Texture as pixel feature for video object segmentation

As texture represents one of the key perceptual attributes of any object, integrating textural information into existing video object segmentation frameworks affords the potential to achieve semantically improved performance. While object segmentation is fundamentally pixel-based classification, texture is normally defined for the entire image, which raises the question of how best to directly specify and characterise texture as a pixel feature. Introduced is a generic strategy for representing textural information so it can be seamlessly incorporated as a pixel feature into any video object segmentation paradigm. Both numerical and perceptual results upon various test sequences reveal considerable improvement in the object segmentation performance when textural information is embedded

A video synchronization approach for coherent key-frame extraction and object segmentation

© 2005 - 2014 JATIT & LLS. All rights reserved. In this paper we discuss a new video frame synchronization approach for coherent key-frame extraction and object segmentation. As two basic units for content-based video analysis, key-frame extraction and object segmentation are usually implemented independently and separately based on different feature sets. Our previous work showed that by exploiting the inherent relationship between key-frames and objects, a set of salient key-frames can be extracted to support robust and efficient object segmentation. This work furthers the previous numerical studies by suggesting a new analytical approach to jointly formulate key-frame extraction and object segmentation via a statistical mixture model where the concept of frame/pixel saliency which is introduced and also this deals with the relationship between the frames. A modified Expectation Maximization algorithm is developed for model estimation that leads to the most salient key-frames for object segmentation. Simulations on both synthetic and real videos show the effectiveness and efficiency of the proposed method

Semi-automatic object tracking in video sequences

A method is presented for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the sequence modelling the a posteriori probabilities of a set of features such as color, position and motion, depth, etc.Facultad de Informátic

Semi-automatic object tracking in video sequences

In this paper we present a method for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the sequence modeling the a posteriori probabilities of a set of features such as: color, position and motion, depth, etcIII Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI

Pixel behaviour metrics for dynamic background modelling with the projected difference pattern method

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Semi-automatic object tracking in video sequences

A method is presented for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the sequence modelling the a posteriori probabilities of a set of features such as color, position and motion, depth, etc.Facultad de Informátic

Semi-automatic object tracking in video sequences

In this paper we present a method for semi-automatic object tracking in video sequences using multiple features and a method for probabilistic relaxation to improve the tracking results producing smooth and accurate tracked borders. Starting from a given initial position of the object in the first frame the proposed method automatically tracks the object in the sequence modeling the a posteriori probabilities of a set of features such as: color, position and motion, depth, etcIII Workshop de Computación Gráfica, Imágenes y Visualización (WCGIV)Red de Universidades con Carreras en Informática (RedUNCI