15156880
Adaptive Foreground and Shadow Detection in Image Sequences
This paper presents a novel method of foreground segmentation that distinguishes moving objects from their moving cast shadows in monocular image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian belief network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. The notion of Markov random field is used to encourage the spatial connectivity of the segmented regions. The solution is obtained by maximizing the posterior possibility density of the segmentation field.
13971731
Video segmentation based on graphical models
This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notions of distance transformation and Markov random field are used to express spatiotemporal constraints. Given consecutive frames, an optimization method is proposed to maximize the conditional probability density of the three fields in an iterative way. Experimental results show that the approach is robust and generates spatiotemporally coherent segmentation results.
8808466
Joint region tracking with switching hypothesized measurements
We propose a switching hypothesized measurements (SHM) model supporting multimodal probability distributions and present the application of the model in handling potential variability in visual environments when tracking multiple objects jointly. For a set of occlusion hypotheses, a frame is measured once under each hypothesis, resulting in a set of measurements at each time instant. A computationally efficient SHM filter is derived for online joint region tracking. Both occlusion relationships and states of the objects are recursively estimated from the history of hypothesized measurements. The reference image is updated adaptively to deal with appearance changes of the objects. The SHM model is generally applicable to various dynamic processes with multiple alternative measurement methods.
6934721
A dynamic conditional random field model for foreground and shadow segmentation
This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences.
60451548
Switching Hypothesized Measurements: A Dynamic Model with Applications to Occlusion Adaptive Joint Tracking
This paper proposes a dynamic model supporting multimodal state space probability distributions and presents the application of the model in dealing with visual occlusions when tracking multiple objects jointly. For a set of hypotheses, multiple measurements are acquired at each time instant. The model switches among a set of hypothesized measurements during the propagation. Two computationally efficient filtering algorithms are derived for online joint tracking. Both the occlusion relationship and state of the objects are recursively estimated from the history of measurement data. The switching hypothesized measurements (SHM) model is generally applicable to describe various dynamic processes with multiple alternative measurement methods.
18961070
A probabilistic approach for foreground and shadow segmentation in monocular image sequences
This paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori-Markov random field estimation is used to boost the spatial connectivity of segmented regions.
6287453
A dynamic assignment problem in a mobile system with limited bandwidth
The assignment problem originally arising from parallel and distributed computing has been investigated intensively since the 70's when Harold Stone proposed a method to solve it with the aid of network flow algorithms. In this paper, we discuss this problem under a mobile environment with limited capacity of wireless links. The assignment problem is to schedule a group of related objects from a resource-constrained mobile device to its powerful proxy machine so that program activity migrates among processors as execution proceeds, and hence the overall performance will improve. First, we reduce this optimization problem to the 0/1 knapsack problem, and therefore show that it is NP-hard. Second, two heuristic algorithms are proposed. One is an adaptive heuristic algorithm with complexity O(NE), here, N and E represent the number of nodes and edges in the object graph abstracted from the mobile application. The other is a non-adaptive heuristic algorithm with complexity O(E), i.e, N times faster than the adaptive counterpart. Finally, we provide a simple distributed algorithm based on Raymond's tree-based distributed mutual exclusion algorithm for solving this problem with message complexity O(N/sup 2/ log N).
35950271
Partition Triangle Meshes into Coarsely Quadrangular Segmentation
A feature captured approach was presented to automatically partition a triangle meshes into a coarsely quadrangular segmentation. The triangle meshes was firstly partitioned into a polygonal segmentation which was further split into triangular and quadrangular patches. After the convex and concave features were identified, the triangular patches were eliminated according to their topological relations to achieve the quadrangular segmentation. The optimization methods based on globally smooth parameterization and active contour model were also adopted to improve the qualities of quadrangular patches and their edges. The final segmentation consisted of pure-quadrangular patches with appropriate sizes according to the model's geometric characteristics, and the features of input model werewell captured.
1208928
Enforcing convexity for improved alignment with constrained local models
Constrained local models (CLMs) have recently demonstrated good performance in non-rigid object alignment/ tracking in comparison to leading holistic approaches (e.g., AAMs). A major problem hindering the development of CLMs further, for non-rigid object alignment/tracking, is how to jointly optimize the global warp update across all local search responses. Previous methods have either used general purpose optimizers (e.g., simplex methods) or graph based optimization techniques. Unfortunately, problems exist with both these approaches when applied to CLMs. In this paper, we propose a new approach for optimizing the global warp update in an efficient manner by enforcing convexity at each local patch response surface. Furthermore, we show that the classic Lucas-Kanade approach to gradient descent image alignment can be viewed as a special case of our proposed framework. Finally, we demonstrate that our approach receives improved performance for the task of non-rigid face alignment/tracking on the MultiPIE database and the UNBC-McMaster archive.
20945105
Real-Time Moving Vehicle Detection With Cast Shadow Removal in Video Based on Conditional Random Field
This paper presents an approach of moving vehicle detection and cast shadow removal for video based traffic monitoring. Based on conditional random field, spatial and temporal dependencies in traffic scenes are formulated under a probabilistic discriminative framework, where contextual constraints during the detection process can be adaptively adjusted in terms of data-dependent neighborhood interaction. Computationally efficient algorithm has been developed to discriminate moving cast shadows and handle nonstationary background processes for real-time vehicle detection in video streams. Experimental results show that the proposed approach effectively fuses contextual dependencies and robustly detects moving vehicles under heavy shadows even in grayscale video.
206733202
Foreground and Shadow Detection Based on Conditional Random Field
This paper presents a conditional random field (CRF) approach to integrate spatial and temporal constraints for moving object detection and cast shadow removal in image sequences. Interactions among both detection (foreground/background/shadow) labels and observed data are unified by a probabilistic framework based on the conditional random field, where the interaction strength can be adaptively adjusted in terms of data similarity of neighboring sites. Experimental results show that the proposed approach effectively fuses contextual dependencies in video sequences and significantly improves the accuracy of object detection.