Posted by navid nourani Wed, September 28, 2016 09:15:11
We equipped our test vehicle (A Siemens Desiro) with LIDAR, Radar and camera systems and showcased topics such as signal recognition and forward collision avoidance systems.
More info here:
Posted by navid nourani Wed, September 28, 2016 09:15:11
Posted by navid nourani Tue, December 10, 2013 02:35:04
Posted by navid nourani Thu, November 07, 2013 04:56:26
O. Pizarro, S.B. Williams, M.V. Jakuba, M. Johnson-Roberson, I. Mahon, M. Bryson, D. Steinberg, A.Friedman, D. Dansereau, N. Nourani-Vatani, D. Bongiorno, M. Bewley, A. Bender, N. Ashan, B. Douillard
Underwater Technology Symposium
Australias Integrated Marine Observing System (IMOS) has a strategic focus on the impact of major boundary currents on continental shelf environments, ecosystems and biodiversity. To improve our understanding of natural, climate change, and human-induced variability in shelf environments, the IMOS Autonomous Underwater Vehicle (AUV) facility has been charged with generating physical and biological observations of benthic variables that cannot be cost-effectively obtained by other means. Starting in 2010, the IMOS AUV facility began collecting precisely navigated benthic imagery using AUVs at selected reference sites on Australia's shelf. This observing program capitalizes on the unique capabilities of AUVs that have allowed repeated visits to the reference sites, providing a critical observational link between oceanographic and benthic processes. This paper provides a brief overview of the relevant capabilities of the AUV facility, the design of the IMOS benthic sampling program, and some preliminary results. We also report on some of the challenges and potential benefits to be realized from a benthic observation system that collects several TB of geo-referenced stereo imagery a year. This includes collaborative semi-automated image analysis, clustering and classification, large scale visualization and data mining, and lighting correction for change detection and characterization. We also mention some of the lessons from operating an AUV-based monitoring program and future work in this area.
Posted by navid nourani Thu, November 07, 2013 04:46:10
N. Nourani-Vatani, M. De Deuge, B. Douillard, S. Williams
ICCV 2013 Workshop on Underwater Vision
While the importance of the choice of color space for color descriptors has been studied extensively, a similar study for image texture descriptors is missing. This publication investigates the effect of color-to-monochrome conversions, image normalization, and metrics on the discriminative power of texture descriptors. The measure of the discriminative power of a feature is formulated as supervised spectral feature analysis. This analysis allows to measure the relative performance of a feature under varying conditions as long as the feature dimension is maintained. Feature discrimination evaluation is applied to Local Binary Patterns texture descriptors and it is shown how the proposed metric directly maps to classification performance. Based on this metric, we demonstrate that the choice of color-to-monochrome conversion and normalization can have a significant effect on the performance of the LBP descriptors.
Posted by navid nourani Thu, November 07, 2013 04:41:25
M.S. Bewley, N. Nourani-Vatani, D. Rao, B. Douillard, O. Pizarro, S.B. Williams
Field and Service Robotics
In recent years, Autonomous Underwater Vehicles (AUVs) have been used extensively to gather imagery and other environmental variables for ocean monitoring. Processing of this vast amount of collected imagery to label content is difficult, expensive and time consuming. Because of this, typically only a small subset of images are labelled, and only at a small number of points. In order to make full use of the raw data returned from the AUV, this process needs to be automated. In this work the single species classification problem is extended to a multi-species classification problem following a taxonomical hierarchy. We demonstrate the application of techniques used in areas such as computer vision, text classification and medical diagnosis to the supervised hierarchical classification of benthic images. We also discuss critical aspects such as training topology and various prediction and scoring methodologies. An interesting aspect of the presented work is that the ground truth labels are sparse and incomplete, i.e. not all labels go to the leaf node, which brings with it other interesting challenges. We find that the best classification results are obtained using Local Binary Patterns (LBP), training a network of binary classifiers with probabilistic output, and applying “one-vs-rest” classification at each level of the hierarchy for prediction. This work presents a working solution that allows AUV images to be automatically labelled with the most appropriate node in a hierarchy of 19 species and morphologies. The result is that the output of the AUV system can include a semantic map using the taxonomy prescribed by marine scientists. This has the potential to not only reduce the manual labelling workload, but also to reduce the current dependence that marine scientists have on extrapolating information from a relatively small number of sparsely labelled points.
Posted by navid nourani Wed, October 23, 2013 05:19:09
Posted by navid nourani Thu, June 27, 2013 11:46:40
M.S. Bewley, N. Nourani-Vatani, B. Douillard, O. Pizarro, S.B. Williams
CVPR Workshop on Fine-Grained Visual Categorization - FGVC^2
Posted by navid nourani Thu, June 27, 2013 11:39:01
B. Douillard∗, N. Nourani-Vatani∗, M. Johnson-Roberson, O. Pizarro, S. Williams, C. Roman, I. Vaughn
(* co-first authors)
Autonomous Robots, Volume 35, Issue 4 , pp 255-269
A method for segmenting three-dimensional data of underwater unstructured terrains is presented. The three-dimensional point clouds are converted to two-dimensional elevation maps and analyzed for seg- mentation in the frequency domain. The lower frequency components represent the slower varying undulations of the underlying ground. The cut-off frequency, below which the frequency components form the ground sur- face, is determined automatically using peak detection. The user can also specify a maximum admissible size of objects to drive the automatic detection of the cut-off frequency. The points above the estimated ground sur- face are clustered via standard proximity clustering to form object segments. The precision of the segmenta- tion is compared against ground truth hand labelled data acquired by a stereo camera pair and a struc- tured light sensor. It is also evaluated for registration error when the extracted segments are used for sub- map alignment. The proposed approach is compared to three point cloud based and two image based segmen- tation algorithms. The results show that the approach is applicable to a range of different terrains and is able to generate features useful for navigation.