"Computational modeling"

Identifying and Pruning Redundant Structures for Deep Neural Networks

Deep convolutional neural networks have achieved considerable success in the field of computer vision. However, it is difficult to deploy state-of-the-art models on resource-constrained platforms due to their high storage, memory bandwidth, and …

Review of ITU-T parametric models for compressed video quality estimation

This paper presents a review of parametric models for video quality estimation standardized in the past few years. The focus of this paper is the estimation of quality degradation caused by the compression artifacts. The models introduced in the …

Avs Encoding Optimization with Perceptual Just Noticeable Distortion Model

Integrating efficient visual perceptual cues into standardized video coding framework can improve performance significantly. In this paper we propose to enhance AVS encoder by using the latest just noticeable distortion (JND) model to adjust DCT …

Video stabilization with L1–L2 optimization

Digital videos often suffer from undesirable camera jitters because of unstable camera motions. In this paper we present a novel video stabilization algorithm by mixed L1-L2 optimization, aiming at removing unwanted camera movements as well as …

Object contour tracking using multi-feature fusion based particle filter

In this paper, a novel object contour tracking framework integrating independent multi-feature fusion object rough location and region-based temporal differencing model is proposed. In our model, the object rough location tracking is realized by …

Hybrid center-symmetric local pattern for dynamic background subtraction

Effective foreground detection in dynamic scenes is a challenging task in computer vision applications. In this paper, we propose a novel background modeling method to tackle this problem. First, we propose a second-order center-symmetric local …

Dynamic background subtraction based on spatial extended center-symmetric local binary pattern

Moving objects detection in dynamic scenes is a challenging task in many computer vision applications. Traditional background modeling methods do not work well in these situations since they assume a nearly static background. In this paper, a novel …