A system for reconstruction from point clouds in 3D: Simplification and mesh representation

Alboul, Lyuba; Chliveros, Georgios

doi:10.1109/icarcv.2010.5707901

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…Such input needs to be converted into the form of connected mesh. Connected meshes could be obtained from point cloud by many triangulation techniques such surface reconstruction from unorganized points in [13], 3D mesh reconstruction from point cloud using elementary Vector and geometry analysis in [14] or reconstruction from point clouds in 3D in [15]. However the data gathered is synthetic 3D objects not a sensory input images as in mentioned in section IV.…”

Section: A Sequential 3d Object Categorization Modelmentioning

confidence: 99%

3D Object Categorization Using Spin-Images with MPI Parallel Implementation

Hegazy

Hamdy

El-Kilany

2014

IJCI. International Journal of Computers and Information

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Object recognition and categorization are two important key features of computer vision. Accuracy aspects representresearchchallengefo rbothobjectrecognitionandcategorization techniques. High performance computing (HPC) technologies usually manage the increasing time and complexity of computations. In this paper, an approach utilizing3Dspin-imagesfor3Dobjectcategorizationisproposed.Themaincontributionofthisapproach is that item ploys the MPI techniques in a unique way to extract spin-images. The technique proposed utilizes the independence between spin-images generated a teach point. Time estimation of this technique has shown dramatic decrease of the categorization time proportion alto number of workers used.

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Section: A Sequential 3d Object Categorization Modelmentioning

confidence: 99%

3D Object Categorization Using Spin-Images with MPI Parallel Implementation

Hegazy

Hamdy

El-Kilany

2014

IJCI. International Journal of Computers and Information

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“…At SHU some results in this direction have been achieved in the remits of the View-Finder project. The data from LRF (laser range finder) and camera have been fused to obtain a 3D photo-realistic representation of the environment, which then was 'inserted' in a 2D map obtained by a SLAM algorithm [24].…”

Section: The Virtual Environmentmentioning

confidence: 99%

Using Agents in Virtual Environments to Assist Controllers to Manage Multiple Assets

Beer

Alboul

Norling

et al. 2013

Lecture Notes in Computer Science

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Abstract. Search and rescue operations often require complex coordination of a range of resources, including human and robotic resources. This paper discusses a proposed new framework that allows agent technology to be used in conjunction with a virtual environment to provide a human controller with an effective visualisation of the distribution of a collection of autonomous objects, in our case, Unmanned Aerial Vehicles (UAVs) so that they can be managed in a way that allows them to successfully complete the task in the minimum possible time. It is our contention that to do this effectively there needs to be two-way initiation of verbal conversations, but that it is not necessary for the system to completely understand the conversations required. An example scenario is presented that illustrates how such a system would be used in practice, illustrating how a single human can verbally communicate with a swarm of semi-autonomous actors verbally and envisage their activities in a swarm based on the visual cues provided within the virtual environment. An agent-based solution is proposed that meets the requirements and provides an effective command station that can manage a search using a collection of UAVs effectively.

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“…The automation of corrosion visual surveying (RGB images) by human inspectors can be seen as an extension of semantic segmentation in images, i.e., the association of pixels to a specific class/label (corrosion) [10][11][12]. Corrosion in the form of isolated area units on a vessel surface is difficult to detect and/or directly predict in an RGB image space using standard techniques (e.g., [13][14][15]). This is mainly due to the diverse geometrical shape, which makes it difficult to postulate prior knowledge on the basis of a generalised morphology.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module

Chliveros,

Tzanetatos,

Kontomaris

2024

Coatings

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This paper involves the automation of a visual characterisation technique for corrosion in marine vessels, as it appears in the hull preventive coatings of marine vessels and their surfaces. We propose a module that maximizes the utilisation of features learned by a deep convolutional neural network to identify areas of corrosion and segment pixels in regions of inspection interest for corrosion detection. Our segmentation module is based on Eigen tree decomposition and information-based decision criteria in order to produce specific corroded spots—regions of interest. To assess performance and compare it with our method, we utilize several state-of-the-art deep learning architectures.The results indicate that our method achieves higher accuracy and precision while maintaining the significance score across the entire dataset. To the best of our knowledge, this is the first Eigen tree-based module in the literature in the context of trained neural network predictors for classifying corrosion in marine vessel images.

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A system for reconstruction from point clouds in 3D: Simplification and mesh representation

Cited by 6 publications

References 11 publications

3D Object Categorization Using Spin-Images with MPI Parallel Implementation

3D Object Categorization Using Spin-Images with MPI Parallel Implementation

Using Agents in Virtual Environments to Assist Controllers to Manage Multiple Assets

A Deep Learning Image Corrosion Classification Method for Marine Vessels Using an Eigen Tree Hierarchy Module

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