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Xavier Otazu, Olivier Penacchio, & Xim Cerda-Company. (2015). An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort. In Barcelona Computational, Cognitive and Systems Neuroscience.
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Xavier Otazu, Olivier Penacchio, & Laura Dempere-Marco. (2012). An investigation into plausible neural mechanisms related to the the CIWaM computational model for brightness induction. In 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision.
Abstract: Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. From a purely computational perspective, we built a low-level computational model (CIWaM) of early sensory processing based on multi-resolution wavelets with the aim of replicating brightness and colour (Otazu et al., 2010, Journal of Vision, 10(12):5) induction effects. Furthermore, we successfully used the CIWaM architecture to define a computational saliency model (Murray et al, 2011, CVPR, 433-440; Vanrell et al, submitted to AVA/BMVA'12). From a biological perspective, neurophysiological evidence suggests that perceived brightness information may be explicitly represented in V1. In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (Li, 1999, Network:Comput. Neural Syst., 10, 187-212) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as visual saliency, which share with brightness induction the relevant effect of contextual influences (the ones modelled by CIWaM). In the proposed model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition taken from the computational model (CIWaM).
This model successfully accounts for well known pyschophysical effects (among them: the White's and modied White's effects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction effects) for static contexts and also for brigthness induction in dynamic contexts defined by modulating the luminance of surrounding areas. From a methodological point of view, we conclude that the results obtained by the computational model (CIWaM) are compatible with the ones obtained by the neurodynamical model proposed here.
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Xavier Boix, Josep M. Gonfaus, Fahad Shahbaz Khan, Joost Van de Weijer, Andrew Bagdanov, Marco Pedersoli, et al. (2009). Combining local and global bag-of-word representations for semantic segmentation. In Workshop on The PASCAL Visual Object Classes Challenge.
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Xavier Baro, Sergio Escalera, Petia Radeva, & Jordi Vitria. (2009). Generic Object Recognition in Urban Image Databases. In 12th International Conference of the Catalan Association for Artificial Intelligence (Vol. 202, pp. 27–34).
Abstract: In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (>500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. All this information is extracted without an object of reference, which allows to search for any type of objects using their visual appearance. A new Visual Content layer is built over Google Maps, allowing the object recognition information to be organized and fused with other content, like satellite images, street maps, and business locations.
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Xavier Baro, Sergio Escalera, Petia Radeva, & Jordi Vitria. (2009). Visual Content Layer for Scalable Recognition in Urban Image Databases, Internet Multimedia Search and Mining. In 10th IEEE International Conference on Multimedia and Expo (1616–1619).
Abstract: Rich online map interaction represents a useful tool to get multimedia information related to physical places. With this type of systems, users can automatically compute the optimal route for a trip or to look for entertainment places or hotels near their actual position. Standard maps are defined as a fusion of layers, where each one contains specific data such height, streets, or a particular business location. In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (> 500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. This allows an efficient and scalable way of accessing maps by visual content.
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Xavier Baro, Sergio Escalera, Isabelle Guyon, Julio C. S. Jacques Junior, Lukasz Romaszko, Lisheng Sun, et al. (2016). Coompetitions in machine learning: case studies. In 30th Annual Conference on Neural Information Processing Systems Worshops.
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Xavier Baro, Jordi Gonzalez, Junior Fabian, Miguel Angel Bautista, Marc Oliu, Hugo Jair Escalante, et al. (2015). ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) (pp. 1–9).
Abstract: Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two performed challenges and obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/.
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X. Orriols, Ricardo Toledo, X. Binefa, Petia Radeva, Jordi Vitria, & Juan J. Villanueva. (2000). Probabilistic Saliency Approach for Elongated Structure Detection using Deformable Models. In 15 th International Conference on Pattern Recognition (Vol. 3, pp. 1006–1009).
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X. Binefa, F. Javier Sanchez, F.X. Perez, Xavier Roca, Jordi Vitria, & Juan J. Villanueva. (1993). Using defocus in optical inspection of integrated circuits. In Institute of Physics Conferences Series (Vol. 135, pp. 389–392). Institute of Physics.
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Wenwen Yu, Mingyu Liu, Mingrui Chen, Ning Lu, Yinlong We, Yuliang Liu, et al. (2023). ICDAR 2023 Competition on Reading the Seal Title. In 17th International Conference on Document Analysis and Recognition (Vol. 14188, 522–535). LNCS.
Abstract: Reading seal title text is a challenging task due to the variable shapes of seals, curved text, background noise, and overlapped text. However, this important element is commonly found in official and financial scenarios, and has not received the attention it deserves in the field of OCR technology. To promote research in this area, we organized ICDAR 2023 competition on reading the seal title (ReST), which included two tasks: seal title text detection (Task 1) and end-to-end seal title recognition (Task 2). We constructed a dataset of 10,000 real seal data, covering the most common classes of seals, and labeled all seal title texts with text polygons and text contents. The competition opened on 30th December, 2022 and closed on 20th March, 2023. The competition attracted 53 participants and received 135 submissions from academia and industry, including 28 participants and 72 submissions for Task 1, and 25 participants and 63 submissions for Task 2, which demonstrated significant interest in this challenging task. In this report, we present an overview of the competition, including the organization, challenges, and results. We describe the dataset and tasks, and summarize the submissions and evaluation results. The results show that significant progress has been made in the field of seal title text reading, and we hope that this competition will inspire further research and development in this important area of OCR technology.
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Wenwen Yu, Chengquan Zhang, Haoyu Cao, Wei Hua, Bohan Li, Huang Chen, et al. (2023). ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images. In 17th International Conference on Document Analysis and Recognition (Vol. 14188, 536–552). LNCS.
Abstract: Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot/Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
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Wenjuan Gong, Jordi Gonzalez, Joao Manuel R. S. Taveres, & Xavier Roca. (2012). A New Image Dataset on Human Interactions. In 7th Conference on Articulated Motion and Deformable Objects (Vol. 7378, pp. 204–209). Springer Berlin Heidelberg.
Abstract: This article describes a new collection of still image dataset which are dedicated to interactions between people. Human action recognition from still images have been a hot topic recently, but most of them are actions performed by a single person, like running, walking, riding bikes, phoning and so on and there is no interactions between people in one image. The dataset collected in this paper are concentrating on human interaction between two people aiming to explore this new topic in the research area of action recognition from still images.
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Wenjuan Gong, Jürgen Brauer, Michael Arens, & Jordi Gonzalez. (2011). Modeling vs. Learning Approaches for Monocular 3D Human Pose Estimation. In 1st IEEE International Workshop on Performance Evaluation on Recognition of Human Actions and Pose Estimation Methods.
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Wenjuan Gong, Andrew Bagdanov, Xavier Roca, & Jordi Gonzalez. (2010). Automatic Key Pose Selection for 3D Human Action Recognition. In 6th International Conference on Articulated Motion and Deformable Objects (Vol. 6169, 290–299). Springer Verlag.
Abstract: This article describes a novel approach to the modeling of human actions in 3D. The method we propose is based on a “bag of poses” model that represents human actions as histograms of key-pose occurrences over the course of a video sequence. Actions are first represented as 3D poses using a sequence of 36 direction cosines corresponding to the angles 12 joints form with the world coordinate frame in an articulated human body model. These pose representations are then projected to three-dimensional, action-specific principal eigenspaces which we refer to as aSpaces. We introduce a method for key-pose selection based on a local-motion energy optimization criterion and we show that this method is more stable and more resistant to noisy data than other key-poses selection criteria for action recognition.
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Weijia Wu, Yuzhong Zhao, Zhuang Li, Jiahong Li, Mike Zheng Shou, Umapada Pal, et al. (2023). ICDAR 2023 Competition on Video Text Reading for Dense and Small Text. In 17th International Conference on Document Analysis and Recognition (Vol. 14188, 405–419). LNCS.
Abstract: Recently, video text detection, tracking and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenario, while ignore extreme video texts challenges, i.e., dense and small text in various scenarios. In this competition report, we establish a video text reading benchmark, named DSText, which focuses on dense and small text reading challenge in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, new challenge for video text spotter. 2) High-proportioned small texts. 3) Various new scenarios, e.g., ‘Game’, ‘Sports’, etc. The proposed DSText includes 100 video clips from 12 open scenarios, supporting two tasks (i.e., video text tracking (Task 1) and end-to-end video text spotting (Task2)). During the competition period (opened on 15th February, 2023 and closed on 20th March, 2023), a total of 24 teams participated in the three proposed tasks with around 30 valid submissions, respectively. In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols and the results summaries of the ICDAR 2023 on DSText competition. Moreover, we hope the benchmark will promise the video text research in the community.
Keywords: Video Text Spotting; Small Text; Text Tracking; Dense Text
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