KEYWORDS: Video, Image segmentation, Video processing, Data centers, Data processing, Image processing algorithms and systems, Switches, Semantic video, Human vision and color perception, Video coding
This paper presents a new method for unsupervised video segmentation based on mean shift clustering in spatio-temporal
domain. The main novelties of the proposed approach are dynamic temporal adaptation of clusters
due to which the segmentation evolves quickly and smoothly over time. The proposed method consists of a short
initialization phase and an update phase. The proposed method significantly reduce the computation load for
the mean shift clustering. In the update phase only the positions of relatively small number of cluster centers are
updated and new frames are segmented based on the segmentation of previous frames. The method segments
video in real-time and tracks video objects effectively.
In this paper we present a new method for joint denoising of depth and luminance images produced by
time-of-flight camera. Here we assume that the sequence does not contain outlier points which can be
present in the depth images. Our method first performs estimation of noise and signal covariance matrices
and then performs vector denoising. Luminance image is segmented into similar contexts using k-means
algorithm, which are used for calculation of covariance matrices. Denoising results are compared with the
ground truth images obtained by averaging of the multiple frames of the still scene.
KEYWORDS: Video, Image segmentation, Video processing, Image processing algorithms and systems, Data processing, Detection and tracking algorithms, Semantic video, Motion estimation, Video coding, Image filtering
Object segmentation is considered as an important step in video analysis and has a wide range of practical
applications. In this paper we propose a novel video segmentation method, based on a combination of watershed
segmentation and mean-shift clustering. The proposed method segments video by clustering spatio-temporal data
in a six-dimensional feature space, where the features are spatio-temporal coordinates and spectral attributes.
The main novelty is an efficient data aggregation method employing watershed segmentation and local feature
averaging. The experimental results show that the proposed algorithm significantly reduces the processing time
by mean-shift algorithm and results in superior video segmentation where video objects are well defined and tracked throughout the time.
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