57,466 research outputs found
Combined wavelet domain and motion compensated filtering compliant with video codecs
In this paper, we introduce the idea of using motion estimation resources from a video codec for video denoising. This is not straightforward because the motion estimators aimed for video compression and coding, tolerate errors in the estimated motion field and hence are not directly applicable to video denoising. To solve this problem, we propose a novel motion field filtering step that refines the accuracy of the motion estimates to a degree that is required for denoising.
We illustrate the use of the proposed motion estimation method within a wavelet-based video denoising scheme. The resulting video denoising method is of low-complexity and receives comparable results with respect to the latest video denoising methods
Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imaging
Depth sensing is useful in a variety of applications that range from
augmented reality to robotics. Time-of-flight (TOF) cameras are appealing
because they obtain dense depth measurements with minimal latency. However, for
many battery-powered devices, the illumination source of a TOF camera is power
hungry and can limit the battery life of the device. To address this issue, we
present an algorithm that lowers the power for depth sensing by reducing the
usage of the TOF camera and estimating depth maps using concurrently collected
images. Our technique also adaptively controls the TOF camera and enables it
when an accurate depth map cannot be estimated. To ensure that the overall
system power for depth sensing is reduced, we design our algorithm to run on a
low power embedded platform, where it outputs 640x480 depth maps at 30 frames
per second. We evaluate our approach on several RGB-D datasets, where it
produces depth maps with an overall mean relative error of 0.96% and reduces
the usage of the TOF camera by 85%. When used with commercial TOF cameras, we
estimate that our algorithm can lower the total power for depth sensing by up
to 73%
Loss-resilient Coding of Texture and Depth for Free-viewpoint Video Conferencing
Free-viewpoint video conferencing allows a participant to observe the remote
3D scene from any freely chosen viewpoint. An intermediate virtual viewpoint
image is commonly synthesized using two pairs of transmitted texture and depth
maps from two neighboring captured viewpoints via depth-image-based rendering
(DIBR). To maintain high quality of synthesized images, it is imperative to
contain the adverse effects of network packet losses that may arise during
texture and depth video transmission. Towards this end, we develop an
integrated approach that exploits the representation redundancy inherent in the
multiple streamed videos a voxel in the 3D scene visible to two captured views
is sampled and coded twice in the two views. In particular, at the receiver we
first develop an error concealment strategy that adaptively blends
corresponding pixels in the two captured views during DIBR, so that pixels from
the more reliable transmitted view are weighted more heavily. We then couple it
with a sender-side optimization of reference picture selection (RPS) during
real-time video coding, so that blocks containing samples of voxels that are
visible in both views are more error-resiliently coded in one view only, given
adaptive blending will erase errors in the other view. Further, synthesized
view distortion sensitivities to texture versus depth errors are analyzed, so
that relative importance of texture and depth code blocks can be computed for
system-wide RPS optimization. Experimental results show that the proposed
scheme can outperform the use of a traditional feedback channel by up to 0.82
dB on average at 8% packet loss rate, and by as much as 3 dB for particular
frames
Online Object Tracking with Proposal Selection
Tracking-by-detection approaches are some of the most successful object
trackers in recent years. Their success is largely determined by the detector
model they learn initially and then update over time. However, under
challenging conditions where an object can undergo transformations, e.g.,
severe rotation, these methods are found to be lacking. In this paper, we
address this problem by formulating it as a proposal selection task and making
two contributions. The first one is introducing novel proposals estimated from
the geometric transformations undergone by the object, and building a rich
candidate set for predicting the object location. The second one is devising a
novel selection strategy using multiple cues, i.e., detection score and
edgeness score computed from state-of-the-art object edges and motion
boundaries. We extensively evaluate our approach on the visual object tracking
2014 challenge and online tracking benchmark datasets, and show the best
performance.Comment: ICCV 201
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