Tube-embodied gradient vector flow fields for unsupervised video object plane (VOP) segmentation
نویسندگان
چکیده
In this paper constrained Gradient Vector Flow (GVF) field generation is performed, for fast and accurate unsupervised stereoscopic semantic segmentation. The scheme utilizes the information provided by a depth segments map, produced by stereo analysis methods and incorporation of a segmentation algorithm. Then a Canny edge detector is applied to the depth region and produces an edge map. The edge map is used for tube estimation inside which the GVF field evolves. After generation of the GVF field an active contour is unsupervisedly initialized onto the outer bound of the tube. Finally a greedy approach is adopted and the active contour, guided by the GVF field, extracts the VOP. Experimental results on real life stereoscopic video sequences indicate the efficiency of the proposed scheme.
منابع مشابه
Non-sequential video structuring based on video object linking: an efficient tool for video browsing and indexing
An efficient system for unsupervised structuring of stereoscopic sequences is presented in this paper, which generates links between similar VOPs of different shots. Particularly after shot cut detection, for each shot, a fast, unsupervised VOP detection and tracking algorithm is applied. Then for each of the foreground VOPs of a frame, a feature vector is constructed using low level features o...
متن کاملInstance Embedding Transfer to Unsupervised Video Object Segmentation
We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each pixel that enables identifying all pixels belonging to the same object. Though trained on static images, the instance embeddings are stable over consecutive video frames, which a...
متن کاملUnsupervised Total Variation Loss for Semi-supervised Deep Learning of Semantic Segmentation
We introduce a novel unsupervised loss function for learning semantic segmentation with deep convolutional neural nets (ConvNet) when densely labeled training images are not available. More specifically, the proposed loss function penalizes the L1-norm of the gradient of the label probability vector image , i.e. total variation, produced by the ConvNet. This can be seen as a regularization term...
متن کاملAutomatic segmentation of moving objects for video object plane generation
The new video coding standard MPEG-4 is enabling content-based functionalities. It takes advantage of a prior decomposition of sequences into video object planes (VOP’s) so that each VOP represents one moving object. A comprehensive review summarizes some of the most important motion segmentation and VOP generation techniques that have been proposed. Then, a new automatic video sequence segment...
متن کاملObject-based video abstraction for video surveillance systems
Key frames are the subset of still images which best represent the content of a video sequence in an abstracted manner. In other words, video abstraction transforms an entire video clip to a small number of representative images. In this paper, we present a scheme for object-based video abstraction facilitated by an efficient video-object segmentation (VOS) system. In such a framework, the conc...
متن کامل