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Patricia Marquez and 6 others. 2014. Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging. 17th International Conference on Medical Image Computing and Computer Assisted Intervention. Springer International Publishing, 231–238. (LNCS.)
Abstract: Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across dierent clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest in uence on OF accuracy drop.
In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three dierent OF methods, including HARP.
Keywords: Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging
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Patricia Marquez, Debora Gil and Aura Hernandez-Sabate. 2012. A Complete Confidence Framework for Optical Flow. In Andrea Fusiello, V.M., Rita Cucchiara, ed. 12th European Conference on Computer Vision – Workshops and Demonstrations. Florence, Italy, October 7-13, 2012, Springer-Verlag, 124–133. (LNCS.)
Abstract: Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations.
Keywords: Optical flow, confidence measures, sparsification plots, error prediction plots
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Patricia Suarez, Angel Sappa and Boris X. Vintimilla. 2017. Cross-Spectral Image Patch Similarity using Convolutional Neural Network. IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics.
Abstract: The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach.
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Patricia Suarez, Angel Sappa and Boris X. Vintimilla. 2017. Learning to Colorize Infrared Images. 15th International Conference on Practical Applications of Agents and Multi-Agent System.
Abstract: This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very dierent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach.
Keywords: CNN in multispectral imaging; Image colorization
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Patricia Suarez, Angel Sappa and Boris X. Vintimilla. 2017. Infrared Image Colorization based on a Triplet DCGAN Architecture. IEEE Conference on Computer Vision and Pattern Recognition Workshops.
Abstract: This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time.
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Patricia Suarez, Angel Sappa and Boris X. Vintimilla. 2017. Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture. 19th international conference on image analysis and processing.
Abstract: This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results.
Keywords: CNN in Multispectral Imaging; Image Colorization
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Petia Radeva and Joan Serrat. 1993. Rubber Snake: Implementation on Signed Distance Potential. Vision Conference.187–194.
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Petia Radeva, Joan Serrat and Enric Marti. 1995. A snake for model-based segmentation. Proc. Conf. Fifth Int Computer Vision.816–821.
Abstract: Despite the promising results of numerous applications, the hitherto proposed snake techniques share some common problems: snake attraction by spurious edge points, snake degeneration (shrinking and attening), convergence and stability of the deformation process, snake initialization and local determination of the parameters of elasticity. We argue here that these problems can be solved only when all the snake aspects are considered. The snakes proposed here implement a new potential eld and external force in order to provide a deformation convergence, attraction by both near and far edges as well as snake behaviour selective according to the edge orientation. Furthermore, we conclude that in the case of model-based seg mentation, the internal force should include structural information about the expected snake shape. Experiments using this kind of snakes for segmenting bones in complex hand radiographs show a signicant improvement.
Keywords: snakes; elastic matching; model-based segmenta tion
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R. de Nijs, Sebastian Ramos, Gemma Roig, Xavier Boix, Luc Van Gool and K. Kühnlenz. 2012. On-line Semantic Perception Using Uncertainty. International Conference on Intelligent Robots and Systems.4185–4191.
Abstract: Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance
Keywords: Semantic Segmentation
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Saad Minhas and 6 others. 2016. LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode. 14th European Conference on Computer Vision Workshops.894–900. (LNCS.)
Abstract: Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
Keywords: Simulation environment; Automated Driving; Driver-Vehicle interaction
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