This paper gives new insights on optical 3D imagery. In this paper we explore the advantages of laser imagery to form a
three-dimensional image of the scene. 3D laser imaging can be used for three-dimensional medical imaging and
surveillance because of ability to identify tumors or concealed objects. We consider the problem of 3D reconstruction
based upon 2D angle-dependent laser images. The objective of this new 3D laser imaging is to provide users a complete
3D reconstruction of objects from available 2D data limited in number. The 2D laser data used in this paper come from
simulations that are based on the calculation of the laser interactions with the different meshed objects of the scene of
interest or from experimental 2D laser images. We show that combining the Radom transform on 2D laser images with
the Maximum Intensity Projection can generate 3D views of the considered scene from which we can extract the 3D
concealed object in real time. With different original numerical or experimental examples, we investigate the effects of
the input contrasts. We show the robustness and the stability of the method. We have developed a new patented method
of 3D laser imaging based on three-dimensional reflective tomographic reconstruction algorithms and an associated
visualization method. In this paper we present the global 3D reconstruction and visualization procedures.
KEYWORDS: 3D image processing, 3D modeling, 3D image reconstruction, 3D acquisition, Laser imaging, Reconstruction algorithms, Clouds, Image processing, Reflectivity, Data acquisition
This paper deals with new optical non-conventional 3D laser imaging. Optical non-conventional imaging explores the advantages of laser imaging to form a three-dimensional image of the scene. 3D laser imaging can be used for threedimensional medical imaging, topography, surveillance, robotic vision because of ability to detect and recognize objects. In this paper, we present a 3D laser imaging for concealed object identification. The objective of this new 3D laser imaging is to provide the user a complete 3D reconstruction of the concealed object from available 2D data limited in number and with low representativeness. The 2D laser data used in this paper come from simulations that are based on the calculation of the laser interactions with the different interfaces of the scene of interest and from experimental results. We show the global 3D reconstruction procedures capable to separate objects from foliage and reconstruct a threedimensional image of the considered object. In this paper, we present examples of reconstruction and completion of three-dimensional images and we analyse the different parameters of the identification process such as resolution, the scenario of camouflage, noise impact and lacunarity degree.
KEYWORDS: Clouds, 3D modeling, Reconstruction algorithms, 3D image processing, Laser scattering, Scattering, 3D image reconstruction, 3D acquisition, Detection and tracking algorithms, Solid modeling
This paper addresses non-conventional three-dimensional imaging with laser systems which explores the advantages of
laser imagery to form a three-dimensional image of the scene. In this paper, we present the 3D laser scattering simulation
of objects hidden behind porous occluders, such as foliage or camouflage. The physics based model presented in this
paper is designed to provide accurate results but also to include all the electromagnetic interaction mechanisms with the
different elements of the scene. A 3D laser cross-section computer model is used to develop reconstruction algorithms to
obtain a high-resolved three-dimensional image. Synthetic images of three-dimensional objects are based on extraction
of laser backscattered signals. But 3D reconstruction must take into account sparse collected data and reconstruction
algorithms must solve a complex multi-parameter inverse problem. The objective of our paper is also to present new
algorithmic approaches for the generation of 3D surface data from 3D sparse point clouds corresponding to our
reconstruction algorithm. The role of this type of algorithmic process is to complete the 3D image at satisfactory levels
for reliable identification of concealed objects. Identifying targets or objects concealed by foliage or camouflage is a
critical requirement for operations in public safety, law enforcement and defense.
There is a considerable interest in the development of new optical imaging systems that are able to give threedimensional
images. Potential applications range across medical imaging, surveillance and robotic vision. Identifying
targets or objects concealed by foliage or camouflage is a critical requirement for operations in public safety, law
enforcement and defense. The most promising techniques for these tasks are 3D laser imaging techniques. Their
principles are to use movable light sources and detectors to collect information on laser scattering and to reconstruct the
3D objects of interest. 3D reconstruction algorithm is a major component in these optical systems for identification of
camouflaged objects. But 3D reconstruction must take into account sparse collected data i.e. concealed objects and
reconstruction algorithms must solve a complex multi-parameter inverse problem. Therefore the inverse problem of
recovering the surface three-dimensional shape function from intensity data is more challenging. The objective of our
paper is to present a new algorithmic approach for the generation of 3D surface data from 3D point clouds corresponding
to reconstruction algorithm. This algorithmic approach is based on research of automatic minimization of an energy
function associated with a sparse structure of 3D points. The role of this type of algorithmic data-driving process is to
complete the incomplete 3D image at satisfactory levels for reliable identification of concealed objects.
The implementation of artificial neural networks (ANN) as CMOS analog integrated circuits shows several attractive features. Stochastic models, especially the Boltzmann Machine, show a number of many attractive features. Recent studies on artificial models point out that classification is their most successful application field, and that real pattern recognition tasks, and especially image processing by artificial neural networks will require large networks. All of the presented implementations of ANN are supposed to be working in ideal conditions but real applications are subject to perturbations. For a digital implementation of ANN perturbation effects could be neglected in a firth order approximation. But for the analog and mixed digital/analog implementation cases, the behavior analysis of the neural network with perturbation conditions is inevitable. Unfortunately, very few papers analyze the behavior of analog neural networks with perturbation or their limitations. In this paper we present the analysis of a Boltzmann Machine model's behavior with physical temperature perturbation. The relation between the T parameter of the Boltzmann Machine model and the physical temperature of circuit has been established. Simulation results are presented and temperature effects compensation is discussed.
The implementation of artificial neural networks (ANN) as CMOS analog integrated circuits shows several attractive features. Stochastic models, and especially the Boltzmann Machine shows a number of many attractive features. Numerous papers show that small size analog networks operate correctly. However, recent studies on artificial models point out that classification is their most successful application field: so real pattern recognition tasks will require large networks. On the other hand, all of the presented implementations of ANN have been supposed to be working in ideal conditions but real applications will subject to perturbations. For a digital implementation of ANN perturbation effects could be neglected in a fifth-order approximation. But for the analog and mixed digital/analog implementation cases, the behavior analysis of the neural network with perturbation conditions is inevitable. Unfortunately, very few papers analyze the behavior of analog neural networks with perturbation or their limitations. In this paper we present the analysis of a CMOS analog implementation of synchronous Boltzmann Machine model's behavior with physical temperature perturbations. The relation between the T parameter of the Boltzmann Machine's model and the physical temperature of circuit has been presented. Simulation results have been given, temperature effects compensation have been discussed, and experimental results have been exposed.
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