Doron Aloni

Three-dimensional photon counting integral imaging reconstruction using penalized maximum likelihood expectation maximization

Recent works have demonstrated that three-dimensional (3D) object reconstruction is possible from integral images captured in severely photon starved conditions. In this paper we propose an iterative approach to implement a maximum likelihood expectation maximization estimator with several types of regularization for 3D reconstruction from photon counting integral images. We show that the proposed algorithms outperform the previously reported approaches for photon counting 3D integral imaging reconstruction. To the best of our knowledge, this is the first report on using iterative statistical reconstruction techniques for 3D photon counting integral imaging.

Automatic 3D object localization and isolation using computational integral imaging.

Detecting objects in three-dimensional (3D) space may be useful for various applications. We present a method to detect the 3D locations of objects using computationally reconstructed images obtained by integral imaging. The new algorithm exploits the space-variant blurring properties of the reconstructed images to detect and isolate objects at their depth locations, while removing traces of objects from other depths. With regard to previous work, the proposed method is more efficient and more resistant to noise; it gives more information about the detected objects depth, and improves object isolation and presentation.

Detection of Object Existence From a Single Reconstructed Plane Obtained by Integral Imaging

We use 3D computational reconstructed integral imaging to detect the existence objects at a 3D space. In order to find the precise location of the objects, we apply an efficient wavelet-based filtering with adaptive thresholding to identify and outline focused regions, which relate to object surfaces located at a single examined reconstructed plane. Finally, we present experimental results of 3D object detection, both in good illumination and under photon-starved conditions.

An overview of 3D visualization with integral imaging in photon starved conditions

Recently it was demonstrated that three-dimensional (3D) object recognition and visualization is possible with integral imaging in photon counting condition or under very low illumination conditions. We present an overview of the reconstruction techniques, imaging performance and compressive sensing ability of integral imaging in photon starved condition.

3D noise-resistant segmentation and tracking of unknown and occluded objects using integral imaging

Three dimensional (3D) object segmentation and tracking can be useful in various computer vision applications, such as: object surveillance for security uses, robot navigation, etc. We present a method for 3D multiple-object tracking using computational integral imaging, based on accurate 3D object segmentation. The method does not employ object detection by motion analysis in a video as conventionally performed (such as background subtraction or block matching). This means that the movement properties do not significantly affect the detection quality. The object detection is performed by analyzing static 3D image data obtained through computational integral imaging With regard to previous works that used integral imaging data in such a scenario, the proposed method performs the 3D tracking of objects without prior information about the objects in the scene, and it is found efficient under severe noise conditions.

Effects of elemental images’ quantity on three-dimensional segmentation using computational integral imaging: publisher’s note

This publishers note corrects the Funding section in Appl. Opt.56, 2132 (2017)APOPAI0003-693510.1364/AO.56.002132.

Feature extraction for 3D object detection from integral imaging

Objects in a 3D space can be located and segmented using information obtained by computational integral imaging. This paper implements an approach for objects isolation based on detecting the sharp edges of the focused regions in the reconstructed confocal images. Several edge feature detection methods are employed and examined. Results show that while the ability to detect the correct object depth locations does not depend on the edge detection method, the resulting quality of the detected object features may be significantly affected by the method used.

Efficient reconstruction of 3D images from photon starved integral imaging using PMLEM

Reconstruction of three dimensional (3D) images from photon counting integral images was recently demonstrated by applying several methods including: maximum likelihood estimation, Bayesian estimation, and statistical estimation involving truncated Poisson statistics. Here we present simulation results for a new estimation approach implementing Penalized Maximum Likelihood Expectation Maximization (PMLEM) that better incorporates prior information in the reconstruction process.