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Author |
David Berga |
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Title |
Understanding Eye Movements: Psychophysics and a Model of Primary Visual Cortex |
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Year |
2019 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Humansmove their eyes in order to learn visual representations of the world. These eye movements depend on distinct factors, either by the scene that we perceive or by our own decisions. To select what is relevant to attend is part of our survival mechanisms and the way we build reality, as we constantly react both consciously and unconsciously to all the stimuli that is projected into our eyes. In this thesis we try to explain (1) how we move our eyes, (2) how to build machines that understand visual information and deploy eyemovements, and (3) how to make these machines understand tasks in order to decide for eye movements.
(1) We provided the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of 230 synthetically-generated image patterns. A total of 15 types of stimuli has been generated (e.g. orientation, brightness, color, size, etc.), with 7 feature contrasts for each feature category. Eye-tracking data was collected from 34 participants during the viewing of the dataset, using Free-Viewing and Visual Search task instructions. Results showed that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. Temporality of fixations, 4. task difficulty and 5. center bias. From such dataset (SID4VAM), we have computed a benchmark of saliency models by testing performance using psychophysical patterns. Model performance has been evaluated considering model inspiration and consistency with human psychophysics. Our study reveals that state-of-the-art Deep Learning saliency models do not performwell with synthetic pattern images, instead, modelswith Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation.
(2) Computations in the primary visual cortex (area V1 or striate cortex) have long been hypothesized to be responsible, among several visual processing mechanisms, of bottom-up visual attention (also named saliency). In order to validate this hypothesis, images from eye tracking datasets have been processed with a biologically plausible model of V1 (named Neurodynamic SaliencyWaveletModel or NSWAM). Following Li’s neurodynamic model, we define V1’s lateral connections with a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation and scale. Early subcortical processes (i.e. retinal and thalamic) are functionally simulated. The resulting saliency maps are generated from the model output, representing the neuronal activity of V1 projections towards brain areas involved in eye movement control. We want to pinpoint that our unified computational architecture is able to reproduce several visual processes (i.e. brightness, chromatic induction and visual discomfort) without applying any type of training or optimization and keeping the same parametrization. The model has been extended (NSWAM-CM) with an implementation of the cortical magnification function to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions. Results show that our model outperforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences, specifically for nature and synthetic images. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) predict attention at distinct image contexts.
(3) Although previous scanpath models have been able to efficiently predict saccades during Free-Viewing, it is well known that stimulus and task instructions can strongly affect eye movement patterns. In particular, task priming has been shown to be crucial to the deployment of eye movements, involving interactions between brain areas related to goal-directed behavior, working and long-termmemory in combination with stimulus-driven eyemovement neuronal correlates. In our latest study we proposed an extension of the Selective Tuning Attentive Reference Fixation ControllerModel based on task demands (STAR-FCT), describing novel computational definitions of Long-TermMemory, Visual Task Executive and Task Working Memory. With these modules we are able to use textual instructions in order to guide the model to attend to specific categories of objects and/or places in the scene. We have designed our memorymodel by processing a visual hierarchy of low- and high-level features. The relationship between the executive task instructions and the memory representations has been specified using a tree of semantic similarities between the learned features and the object category labels. Results reveal that by using this model, the resulting object localizationmaps and predicted saccades have a higher probability to fall inside the salient regions depending on the distinct task instructions compared to saliency. |
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July 2019 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Editor |
Xavier Otazu |
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ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-84-948531-8-0 |
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NEUROBIT |
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Admin @ si @ Ber2019 |
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3390 |
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Author |
Xavier Soria |
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Title |
Single sensor multi-spectral imaging |
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Year |
2019 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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The image sensor, nowadays, is rolling the smartphone industry. While some phone brands explore equipping more image sensors, others, like Google, maintain their smartphones with just one sensor; but this sensor is equipped with Deep Learning to enhance the image quality. However, what all brands agree on is the need to research new image sensors; for instance, in 2015 Omnivision and PixelTeq presented new CMOS based image sensors defined as multispectral Single Sensor Camera (SSC), which are capable of capturing multispectral bands. This dissertation presents the benefits of using a multispectral SSCs that, as aforementioned, simultaneously acquires images in the visible and near-infrared (NIR) bands. The principal benefits while addressing problems related to image bands in the spectral range of 400 to 1100 nanometers, there are cost reductions in the hardware and software setup because only one SSC is needed instead of two, and the images alignment are not required any more. Concerning to the NIR spectrum, many works in literature have proven the benefits of working with NIR to enhance RGB images (e.g., image enhancement, remove shadows, dehazing, etc.). In spite of the advantage of using SSC (e.g., low latency), there are some drawback to be solved. One of this drawback corresponds to the nature of the silicon-based sensor, which in addition to capture the RGB image, when the infrared cut off filter is not installed it also acquires NIR information into the visible image. This phenomenon is called RGB and NIR crosstalking. This thesis firstly faces this problem in challenging images and then it shows the benefit of using multispectral images in the edge detection task.
The RGB color restoration from RGBN image is the topic tackled in RGB and NIR crosstalking. Even though in the literature a set of processes have been proposed to face this issue, in this thesis novel approaches, based on DL, are proposed to subtract the additional NIR included in the RGB channel. More precisely, an Artificial Neural Network (NN) and two Convolutional Neural Network (CNN) models are proposed. As the DL based models need a dataset with a large collection of image pairs, a large dataset is collected to address the color restoration. The collected images are from challenging scenes where the sunlight radiation is sufficient to give absorption/reflectance properties to the considered scenes. An extensive evaluation has been conducted on the CNN models, differences from most of the restored images are almost imperceptible to the human eye. The next proposal of the thesis is the validation of the usage of SSC images in the edge detection task. Three methods based on CNN have been proposed. While the first one is based on the most used model, holistically-nested edge detection (HED) termed as multispectral HED (MS-HED), the other two have been proposed observing the drawbacks of MS-HED. These two novel architectures have been designed from scratch (training from scratch); after the first architecture is validated in the visible domain a slight redesign is proposed to tackle the multispectral domain. Again, another dataset is collected to face this problem with SSCs. Even though edge detection is confronted in the multispectral domain, its qualitative and quantitative evaluation demonstrates the generalization in other datasets used for edge detection, improving state-of-the-art results. |
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September 2019 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Editor |
Angel Sappa |
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ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-84-948531-9-7 |
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MSIAU; 600.122 |
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Admin @ si @ Sor2019 |
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3391 |
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Author |
Robert Benavente; C. Alejandro Parraga; Maria Vanrell |
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Title |
La influencia del contexto en la definicion de las fronteras entre las categorias cromaticas |
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Conference Article |
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2010 |
Publication |
9th Congreso Nacional del Color |
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92–95 |
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Categorización del color; Apariencia del color; Influencia del contexto; Patrones de Mondrian; Modelos paramétricos |
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En este artículo presentamos los resultados de un experimento de categorización de color en el que las muestras se presentaron sobre un fondo multicolor (Mondrian) para simular los efectos del contexto. Los resultados se comparan con los de un experimento previo que, utilizando un paradigma diferente, determinó las fronteras sin tener en cuenta el contexto. El análisis de los resultados muestra que las fronteras obtenidas con el experimento en contexto presentan menos confusión que las obtenidas en el experimento sin contexto. |
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Alicante (Spain) |
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978-84-9717-144-1 |
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CNC |
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CIC |
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CAT @ cat @ BPV2010 |
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1327 |
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Author |
Lasse Martensson; Anders Hast; Alicia Fornes |
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Title |
Word Spotting as a Tool for Scribal Attribution |
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Conference Article |
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2017 |
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2nd Conference of the association of Digital Humanities in the Nordic Countries |
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87-89 |
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Gothenburg; Suecia; March 2017 |
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ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-91-88348-83-8 |
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DAG; 600.097; 600.121 |
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Admin @ si @ MHF2017 |
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2954 |
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Author |
Carles Fernandez; Jordi Gonzalez; Joao Manuel R. S. Taveres; Xavier Roca |
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Title |
Towards Ontological Cognitive System |
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Book Chapter |
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Year |
2013 |
Publication |
Topics in Medical Image Processing and Computational Vision |
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8 |
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87-99 |
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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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Springer Netherlands |
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2212-9391 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-94-007-0725-2 |
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ISE; 605.203; 302.018; 600.049 |
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Admin @ si @ FGT2013 |
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2287 |
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Author |
Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Oscar Vilarroya; Petia Radeva |
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Title |
A Supervised Graph-cut Deformable Model for Brain MRI Segmentation. Deformation models: tracking, animation and applications |
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2012 |
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Computational Vision and Biomechanics |
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Springer Netherlands |
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LNCS |
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978-94-007-5445-4 |
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MILAB;HuPBA |
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Admin @ si @ ISH2012b |
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2066 |
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Author |
David Roche; Debora Gil; Jesus Giraldo |
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Title |
Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? |
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Book Chapter |
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2014 |
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G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology |
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796 |
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3 |
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159-181 |
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β-arrestin; biased agonism; curve fitting; empirical modeling; evolutionary algorithm; functional selectivity; G protein; GPCR; Hill coefficient; intrinsic efficacy; inverse agonism; mathematical modeling; mechanistic modeling; operational model; parameter optimization; receptor dimer; receptor oligomerization; receptor constitutive activity; signal transduction; two-state model |
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Abstract |
Empirical and mechanistic models differ in their approaches to the analysis of pharmacological effect. Whereas the parameters of the former are not physical constants those of the latter embody the nature, often complex, of biology. Empirical models are exclusively used for curve fitting, merely to characterize the shape of the E/[A] curves. Mechanistic models, on the contrary, enable the examination of mechanistic hypotheses by parameter simulation. Regretfully, the many parameters that mechanistic models may include can represent a great difficulty for curve fitting, representing, thus, a challenge for computational method development. In the present study some empirical and mechanistic models are shown and the connections, which may appear in a number of cases between them, are analyzed from the curves they yield. It may be concluded that systematic and careful curve shape analysis can be extremely useful for the understanding of receptor function, ligand classification and drug discovery, thus providing a common language for the communication between pharmacologists and medicinal chemists. |
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Springer Netherlands |
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0065-2598 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-94-007-7422-3 |
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IAM; 600.075 |
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IAM @ iam @ RGG2014 |
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2197 |
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Author |
Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre |
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Title |
Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources |
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2016 |
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The future of historical demography. Upside down and inside out |
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127-131 |
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Acco Publishers |
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K.Matthijs; S.Hin; H.Matsuo; J.Kok |
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978-94-6292-722-3 |
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DAG; 600.097 |
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Admin @ si @ PFL2016 |
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2907 |
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Thierry Brouard; A. Delaplace; Muhammad Muzzamil Luqman; H. Cardot; Jean-Yves Ramel |
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Title |
Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures |
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2010 |
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Bayesian Network |
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13-37 |
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Sciyo |
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Ahmed Rebai |
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978-953-307-124-4 |
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Admin @ si @ BDL2010 |
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1461 |
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Jorge Bernal; Fernando Vilariño; F. Javier Sanchez |
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Title |
Towards Intelligent Systems for Colonoscopy |
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Book Chapter |
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2011 |
Publication |
Colonoscopy |
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1 |
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257-282 |
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In this chapter we present tools that can be used to build intelligent systems for colonoscopy.
The idea is, by using methods based on computer vision and artificial intelligence, add significant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions |
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Intech |
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Paul Miskovitz |
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978-953-307-568-6 |
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800 |
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MV;SIAI |
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IAM @ iam @ BVS2011 |
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1697 |
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Author |
Aura Hernandez-Sabate; Debora Gil |
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Title |
The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries |
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Book Chapter |
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2012 |
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Intravascular Ultrasound |
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185-206 |
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Intech |
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Yasuhiro Honda |
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English |
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english |
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978-953-307-900-4 |
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IAM; ADAS |
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IAM @ iam @ HeG2012 |
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1684 |
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Author |
Cristhian Aguilera; M.Ramos; Angel Sappa |
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Title |
Simulated Annealing: A Novel Application of Image Processing in the Wood Area |
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2012 |
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Simulated Annealing – Advances, Applications and Hybridizations |
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91-104 |
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Marcos de Sales Guerra Tsuzuki |
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978-953-51-0710-1 |
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ADAS |
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no |
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Admin @ si @ ARS2012 |
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2156 |
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Author |
Jorge Bernal; F. Javier Sanchez; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach |
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Title |
Bulding up the future of colonoscopy: A synergy between clinicians and computer scientists |
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Book Chapter |
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2015 |
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Colonoscopy and Colorectal Cancer |
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Intelligent systems; Image properties; Validation; Clinical drawbacks; Endoluminal scene description |
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Recent advances in endoscopic technology have generated an increasing interest in strengthening the collaboration between clinicians and computers scientist to develop intelligent systems that can provide additional information to clinicians in the different stages of an intervention. The objective of this chapter is to identify clinical drawbacks of colonoscopy in order to define potential areas of collaboration. Once areas are defined, we present the challenges that colonoscopy images present in order computational methods to provide with meaningful output, including those related to image formation and acquisition, as they are proven to have an impact in the performance of an intelligent system. Finally, we also propose how to define validation frameworks in order to assess the performance of a given method, making an special emphasis on how databases should be created and annotated and which metrics should be used to evaluate systems correctly. |
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978-953-51-2225-8 |
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MV |
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Admin @ si @ BSR2015 |
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2624 |
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Author |
Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora |
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Title |
Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images |
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Book Chapter |
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2020 |
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Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends |
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World Scientific |
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978-981-120-323-7 |
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DAG; 600.140; 600.121 |
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Admin @ si @ FLP2020 |
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3350 |
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Author |
V. Valev; Petia Radeva |
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Title |
Determining Structural Description by Boolean Formulas. |
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Book Chapter |
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Year |
1992 |
Publication |
Advances in Structural and Syntactic Pattern Recognition |
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5 |
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131–140 |
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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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World Scientific |
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H. Bunke |
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Machine Perception and Artificial Intelligence: |
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978-981-279-791-9 |
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MILAB |
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BCNPCL @ bcnpcl @ VaR1992c |
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254 |
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