16302121
Smooth Foreground-Background Segmentation for Video Processing
We propose an efficient way to account for spatial smoothness in foreground-background segmentation of video sequences. Most statistical background modeling techniques regard the pixels in an image as independent and disregard the fundamental concept of smoothness. In contrast, we model smoothness of the foreground and background with a Markov random field, in such a way that it can be globally optimized at video frame rate. As a background model, the mixture-of-Gaussian (MOG) model is adopted and enhanced with several improvements developed for other background models. Experimental results show that the MOG model is still competitive, and that segmentation with the smoothness prior outperforms other methods.
5982516
Robust Fitting by Adaptive-Scale Residual Consensus
Computer vision tasks often require the robust fit of a model to some data. In a robust fit, two major steps should be taken: i) robustly estimate the parameters of a model, and ii) differentiate inliers from outliers. We propose a new estimator called Adaptive-Scale Residual Consensus (ASRC). ASRC scores a model based on both the residuals of inliers and the corresponding scale estimate determined by those inliers. ASRC is very robust to multiple-structural data containing a high percentage of outliers. Compared with RANSAC, ASRC requires no pre-determined inlier threshold as it can simultaneously estimate the parameters of a model and the scale of inliers belonging to that model. Experiments show that ASRC has better robustness to heavily corrupted data than other robust methods. Our experiments address two important computer vision tasks: range image segmentation and fundamental matrix calculation. However, the range of potential applications is much broader than these.
2318961
A Novel Robust Statistical Method for Background Initialization and Visual Surveillance
In many visual tracking and surveillance systems, it is important to initialize a background model using a training video sequence which may include foreground objects. In such a case, robust statistical methods are required to handle random occurrences of foreground objects (i.e., outliers), as well as general image noise. The robust statistical method Median has been employed for initializing the background model. However, the Median can tolerate up to only 50% outliers, which cannot satisfy the requirements of some complicated environments. In this paper, we propose a novel robust method for the background initialization. The proposed method can tolerate more than 50% of foreground pixels and noise. We give quantitative evaluations on a number of video sequences and compare our proposed method with five other methods. Experiments show that our method can achieve very promising results in background initialization: including applications in video segmentation, visual tracking and surveillance.
14076500
False-Peaks-Avoiding Mean Shift Method for Unsupervised Peak-Valley Sliding Image Segmentation
The mean shift (MS) algorithm is sensitive to local peaks. In this paper, we show both empirically and analytically that when using sample data, the reconstructed PDF may have false peaks. We show how the occurrence of the false peaks is related to the bandwidth h of the kernel density estimator, using examples of gray-level image segmentation. It is well known that in MS-based approaches, the choice of h is important: we provide a quantitative relationship between false peaks and h. For the gray-level image segmentation problem, we provide a complete unsupervised peak-valley sliding algorithm for gray- level image segmentation.
17118926
Background Subtraction Based on a Robust Consensus Method
Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model per pixel. Numerous experiments on both indoor and outdoor video sequences show that the proposed method, compared with several state-of-the-art methods, can achieve very promising performance
16354227
Using symmetry in robust model fitting
The pattern recognition and computer vision communities often employ robust methods for model fitting. In particular, high breakdown-point methods such as least median of squares (LMedS) and least trimmed squares (LTS) have often been used in situations where the data are contaminated with outliers. However, though the breakdown point of these methods can be as high as 50% (they can be robust to up to 50% contamination), they can break down at unexpectedly lower percentages when the outliers are clustered. In this paper, we demonstrate the fragility of LMedS and LTS and analyze the reasons that cause the fragility of these methods in the situation when a large percentage of clustered outliers exist in the data. We adapt the concept of "symmetry distance" to formulate an improved regression method, called the least trimmed symmetry distance (LTSD). Experimental results are presented to show that the LTSD performs better than LMedS and LTS under a large percentage of clustered outliers and large standard variance of inliers.
13939828
Mutual information regularized Bayesian framework for multiple image restoration
Bayesian methods have been extensively used in various applications. However, there are two intrinsic issues rarely addressed, namely generalization and validity, in the context of multiple image restoration, we show that traditional Bayesian methods are sensitive to model errors and cannot guarantee valid results satisfying the underlying prior knowledge, e.g. independent noise property. To improve the Bayesian framework's generalization, we propose to explicitly enforce the validity of the result. Independent noise prior is very important but largely under-utilized in previous literature. In this paper, we use mutual information (MI) to explicitly enforce the independence. Efficient approximations based on Taylor expansion are proposed to adapt MI into standard energy forms to regularize the Bayesian methods. The new regularized Bayesian framework effectively utilizes the traditional generative signal/noise models but is much more robust to various model errors, as demonstrated in experiments on some demanding imaging applications.
17778034
Evolutionary Gibbs sampler for image segmentation
We propose a novel evolutionary algorithm for the function optimization problem in Bayesian image segmentation with Markov random field prior. Function variables are partitioned into several codings. A pivot coding is selected and variables in it are evolved respectively according to their probability distributions which encode both the evolutionary pressure and contextual constraints from neighboring pixels. Variables in other codings are evolved according to their conditional probabilities. In summary, the algorithm is about building probabilistic models to guide search. It achieves the efficiency and flexibility by incorporating Gibbs sampler in an evolutionary approach. Remarkable performance is observed in some experiments.
14876128
A native extension of SQL for mining data streams
ESL1 enables users to develop stream applications in an SQL-like high level language that provides the ease-of-use of a declarative language, which is Turing complete in terms of expressive power [11].
9329231
Idea Storming Cube: A Game-based System to Support Creative Thinking
This paper describes a collaborative game-based creativity support system, idea storming cube, in support of creative thinking. It aims to make people form a creative and perspective-shift thinking habit. The system acquires knowledge from domain expert, user inputs history, and individuals in the current brainstorming group, and then provides user-, goal- and context-sensitive supports. Comparing to classic tutoring systems, it focuses more on stimulating divergent thinking. The system can be put into the basic mode or the idea generation mode in order to support different gaming objectives. A case study for preliminary evaluation of the proposed HCI tool for collaborative idea generation is also reported in this paper.
14060305
Effectively Detecting All-Zero DCT Blocks for H.264 Optimization
This paper presents a novel efficient algorithm to reduce redundant DCT and quantization computations for H.264 encoding. A theoretical analysis is performed to study the sufficient condition for DCT coefficients to be quantized to zeros in H.264. As a result, a more tight sufficient condition is derived to early detect all-zero 4x4 DCT blocks. Compared with other algorithms, the proposed algorithm provides a more precise and efficient condition to predict all-zero DCT blocks. The experimental results demonstrate that the proposed algorithm outperforms other algorithms in terms of all the evaluated performances.