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Enric Marti, Debora Gil, & Carme Julia. (2005). Una experiència en PBL per a la docència de Gràfics per Computador.
Abstract: En aquest article es presenta una experiència en ABP feta el curs 2004-05 en Gràfics per Computador 2, assignatura optativa de 3er curs d’Enginyeria Informàtica impartida a l’ETSE. En l’article s’explica l’organització docent abans d’ABP, basada en classes magistrals. Després es mostra l’organització en ABP i es quantifica en ECTS l’esforç de l’alumne en ambdues organitzacions. Essent conscient del diferent interès de l’alumnat per l’assignatura, se’ls hi ofereix dos itineraris: el de classes magistrals i d’ABP. Es mostren alguns resultats dels alumnes d’ABP i també les primeres enquestes realitzades als alumnes. S’exposen les conclusions en el primer any de l’experiència, plantejant temes de discussió. S’ha procurat que la proposta no desbordi l’esforç del professorat. Per això s’ofereix el doble itinerari, per a canalitzar per ABP els alumnes més interessats i permetre a la resta que realitzin el curs amb l’organització clàsica de l’assignatura: classes magistrals de teoria, problemes i pràctiques.
Keywords: Aprenentatge Basat en Projectes; Aprenentatge Basat en Problemes; Problem Based Learning; ECTS; EEES; Computer Graphics; OpenGL.
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Albert Andaluz, Francesc Carreras, Debora Gil, & Jaume Garcia. (2010). Una aplicació amigable pel càlcul de indicadors clínics del ventricle esquerre. Barcelona: Biocat.
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Jaume Garcia, Debora Gil, Francesc Carreras, Sandra Pujades, R.Leta, Xavier Alomar, et al. (2008). Un Model 3D del Ventricle Esquerre Integrant Anatomia i Funcionalitat. In XX Congrés de la Societat Catalana de Cardiologia, Actes del Congres (122). Barcelona.
Abstract: Els canvis en la dinàmica del Ventricle Esquerre (VE) reflecteixen la majoria de malalties cardiovasculars . Els avenços en imatge mèdica han impulsat la recerca en models i simulacions de la dinàmica 3D del VE . La majoria dels models existents sols consideren l’anatomia externa del VE i no permeten una avaluació de l’acoblament electromecànic . Donat que la mecànica d’un muscle depèn de la orientació de les seves fibres, un model realista hauria d’incloure la disposició espacial de la banda ventricular helicoidal (BVH) .
Proposem desenvolupar un model del VE adaptat a cada pacient que integri, per primer cop, l’anatomia de la banda ventricular, l’anatomia externa del VE i la seva funcionalitat, per a una millor determinació del patró d’activació electromecànica
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Francisco Jose Perales, Yuhua Luo, & Juan J. Villanueva. (1991). Un metodo Automatico de Rotoscopia Sin Marcas para el Estudio del Movimiento Humano Basado en un modelo Biomecanico. In Primer Congreso Español de Informatica Grafica (pp. 53–65).
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R. Xandri. (2002). Un metode de vectoritzacio basat en l’aprimament.
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J. Queralt. (2000). Un metode de vectoritzacio basat en la correspondencia de contorns i en la Teoria de Grafs.
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Javier Jimenez, Antonio Lopez, & Joan Serrat. (2007). Un enfoque ABP aplicado a Ingenieria del Software.
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M. Bressan. (2001). Un analisis de viabilidad para la confeccion semisupervisada de un mapa de usos del suelo de Catalunya.
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Joan Oliver, Ricardo Toledo, J. Pujol, J. Sorribes, & E. Valderrama. (2009). Un ABP basado en la robotica para las ingenierias informaticas.
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Marta Diez-Ferrer, Arturo Morales, Rosa Lopez Lisbona, Noelia Cubero, Cristian Tebe, Susana Padrones, et al. (2019). Ultrathin Bronchoscopy with and without Virtual Bronchoscopic Navigation: Influence of Segmentation on Diagnostic Yield. RES - Respiration, 97(3), 252–258.
Abstract: Background: Bronchoscopy is a safe technique for diagnosing peripheral pulmonary lesions (PPLs), and virtual bronchoscopic navigation (VBN) helps guide the bronchoscope to PPLs. Objectives: We aimed to compare the diagnostic yield of VBN-guided and unguided ultrathin bronchoscopy (UTB) and explore clinical and technical factors associated with better results. We developed a diagnostic algorithm for deciding whether to use VBN to reach PPLs or choose an alternative diagnostic approach. Methods: We compared diagnostic yield between VBN-UTB (prospective cases) and unguided UTB (historical controls) and analyzed the VBN-UTB subgroup to identify clinical and technical variables that could predict the success of VBN-UTB. Results: Fifty-five cases and 110 controls were included. The overall diagnostic yield did not differ between the VBN-guided and unguided arms (47 and 40%, respectively; p = 0.354). Although the yield was slightly higher for PPLs ≤20 mm in the VBN-UTB arm, the difference was not significant (p = 0.069). No other clinical characteristics were associated with a higher yield in a subgroup analysis, but an 85% diagnostic yield was observed when segmentation was optimal and the PPL was endobronchial (vs. 30% when segmentation was suboptimal and 20% when segmentation was optimal but the PPL was extrabronchial). Conclusions: VBN-guided UTB is not superior to unguided UTB. A greater impact of VBN-guided over unguided UTB is highly dependent on both segmentation quality and an endobronchial location of the PPL. Segmentation quality should be considered before starting a procedure, when an alternative technique that may improve yield can be chosen, saving time and resources.
Keywords: Lung cancer; Peripheral lung lesion; Diagnosis; Bronchoscopy; Ultrathin bronchoscopy; Virtual bronchoscopic navigation
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David Lloret, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2003). Ultrasound to MR Volume Registration for Brain Sinking Measurement.
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David Lloret, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2003). Ultrasound to magnetic resonance volume registration for brain sinking measurement.
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Jose Seabra, F. Javier Sanchez, Francesco Ciompi, & Petia Radeva. (2010). Ultrasonographic Plaque Characterization using a Rayleigh Mixture Model. In 7th IEEE International Symposium on Biomedical Imaging (1–4).
Abstract: From Nano to Macro
A correct modelling of tissue morphology is determinant for the identification of vulnerable plaques. This paper aims at describing the plaque composition by means of a Rayleigh Mixture Model applied to ultrasonic data. The effectiveness of using a mixture of distributions is established through synthetic and real ultrasonic data samples. Furthermore, the proposed mixture model is used in a plaque classification problem in Intravascular Ultrasound (IVUS) images of coronary plaques. A classifier tested on a set of 67 in-vitro plaques, yields an overall accuracy of 86% and sensitivity of 92%, 94% and 82%, for fibrotic, calcified and lipidic tissues, respectively. These results strongly suggest that different plaques types can be distinguished by means of the coefficients and Rayleigh parameters of the mixture distribution.
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Dorota Kaminska, Kadir Aktas, Davit Rizhinashvili, Danila Kuklyanov, Abdallah Hussein Sham, Sergio Escalera, et al. (2021). Two-stage Recognition and Beyond for Compound Facial Emotion Recognition. ELEC - Electronics, 10(22), 2847.
Abstract: Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.
Keywords: compound emotion recognition; facial expression recognition; dominant and complementary emotion recognition; deep learning
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Ayan Banerjee, Palaiahnakote Shivakumara, Parikshit Acharya, Umapada Pal, & Josep Llados. (2022). TWD: A New Deep E2E Model for Text Watermark Detection in Video Images. In 26th International Conference on Pattern Recognition.
Abstract: Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge
Keywords: Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection
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