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Oriol Rodriguez-Leor, E. Fernandez-Nofrerias, J. Mauri, C. Garcia, R. Villuendas, V. Valle, et al. (2003). Intravascular ultrasound segmentation using local binary patterns. European Heart Journal (IF: 5.997), ESC Congress 2003.
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Oriol Rodriguez-Leor, E. Fernandez-Nofrerias, J. Mauri, R. Villuendas, C. Garcia, V. Valle, et al. (2003). An empiric model for three-dimensional reconstruction of coronary vessels from X-ray angiography. European Heart Journal (IF: 5.997), ESC Congress 2003.
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Oriol Rodriguez-Leor, J. Mauri, E. Fernandez-Nofrerias, J. Lopez, M. Gomez, V. Valle, et al. (2003). Coronary arteries three-dimensional quantification using intravascular ultrasound and angiography. European Heart Journal (IF: 5.997), ESC Congress 2003.
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Oriol Rodriguez-Leor, J. Mauri, Eduard Fernandez-Nofrerias, Antonio Tovar, Vicente del Valle, Aura Hernandez-Sabate, et al. (2004). Utilizacion de la estructura de los campos vectoriales para la deteccion de la Adventicia en imagenes de Ecografia Intracoronaria. REC - Revista Española de Cardiología, 100.
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Oriol Rodriguez-Leor, J. Mauri, Eduard Fernandez-Nofrerias, M. Gomez, Antonio Tovar, L. Cano, et al. (2002). Ecografia Intracoronaria: Segmentacio Automatica de area de la llum. Revista Societat Catalana de Cardiologia, 42.
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Patricia Suarez, Henry Velesaca, Dario Carpio, & Angel Sappa. (2023). Corn kernel classification from few training samples. Artificial Intelligence in Agriculture, 89–99.
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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Patrick Brandao, O. Zisimopoulos, E. Mazomenos, G. Ciutib, Jorge Bernal, M. Visentini-Scarzanell, et al. (2018). Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks. JMRR - Journal of Medical Robotics Research.
Abstract: Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp
detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance.
Keywords: convolutional neural networks; colonoscopy; computer aided diagnosis
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Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, & Xavier Roca. (2019). Integrating Vision and Language in Social Networks for Identifying Visual Patterns of Personality Traits. IJSSH - International Journal of Social Science and Humanity, 6–12.
Abstract: Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. In this sense, user text interactions are widely used to sense the whys of certain social user’s demands and cultural- driven interests. However, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited. Following this trend on visual-based social analysis, we present a novel methodology based on neural networks to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So, the key contribution in this work is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between posted images and the personality estimated from their accompanying texts. Thus, the experimental results are consistent with previous cyber-psychology results based on texts, suggesting that images could also be used for personality estimation: classification results on some personality traits show that specific and characteristic visual patterns emerge, in essence representing abstract concepts. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts, and to further substitute current textual personality questionnaires by image-based ones.
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Petia Radeva, & Jordi Vitria. (2004). Corkinspect: Statistical Learning of Natural Material. Italian Beverage Technology, 13(38):11–18.
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R. Clariso, David Masip, & A. Rius. (2014). Student projects empowering mobile learning in higher education. RUSC - Revista de Universidad y Sociedad del Conocimiento, 192–207.
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Razieh Rastgoo, Kourosh Kiani, & Sergio Escalera. (2022). Real-time Isolated Hand Sign Language RecognitioN Using Deep Networks and SVD. Journal of Ambient Intelligence and Humanized Computing, 591–611.
Abstract: One of the challenges in computer vision models, especially sign language, is real-time recognition. In this work, we present a simple yet low-complex and efficient model, comprising single shot detector, 2D convolutional neural network, singular value decomposition (SVD), and long short term memory, to real-time isolated hand sign language recognition (IHSLR) from RGB video. We employ the SVD method as an efficient, compact, and discriminative feature extractor from the estimated 3D hand keypoints coordinators. Despite the previous works that employ the estimated 3D hand keypoints coordinates as raw features, we propose a novel and revolutionary way to apply the SVD to the estimated 3D hand keypoints coordinates to get more discriminative features. SVD method is also applied to the geometric relations between the consecutive segments of each finger in each hand and also the angles between these sections. We perform a detailed analysis of recognition time and accuracy. One of our contributions is that this is the first time that the SVD method is applied to the hand pose parameters. Results on four datasets, RKS-PERSIANSIGN (99.5±0.04), First-Person (91±0.06), ASVID (93±0.05), and isoGD (86.1±0.04), confirm the efficiency of our method in both accuracy (mean+std) and time recognition. Furthermore, our model outperforms or gets competitive results with the state-of-the-art alternatives in IHSLR and hand action recognition.
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2008). Parametric Fuzzy Sets for Automatic Color Naming. Journal of the Optical Society of America A, 2582–2593.
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2006). A data set for fuzzy colour naming. Color Research & Application, 31(1):48–56.
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2004). Estimation of Fuzzy Sets for Computational Colour Categorization. Color Research and Application, 29(5):342–353 (IF: 0.739).
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Roger Max Calle Quispe, Maya Aghaei Gavari, & Eduardo Aguilar Torres. (2023). Towards real-time accurate safety helmets detection through a deep learning-based method. Ingeniare. Revista chilena de ingenieria.
Abstract: Occupational safety is a fundamental activity in industries and revolves around the management of the necessary controls that must be present to mitigate occupational risks. These controls include verifying the use of Personal Protection Equipment (PPE). Within PPE, safety helmets are vital to reducing severe or fatal consequences caused by head injuries. This problem has been addressed recently by various research based on deep learning to detect the usage of safety helmets by the present people in the industrial field.
These works have achieved promising results for safety helmet detection using object detection methods from the YOLO family. In this work, we propose to analyze the performance of Scaled-YOLOv4, a novel model of the YOLO family that has yet to be previously studied for this problem. The performance of the Scaled-YOLOv4 is evaluated on two public databases, carefully selected among the previously proposed datasets for the occupational safety framework. We demonstrate the superiority of Scaled-YOLOv4 in terms of mAP and Fl-score concerning the previous works for both databases. Further, we summarize the currently available datasets for safety helmet detection purposes and discuss their suitability.
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