Panagiota Spyridonos, Fernando Vilariño, Jordi Vitria, Fernando Azpiroz, & Petia Radeva. (2006). Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions. In and J. Sporring M. N. R. Larsen (Ed.), 9th International Conference on Medical Image Computing and Computer–Assisted Intervention (Vol. 4191, 161–168). LNCS. Berlin Heidelberg: Springer Verlag.
Abstract: Wireless endoscopy is a very recent and at the same time unique technique allowing to visualize and study the occurrence of con- tractions and to analyze the intestine motility. Feature extraction is es- sential for getting efficient patterns to detect contractions in wireless video endoscopy of small intestine. We propose a novel method based on anisotropic image filtering and efficient statistical classification of con- traction features. In particular, we apply the image gradient tensor for mining informative skeletons from the original image and a sequence of descriptors for capturing the characteristic pattern of contractions. Fea- tures extracted from the endoluminal images were evaluated in terms of their discriminatory ability in correct classifying images as either belong- ing to contractions or not. Classification was performed by means of a support vector machine classifier with a radial basis function kernel. Our classification rates gave sensitivity of the order of 90.84% and specificity of the order of 94.43% respectively. These preliminary results highlight the high efficiency of the selected descriptors and support the feasibility of the proposed method in assisting the automatic detection and analysis of contractions.
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Sergio Vera, Miguel Angel Gonzalez Ballester, & Debora Gil. (2012). Optimal Medial Surface Generation for Anatomical Volume Representations. In MichaelW. David and Vannier H. and H. Yoshida (Ed.), Abdominal Imaging. Computational and Clinical Applications (Vol. 7601, pp. 265–273). Lecture Notes in Computer Science. Springer Berlin Heidelberg.
Abstract: Medial representations are a widely used technique in abdominal organ shape representation and parametrization. Those methods require good medial manifolds as a starting point. Any medial
surface used to parametrize a volume should be simple enough to allow an easy manipulation and complete enough to allow an accurate reconstruction of the volume. Obtaining good quality medial
surfaces is still a problem with current iterative thinning methods. This forces the usage of generic, pre-calculated medial templates that are adapted to the final shape at the cost of a drop in volume reconstruction.
This paper describes an operator for generation of medial structures that generates clean and complete manifolds well suited for their further use in medial representations of abdominal organ volumes. While being simpler than thinning surfaces, experiments show its high performance in volume reconstruction and preservation of medial surface main branching topology.
Keywords: Medial surface representation; volume reconstruction
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Fadi Dornaika, & Bogdan Raducanu. (2011). Subtle Facial Expression Recognition in Still Images and Videos. In Yu-Jin Zhang (Ed.), Advances in Face Image Analysis: Techniques and Technologies (pp. 259–277). New York, USA: IGI-Global.
Abstract: This chapter addresses the recognition of basic facial expressions. It has three main contributions. First, the authors introduce a view- and texture independent schemes that exploits facial action parameters estimated by an appearance-based 3D face tracker. they represent the learned facial actions associated with different facial expressions by time series. Two dynamic recognition schemes are proposed: (1) the first is based on conditional predictive models and on an analysis-synthesis scheme, and (2) the second is based on examples allowing straightforward use of machine learning approaches. Second, the authors propose an efficient recognition scheme based on the detection of keyframes in videos. Third, the authors compare the dynamic scheme with a static one based on analyzing individual snapshots and show that in general the former performs better than the latter. The authors then provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM).
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Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, & Stan Z Li. (2023). Face Presentation Attack Detection (PAD) Challenges. In Advances in Face Presentation Attack Detection (17–35). SLCV.
Abstract: In recent years, the security of face recognition systems has been increasingly threatened. Face Anti-spoofing (FAS) is essential to secure face recognition systems primarily from various attacks. In order to attract researchers and push forward the state of the art in Face Presentation Attack Detection (PAD), we organized three editions of Face Anti-spoofing Workshop and Competition at CVPR 2019, CVPR 2020, and ICCV 2021, which have attracted more than 800 teams from academia and industry, and greatly promoted the algorithms to overcome many challenging problems. In this chapter, we introduce the detailed competition process, including the challenge phases, timeline and evaluation metrics. Along with the workshop, we will introduce the corresponding dataset for each competition including data acquisition details, data processing, statistics, and evaluation protocol. Finally, we provide the available link to download the datasets used in the challenges.
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Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, & Stan Z Li. (2023). Best Solutions Proposed in the Context of the Face Anti-spoofing Challenge Series. In Advances in Face Presentation Attack Detection (37–78).
Abstract: The PAD competitions we organized attracted more than 835 teams from home and abroad, most of them from the industry, which shows that the topic of face anti-spoofing is closely related to daily life, and there is an urgent need for advanced algorithms to solve its application needs. Specifically, the Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round; the Chalearn Face Anti-spoofing Attack Detection Challenge attracted 340 teams in the development stage, and finally, 11 and 8 teams have submitted their codes in the single-modal and multi-modal face anti-spoofing recognition challenges, respectively; the 3D High-Fidelity Mask Face Presentation Attack Detection Challenge attracted 195 teams for the development phase with a total of 18 teams qualifying for the final round. All the results were verified and re-run by the organizing team, and the results were used for the final ranking. In this chapter, we briefly the methods developed by the teams participating in each competition, and introduce the algorithm details of the top-three ranked teams in detail.
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Jun Wan, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, & Stan Z Li. (2023). Face Anti-spoofing Progress Driven by Academic Challenges. In Advances in Face Presentation Attack Detection (1–15). SLCV.
Abstract: With the ubiquity of facial authentication systems and the prevalence of security cameras around the world, the impact that facial presentation attack techniques may have is huge. However, research progress in this field has been slowed by a number of factors, including the lack of appropriate and realistic datasets, ethical and privacy issues that prevent the recording and distribution of facial images, the little attention that the community has given to potential ethnic biases among others. This chapter provides an overview of contributions derived from the organization of academic challenges in the context of face anti-spoofing detection. Specifically, we discuss the limitations of benchmarks and summarize our efforts in trying to boost research by the community via the participation in academic challenges
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Quan-sen Sun, Pheng-ann Heng, Zhong Jin, & De-shen Xia. (2005). Face recognition based on generalized canonical correlation analysis. In Advances in Intelligent Computing, Lecture Notes in Computer Science, 3645: 958–967.
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Enric Marti, Jordi Regincos, Juan Jose Villanueva, & Jaime Lopez-Krahe. (1994). Line drawing interpretation as polyhedral objects to man-machine interaction in CAD systems. In Advances in Pattern Recognition and Image Analysis, (pp. 158–169). World Scientific Pub.
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V. Valev, & Petia Radeva. (1992). Determining Structural Description by Boolean Formulas. In H. Bunke (Ed.), Advances in Structural and Syntactic Pattern Recognition (Vol. 5, 131–140). Machine Perception and Artificial Intelligence:. World Scientific.
Abstract: Pattern recognition is an active area of research with many applications, some of which have reached commercial maturity. Structural and syntactic methods are very powerful. They are based on symbolic data structures together with matching, parsing, and reasoning procedures that are able to infer interpretations of complex input patterns.
This book gives an overview of the latest developments and achievements in the field.
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Pedro Herruzo, Marc Bolaños, & Petia Radeva. (2016). Can a CNN Recognize Catalan Diet? In AIP Conference Proceedings (Vol. 1773).
Abstract: CoRR abs/1607.08811
Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle.
With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes.
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Bogdan Raducanu, & Fadi Dornaika. (2008). Dynamic Vs. Static Recognition of Facial Expressions. In Rabuñal (Ed.), Ambient Intelligence. European Conference (Vol. 5355, 13–25). LNCS.
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Xavier Baro, & Jordi Vitria. (2008). Evolutionary Object Detection by Means of Naive Bayes Models Estimation. In M. Giacobini (Ed.), Applications of Evolutionary Computing. EvoWorkshops (Vol. 4974, 235–244). LNCS.
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Ivan Huerta, Ariel Amato, Jordi Gonzalez, & Juan J. Villanueva. (2008). Fusing Edge Cues to Handle Colour Problems in Image Segmentation. In Articulated Motion and Deformable Objects, 5th International Conference (Vol. 5098, 279–288). LNCS.
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Bhaskar Chakraborty, Marco Pedersoli, & Jordi Gonzalez. (2008). View-Invariant Human Action Detection using Component-Wise HMM of Body Parts. In Articulated Motion and Deformable Objects, 5th International Conference (Vol. 5098, 208–217). LNCS.
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Pau Baiget, Xavier Roca, & Jordi Gonzalez. (2008). Autonomous Virtual Agents for Performance Evaluation of Tracking Algorithms. In Articulated Motion and Deformable Objects, 5th International Conference AMDO 2008, (Vol. 5098, pp. 299–308). LNCS.
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