Svebor Karaman, Giuseppe Lisanti, Andrew Bagdanov, & Alberto del Bimbo. (2014). From re-identification to identity inference: Labeling consistency by local similarity constraints. In Person Re-Identification (Vol. 2, pp. 287–307). Springer London.
Abstract: In this chapter, we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multishot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multishot re-identification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labeled images in the gallery.
Keywords: re-identification; Identity inference; Conditional random fields; Video surveillance
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David Vazquez, Antonio Lopez, Daniel Ponsa, & Javier Marin. (2011). Virtual Worlds and Active Learning for Human Detection. In 13th International Conference on Multimodal Interaction (pp. 393–400). New York, NY, USA, USA: ACM DL.
Abstract: Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid.
Keywords: Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning
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Ruth Aylett, Ginevra Castellano, Bogdan Raducanu, Ana Paiva, & Marc Hanheide. (2011). Long-term socially perceptive and interactive robot companions: challenges and future perspectives. In 13th International Conference on Multimodal Interaction (pp. 323–326). ACM.
Abstract: This paper gives a brief overview of the challenges for multi-model perception and generation applied to robot companions located in human social environments. It reviews the current position in both perception and generation and the immediate technical challenges and goes on to consider the extra issues raised by embodiment and social context. Finally, it briefly discusses the impact of systems that must function continually over months rather than just for a few hours.
Keywords: human-robot interaction, multimodal interaction, social robotics
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Naila Murray, Sandra Skaff, Luca Marchesotti, & Florent Perronnin. (2011). Towards Automatic Concept Transfer. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering (167.176). ACM Press.
Abstract: This paper introduces a novel approach to automatic concept transfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The approach modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This approach is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. The user may adjust the intensity level of the concept transfer to his/her liking with a single parameter. The proposed approach uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. It also uses the Earth-Mover's Distance to compute a mapping between the models of the input image and the target chromatic concept. Results show that our approach yields transferred images which effectively represent concepts, as confirmed by a user study.
Keywords: chromatic modeling, color concepts, color transfer, concept transfer
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Debora Gil, Agnes Borras, Manuel Ballester, Francesc Carreras, Ruth Aris, Manuel Vazquez, et al. (2011). MIOCARDIA: Integrating cardiac function and muscular architecture for a better diagnosis. In Association for Computing Machinery (Ed.), 14th International Symposium on Applied Sciences in Biomedical and Communication Technologies. Barcelona, Spain.
Abstract: Deep understanding of myocardial structure of the heart would unravel crucial knowledge for clinical and medical procedures. The MIOCARDIA project is a multidisciplinary project in cooperation with l'Hospital de la Santa Creu i de Sant Pau, Clinica la Creu Blanca and Barcelona Supercomputing Center. The ultimate goal of this project is defining a computational model of the myocardium. The model takes into account the deep interrelation between the anatomy and the mechanics of the heart. The paper explains the workflow of the MIOCARDIA project. It also introduces a multiresolution reconstruction technique based on DT-MRI streamlining for simplified global myocardial model generation. Our reconstructions can restore the most complex myocardial structures and provides evidences of a global helical organization.
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Simone Balocco, Carlo Gatta, Xavier Carrillo, F. Mauri, & Petia Radeva. (2011). Plaque Type, Plaque Burden and Wall Shear Stress Relation in Coronary Arteries Assessed by X-ray Angiography and Intravascular Ultrasound: a Qualitative Study. In 14th International Symposium on Applied Sciences in Biomedical and Communication Technologies.
Abstract: In this paper, we present a complete framework that automatically provides fluid-dynamic and plaque analysis from IVUS and Angiographic sequences. Such framework is used to analyze, in three coronary arteries, the relation between wall shear stress with type and amount of plaque. Preliminary qualitative results show an inverse relation between the wall shear stress and the plaque burden, which is confirmed by the fact that the plaque growth is higher on the wall having concave curvature. Regarding the plaque type it was observed that regions having low shear stress are predominantly fibro-lipidic while the heavy calcifications are in general located in areas of the vessel having high WSS.
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Alicia Fornes, Volkmar Frinken, Andreas Fischer, Jon Almazan, G. Jackson, & Horst Bunke. (2011). A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors. In Proceedings of the 2011 Workshop on Historical Document Imaging and Processing (pp. 83–90). ACM.
Abstract: The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach.
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Cesar Isaza, Joaquin Salas, & Bogdan Raducanu. (2012). Synthetic ground truth dataset to detect shadow cast by static objects in outdoor. In 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications (art. 11). ACM.
Abstract: In this paper, we propose a precise synthetic ground truth dataset to study the problem of detection of the shadows cast by static objects in outdoor environments during extended periods of time (days). For our dataset, we have created a virtual scenario using a rendering software. To increase the realism of the simulated environment, we have defined the scenario in a precise geographical location. In our dataset the sun is by far the main illumination source. The sun position during the simulation time takes into consideration factors related to the geographical location, such as the latitude, longitude, elevation above sea level, and precise image capturing day and time. In our simulation the camera remains fixed. The dataset consists of seven days of simulation, from 10:00am to 5:00pm. Images are captured every 10 seconds. The shadows' ground truth is automatically computed by the rendering software.
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2013). Document noise removal using sparse representations over learned dictionary. In Symposium on Document engineering (pp. 161–168).
Abstract: best paper award
In this paper, we propose an algorithm for denoising document images using sparse representations. Following a training set, this algorithm is able to learn the main document characteristics and also, the kind of noise included into the documents. In this perspective, we propose to model the noise energy based on the normalized cross-correlation between pairs of noisy and non-noisy documents. Experimental
results on several datasets demonstrate the robustness of our method compared with the state-of-the-art.
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David Fernandez, Simone Marinai, Josep Llados, & Alicia Fornes. (2013). Contextual Word Spotting in Historical Manuscripts using Markov Logic Networks. In 2nd International Workshop on Historical Document Imaging and Processing (pp. 36–43).
Abstract: Natural languages can often be modelled by suitable grammars whose knowledge can improve the word spotting results. The implicit contextual information is even more useful when dealing with information that is intrinsically described as one collection of records. In this paper, we present one approach to word spotting which uses the contextual information of records to improve the results. The method relies on Markov Logic Networks to probabilistically model the relational organization of handwritten records. The performance has been evaluated on the Barcelona Marriages Dataset that contains structured handwritten records that summarize marriage information.
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Volkmar Frinken, Andreas Fischer, & Carlos David Martinez Hinarejos. (2013). Handwriting Recognition in Historical Documents using Very Large Vocabularies. In 2nd International Workshop on Historical Document Imaging and Processing (pp. 67–72).
Abstract: Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words.
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Sergio Escalera, Jordi Gonzalez, Xavier Baro, Miguel Reyes, Oscar Lopes, Isabelle Guyon, et al. (2013). Multi-modal Gesture Recognition Challenge 2013: Dataset and Results. In 15th ACM International Conference on Multimodal Interaction (pp. 445–452).
Abstract: The recognition of continuous natural gestures is a complex and challenging problem due to the multi-modal nature of involved visual cues (e.g. fingers and lips movements, subtle facial expressions, body pose, etc.), as well as technical limitations such as spatial and temporal resolution and unreliable
depth cues. In order to promote the research advance on this field, we organized a challenge on multi-modal gesture recognition. We made available a large video database of 13; 858 gestures from a lexicon of 20 Italian gesture categories recorded with a KinectTM camera, providing the audio, skeletal model, user mask, RGB and depth images. The focus of the challenge was on user independent multiple gesture learning. There are no resting positions and the gestures are performed in continuous sequences lasting 1-2 minutes, containing between 8 and 20 gesture instances in each sequence. As a result, the dataset contains around 1:720:800 frames. In addition to the 20 main gesture categories, ‘distracter’ gestures are included, meaning that additional audio
and gestures out of the vocabulary are included. The final evaluation of the challenge was defined in terms of the Levenshtein edit distance, where the goal was to indicate the real order of gestures within the sequence. 54 international teams participated in the challenge, and outstanding results
were obtained by the first ranked participants.
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Victor Ponce, Sergio Escalera, & Xavier Baro. (2013). Multi-modal Social Signal Analysis for Predicting Agreement in Conversation Settings. In 15th ACM International Conference on Multimodal Interaction (pp. 495–502).
Abstract: In this paper we present a non-invasive ambient intelligence framework for the analysis of non-verbal communication applied to conversational settings. In particular, we apply feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues coming from the fields of psychology and observational methodology. We test our methodology over data captured in victim-offender mediation scenarios. Using different state-of-the-art classification approaches, our system achieve upon 75% of recognition predicting agreement among the parts involved in the conversations, using as ground truth the experts opinions.
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Ariel Amato, Angel Sappa, Alicia Fornes, Felipe Lumbreras, & Josep Llados. (2013). Divide and Conquer: Atomizing and Parallelizing A Task in A Mobile Crowdsourcing Platform. In 2nd International ACM Workshop on Crowdsourcing for Multimedia (pp. 21–22).
Abstract: In this paper we present some conclusions about the advantages of having an efficient task formulation when a crowdsourcing platform is used. In particular we show how the task atomization and distribution can help to obtain results in an efficient way. Our proposal is based on a recursive splitting of the original task into a set of smaller and simpler tasks. As a result both more accurate and faster solutions are obtained. Our evaluation is performed on a set of ancient documents that need to be digitized.
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Alicia Fornes, Josep Llados, Joan Mas, Joana Maria Pujadas-Mora, & Anna Cabre. (2014). A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts. In Digital Access to Textual Cultural Heritage Conference (pp. 103–108).
Abstract: In this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts.
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