A new two-stage global/local motion estimation based on a background/foreground segmentation

Abstract

In the framework of sequence coding, motion estimation and compensation has been shown to be very efcient at removing temporal redundancy. The motion existing in a scene can be mainly seen as arising from local motions superimposed to the camera motion. In this paper, a new two stage global local motion estimation approach is presented. The global motion estimation only relies on the background information. It is based on a matching technique and the global motion model is chosen to be a ne. Simulation results show signi cant improvements obtained with the proposed method compared to usual methods.

Fast Video Segmentation Algorithm With Shadow Cancellation, Global Motion Compensation, and Adaptive Threshold Techniques

“…These algorithms are based on the motion model of two (translation model), four (isotropic model), six (affine model), eight (perspective model), or 12 parameters (parabolic model). They can be classified into three types: frame matching, differential technique, and feature points based algorithms [18]. Frame matching algorithm matches the whole frame with the candidate motion parameters to find the global motion vector [19], [20].…”

“…When lighting is changed, we can model the irradiance as (18) where is light changing factor and is a constant. For background object, the dicriminant function is (19) Therefore, the background object may also be taken as foreground objects.…”

Motion-Adaptive Modelling of Scene Content for Very Low Bit Rate Model-Assisted Coding of Video

“…Many foreground/background segmentation an average rate of about 86%, whereas an algorithm which techniques have been proposed in the literature, initially does not perform background removal could only achieve less in the relatively simple case of stationary (still) backthan 10% of successful tracking. Initial coding experiments grounds [3,4], then in the more general case of moving using the information obtained from face tracking for modelbackgrounds [5][6][7][8][9]. In the former case, the techniques are assisted coding of video in QCIF format at 16 kbps demonstrate typically based on frame differencing, followed by thresh-…”

Visual alignment of arteries in coronary cine-angiogram using global motion stabilisation approach

“…Hence, it is desirable to conduct an objective assessment of the detected stenosis Non-uniform illumination and noise degrade the visual clarity of vessel structures in CCA frames (Dehkordi et al, 2011). Additionally, CCAs consist of various types of motion artifacts (Moscheni et al, 1995;Yamamoto et al, 2009). There are three main types of motions in CCAs, namely, the global, radial and local motions.…”