Mohamad Motasem Nawaf

Replica update strategy in mobile ad hoc networks

In mobile ad hoc networks, partitioning occurs frequently. Data replication techniques are used to improve data accessibility but require data consistency to be maintained in case of update. In this paper, we propose hybrid push-pull data update propagation. The idea is to divide replica holders into SH(Push) and LL(Pull) categories. Updates are pushed to SH nodes whenever they occur. LL nodes pull the updates from SH nodes in a frequency suitable for their needs. The novelty of this method that it minimizes communication cost when saving an adapted level -to mobile hosts needs- for data consistency.

Color and flow based superpixels for 3D geometry respecting meshing

We present an adaptive weight based superpixel segmentation method for the goal of creating mesh representation that respects the 3D scene structure. We propose a new fusion framework which employs both dense optical flow and color images to compute the probability of boundaries. The main contribution of this work is that we introduce a new color and optical flow pixel-wise weighting model that takes into account the non-linear error distribution of the depth estimation from optical flow. Experiments show that our method is better than the other state-of-art methods in terms of smaller error in the final produced mesh.

Fusion of dense spatial features and sparse temporal features for three-dimensional structure estimation in urban scenes

The authors present a novel approach to improve three-dimensional (3D) structure estimation from an image stream in urban scenes. The authors consider a particular setup, where the camera is installed on a moving vehicle. Applying traditional structure from motion (SfM) technique in this case generates poor estimation of the 3D structure because of several reasons such as texture-less images, small baseline variations and dominant forward camera motion. The authors idea is to introduce the monocular depth cues that exist in a single image, and add time constraints on the estimated 3D structure. The scene is modelled as a set of small planar patches obtained using over-segmentation, and the goal is to estimate the 3D positioning of these planes. The authors propose a fusion scheme that employs Markov random field model to integrate spatial and temporal depth features. Spatial depth is obtained by learning a set of global and local image features. Temporal depth is obtained via sparse optical flow based SfM approach. That allows decreasing the estimation ambiguity by forcing some constraints on camera motion. Finally, the authors apply a fusion scheme to create unique 3D structure estimation.

Monocular 3D structure estimation for urban scenes

We propose a 3D structure estimation framework that adopts the slanted-planes representation in order to provide a dense estimation. The proposed approach fuses sparse 3D reconstructed point cloud obtained using several feature matching methods and noisy dense optical flow in order to perform accurate structure fitting and visually appealing results. We formulate the problem as a weighted total least square model that takes into account the occlusion boundaries between neighboring planes. We also propose an extended flow-based superpixel segmentation which is adaptive to the sparse feature points density for more balanced reconstruction. To validate our approach, we present 3D models obtained using the KITTI dataset [1] compared with other methods.

Joint spatio-temporal depth features fusion framework for 3d structure estimation in urban environment

We present a novel approach to improve 3D structure estimation from an image stream in urban scenes. We consider a particular setup where the camera is installed on a moving vehicle. Applying traditional structure from motion (SfM) technique in this case generates poor estimation of the 3d structure due to several reasons such as texture-less images, small baseline variations and dominant forward camera motion. Our idea is to introduce the monocular depth cues that exist in a single image, and add time constraints on the estimated 3D structure. We assume that our scene is made up of small planar patches which are obtained using over-segmentation method, and our goal is to estimate the 3D positioning for each of these planes. We propose a fusion framework that employs Markov Random Field (MRF) model to integrate both spatial and temporal depth information. An advantage of our model is that it performs well even in the absence of some depth information. Spatial depth information is obtained through a global and local feature extraction method inspired by Saxena et al. [1]. Temporal depth information is obtained via sparse optical flow based structure from motion approach. That allows decreasing the estimation ambiguity by forcing some constraints on camera motion. Finally, we apply a fusion scheme to create unique 3D structure estimation.

Fast Visual Odometry for a Low-Cost Underwater Embedded Stereo System †

This paper provides details of hardware and software conception and realization of a stereo embedded system for underwater imaging. The system provides several functions that facilitate underwater surveys and run smoothly in real-time. A first post-image acquisition module provides direct visual feedback on the quality of the taken images which helps appropriate actions to be taken regarding movement speed and lighting conditions. Our main contribution is a light visual odometry method adapted to the underwater context. The proposed method uses the captured stereo image stream to provide real-time navigation and a site coverage map which is necessary to conduct a complete underwater survey. The visual odometry uses a stochastic pose representation and semi-global optimization approach to handle large sites and provides long-term autonomy, whereas a novel stereo matching approach adapted to underwater imaging and system attached lighting allows fast processing and suitability to low computational resource systems. The system is tested in a real context and shows its robustness and promising future potential.

Cultural Heritage Resources Profiling: Ontology-based Approach.

Cultural heritage (CH) resources are very heterogeneous since the information was collected from vast diversity of cultural sites and digitally recorded in different formats. With the progress of 3D technologies, photogrammetry techniques become the adopted solution for representing CH artifacts by turning photos from small finds, to entire landscapes, into accurate 3D models. To meet knowledge representation with cultural heritage photogrammetry, this paper proposes an ontology-profiling method for modeling a real case of archaeological amphorae. The ontological profile consists of all needed information to represent a CH resource including typology attributes, geo-spatial information and photogrammetry process. An example illustrating the applicability of this profiling method to the problem of CH resources conceptualization is presented. We also outline our perspectives for using ontologies in data-driven science, in particular on modeling a complete pipeline that manages both the photogrammetric process and the archaeological knowledge.