Multimodality semantic segmentation based on polarization and color images

Abstract

Semantic segmentation gives a meaningful class label to every pixel in an image. It enables intelligent devices to understand the scene and has received sufficient attention during recent years. Traditional imaging systems always apply their methods on RGB, RGB-D or even RGB combined with geometric information. However, for outdoor applications, strong reflection or poor illumination appears to reduce the visualization of the real shape or texture of the objects, thus limiting the performance of semantic segmentation algorithms. To tackle this problem, this paper adopts polarization imaging as it can provide complementary information by describing some imperceptible light properties, which varies from different materials. For acceleration, SLIC superpixel segmentation is used to speed up the system. HOG and LBP features are extracted from both color and polarization images. After quantization using visual codebooks, Joint Boosting classifier is trained to label each pixel based on the quantized features. The proposed method was evaluated both on Day-set and Dusk-set. The experimental results show that using polarization setup can provide complementary information to improve the semantic segmentation accuracy. Especially, a large improvement on Dusk-set shows its capacity for intelligent vehicle applications under dark illumination condition.