Region-Based Semantic Segmentation with End-to-End Training

We propose a novel method for semantic segmentation, the task of labeling each pixel in an image with a semantic class. Our method combines the advantages of the two main competing paradigms. Methods based on region classification offer proper spatial support for appearance measurements, but typically operate in two separate stages, none of which targets pixel labeling performance at the end of the pipeline. More recent fully convolutional methods are capable of end-to-end training for the final pixel labeling, but resort to fixed patches as spatial support. We show how to modify modern region-based approaches to enable end-to-end training for semantic segmentation. This is achieved via a differentiable region-to-pixel layer and a differentiable free-form Region-of-Interest pooling layer. Our method improves the state-of-the-art in terms of class-average accuracy with 64.0% on SIFT Flow and 49.9% on PASCAL Context, and is particularly accurate at object boundaries.Comment: ECCV 2016 camera-read

A Study on Semantic Segmentation for Autonomous Vehicles

ABSTRACTAutonomous vehicles are already a reality, and there are still severalchallenges to overcome. One important challenge for the adoptionof these vehicles is perceiving its surroundings. This necessity ofperception can be fulfilled by digital cameras. When working withdigital image processing, the quality will be limited by real-timeconstraints. As several works indicate, this real-time constraint forautonomous vehicles is at most 100ms per frame. Also, by improvingthe processing time, the chances of accidents involving autonomousvehicles may be decreased. This paper analyses the advantages anddrawbacks of semantic segmentation and also presents a study toimplement perception for autonomous vehicles by accelerating asemantic segmentation algorithm, also used by other works on thefield. To accelerate the algorithm, spacial parallelism will be used