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Fernando Barrera, Felipe Lumbreras and Angel Sappa. 2012. Evaluation of Similarity Functions in Multimodal Stereo. 9th International Conference on Image Analysis and Recognition. Springer Berlin Heidelberg, 320–329. (LNCS.)
Abstract: This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head.
Keywords: Aveiro, Portugal
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Miguel Oliveira, Angel Sappa and V. Santos. 2012. Color Correction using 3D Gaussian Mixture Models. 9th International Conference on Image Analysis and Recognition. Springer Berlin Heidelberg, 97–106. (LNCS.)
Abstract: The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
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Fernando Barrera, Felipe Lumbreras, Cristhian Aguilera and Angel Sappa. 2012. Planar-Based Multispectral Stereo. 11th Quantitative InfraRed Thermography.
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Cristhian Aguilera, Fernando Barrera, Angel Sappa and Ricardo Toledo. 2012. A Novel SIFT-Like-Based Approach for FIR-VS Images Registration. 11th Quantitative InfraRed Thermography.
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Monica Piñol, Angel Sappa, Angeles Lopez and Ricardo Toledo. 2012. Feature Selection Based on Reinforcement Learning for Object Recognition. Adaptive Learning Agents Workshop.33–39.
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German Ros, Angel Sappa, Daniel Ponsa and Antonio Lopez. 2012. Visual SLAM for Driverless Cars: A Brief Survey. IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles.
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Naveen Onkarappa and Angel Sappa. 2012. An Empirical Study on Optical Flow Accuracy Depending on Vehicle Speed. IEEE Intelligent Vehicles Symposium. IEEE Xplore, 1138–1143.
Abstract: Driver assistance and safety systems are getting attention nowadays towards automatic navigation and safety. Optical flow as a motion estimation technique has got major roll in making these systems a reality. Towards this, in the current paper, the suitability of polar representation for optical flow estimation in such systems is demonstrated. Furthermore, the influence of individual regularization terms on the accuracy of optical flow on image sequences of different speeds is empirically evaluated. Also a new synthetic dataset of image sequences with different speeds is generated along with the ground-truth optical flow.
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Miguel Oliveira, Angel Sappa and V. Santos. 2012. Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models. IEEE Intelligent Vehicles Symposium. IEEE Xplore, 299–303.
Abstract: The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
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Jose Manuel Alvarez, Theo Gevers, Y. LeCun and Antonio Lopez. 2012. Road Scene Segmentation from a Single Image. 12th European Conference on Computer Vision. Springer Berlin Heidelberg, 376–389. (LNCS.)
Abstract: Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images.
From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined
Keywords: road detection
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German Ros, Jesus Martinez del Rincon and Gines Garcia-Mateos. 2012. Articulated Particle Filter for Hand Tracking. 21st International Conference on Pattern Recognition.3581–3585.
Abstract: This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper.
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