5,243 research outputs found
Tree leaves extraction in natural images: Comparative study of pre-processing tools and segmentation methods
International audienceIn this paper, we propose a comparative study of various segmentation methods applied to the extraction of tree leaves from natural images. This study follows the design of a mobile application, developed by Cerutti et al. (published in ReVeS Participation-Tree Species Classification Using Random Forests and Botanical Features. CLEF 2012), to highlight the impact of the choices made for segmentation aspects. All the tests are based on a database of 232 images of tree leaves depicted on natural background from smartphones acquisitions. We also propose to study the improvements, in terms of performance, by using pre-processing tools such as the interaction between the user and the application through an input stroke, as well as the use of color distance maps. The results presented in this paper shows that the method developed by Cerutti et al. (denoted Guided Active Contour), obtains the best score for almost all observation criteria. Finally we detail our online benchmark composed of 14 unsupervised methods and 6 supervised ones
Real-time manhattan world rotation estimation in 3D
Drift of the rotation estimate is a well known problem in visual odometry systems as it is the main source of positioning inaccuracy. We propose three novel algorithms to estimate the full 3D rotation to the surrounding Manhattan World (MW) in as short as 20 ms using surface-normals derived from the depth channel of a RGB-D camera. Importantly, this rotation estimate acts as a structure compass which can be used to estimate the bias of an odometry system, such as an inertial measurement unit (IMU), and thus remove its angular drift. We evaluate the run-time as well as the accuracy of the proposed algorithms on groundtruth data. They achieve zerodrift rotation estimation with RMSEs below 3.4° by themselves and below 2.8° when integrated with an IMU in a standard extended Kalman filter (EKF). Additional qualitative results show the accuracy in a large scale indoor environment as well as the ability to handle fast motion. Selected segmentations of scenes from the NYU depth dataset demonstrate the robustness of the inference algorithms to clutter and hint at the usefulness of the segmentation for further processing.United States. Office of Naval Research. Multidisciplinary University Research Initiative6 (Awards N00014-11-1-0688 and N00014-10-1-0936)National Science Foundation (U.S.) (Award IIS-1318392
Efficient Retrieval of Images with Irregular Patterns using Morphological Image Analysis: Applications to Industrial and Healthcare datasets
Image retrieval is the process of searching and retrieving images from a
database based on their visual content and features. Recently, much attention
has been directed towards the retrieval of irregular patterns within industrial
or medical images by extracting features from the images, such as deep
features, colour-based features, shape-based features and local features. This
has applications across a spectrum of industries, including fault inspection,
disease diagnosis, and maintenance prediction. This paper proposes an image
retrieval framework to search for images containing similar irregular patterns
by extracting a set of morphological features (DefChars) from images; the
datasets employed in this paper contain wind turbine blade images with defects,
chest computerised tomography scans with COVID-19 infection, heatsink images
with defects, and lake ice images. The proposed framework was evaluated with
different feature extraction methods (DefChars, resized raw image, local binary
pattern, and scale-invariant feature transforms) and distance metrics to
determine the most efficient parameters in terms of retrieval performance
across datasets. The retrieval results show that the proposed framework using
the DefChars and the Manhattan distance metric achieves a mean average
precision of 80% and a low standard deviation of 0.09 across classes of
irregular patterns, outperforming alternative feature-metric combinations
across all datasets. Furthermore, the low standard deviation between each class
highlights DefChars' capability for a reliable image retrieval task, even in
the presence of class imbalances or small-sized datasets.Comment: 35 pages, 5 figures, 19 tables (17 tables in appendix), submitted to
Special Issue: Advances and Challenges in Multimodal Machine Learning 2nd
Edition, Journal of Imaging, MDP
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