8226839
Robust adaptive-scale parametric model estimation for computer vision
Robust model fitting essentially requires the application of two estimators. The first is an estimator for the values of the model parameters. The second is an estimator for the scale of the noise in the (inlier) data. Indeed, we propose two novel robust techniques: the two-step scale estimator (TSSE) and the adaptive scale sample consensus (ASSC) estimator. TSSE applies nonparametric density estimation and density gradient estimation techniques, to robustly estimate the scale of the inliers. The ASSC estimator combines random sample consensus (RANSAC) and TSSE, using a modified objective function that depends upon both the number of inliers and the corresponding scale. ASSC is very robust to discontinuous signals and data with multiple structures, being able to tolerate more than 80 percent outliers. The main advantage of ASSC over RANSAC is that prior knowledge about the scale of inliers is not needed. ASSC can simultaneously estimate the parameters of a model and the scale of the inliers belonging to that model. Experiments on synthetic data show that ASSC has better robustness to heavily corrupted data than least median squares (LMedS), residual consensus (RESC), and adaptive least Kth order squares (ALKS). We also apply ASSC to two fundamental computer vision tasks: range image segmentation and robust fundamental matrix estimation. Experiments show very promising results.
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.
1005847
Color Image Segmentation Using Global Information and Local Homogeneity
This paper proposes a new method of color image segmentation considering both global information and local homogeneity. The method applies the mean shift algorithm in the hue and intensity subspace of HSV. The cyclic property of the hue component is also considered in the proposed method. Experiments on natural color images show promising results.
6321624
Efficient Visual Tracking by Probabilistic Fusion of Multiple Cues
It has been shown that integrating multiple cues will increase the reliability and robustness of a vision system in situations that no single cue is reliable. In this paper, we propose a method by fusing multiple cues (i.e., the color cue and the edge cue). In contrast to previous work, we propose a novel shape similarity measure which includes the spatial distribution of the number of and the gradient intensity of the edge points. We integrate this shape similarity measure with our recently proposed SMOG-based color similarity measure in the framework of particle filter (PF). Experimental results demonstrate the high robustness and effectiveness of our method in handling appearance changes, cluttered background, moving camera, and occlusions
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.
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.
14953442
Estimating the Selectivity of XML Path Expression with Predicates by Histograms
Selectivity estimation of path expressions in querying XML data plays an important role in query optimization. A path expression may contain multiple branches with predicates, each of which having its impact on the selectivity of the entire query. In this paper, we propose a novel method based on 2-dimensional value histograms to estimate the selectivity of path expressions embedded with predicates. The value histograms capture the correlation between the structures and the values in the XML data. We define a set of operations on the value histograms as well as on the traditional histograms that capture nodes positional distribution. We then construct a cost tree based on such operations. The selectivity of any node (or branch) in a path expression can be estimated by executing the cost tree. Compared with previous methods (which ignore value distribution) our method offers much better estimation accuracy.
14477369
Markov random field modeled range image segmentation
In this paper, range image segmentation is studied in the framework of the maximum a posteriori estimation and Markov random field modeling. A novel range image segmentation model is proposed. The model serves as an evaluator for a small number of segmentation candidates obtained through a fast edge detection algorithm. A local method is employed to search for the optimal segmentation from the candidates. Experimental results show that such combination of heuristics and model-based evaluation leads to a fast and accurate segmentation.
44405113
Data hiding techniques for printed binary images
The objective of this research is to develop a method to hide information inside a binary image by digital halftoning techniques with certain modifications. Two modified digital halftoning techniques, modified ordered dithering and modified multiscale error diffusion, are used in this research. The data is encoded pixel by pixel in the halftone image according to position at the image and sequence of binarization, respectively. The eye model and mean square error are used to measure the image quality. A computer vision method has been developed to recognize the printed binary image. The results show that thousands of binary images similar to human vision but quite distinct from each other by computer vision can be generated. The eye model and computer vision are useful for both binary image quality measurement and data recognition. These new techniques have great potential in printing security documents such as currency, ID card as well as confidential documents.
43248584
Histogram Features-Based Fisher Linear Discriminant for Face Detection
The face pattern is described by pairs of template-based histogram and Fisher projection orientation under the paradigm of AdaBoost learning in this paper. We assume that a set of templates are available first. To avoid making strong assumptions about distributional structure while still retaining good properties for estimation, the classical statistical model, histogram, is used to summarize the response of each template. By introducing a novel “integral histogram image”, we can compute histograms rapidly. Then we turn to Fisher linear discriminant for each template to project histograms from d–dimensional to one-dimensional subspace. Best features, used to describe face pattern, are selected by AdaBoost learning. The results of preliminary experiments demonstrate that the selected features are much more powerful to represent the face pattern than the simple rectangle features used by Viola and Jones and some variants.
2984995
Query-By-Example Technique for Retrieving Cover Versions of Popular Songs with Similar Melodies
Retrieving audio material based on audio queries is an important and challenging issue in the research field of content-based access to popular music. As part of this research field, we present a preliminary investigation into retrieving cover versions of songs specified by users. The technique enables users to listen to songs with an identical tune, but performed by different singers, in different languages, genres, and so on. The proposed system is built on a query-by-example framework, which takes a fragment of the song submitted by the user as input, and returns songs similar to the query in terms of the main melody as output. To handle the likely discrepancies, e.g., tempos, transpositions, and accompaniments between cover versions and the original song, methods are presented to remove the non-vocal portions of the song, extract the sung notes from the accompanied vocals, and compare the similarities between the sung note sequences.