23 research outputs found
Deep Hierarchical Parsing for Semantic Segmentation
This paper proposes a learning-based approach to scene parsing inspired by
the deep Recursive Context Propagation Network (RCPN). RCPN is a deep
feed-forward neural network that utilizes the contextual information from the
entire image, through bottom-up followed by top-down context propagation via
random binary parse trees. This improves the feature representation of every
super-pixel in the image for better classification into semantic categories. We
analyze RCPN and propose two novel contributions to further improve the model.
We first analyze the learning of RCPN parameters and discover the presence of
bypass error paths in the computation graph of RCPN that can hinder contextual
propagation. We propose to tackle this problem by including the classification
loss of the internal nodes of the random parse trees in the original RCPN loss
function. Secondly, we use an MRF on the parse tree nodes to model the
hierarchical dependency present in the output. Both modifications provide
performance boosts over the original RCPN and the new system achieves
state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler
urban datasets.Comment: IEEE CVPR 201
Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality
Real-time occlusion handling is a major problem in outdoor mixed reality
system because it requires great computational cost mainly due to the
complexity of the scene. Using only segmentation, it is difficult to accurately
render a virtual object occluded by complex objects such as trees, bushes etc.
In this paper, we propose a novel occlusion handling method for real-time,
outdoor, and omni-directional mixed reality system using only the information
from a monocular image sequence. We first present a semantic segmentation
scheme for predicting the amount of visibility for different type of objects in
the scene. We also simultaneously calculate a foreground probability map using
depth estimation derived from optical flow. Finally, we combine the
segmentation result and the probability map to render the computer generated
object and the real scene using a visibility-based rendering method. Our
results show great improvement in handling occlusions compared to existing
blending based methods
Sample and Filter: Nonparametric Scene Parsing via Efficient Filtering
Scene parsing has attracted a lot of attention in computer vision. While
parametric models have proven effective for this task, they cannot easily
incorporate new training data. By contrast, nonparametric approaches, which
bypass any learning phase and directly transfer the labels from the training
data to the query images, can readily exploit new labeled samples as they
become available. Unfortunately, because of the computational cost of their
label transfer procedures, state-of-the-art nonparametric methods typically
filter out most training images to only keep a few relevant ones to label the
query. As such, these methods throw away many images that still contain
valuable information and generally obtain an unbalanced set of labeled samples.
In this paper, we introduce a nonparametric approach to scene parsing that
follows a sample-and-filter strategy. More specifically, we propose to sample
labeled superpixels according to an image similarity score, which allows us to
obtain a balanced set of samples. We then formulate label transfer as an
efficient filtering procedure, which lets us exploit more labeled samples than
existing techniques. Our experiments evidence the benefits of our approach over
state-of-the-art nonparametric methods on two benchmark datasets.Comment: Please refer to the CVPR-2016 version of this manuscrip
Layered Interpretation of Street View Images
We propose a layered street view model to encode both depth and semantic
information on street view images for autonomous driving. Recently, stixels,
stix-mantics, and tiered scene labeling methods have been proposed to model
street view images. We propose a 4-layer street view model, a compact
representation over the recently proposed stix-mantics model. Our layers encode
semantic classes like ground, pedestrians, vehicles, buildings, and sky in
addition to the depths. The only input to our algorithm is a pair of stereo
images. We use a deep neural network to extract the appearance features for
semantic classes. We use a simple and an efficient inference algorithm to
jointly estimate both semantic classes and layered depth values. Our method
outperforms other competing approaches in Daimler urban scene segmentation
dataset. Our algorithm is massively parallelizable, allowing a GPU
implementation with a processing speed about 9 fps.Comment: The paper will be presented in the 2015 Robotics: Science and Systems
Conference (RSS