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| Author | Patricia Suarez; Henry Velesaca; Dario Carpio; Angel Sappa | ||||
| Title | Corn kernel classification from few training samples | Type | Journal | ||
| Year | 2023 | Publication | Artificial Intelligence in Agriculture | Abbreviated Journal | |
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9 | Issue | Pages | 89-99 | |
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| Abstract | This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types. | ||||
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| Notes | MSIAU | Approved | no | ||
| Call Number | Admin @ si @ SVC2023 | Serial | 3892 | ||
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| Author | Josep Llados; Enric Marti | ||||
| Title | A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems | Type | Journal Article | ||
| Year | 1999 | Publication | Machine Graphics & Vision | Abbreviated Journal | |
| Volume |
8 | Issue | Pages | 195-211 | |
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| Notes | DAG;IAM; | Approved | no | ||
| Call Number | IAM @ iam @ LIM1999 | Serial | 1568 | ||
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| Author | S. Chanda; Umapada Pal; Oriol Ramos Terrades | ||||
| Title | Word-Wise Thai and Roman Script Identification | Type | Journal | ||
| Year | 2009 | Publication | ACM Transactions on Asian Language Information Processing | Abbreviated Journal | TALIP |
| Volume |
8 | Issue | 3 | Pages | 1-21 |
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| Abstract | In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1530-0226 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ CPR2009f | Serial | 1869 | ||
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| Author | Carles Fernandez; Jordi Gonzalez; Joao Manuel R. S. Taveres; Xavier Roca | ||||
| Title | Towards Ontological Cognitive System | Type | Book Chapter | ||
| Year | 2013 | Publication | Topics in Medical Image Processing and Computational Vision | Abbreviated Journal | |
| Volume |
8 | Issue | Pages | 87-99 | |
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| Abstract | The increasing ubiquitousness of digital information in our daily lives has positioned video as a favored information vehicle, and given rise to an astonishing generation of social media and surveillance footage. This raises a series of technological demands for automatic video understanding and management, which together with the compromising attentional limitations of human operators, have motivated the research community to guide its steps towards a better attainment of such capabilities. As a result, current trends on cognitive vision promise to recognize complex events and self-adapt to different environments, while managing and integrating several types of knowledge. Future directions suggest to reinforce the multi-modal fusion of information sources and the communication with end-users. | ||||
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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 2212-9391 | ISBN | 978-94-007-0725-2 | Medium | |
| Area | Expedition | Conference | |||
| Notes | ISE; 605.203; 302.018; 600.049 | Approved | no | ||
| Call Number | Admin @ si @ FGT2013 | Serial | 2287 | ||
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| Author | Olivier Penacchio; Xavier Otazu; Laura Dempere-Marco | ||||
| Title | A Neurodynamical Model of Brightness Induction in V1 | Type | Journal Article | ||
| Year | 2013 | Publication | PloS ONE | Abbreviated Journal | Plos |
| Volume |
8 | Issue | 5 | Pages | e64086 |
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| Abstract | Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Recent neurophysiological evidence suggests that brightness information might be explicitly represented in V1, in contrast to the more common assumption that the striate cortex is an area mostly responsive to sensory information. Here we investigate possible neural mechanisms that offer a plausible explanation for such phenomenon. To this end, a neurodynamical model which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual influences is presented. The proposed computational model successfully accounts for well known psychophysical effects for static contexts and also for brightness induction in dynamic contexts defined by modulating the luminance of surrounding areas. This work suggests that intra-cortical interactions in V1 could, at least partially, explain brightness induction effects and reveals how a common general architecture may account for several different fundamental processes, such as visual saliency and brightness induction, which emerge early in the visual processing pathway. | ||||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ POD2013 | Serial | 2242 | ||
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| Author | Yagmur Gucluturk; Umut Guclu; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Marcel A. J. van Gerven; Rob van Lier | ||||
| Title | Multimodal First Impression Analysis with Deep Residual Networks | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Transactions on Affective Computing | Abbreviated Journal | TAC |
| Volume |
8 | Issue | 3 | Pages | 316-329 |
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| Abstract | People form first impressions about the personalities of unfamiliar individuals even after very brief interactions with them. In this study we present and evaluate several models that mimic this automatic social behavior. Specifically, we present several models trained on a large dataset of short YouTube video blog posts for predicting apparent Big Five personality traits of people and whether they seem suitable to be recommended to a job interview. Along with presenting our audiovisual approach and results that won the third place in the ChaLearn First Impressions Challenge, we investigate modeling in different modalities including audio only, visual only, language only, audiovisual, and combination of audiovisual and language. Our results demonstrate that the best performance could be obtained using a fusion of all data modalities. Finally, in order to promote explainability in machine learning and to provide an example for the upcoming ChaLearn challenges, we present a simple approach for explaining the predictions for job interview recommendations | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ GGB2018 | Serial | 3210 | ||
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| Author | Fei Yang; Yongmei Cheng; Joost Van de Weijer; Mikhail Mozerov | ||||
| Title | Improved Discrete Optical Flow Estimation With Triple Image Matching Cost | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume |
8 | Issue | Pages | 17093 - 17102 | |
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| Abstract | Approaches that use more than two consecutive video frames in the optical flow estimation have a long research history. However, almost all such methods utilize extra information for a pre-processing flow prediction or for a post-processing flow correction and filtering. In contrast, this paper differs from previously developed techniques. We propose a new algorithm for the likelihood function calculation (alternatively the matching cost volume) that is used in the maximum a posteriori estimation. We exploit the fact that in general, optical flow is locally constant in the sense of time and the likelihood function depends on both the previous and the future frame. Implementation of our idea increases the robustness of optical flow estimation. As a result, our method outperforms 9% over the DCFlow technique, which we use as prototype for our CNN based computation architecture, on the most challenging MPI-Sintel dataset for the non-occluded mask metric. Furthermore, our approach considerably increases the accuracy of the flow estimation for the matching cost processing, consequently outperforming the original DCFlow algorithm results up to 50% in occluded regions and up to 9% in non-occluded regions on the MPI-Sintel dataset. The experimental section shows that the proposed method achieves state-of-the-arts results especially on the MPI-Sintel dataset. | ||||
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| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ YCW2020 | Serial | 3345 | ||
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| Author | Alejandro Cartas; Petia Radeva; Mariella Dimiccoli | ||||
| Title | Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume |
8 | Issue | Pages | 77344 - 77363 | |
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| Abstract | Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications. | ||||
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ CRD2020 | Serial | 3436 | ||
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| Author | Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Esmitt Ramirez; Carles Sanchez | ||||
| Title | Intraoperative Extraction of Airways Anatomy in VideoBronchoscopy | Type | Journal Article | ||
| Year | 2020 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume |
8 | Issue | Pages | 159696 - 159704 | |
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| Abstract | A main bottleneck in bronchoscopic biopsy sampling is to efficiently reach the lesion navigating across bronchial levels. Any guidance system should be able to localize the scope position during the intervention with minimal costs and alteration of clinical protocols. With the final goal of an affordable image-based guidance, this work presents a novel strategy to extract and codify the anatomical structure of bronchi, as well as, the scope navigation path from videobronchoscopy. Experiments using interventional data show that our method accurately identifies the bronchial structure. Meanwhile, experiments using simulated data verify that the extracted navigation path matches the 3D route. | ||||
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| Notes | IAM; 600.139; 600.145 | Approved | no | ||
| Call Number | Admin @ si @ GEB2020 | Serial | 3467 | ||
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| Author | G. Gasbarri; Matias Bilkis; E. Roda Salichs; J. Calsamiglia | ||||
| Title | Sequential hypothesis testing for continuously-monitored quantum systems | Type | Journal Article | ||
| Year | 2024 | Publication | Quantum | Abbreviated Journal | |
| Volume |
8 | Issue | 1289 | Pages | |
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| Abstract | We consider a quantum system that is being continuously monitored, giving rise to a measurement signal. From such a stream of data, information needs to be inferred about the underlying system's dynamics. Here we focus on hypothesis testing problems and put forward the usage of sequential strategies where the signal is analyzed in real time, allowing the experiment to be concluded as soon as the underlying hypothesis can be identified with a certified prescribed success probability. We analyze the performance of sequential tests by studying the stopping-time behavior, showing a considerable advantage over currently-used strategies based on a fixed predetermined measurement time. | ||||
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| Notes | xxxx | Approved | no | ||
| Call Number | Admin @ si @ GBR2024 | Serial | 3847 | ||
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| Author | Jose Manuel Alvarez; Antonio Lopez | ||||
| Title | Photometric Invariance by Machine Learning | Type | Book Chapter | ||
| Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
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7 | Issue | Pages | 113-134 | |
| Keywords | road detection | ||||
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| Publisher | iConcept Press Ltd | Place of Publication | Editor | Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek | |
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| ISSN | ISBN | 978-0-470-89084-4 | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ AlL2012 | Serial | 2186 | ||
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| Author | Onur Ferhat; Fernando Vilariño; F. Javier Sanchez | ||||
| Title | A cheap portable eye-tracker solution for common setups. | Type | Journal Article | ||
| Year | 2014 | Publication | Journal of Eye Movement Research | Abbreviated Journal | JEMR |
| Volume |
7 | Issue | 3 | Pages | 1-10 |
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| Abstract | We analyze the feasibility of a cheap eye-tracker where the hardware consists of a single webcam and a Raspberry Pi device. Our aim is to discover the limits of such a system and to see whether it provides an acceptable performance. We base our work on the open source Opengazer (Zielinski, 2013) and we propose several improvements to create a robust, real-time system which can work on a computer with 30Hz sampling rate. After assessing the accuracy of our eye-tracker in elaborated experiments involving 12 subjects under 4 different system setups, we install it on a Raspberry Pi to create a portable stand-alone eye-tracker which achieves 1.42° horizontal accuracy with 3Hz refresh rate for a building cost of 70 Euros. | ||||
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| Notes | ;SIAI | Approved | no | ||
| Call Number | Admin @ si @ FVS2014 | Serial | 2435 | ||
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| Author | L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink | ||||
| Title | A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting | Type | Journal | ||
| Year | 2014 | Publication | Manuscript Cultures | Abbreviated Journal | |
| Volume |
7 | Issue | Pages | 47-58 | |
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| Abstract | With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline. |
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| Notes | DAG; 600.061; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3190 | ||
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| Author | Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Farhan Akram; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig | ||||
| Title | Recognizing Food Places in Egocentric Photo-Streams Using Multi-Scale Atrous Convolutional Networks and Self-Attention Mechanism | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume |
7 | Issue | Pages | 39069-39082 | |
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| Abstract | Wearable sensors (e.g., lifelogging cameras) represent very useful tools to monitor people's daily habits and lifestyle. Wearable cameras are able to continuously capture different moments of the day of their wearers, their environment, and interactions with objects, people, and places reflecting their personal lifestyle. The food places where people eat, drink, and buy food, such as restaurants, bars, and supermarkets, can directly affect their daily dietary intake and behavior. Consequently, developing an automated monitoring system based on analyzing a person's food habits from daily recorded egocentric photo-streams of the food places can provide valuable means for people to improve their eating habits. This can be done by generating a detailed report of the time spent in specific food places by classifying the captured food place images to different groups. In this paper, we propose a self-attention mechanism with multi-scale atrous convolutional networks to generate discriminative features from image streams to recognize a predetermined set of food place categories. We apply our model on an egocentric food place dataset called “EgoFoodPlaces” that comprises of 43 392 images captured by 16 individuals using a lifelogging camera. The proposed model achieved an overall classification accuracy of 80% on the “EgoFoodPlaces” dataset, respectively, outperforming the baseline methods, such as VGG16, ResNet50, and InceptionV3. | ||||
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| Notes | MILAB; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SRA2019 | Serial | 3296 | ||
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| Author | Jiaolong Xu; Liang Xiao; Antonio Lopez | ||||
| Title | Self-supervised Domain Adaptation for Computer Vision Tasks | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
| Volume |
7 | Issue | Pages | 156694 - 156706 | |
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| Abstract | Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link. https://github.com/Jiaolong/self-supervised-da. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ XXL2019 | Serial | 3302 | ||
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