Using volumetric computational II reconstruction, we are able to recognize distorted occluded objects with correlation based recognition algorithms. We present experimental results which show recognition of 3D rotated targets in a reconstructed occluded scene. We also show the ability of the proposed technique to recognize distorted occluded 3D non-training targets.
Proc. SPIE. 6392, Three-Dimensional TV, Video, and Display V
KEYWORDS: Microlens array, 3D image reconstruction, Image processing, Image resolution, Digital cameras, Feature extraction, Reconstruction algorithms, 3D displays, Integral imaging, 3D image processing
In this paper, we present a system to reconstruct perspective view of a partially occluded object by using computational integral imaging. The system is analyzed to extract information of off-centered views from a elemental images set. To obtain unobstructed images with high resolution, low focus error, and large depth of focus, synthetic aperture integral imaging utilizing a digital camera has been adopted.
KEYWORDS: Microlens array, Three dimensional sensing, 3D image reconstruction, Visualization, Image resolution, Reconstruction algorithms, 3D displays, 3D visualizations, Integral imaging, 3D image processing
In this paper, we present a system to reconstruct a free view of a partially occluded object by using computational integral imaging. The system is analyzed to sense information of off-center views from a elemental images set. To obtain unobstructed images with high resolution, low focus error, and large depth of focus, synthetic aperture integral imaging utilizing a digital camera has been adopted. Two novel algorithms are proposed: 1) an algorithm for reconstructing volumetric perspective images and 2) an algorithm for scaling reconstruction images at arbitrary distances.
We present a fusion method for multi-wavelengths holographic images using Discrete Wavelet transform (DWT). The advantage of DWT is more control on high as well as low frequencies to get better quality. Fusion results include monochromatic fused images and color fused images from holographic images reconstructed from holograms recorded with multiple wavelengths from 567nm to 613nm.
In this paper, we present 3D image fusion using digital holography. We demonstrate experimentally that through image fusion technique using multi-resolution wavelet decomposition it is possible to increase details and contrast of the 3D reconstructed computational holographic images obtained by multi-wavelengths digital holograms.
We propose an optimum nonlinear filter to detect the target's 3D coordinates within the input 3D scene using LADAR data. The 2D encoded LADAR range image is converted into 3D binary profile, and then the 3D optimum nonlinear filtering technique is used to detect the 3D coordinates of targets including the target distance from the sensor. The 3D optimum nonlinear filter is designed to detect distorted targets, i.e., out-of-plane and in-plane rotations and scale, and to be noise robust. The nonlinear filter is derived to minimize the mean of the output energy due to the disjoint background noise and additive noise and output energy due to the input scene, while maintaining a fixed output peak for the members of the true class target training set. The system is tested using real LADAR imagery in the presence of background clutter. The correlation outputs of LADAR images show dominant peaks at the target 3D coordinates.
Laser radar (LADAR) is a device to acquire the profile of an object. A 3D LADAR range image is encoded as a 2D image whose gray values represent the measured range. It is possible to apply 2D filters for recognition of 2D encoded range image to detect the location of a target. However, if we want to detect the target coordinate within the input scene, that is not only the location of a target but also the distance from a sensor, 3D data processing is needed. First, it is needed to convert 2D range image back into 3D space, and then apply a 3D filtering technique to detect the target location and the distance from the sensor. We set up and solve the minimization problem that leads to the derivation of the 3D distortion tolerant nonlinear filter by minimizing the output energy due to the additive noise and the energy of correlation output in response to the input scene with the false objects. This filter also has a capability to reject a certain target if we know exactly what to reject. The correlation output at the distance level of the target shows a dominant peak at the target location, and the correlation outputs at the distance of the adjacent target level shows a very small peak at the target location. We can detect the location and the distance at the same time of the distorted target using proposed 3D optimum filtering.