Adaptive Feature-specific Imaging Research Articles

Adaptive three-dimensional feature specific imaging

Feature specific imaging is a computational imaging technique that minimizes the number of measurements needed to sufficiently reconstruct a scene by using a priori knowledge (e.g., the scene’s second-order statistics) to judiciously, as well as possibly adaptively, choose the projection vectors to be measured. Here, we have developed an approach to three-dimensional adaptive feature specific imaging that takes into account the obstruction of distant objects by closer objects in the adaption of the projections and in the reconstruction algorithm. The developed system reconstructs the cross-range image of the scene at each range bin from a set of range resolved measurements from all the return from the scene at that range using only a single photodetector, while adapting to the obstruction of the scene by closer objects. Simulations and a proof-of-concept demonstration of adaptive three-dimensional feature specific imaging are presented.

Adaptive feature-specific imaging for recognition of non-Gaussian classes

We present an adaptive feature-specific imaging (AFSI) system for application to an M-class recognition task. The proposed system uses nearest-neighbor-based density estimation to compute the (non-Gaussian) class-conditional densities. We refine the density estimates based on the training data and the knowledge from previous measurements at each step. The projection basis for the AFSI system is also adapted based on the previous measurements at each step. The decision-making process is based on sequential hypothesis testing. We quantify the number of measurements required to achieve a specified probability of error (P(e)) and we compare the AFSI system with an adaptive-conventional (ACONV) system. The AFSI system exhibits significant improvement compared to the ACONV system at low signal-to-noise ratio (SNR), and it is shown that, for an M=4 hypotheses, SNR=-10 dB, and a desired P(e)=10(-2), the AFSI system requires 30 times fewer measurements than the ACONV system. Experimental results validating the AFSI system are presented.

Recognition using information-optimal adaptive feature-specific imaging

We present an information-theoretic adaptive feature-specific imaging (AFSI) system for a M-class recognition task. The proposed system utilizes the recently developed task-specific information (TSI) framework to incorporate the knowledge from previous measurements and adapt the projection matrix at each step. The decision-making framework is based on sequential hypothesis testing. We quantify the number of measurements required to achieve a specified probability of misclassification (P(e)), and we compare the performances of three approaches: the new TSI-based AFSI system, a previously reported statistical AFSI system, and static FSI (SFSI). The TSI-based AFSI system exhibits significant improvement compared with SFSI and statistical AFSI at low signal-to-noise ratio (SNR). It is shown that for M=4 hypotheses, SNR=-20 dB and desired P(e)=10(-2), TSI-based AFSI requires 3 times fewer measurements than statistical AFSI, and 16 times fewer measurements than SFSI. We also describe an extension of the proposed method that is suitable for recognition in the presence of nuisance parameters such as illumination conditions and target orientations.

Adaptive feature-specific imaging: a face recognition example

We present an adaptive feature-specific imaging (AFSI) system and consider its application to a face recognition task. The proposed system makes use of previous measurements to adapt the projection basis at each step. Using sequential hypothesis testing, we compare AFSI with static-FSI (SFSI) and static or adaptive conventional imaging in terms of the number of measurements required to achieve a specified probability of misclassification (Pe). The AFSI system exhibits significant improvement compared to SFSI and conventional imaging at low signal-to-noise ratio (SNR). It is shown that for M=4 hypotheses and desired Pe=10(-2), AFSI requires 100 times fewer measurements than the adaptive conventional imager at SNR= -20 dB. We also show a trade-off, in terms of average detection time, between measurement SNR and adaptation advantage, resulting in an optimal value of integration time (equivalent to SNR) per measurement.