14368338
Optimal radial contour tracking by dynamic programming
A common problem in most active contour methods is that the recursive searching scheme can only return a local optimal solution. Furthermore, the internal energy of the snake is not strong enough to control the shape of the contour. To overcome these difficulties, we develop a causal internal energy term based on a radial contour representation to encode the smooth constraint of the contour, and develop a global shape priori to control contour's shape and position based on object's dynamics. The causality nature of the representation allows us to efficiently find global optimal solution using dynamic programming. To validate the efficacy and robustness of the proposed approach, we apply this approach to track people in bad illumination and cluttered environments. We report promising results in the paper.
6471646
Multicue HMM-UKF for real-time contour tracking
We propose an HMM model for contour detection based on multiple visual cues in spatial domain and improve it by joint probabilistic matching to reduce background clutter. It is further integrated with unscented Kalman filter to exploit object dynamics in nonlinear systems for robust contour tracking
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.
31770762
Hierarchical Segmentation and Representation with Dynamic Link Architecture Neural Network
A segmentation scheme based on tracing objects through scale space is proposed. For analyzing the image structure in scale space, a hierarchical neuraloscillator network is proposed. Its intrinsic dynamics provides a natural and reliable representation of multiple segmented patterns. The pyramid-like structure and the local interactions between the neurons can easily integrate the local context information of pixels and global image structure. This model assumes that neural cells show oscillatory activities and the segmentation result is coded by synchronization of activities. This representation framework for neural network suggested by time correlation theory receives direct experimental support from neurophysiology experiment. Computer simulations demonstrate that the network can detect the topological character of images within several oscillatory cycles. Experiment on real image showed the model's ability of segmentation.
14712495
One-class SVM for learning in image retrieval
Relevance feedback schemes using linear/quadratic estimators have been applied in content-based image retrieval to improve retrieval performance significantly. One major difficulty in relevance feedback is to estimate the support of target images in high dimensional feature space with a relatively small number of training samples. We develop a novel scheme based on one-class SVM, which fits a tight hyper-sphere in the nonlinearly transformed feature space to include most of the target images based on positive examples. The use of a kernel provides us an elegant way to deal with nonlinearity in the distribution of the target images, while the regularization term in SVM provides good generalization ability. To validate the efficacy of the proposed approach, we test it on both synthesized data and real-world images. Promising results are achieved in both cases.
12006939
Mode-based multi-hypothesis head tracking using parametric contours
The paper describes a probabilistic mode-based multi-hypothesis tracking (MHT) algorithm. The modes are the local maximums refined from initial samples in a parametric state space. Because the modes are highly representative, this technique allows us to use a small number of hypotheses to effectively model nonlinear probabilistic distributions. To ensure real-time tracking performance, we propose a novel parametric causal contour model and an efficient dynamic programming scheme to refine the initial contours to nearby modes. Furthermore, to overcome the common drawback of conventional MHT techniques, i.e., producing only the maximum likelihood estimates instead of the desired posterior, we introduce the highly effective importance sampling framework into MHT, and develop a novel procedure to estimate the posterior from the importance function. Experiments on a challenging real-world video sequence demonstrate that the proposed tracking technique is both robust in complex environment (e.g., clutter background and partial occlusion) and efficient in computation.
1284213
An algebraic approach to practical and scalable overlay network monitoring
Overlay network monitoring enables distributed Internet applications to detect and recover from path outages and periods of degraded performance within seconds. For an overlay network with n end hosts, existing systems either require O(n2) measurements, and thus lack scalability, or can only estimate the latency but not congestion or failures. Our earlier extended abstract [1] briefly proposes an algebraic approach that selectively monitors k linearly independent paths that can fully describe all the O(n2) paths. The loss rates and latency of these k paths can be used to estimate the loss rates and latency of all other paths. Our scheme only assumes knowledge of the underlying IP topology, with links dynamically varying between lossy and normal.In this paper, we improve, implement and extensively evaluate such a monitoring system. We further make the following contributions: i) scalability analysis indicating that for reasonably large n (e.g., 100), the growth of k is bounded as O(n log n), ii) efficient adaptation algorithms for topology changes, such as the addition or removal of end hosts and routing changes, iii) measurement load balancing schemes, and iv) topology measurement error handling. Both simulation and Internet experiments demonstrate we obtain highly accurate path loss rate estimation while adapting to topology changes within seconds and handling topology errors.
40089095
Ray-guided global optimization method for training neural networks
Abstract A novel method for globally searching to find the good minima is proposed in this paper. Starting from a local minimum, the weight space around it is scanned with the process being guided by terrain-independent emanating rays. During the search, starting points for further exploration are identified and used to find corresponding local minima. Based on the correct classification rate (CCR) on the validation data, the best minimum is found.
14272034
Optimal Anytime Constrained Simulated Annealing for Constrained Global Optimization
In this paper we propose an optimal anytime version of constrained simulated annealing (CSA) for solving constrained nonlinear programming problems (NLPs). One of the goals of the algorithm is to generate feasible solutions of certain prescribed quality using an average time of the same order of magnitude as that spent by the original CSA with an optimal cooling schedule in generating a solution of similar quality. Here, an optimal cooling schedule is one that leads to the shortest average total number of probes when the original CSA with the optimal schedule is run multiple times until it finds a solution. Our second goal is to design an anytime version of CSA that generates gradually improving feasible solutions as more time is spent, eventually finding a constrained global minimum (CGM). In our study, we have observed a monotonically non-decreasing function relating the success probability of obtaining a solution and the average completion time of CSA, and an exponential function relating the objective target that CSA is looking for and the average completion time. Based on these observations, we have designed CSAAT-ID the anytime CSA with iterative deepening that schedules multiple runs of CSA using a set of increasing cooling schedules and a set of improving objective targets. We then prove the optimality of our schedules and demonstrate experimentally the results on four continuous constrained NLPs. CSAAT-ID can be generalized to solving discrete, continuous, and mixed-integer NLPs, since CSA is applicable to solve problems in these three classes. Our approach can also be generalized to other stochastic search algorithms, such as genetic algorithms, and be used to determine the optimal time for each run of such algorithms.
9845542
Back Propagation with Randomized Cost Function for Training Neural Networks
A novel method to improve both the generalization and convergence performance of the back propagation algorithm (BP) by using multiple cost functions with a randomizing scheme is proposed in this paper. Under certain conditions, the randomized technique will converge to the global minimum with probability one. Experimental results on benchmark Encoder-Decoder problems and the NC2 classification problem show that the method is effective in enhancing BP's convergence and generalization performance
14540904
Camera calibration with a motorized zoom lens
This paper presents a simple and efficient method of calibrating the intrinsic camera parameters for all the lens settings of a motorized zoom lens. We fix the aperture setting and perform the camera calibration, adaptively, over the ranges of the room and focus settings. Bilinear interpolation is used to provide the values of the intrinsic camera parameters for those lens settings where no observations are taken. Our experiments show that the proposed method can provide accurate intrinsic camera parameters for all the lens settings, even though camera calibration is performed only for a small number of sampled lens settings. A calibration object suitable for zoom lens calibration is also presented.