4 research outputs found
Fully automatic extraction of salient objects from videos in near real-time
Automatic video segmentation plays an important role in a wide range of
computer vision and image processing applications. Recently, various methods
have been proposed for this purpose. The problem is that most of these methods
are far from real-time processing even for low-resolution videos due to the
complex procedures. To this end, we propose a new and quite fast method for
automatic video segmentation with the help of 1) efficient optimization of
Markov random fields with polynomial time of number of pixels by introducing
graph cuts, 2) automatic, computationally efficient but stable derivation of
segmentation priors using visual saliency and sequential update mechanism, and
3) an implementation strategy in the principle of stream processing with
graphics processor units (GPUs). Test results indicates that our method
extracts appropriate regions from videos as precisely as and much faster than
previous semi-automatic methods even though any supervisions have not been
incorporated.Comment: submitted to Special Issue on High Performance Computation on
Hardware Accelerators, the Computer Journa
Graph-Theoretic Spatiotemporal Context Modeling for Video Saliency Detection
As an important and challenging problem in computer vision, video saliency
detection is typically cast as a spatiotemporal context modeling problem over
consecutive frames. As a result, a key issue in video saliency detection is how
to effectively capture the intrinsical properties of atomic video structures as
well as their associated contextual interactions along the spatial and temporal
dimensions. Motivated by this observation, we propose a graph-theoretic video
saliency detection approach based on adaptive video structure discovery, which
is carried out within a spatiotemporal atomic graph. Through graph-based
manifold propagation, the proposed approach is capable of effectively modeling
the semantically contextual interactions among atomic video structures for
saliency detection while preserving spatial smoothness and temporal
consistency. Experiments demonstrate the effectiveness of the proposed approach
over several benchmark datasets.Comment: ICIP 201
Computational models of attention
This chapter reviews recent computational models of visual attention. We
begin with models for the bottom-up or stimulus-driven guidance of attention to
salient visual items, which we examine in seven different broad categories. We
then examine more complex models which address the top-down or goal-oriented
guidance of attention towards items that are more relevant to the task at hand
Computational models: Bottom-up and top-down aspects
Computational models of visual attention have become popular over the past
decade, we believe primarily for two reasons: First, models make testable
predictions that can be explored by experimentalists as well as theoreticians,
second, models have practical and technological applications of interest to the
applied science and engineering communities. In this chapter, we take a
critical look at recent attention modeling efforts. We focus on {\em
computational models of attention} as defined by Tsotsos \& Rothenstein
\shortcite{Tsotsos_Rothenstein11}: Models which can process any visual stimulus
(typically, an image or video clip), which can possibly also be given some task
definition, and which make predictions that can be compared to human or animal
behavioral or physiological responses elicited by the same stimulus and task.
Thus, we here place less emphasis on abstract models, phenomenological models,
purely data-driven fitting or extrapolation models, or models specifically
designed for a single task or for a restricted class of stimuli. For
theoretical models, we refer the reader to a number of previous reviews that
address attention theories and models more generally
\cite{Itti_Koch01nrn,Paletta_etal05,Frintrop_etal10,Rothenstein_Tsotsos08,Gottlieb_Balan10,Toet11,Borji_Itti12pami}