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Author |
Naveen Onkarappa; Sujay M. Veerabhadrappa; Angel Sappa |
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Title |
Optical Flow in Onboard Applications: A Study on the Relationship Between Accuracy and Scene Texture |
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Conference Article |
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Year |
2012 |
Publication |
4th International Conference on Signal and Image Processing |
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Volume |
221 |
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Pages |
257-267 |
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Abstract |
Optical flow has got a major role in making advanced driver assistance systems (ADAS) a reality. ADAS applications are expected to perform efficiently in all kinds of environments, those are highly probable, that one can drive the vehicle in different kinds of roads, times and seasons. In this work, we study the relationship of optical flow with different roads, that is by analyzing optical flow accuracy on different road textures. Texture measures such as TeX , TeX and TeX are evaluated for this purpose. Further, the relation of regularization weight to the flow accuracy in the presence of different textures is also analyzed. Additionally, we present a framework to generate synthetic sequences of different textures in ADAS scenarios with ground-truth optical flow. |
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Coimbatore, India |
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1876-1100 |
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978-81-322-0996-6 |
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ICSIP |
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ADAS |
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no |
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Admin @ si @ OVS2012 |
Serial |
2356 |
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Author |
Monica Piñol; Angel Sappa; Ricardo Toledo |
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Title |
MultiTable Reinforcement for Visual Object Recognition |
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Conference Article |
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Year |
2012 |
Publication |
4th International Conference on Signal and Image Processing |
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221 |
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469-480 |
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This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach. |
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Coimbatore, India |
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Springer India |
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1876-1100 |
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978-81-322-0996-6 |
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ICSIP |
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ADAS |
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no |
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Admin @ si @ PST2012 |
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2157 |
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Author |
David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras |
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Title |
Visual Registration Method For A Low Cost Robot: Computer Vision Systems |
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Conference Article |
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2009 |
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7th International Conference on Computer Vision Systems |
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5815 |
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204–214 |
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An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance. |
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Belgica |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-04666-7 |
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ICVS |
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ADAS |
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no |
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Admin @ si @ ATR2009b |
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1247 |
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Author |
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate; Daniel Kondermann |
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Title |
When Is A Confidence Measure Good Enough? |
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Conference Article |
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Year |
2013 |
Publication |
9th International Conference on Computer Vision Systems |
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7963 |
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344-353 |
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Keywords |
Optical flow, confidence measure, performance evaluation |
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Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions,
thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality. |
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St Petersburg; Russia; July 2013 |
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Springer Link |
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LNCS |
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0302-9743 |
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978-3-642-39401-0 |
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ICVS |
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IAM;ADAS; 600.044; 600.057; 600.060; 601.145 |
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IAM @ iam @ MGH2013a |
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2218 |
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Author |
Andrew Nolan; Daniel Serrano; Aura Hernandez-Sabate; Daniel Ponsa; Antonio Lopez |
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Title |
Obstacle mapping module for quadrotors on outdoor Search and Rescue operations |
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Conference Article |
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Year |
2013 |
Publication |
International Micro Air Vehicle Conference and Flight Competition |
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UAV |
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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. |
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Toulouse; France; September 2013 |
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IMAV |
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ADAS; 600.054; 600.057;IAM |
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Admin @ si @ NSH2013 |
Serial |
2371 |
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Author |
Konstantia Georgouli; Katerine Diaz; Jesus Martinez del Rincon; Anastasios Koidis |
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Title |
Building generic, easily-updatable chemometric models with harmonisation and augmentation features: The case of FTIR vegetable oils classification |
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Conference Article |
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2017 |
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3rd Ιnternational Conference Metrology Promoting Standardization and Harmonization in Food and Nutrition |
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Thessaloniki; Greece; October 2017 |
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IMEKOFOODS |
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ADAS; 600.118 |
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Admin @ si @ GDM2017 |
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3081 |
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Author |
R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. |
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Title |
On-line Semantic Perception Using Uncertainty |
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Conference Article |
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Year |
2012 |
Publication |
International Conference on Intelligent Robots and Systems |
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IROS |
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4185-4191 |
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Keywords |
Semantic Segmentation |
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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 |
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IROS |
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ADAS |
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ADAS @ adas @ NRR2012 |
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2378 |
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Author |
Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom |
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Title |
Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains |
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Conference Article |
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2015 |
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International Conference on Intelligent Robots and Systems |
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2488 - 2495 |
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Visual Learning; Computer Vision; Autonomous Agents |
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In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks. |
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Hamburg; Germany; October 2015 |
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ADAS; 600.076 |
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Call Number |
Admin @ si @ OSL2015 |
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2664 |
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Author |
Yi Xiao; Felipe Codevilla; Diego Porres; Antonio Lopez |
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Title |
Scaling Vision-Based End-to-End Autonomous Driving with Multi-View Attention Learning |
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Conference Article |
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2023 |
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International Conference on Intelligent Robots and Systems |
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On end-to-end driving, human driving demonstrations are used to train perception-based driving models by imitation learning. This process is supervised on vehicle signals (e.g., steering angle, acceleration) but does not require extra costly supervision (human labeling of sensor data). As a representative of such vision-based end-to-end driving models, CILRS is commonly used as a baseline to compare with new driving models. So far, some latest models achieve better performance than CILRS by using expensive sensor suites and/or by using large amounts of human-labeled data for training. Given the difference in performance, one may think that it is not worth pursuing vision-based pure end-to-end driving. However, we argue that this approach still has great value and potential considering cost and maintenance. In this paper, we present CIL++, which improves on CILRS by both processing higher-resolution images using a human-inspired HFOV as an inductive bias and incorporating a proper attention mechanism. CIL++ achieves competitive performance compared to models which are more costly to develop. We propose to replace CILRS with CIL++ as a strong vision-based pure end-to-end driving baseline supervised by only vehicle signals and trained by conditional imitation learning. |
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Detroit; USA; October 2023 |
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ADAS |
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Admin @ si @ XCP2023 |
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3930 |
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Author |
David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras |
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Title |
Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing |
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Conference Article |
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2009 |
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5th International Symposium on Visual Computing |
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5875 |
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44–55 |
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In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost. |
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Las Vegas, USA |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-10330-8 |
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ISVC |
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ADAS |
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Admin @ si @ ATR2009a |
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1246 |
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