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Author |
Javier Vazquez; Graham D. Finlayson; Luis Herranz |
![goto web page url](img/www.gif)
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Title |
Improving the perception of low-light enhanced images |
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Journal Article |
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Year |
2024 |
Publication |
Optics Express |
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32 |
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4 |
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5174-5190 |
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Improving images captured under low-light conditions has become an important topic in computational color imaging, as it has a wide range of applications. Most current methods are either based on handcrafted features or on end-to-end training of deep neural networks that mostly focus on minimizing some distortion metric —such as PSNR or SSIM— on a set of training images. However, the minimization of distortion metrics does not mean that the results are optimal in terms of perception (i.e. perceptual quality). As an example, the perception-distortion trade-off states that, close to the optimal results, improving distortion results in worsening perception. This means that current low-light image enhancement methods —that focus on distortion minimization— cannot be optimal in the sense of obtaining a good image in terms of perception errors. In this paper, we propose a post-processing approach in which, given the original low-light image and the result of a specific method, we are able to obtain a result that resembles as much as possible that of the original method, but, at the same time, giving an improvement in the perception of the final image. More in detail, our method follows the hypothesis that in order to minimally modify the perception of an input image, any modification should be a combination of a local change in the shading across a scene and a global change in illumination color. We demonstrate the ability of our method quantitatively using perceptual blind image metrics such as BRISQUE, NIQE, or UNIQUE, and through user preference tests. |
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MACO |
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no |
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Admin @ si @ VFH2024 |
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4018 |
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Author |
Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang |
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Historical Cryptology |
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2024 |
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Learning and Experiencing Cryptography with CrypTool and SageMath |
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Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources. |
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Admin @ si @ MFK2024 |
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4020 |
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Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane |
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Title |
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains |
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Miscellaneous |
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Year |
2024 |
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Arxiv |
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We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL. |
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HUPBA |
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no |
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Admin @ si @ HPF2024 |
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4021 |
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Author |
German Barquero; Sergio Escalera; Cristina Palmero |
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Title |
Seamless Human Motion Composition with Blended Positional Encodings |
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Miscellaneous |
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2024 |
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Arxiv |
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Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions. |
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HUPBA |
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no |
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Admin @ si @ BEP2024 |
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4022 |
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Author |
Ayan Banerjee; Sanket Biswas; Josep Llados; Umapada Pal |
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Title |
GraphKD: Exploring Knowledge Distillation Towards Document Object Detection with Structured Graph Creation |
Type |
Miscellaneous |
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Year |
2024 |
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Arxiv |
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Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impractical for deployment on resource constrained devices. Knowledge distillation allows us to create small and more efficient models that retain much of the performance of their larger counterparts. Here we present a graph-based knowledge distillation framework to correctly identify and localize the document objects in a document image. Here, we design a structured graph with nodes containing proposal-level features and edges representing the relationship between the different proposal regions. Also, to reduce text bias an adaptive node sampling strategy is designed to prune the weight distribution and put more weightage on non-text nodes. We encode the complete graph as a knowledge representation and transfer it from the teacher to the student through the proposed distillation loss by effectively capturing both local and global information concurrently. Extensive experimentation on competitive benchmarks demonstrates that the proposed framework outperforms the current state-of-the-art approaches. The code will be available at: this https URL. |
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DAG |
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no |
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Admin @ si @ BBL2024b |
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4023 |
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Author |
Tao Wu; Kai Wang; Chuanming Tang; Jianlin Zhang |
![goto web page url](img/www.gif)
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Title |
Diffusion-based network for unsupervised landmark detection |
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Journal Article |
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Year |
2024 |
Publication |
Knowledge-Based Systems |
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Volume |
292 |
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Pages |
111627 |
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Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience a notable decline in accuracy. To address these drawbacks, we propose a novel diffusion-based network (DBN) for unsupervised landmark detection, which leverages the generation ability of the diffusion models to detect the landmark locations. In particular, we introduce a dual-branch encoder (DualE) for extracting visual features and predicting landmarks. Additionally, we lighten the decoder structure for faster inference, referred to as LightD. By this means, we avoid relying on extensive data comparison and the necessity of designing complex architectures as in previous methods. Experiments on CelebA, AFLW, 300W and Deepfashion benchmarks have shown that DBN performs state-of-the-art compared to the existing methods. Furthermore, DBN shows robustness even when faced with limited data cases. |
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LAMP |
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no |
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Admin @ si @ WWT2024 |
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4024 |
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Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
Type |
Journal Article |
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Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
27 |
Issue |
8 |
Pages |
875–881 |
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ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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800 |
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MILAB;MV;SIAI |
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no |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
Serial |
647 |
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Author |
Panagiota Spyridonos; Fernando Vilariño; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Anisotropic Feature Extraction from Endoluminal Images for Detection of Intestinal Contractions |
Type |
Book Chapter |
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Year |
2006 |
Publication |
9th International Conference on Medical Image Computing and Computer–Assisted Intervention |
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Volume |
4191 |
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161–168 |
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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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Copenhagen (Denmark) |
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Springer Verlag |
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Berlin Heidelberg |
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R. Larsen, M. Nielsen, and J. Sporring |
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800 |
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MICCAI06 |
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MV;OR;MILAB;SIAI |
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no |
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BCNPCL @ bcnpcl @ SVV2006; IAM @ iam @ SVV2006 |
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725 |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Cascade analysis for intestinal contraction detection |
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Conference Article |
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Year |
2006 |
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20th International Congress and exhibition Computer Assisted Radiology and Surgery |
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9-10 |
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intestine video analysis, anisotropic features, support vector machine, cascade of classifiers |
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In this work, we address the study of intestinal contractions in a novel approach based on a machine learning framework to process data from Wireless Capsule Video Endoscopy. Wireless endoscopy represents a unique way to visualize the intestine motility by creating long videos to visualize intestine dynamics. In this paper we argue that to analyze huge amount of wireless endoscopy data and define robust methods for contraction detection we should base our approach on sophisticated machine learning techniques. In particular, we propose a cascade of classifiers in order to remove different physiological phenomenon and obtain the motility pattern of small intestines. Our results show obtaining high specificity and sensitivity rates that highlight the high efficiency of the selected approach and support the feasibility of the proposed methodology in the automatic detection and analysis of intestine contractions. |
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Osaka (Japan) |
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800 |
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CARS |
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MV;OR;MILAB;SIAI |
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no |
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BCNPCL @ bcnpcl @ VSV2006a; IAM @ iam @ VSV2006h |
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726 |
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Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy |
Type |
Conference Article |
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Year |
2006 |
Publication |
18th International Conference on Pattern Recognition |
Abbreviated Journal |
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4 |
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719-722 |
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Clinical diagnosis , Endoscopes , Fluids and secretions , Gabor filters , Hospitals , Image sequence analysis , Intestines , Lighting , Shape , Visualization |
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Wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. In this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. We applied Gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. Our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. The proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found. |
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Hong Kong |
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1051-4651 |
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0-7695-2521-0 |
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800 |
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ICPR |
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MV;OR;MILAB;SIAI |
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no |
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BCNPCL @ bcnpcl @ VSV2006b; IAM @ iam @ VSV2006g |
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727 |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions |
Type |
Book Chapter |
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Year |
2006 |
Publication |
11th Iberoamerican Congress on Pattern Recognition |
Abbreviated Journal |
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4225 |
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178–187 |
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This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns. |
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Cancun (Mexico) |
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Springer Verlag |
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Berlin Heidelberg |
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.F. Mart ́ınez-Trinidad et al |
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Area ![sorted by Area field, ascending order (up)](img/sort_asc.gif) |
800 |
Expedition |
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Conference |
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|
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Notes |
MV;OR;MILAB;SIAI |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f |
Serial |
728 |
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Permanent link to this record |
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|
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Author |
Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A Machine Learning framework using SOMs: Applications in the Intestinal Motility Assessment |
Type |
Book Chapter |
|
Year |
2006 |
Publication |
11th Iberoamerican Congress on Pattern Recognition |
Abbreviated Journal |
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|
|
Volume |
4225 |
Issue |
|
Pages |
188–197 |
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Keywords |
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Abstract |
Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment. |
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Address |
Cancun (Mexico) |
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Corporate Author |
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Thesis |
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Publisher |
Springer Verlag |
Place of Publication |
Berlin-Heidelberg |
Editor |
J.P. Martinez–Trinidad et al |
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Language |
|
Summary Language |
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Original Title |
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Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
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|
Series Volume |
|
Series Issue |
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Edition |
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ISSN |
|
ISBN |
|
Medium |
|
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Area ![sorted by Area field, ascending order (up)](img/sort_asc.gif) |
800 |
Expedition |
|
Conference |
CIARP06 |
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Notes |
MV;OR;MILAB;SIAI |
Approved |
no |
|
|
Call Number |
BCNPCL @ bcnpcl @ VSV2006d; IAM @ iam @ VSV2006e |
Serial |
729 |
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Permanent link to this record |
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Author |
Fernando Vilariño |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy |
Type |
Book Whole |
|
Year |
2006 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Volume |
|
Issue |
|
Pages |
|
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Keywords |
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Abstract |
Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions obtained by labelling all the motility events present in a video provided by a capsule with a wireless micro-camera, which is ingested by the patient. However, the visual analysis of these video sequences presents several im- portant drawbacks, mainly related to both the large amount of time needed for the visualization process, and the low prevalence of intestinal contractions in video.
In this work we propose a machine learning system to automatically detect the intestinal contractions in video capsule endoscopy, driving a very useful but not fea- sible clinical routine into a feasible clinical procedure. Our proposal is divided into two different parts: The first part tackles the problem of the automatic detection of phasic contractions in capsule endoscopy videos. Phasic contractions are dynamic events spanning about 4-5 seconds, which show visual patterns with a high variability. Our proposal is based on a sequential design which involves the analysis of textural, color and blob features with powerful classifiers such as SVM. This approach appears to cope with two basic aims: the reduction of the imbalance rate of the data set, and the modular construction of the system, which adds the capability of including domain knowledge as new stages in the cascade. The second part of the current work tackles the problem of the automatic detection of tonic contractions. Tonic contrac- tions manifest in capsule endoscopy as a sustained pattern of the folds and wrinkles of the intestine, which may be prolonged for an undetermined span of time. Our proposal is based on the analysis of the wrinkle patterns, presenting a comparative study of diverse features and classification methods, and providing a set of appro- priate descriptors for their characterization. We provide a detailed analysis of the performance achieved by our system both in a qualitative and a quantitative way. |
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Address |
CVC (UAB) |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Publisher |
|
Place of Publication |
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Editor |
Petia Radeva |
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Language |
|
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
84-933652-7-0 |
Edition |
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ISSN |
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ISBN |
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Medium |
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Area ![sorted by Area field, ascending order (up)](img/sort_asc.gif) |
800 |
Expedition |
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Conference |
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Notes |
MV;SIAI |
Approved |
no |
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Call Number |
Admin @ si @ Vil2006; IAM @ iam @ Vil2006 |
Serial |
738 |
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Permanent link to this record |
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Author |
Santiago Segui; Laura Igual; Fernando Vilariño; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy |
Type |
Book Chapter |
|
Year |
2008 |
Publication |
Computer Vision Systems. 6th International |
Abbreviated Journal |
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Volume |
5008 |
Issue |
|
Pages |
251–260 |
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Keywords |
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Abstract |
Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are pro- vided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learn- ing framework in order to obtain the characterization of diverse motil- ity events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists. |
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Address |
Santorini (Greece) |
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Corporate Author |
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Thesis |
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Publisher |
Springer-Verlag |
Place of Publication |
Berlin Heidelberg |
Editor |
A. Gasteratos, M. Vincze, and J.K. Tsotsos |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
|
Abbreviated Series Title |
LNCS |
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|
Series Volume |
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Series Issue |
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Edition |
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ISSN |
|
ISBN |
978-3-540-79546-9 |
Medium |
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Area ![sorted by Area field, ascending order (up)](img/sort_asc.gif) |
800 |
Expedition |
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Conference |
ICVS |
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Notes |
OR; MV; MILAB; SIAI |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ SIV2008; IAM @ iam @ SIV2008 |
Serial |
962 |
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Permanent link to this record |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions |
Type |
Journal Article |
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Year |
2010 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
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Volume |
29 |
Issue |
2 |
Pages |
246-259 |
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Keywords |
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Abstract |
Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions. |
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Address |
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Corporate Author |
IEEE |
Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
|
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
|
Abbreviated Series Title |
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Series Volume |
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Series Issue |
|
Edition |
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ISSN |
0278-0062 |
ISBN |
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Medium |
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Area ![sorted by Area field, ascending order (up)](img/sort_asc.gif) |
800 |
Expedition |
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Conference |
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|
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Notes |
MILAB;MV;OR;SIAI |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
Serial |
1281 |
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Permanent link to this record |