We present an overview of the 3D integral imaging based human recognition under degraded environments and its performance comparison with that of RGB-D sensors. In this work, we considered the problem of continuous gesture recognition under degradations such as partial occlusion. The 3D integral imaging helps to improve the recognition under these degradations which has been demonstrated through experimental results. Additionally, its performance is better compared to that of RGB-D sensors under the experimental conditions considered.
In this paper, we overview the previously reported underwater signal detection system using 1D integral imaging convolutional neural networks (1DInImCNN). The 1DInImCNN system comprises cameras arranged in a one-dimensional configuration for optical signal collection and the 1DInImCNN approach for signal detection. The 1D camera array is used to capture the spatial and temporal information, encoded using Gold code and transmitted by a Light-emitting Diode (LED). Various turbidities and occlusions are created in a water tank to test the performance of the proposed method under such degradations. The 1DInImCNN method is compared to the previously proposed 3D integral imaging (3D InIm) with Convolutional neural network (CNN) and Bi-Long Short-term memory (Bi-LSTM) approach. The results suggest that the 1DInImCNN-based approach outperforms the previously proposed 3D InIm with the CNN-BiLSTM approach in terms of computation costs and detection performance.
KEYWORDS: Signal detection, Integral imaging, Turbidity, Imaging systems, Polarimetry, Polarization, Image restoration, 3D image processing, Nonlinear dynamics, 3D image reconstruction
We overview a polarimetric integral imaging system for optical signal sensing and imaging. The proposed system is demonstrated to enhance signal detection and visualization in turbid water. For optical signal detection, a temporally encoded optical signal is recorded using single-shot polarimetric integral imaging and detected using nonlinear correlation. Furthermore, we also presented an integral imaging-based polarization dehazing method for polarization-based image recovery in turbid and occluded mediums. Reconstruction based on integral imaging reduces noise and improves estimating the intermediate parameters required for polarization-based image recovery. The above-overviewed systems enhance the detection capabilities compared to conventional 2D imaging methods.
We overview our recently published multi-dimensional integral imaging-based system for underwater optical signal detection. For robust signal detection, an optical signal propagating through the turbid water is encoded using multiple light sources and coded with spread spectrum techniques. An array of optical sensors captures video sequences of elemental images, which are reconstructed using multi-dimensional integral imaging followed by a 4D correlation to detect the transmitted signal. The area under the curve (AUC) and the number of detection errors were used as metrics to assess the performance of the system. The overviewed system successfully detects an optical signal under higher turbidity conditions than possible using conventional sensing and detection approaches.
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