No abstract

We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.

No abstract

We propose a novel multi-sensor system for accurate and power-efficient dynamic car-driver hand-gesture recognition, using a short-range radar, a color camera, and a depth camera, which together make the system robust against variable lighting conditions. We present a procedure to jointly calibrate the radar and depth sensors. We employ convolutional deep neural networks to fuse data from multiple sensors and to classify the gestures. Our algorithm accurately recognizes 10 different gestures acquired indoors and outdoors in a car during the day and at night. It consumes significantly less power than purely vision-based systems.

No abstract