KEYWORDS: Image processing, 3D modeling, Image segmentation, Unmanned aerial vehicles, 3D image processing, Image enhancement, Digital filtering, Systems modeling, Photogrammetry, Imaging systems
Unmanned Aerial Systems (UAS) are extensively used in diverse fields, wherever inexpensive and easy-to-deploy platforms are required for close-range remote sensing.
Applications proposed in archaeology to date include ortho-photography and 3-D modeling. On the other hand, use of image processing and feature detection methods, well developed in other fields is hardly used.
After reviewing technologies and methods for UAS-based surveying and surface modeling, we propose feature detection methods (e.g. line detection, texture segmentation) dedicated to extraction of structures in the images that are significant for archaeological survey, planning, and documentation and show results on selected case studies.
This work, after a preliminary feasibility study using a Matlab environment simulation, defines the design and the real
hardware testing of a new bio-inspired decision chain for UAV sense-and-avoid applications. Relying on a single and
cheap visible camera sensor, computer vision, bio-inspired and automatic decision algorithms have been adopted and
implemented on a specific ARM embedded platform through C++/OpenCV coding. A first data set processing, really
captured on flight, has been presented.
In this paper, starting from the GOFR algorithm, a new Forward Regression algorithm for landmine detection and
localization using thermal methods is presented. The efficiency of such algorithm is described by showing a valid
representation of the typical temperature waveforms taken after heating the ground surface, and detection of
temperature anomalies due to the presence of hidden objects. Optimizations to the algorithm are then showed, with the
aim of a significant sampling density reduction in space and time.
This paper presents new techniques of landmine detection and localization using thermal methods. Described methods
use both dynamical and static analysis. The work is based on datasets obtained from the Humanitarian Demining
Laboratory of Università La Sapienza di Roma, Italy.
In this paper we present results of experimental validation of a new methodology for anti-personnel mine (APM)
detection for humanitarian demining, proposed by the authors and previously validated only by simulation. The
technique is based on local heating and sensing by contactless thermometers (pyrometers). A large sand box (2.6m3) has
been realized and fitted with a cart moving on rails and holding instrumentation. Accurate mine surrogates have been
hidden in the sand together with confounders. Preliminary measurements are consistent with simulations and prove
validity of the approach.
Non parametric inference error, the error arising from estimating the regression function based on a labeled set of
training examples could be divided into two main contributions: the bias and the variance. Neural network is one of
the existing models in non parametric inference whose bias/variance trade off is hidden below the network architecture.
In recent years new and powerful tools for neural networks selection were invented to impact the bias variance dilemma and the results in the implemented solution were satisfying [11,12]. We exploited the new measures introduced in these works for implementing a genetic algorithm to train neural networks. This method enables a reliable generalization error estimation for neural model. Estimating the error performance permits to drive correctly the genetic evolution that will lead to a fitting model with the desired characteristics. After a brief description of the estimation technique we used the genetic algorithm implementation for artificial data as a test. Finally the results of the fully automatic algorithm for NN training and model selection applied to investigation of defect structure of semi-insulating materials based on photo-induced transient spectroscopy experiments.
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