Anomaly Detection with Adversarial Dual Autoencoders

Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. However, GAN training is somewhat challenging and unstable. Inspired from previous work in GAN-based image generation, we introduce a GAN-based anomaly detection framework - Adversarial Dual Autoencoders (ADAE) - consists of two autoencoders as generator and discriminator to increase training stability. We also employ discriminator reconstruction error as anomaly score for better detection performance. Experiments across different datasets of varying complexity show strong evidence of a robust model that can be used in different scenarios, one of which is brain tumor detection.

Mobile Camera Array Calibration for Light Field Acquisition

The light field camera is useful for computer graphics and vision applications. Calibration is an essential step for these applications. After calibration, we can rectify the captured image by using the calibrated camera parameters. However, the large camera array calibration method, which assumes that all cameras are on the same plane, ignores the orientation and intrinsic parameters. The multi-camera calibration technique usually assumes that the working volume and viewpoints are fixed. In this paper, we describe a calibration algorithm suitable for a mobile camera array based light field acquisition system. The algorithm performs in Zhang's style by moving a checkerboard, and computes the initial parameters in closed form. Global optimization is then applied to refine all the parameters simultaneously. Our implementation is rather flexible in that users can assign the number of viewpoints and refinement of intrinsic parameters is optional. Experiments on both simulated data and real data acquired by a commercial product show that our method yields good results. Digital refocusing application shows the calibrated light field can well focus to the target object we desired.