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A. Pujol, Felipe Lumbreras, Javier Varona, & Juan J. Villanueva. (2000). Locating people in indoor scenes for real applications. In 15 th International Conference on Pattern Recognition (Vol. 4, pp. 632–635).
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Raul Gomez, Jaume Gibert, Lluis Gomez, & Dimosthenis Karatzas. (2020). Location Sensitive Image Retrieval and Tagging. In 16th European Conference on Computer Vision.
Abstract: People from different parts of the globe describe objects and concepts in distinct manners. Visual appearance can thus vary across different geographic locations, which makes location a relevant contextual information when analysing visual data. In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth. We present LocSens, a model that learns to rank triplets of images, tags and coordinates by plausibility, and two training strategies to balance the location influence in the final ranking. LocSens learns to fuse textual and location information of multimodal queries to retrieve related images at different levels of location granularity, and successfully utilizes location information to improve image tagging.
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A.Kesidis, & Dimosthenis Karatzas. (2014). Logo and Trademark Recognition. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 591–646). Springer London.
Abstract: The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images.
Keywords: Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems
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Diego Velazquez, Josep M. Gonfaus, Pau Rodriguez, Xavier Roca, Seiichi Ozawa, & Jordi Gonzalez. (2021). Logo Detection With No Priors. ACCESS - IEEE Access, 9, 106998–107011.
Abstract: In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors.
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2013). Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers. In 26th Canadian Conference on Artificial Intelligence (Vol. 7884, pp. 1–12). Springer Berlin Heidelberg.
Abstract: Best paper award
The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology.
Keywords: Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature
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Marçal Rusiñol, & Josep Llados. (2009). Logo Spotting by a Bag-of-words Approach for Document Categorization. In 10th International Conference on Document Analysis and Recognition (111–115).
Abstract: In this paper we present a method for document categorization which processes incoming document images such as invoices or receipts. The categorization of these document images is done in terms of the presence of a certain graphical logo detected without segmentation. The graphical logos are described by a set of local features and the categorization of the documents is performed by the use of a bag-of-words model. Spatial coherence rules are added to reinforce the correct category hypothesis, aiming also to spot the logo inside the document image. Experiments which demonstrate the effectiveness of this system on a large set of real data are presented.
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Volkmar Frinken, Francisco Zamora, Salvador España, Maria Jose Castro, Andreas Fischer, & Horst Bunke. (2012). Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition. In 21st International Conference on Pattern Recognition (pp. 701–704).
Abstract: Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models.
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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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Murad Al Haj. (2013). Looking at Faces: Detection, Tracking and Pose Estimation (Jordi Gonzalez, & Xavier Roca, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Humans can effortlessly perceive faces, follow them over space and time, and decode their rich content, such as pose, identity and expression. However, despite many decades of research on automatic facial perception in areas like face detection, expression recognition, pose estimation and face recognition, and despite many successes, a complete solution remains elusive. This thesis is dedicated to three problems in automatic face perception, namely face detection, face tracking and pose estimation.
In face detection, an initial simple model is presented that uses pixel-based heuristics to segment skin locations and hand-crafted rules to determine the locations of the faces present in an image. Different colorspaces are studied to judge whether a colorspace transformation can aid skin color detection. The output of this study is used in the design of a more complex face detector that is able to successfully generalize to different scenarios.
In face tracking, a framework that combines estimation and control in a joint scheme is presented to track a face with a single pan-tilt-zoom camera. While this work is mainly motivated by tracking faces, it can be easily applied atop of any detector to track different objects. The applicability of this method is demonstrated on simulated as well as real-life scenarios.
The last and most important part of this thesis is dedicate to monocular head pose estimation. In this part, a method based on partial least squares (PLS) regression is proposed to estimate pose and solve the alignment problem simultaneously. The contributions of this work are two-fold: 1) demonstrating that the proposed method achieves better than state-of-the-art results on the estimation problem and 2) developing a technique to reduce misalignment based on the learned PLS factors that outperform multiple instance learning (MIL) without the need for any re-training or the inclusion of misaligned samples in the training process, as normally done in MIL.
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Sergio Escalera, Jordi Gonzalez, Hugo Jair Escalante, Xavier Baro, & Isabelle Guyon. (2018). Looking at People Special Issue. IJCV - International Journal of Computer Vision, 126(2-4), 141–143.
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Valeriya Khan, Sebastian Cygert, Bartlomiej Twardowski, & Tomasz Trzcinski. (2023). Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops (pp. 3496–3500).
Abstract: In this work, we improve the generative replay in a continual learning setting. We notice that in VAE-based generative replay, the generated features are quite far from the original ones when mapped to the latent space. Therefore, we propose modifications that allow the model to learn and generate complex data. More specifically, we incorporate the distillation in latent space between the current and previous models to reduce feature drift. Additionally, a latent matching for the reconstruction and original data is proposed to improve generated features alignment. Further, based on the observation that the reconstructions are better for preserving knowledge, we add the cycling of generations through the previously trained model to make them closer to the original data. Our method outperforms other generative replay methods in various scenarios.
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Joan Serrat, Ferran Diego, & Felipe Lumbreras. (2008). Los faros delanteros a traves del objetivo. UAB Divulga, Revista de divulgacion cientifica.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Loss-Weighted Decoding for Error-Correcting Output Coding. In 3rd International Conference on Computer Vision Theory and Applications, (Vol. 2, 117–122).
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Giacomo Magnifico, Beata Megyesi, Mohamed Ali Souibgui, Jialuo Chen, & Alicia Fornes. (2022). Lost in Transcription of Graphic Signs in Ciphers. In International Conference on Historical Cryptology (HistoCrypt 2022) (pp. 153–158).
Abstract: Hand-written Text Recognition techniques with the aim to automatically identify and transcribe hand-written text have been applied to historical sources including ciphers. In this paper, we compare the performance of two machine learning architectures, an unsupervised method based on clustering and a deep learning method with few-shot learning. Both models are tested on seen and unseen data from historical ciphers with different symbol sets consisting of various types of graphic signs. We compare the models and highlight their differences in performance, with their advantages and shortcomings.
Keywords: transcription of ciphers; hand-written text recognition of symbols; graphic signs
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Onur Ferhat, & Fernando Vilariño. (2016). Low Cost Eye Tracking: The Current Panorama. CIN - Computational Intelligence and Neuroscience, , Article ID 8680541.
Abstract: Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools.
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