Fast Bilateral Solver for Semantic Video Segmentation
نویسندگان
چکیده
We apply the fast bilateral solver technique to the problem of real-time semantic video segmentation. While structured prediction by a dense CRF is accurate on video datasets, the performance is not adequate for real-time segmentation. We hope to utilize the efficient smoothing methodology from the fast bilateral solver within the video segmentation framework introduced by Kundu et al. [9], improving the accuracy of the segmentation, maintaining temporal and spatial coherence, and achieving the speedups necessary for real-time performance.
منابع مشابه
The Fast Bilateral Solver
We present the bilateral solver, a novel algorithm for edge-aware smoothing that combines the flexibility and speed of simple filtering approaches with the accuracy of domain-specific optimization algorithms. Our technique is capable of matching or improving upon state-of-the-art results on several different computer vision tasks (stereo, depth superresolution, colorization, and semantic segmen...
متن کاملSegmentation Using Fast Marching Method
The paper deals with a novel segmentation technique applicable to colour video sequences. The algorithm uses Fast Marching Method for automatic extraction of semantic objects from natural colour video sequences by joint motion and colour analysis. The algorithm handles background in the same way as other objects, thus it does not need global motion compensation. The number of control parameters...
متن کاملSIDF: A Novel Framework for Accurate Surgical Instrument Detection in Laparoscopic Video Frames
Background and Objectives: Identification of surgical instruments in laparoscopic video images has several biomedical applications. While several methods have been proposed for accurate detection of surgical instruments, the accuracy of these methods is still challenged high complexity of the laparoscopic video images. This paper introduces a Surgical Instrument Detection Framework (SIDF) for a...
متن کاملFast Semantic Image Segmentation with High Order Context and Guided Filtering
This paper describes a fast and accurate semantic image segmentation approach that encodes not only the discriminative features from deep neural networks, but also the high-order context compatibility among adjacent objects as well as low level image features. We formulate the underlying problem as the conditional random field that embeds local feature extraction, clique potential construction,...
متن کاملBeyond Semantic Image Segmentation : Exploring Efficient Inference in Video
Deep convolutional neural networks (DCNNs) trained on a large number of images with pixel-level annotations or a combination of strongly labeled and weakly-labeled images have recently been the state-of-the-art in semantic image segmentation with significant performance improvement. However, due to the very invariance properties that make DCNNs good for high level tasks such as classification, ...
متن کامل