Three-dimensional object-distortion-tolerant recognition for integral imaging using independent component analysis

Human gesture recognition using three-dimensional integral imaging

Three-dimensional (3D) integral imaging allows one to reconstruct a 3D scene, including range information, and provides sectional refocused imaging of 3D objects at different ranges. This paper explores the potential use of 3D passive sensing integral imaging for human gesture recognition tasks from sequences of reconstructed 3D video scenes. As a preliminary testbed, the 3D integral imaging sensing is implemented using an array of cameras with the appropriate algorithms for 3D scene reconstruction. Recognition experiments are performed by acquiring 3D video scenes of multiple hand gestures performed by ten people. We analyze the capability and performance of gesture recognition using 3D integral imaging representations at given distances and compare its performance with the use of standard two-dimensional (2D) single-camera videos. To the best of our knowledge, this is the first report on using 3D integral imaging for human gesture recognition.

Probabilistic 3D object recognition and pose estimation using multiple interpretations generation

This paper presents a probabilistic object recognition and pose estimation method using multiple interpretation generation in cluttered indoor environments. How to handle pose ambiguity and uncertainty is the main challenge in most recognition systems. In order to solve this problem, we approach it in a probabilistic manner. First, given a three-dimensional (3D) polyhedral object model, the parallel and perpendicular line pairs, which are detected from stereo images and 3D point clouds, generate pose hypotheses as multiple interpretations, with ambiguity from partial occlusion and fragmentation of 3D lines especially taken into account. Different from the previous methods, each pose interpretation is represented as a region instead of a point in pose space reflecting the measurement uncertainty. Then, for each pose interpretation, more features around the estimated pose are further utilized as additional evidence for computing the probability using the Bayesian principle in terms of likelihood and unlikelihood. Finally, fusion strategy is applied to the top ranked interpretations with high probabilities, which are further verified and refined to give a more accurate pose estimation in real time. The experimental results show the performance and potential of the proposed approach in real cluttered domestic environments.

Distortion-tolerant 3D recognition of underwater objects using neural networks

A method for distortion-tolerant recognition of objects in water using three-dimensional (3D) integral imaging with a neural network classification architecture is presented. Recognition algorithms are developed and experimental results are presented with rotation-variable 3D objects. To test the robustness of the system, objects are placed under a variety of water conditions, including variable Maalox-induced scattering levels and occlusion using pine needles. Neural networks have long been used for two-dimensional recognition and have recently been used for 3D digital holographic recognition. To the best of our knowledge, this is the first use of neural networks for passive 3D integral imaging and recognition of underwater objects.

Recent progress in three-dimensional information processing based on integral imaging

Recently developed integral imaging techniques are reviewed. Integral imaging captures and reproduces the light rays from the object space, enabling the acquisition and the display of the three-dimensional information of the object in an efficient way. Continuous effort on integral imaging has been improving the performance of the capture and display process in various aspects, including distortion, resolution, viewing angle, and depth range. Digital data processing of the captured light rays can now visualize the three-dimensional structure of the object with a high degree of freedom and enhanced quality. This recent progress is of high interest for both industrial applications and academic research.