACV 2023 Pedestrian Attribute Recognition and Attributed-based Per-son Retrieval Challenge (UPAR-Challenge) aimed to spot-light the problem of domain gaps in a real-world surveil-lance context and highlight the challenges and limitations of existing methods. The UPAR dataset, composed of 40 important binary attributes over 12 attribute categories across four datasets, was extended with data captured from a low-flying UAV from the P-DESTRE dataset. To this aim, 0.6M additional annotations were manually labeled and vali-dated. Each track evaluated the robustness of the competing methods to domain shifts by training on limited data from a specific domain and evaluating using data from unseen do-mains. The challenge attracted 41 registered participants, but only one team managed to outperform the baseline on one track, emphasizing the task's difficulty. This work de-scribes the challenge design, the adopted dataset, obtained results, as well as future directions on the topic. |
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Waikoloa; Hawai; USA; January 2023 |
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WACVW |
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HUPBA |
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no |
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Admin @ si @ CSJ2023 |
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3902 |
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| Author |
Aniol Lidon; Xavier Giro; Marc Bolaños; Petia Radeva; Markus Seidl; Matthias Zeppelzauer |
| Title |
UPC-UB-STP @ MediaEval 2015 diversity task: iterative reranking of relevant images |
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Conference Article |
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2015 |
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2015 MediaEval Retrieving Diverse Images Task |
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This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task. The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity. |
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Wurzen; Germany; September 2015 |
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MediaEval |
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MILAB |
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no |
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Admin @ si @LGB2016 |
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2793 |
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