|
|
Patricia Marquez, Debora Gil and Aura Hernandez-Sabate. 2011. A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth. IEEE International Conference on Computer Vision – Workshops. Barcelona (Spain), IEEE, 2042–2049.
Abstract: Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems.
Keywords: IEEE International Conference on Computer Vision – Workshops
|
|
|
|
David Vazquez, Antonio Lopez, Daniel Ponsa and Javier Marin. 2011. Virtual Worlds and Active Learning for Human Detection. 13th International Conference on Multimodal Interaction. New York, NY, USA, USA, ACM DL, 393–400.
Abstract: Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid.
Keywords: Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning
|
|
|
|
Andrew Nolan, Daniel Serrano, Aura Hernandez-Sabate, Daniel Ponsa and Antonio Lopez. 2013. Obstacle mapping module for quadrotors on outdoor Search and Rescue operations. International Micro Air Vehicle Conference and Flight Competition.
Abstract: Obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAV), due to their limited payload capacity to carry advanced sensors. Unlike larger vehicles, MAV can only carry light weight sensors, for instance a camera, which is our main assumption in this work. We explore passive monocular depth estimation and propose a novel method Position Aided Depth Estimation
(PADE). We analyse PADE performance and compare it against the extensively used Time To Collision (TTC). We evaluate the accuracy, robustness to noise and speed of three Optical Flow (OF) techniques, combined with both depth estimation methods. Our results show PADE is more accurate than TTC at depths between 0-12 meters and is less sensitive to noise. Our findings highlight the potential application of PADE for MAV to perform safe autonomous navigation in
unknown and unstructured environments.
Keywords: UAV
|
|
|
|
Naveen Onkarappa and Angel Sappa. 2011. Space Variant Representations for Mobile Platform Vision Applications. In P. Real, D.D., H. Molina, A. Berciano, W. Kropatsch, ed. 14th International Conference on Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 146–154.
Abstract: The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.
|
|
|
|
David Vazquez, Antonio Lopez, Daniel Ponsa and Javier Marin. 2011. Cool world: domain adaptation of virtual and real worlds for human detection using active learning. NIPS Domain Adaptation Workshop: Theory and Application. Granada, Spain.
Abstract: Image based human detection is of paramount interest for different applications. The most promising human detectors rely on discriminatively learnt classifiers, i.e., trained with labelled samples. However, labelling is a manual intensive task, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, in Marin et al. we have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera and the same type of scenario. Accordingly, in Vazquez et al. we cast the problem as one of supervised domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we use an active learning technique. Thus, ultimately our human model is learnt by the combination of virtual- and real-world labelled samples which, to the best of our knowledge, was not done before. Here, we term such combined space cool world. In this extended abstract we summarize our proposal, and include quantitative results from Vazquez et al. showing its validity.
Keywords: Pedestrian Detection; Virtual; Domain Adaptation; Active Learning
|
|
|
|
Miguel Oliveira, Angel Sappa and V.Santos. 2011. Unsupervised Local Color Correction for Coarsely Registered Images. IEEE conference on Computer Vision and Pattern Recognition.201–208.
Abstract: The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time.
|
|
|
|
Arnau Ramisa, David Aldavert, Shrihari Vasudevan, Ricardo Toledo and Ramon Lopez de Mantaras. 2011. The IIIA30 MObile Robot Object Recognition Datset. 11th Portuguese Robotics Open.
Abstract: Object perception is a key feature in order to make mobile robots able to perform high-level tasks. However, research aimed at addressing the constraints and limitations encountered in a mobile robotics scenario, like low image resolution, motion blur or tight computational constraints, is still very scarce. In order to facilitate future research in this direction, in this work we present an object detection and recognition dataset acquired using a mobile robotic platform. As a baseline for the dataset, we evaluated the cascade of weak classifiers object detection method from Viola and Jones.
|
|
|
|
Mohammad Rouhani and Angel Sappa. 2011. Implicit B-Spline Fitting Using the 3L Algorithm. 18th IEEE International Conference on Image Processing.893–896.
|
|
|
|
Marçal Rusiñol, David Aldavert, Ricardo Toledo and Josep Llados. 2011. Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method. 11th International Conference on Document Analysis and Recognition.63–67.
Abstract: In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts.
|
|
|
|
Mohammad Rouhani and Angel Sappa. 2011. Correspondence Free Registration through a Point-to-Model Distance Minimization. 13th IEEE International Conference on Computer Vision.2150–2157.
Abstract: This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework.
|
|
