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Data mining techniques were applied for the reduction of false positives in aviation explosives detection CT (computed tomography) imaging systems. An inductive post-detection classifier (PDC) was trained, implemented, and fielded. The PDC can only eliminate alarms generated from the existing detection system - it does not detect new alarms.
Image transmission is sometimes accompanied with geometric distortions. A novel image recovery scheme with multi-bit binary message is proposed in this paper. In the proposed scheme, several predefined templates are introduced to an image in the discrete wavelet transform domain. A blind template detection algorithm is performed on the geometrically distorted image to extract locations of the templates...
Moving objects detection in dynamic scenes is a challenging task in many computer vision applications. Traditional background modeling methods do not work well in these situations since they assume a nearly static background. In this paper, a novel operator named spatial extended center-symmetric local binary pattern (SCS-LBP) for background modeling is proposed. It extracts spatial and temporal information...
In order to protect the copyright of digital productions, a novel watermarking algorithm based on feature points and singular value decomposition (SVD) is presented. By introducing the SIFT's key points matching and geometric distortion estimation, the attacked watermarked image can be corrected and the watermark synchronization is realized. In this way, the feature regions detected by the Harris...
In this paper, we present a key point recognition scheme, which consists of a novel feature detector and an efficient descriptor. Inspired by FAST (features from accelerated segment test), our feature detector is easy to compute and has high repeatability. Scale-invariance and optimized robustness are gained by extending traditional FAST to scale space.We combine this detector with an adapted version...
Reliable tracking of objects is an inevitable prerequisite for automated video surveillance systems. As most object detection methods, which are based on machine learning, require adequate data for the application scenario, foreground segmentation is a popular method to find possible regions of interest. These usually require a specific learning phase and adaptation over time. In this work we will...
In order to solve the problem of slowly moving object detection and tracking, this paper proposes a new weighted accumulative difference method and works out a weights setting algorithm to fast detect the slowly moving object. We obtain the interest points from the moving area detected, then fix the matching range with the maximum velocity principle and work out a non-retrospective mismatch detection...
In view of the current status quo, which the feature-based digital watermarking is lack of robustness to non uniform scaling attacks, this paper puts forward a watermarking algorithm based template and local feature. Firstly, the template information is embedded in spatial domain. Then the improved Harris-Laplacian detector is utilized to extract steady feature points from the host image ,the local...
Freeway monitoring is one of the key elements in ITS (intelligent transportation system). Moving vehicle extraction is an important preprocessing step in freeway monitoring. It has great influence on the following steps, such as object tracking, classification and behavior analysis. This paper presents an effective method to extract moving objects. Our approach proved to be robust to sudden light...
Multiple-extremum issue including the well-known ??singularity?? problem is one of the major defects in kernel-based object tracking. This paper studies this important problem and presents a novel approach called section-based tracking (SBT) that is based on the section information provided by the division of the object's weight image. This approach serves to eliminate fake extremal points and make...
One of the challenges to creating robust trackers is the construction of robust appearance Model. This paper presents a robust appearance model for object tracking. The robust object distribution is acquired by comparing the two Gaussian Mixture Models of the object and background. The probability image generated by the robust object distribution is used for the CAMSHIFT tracking. Experiments on several...
This paper proposes a novel geometric distortion resilient image copy detection scheme based on scale invariant feature transform (SIFT) detector. By using the SIFT detector, the proposed copy detection scheme first construct a series of robust, homogenous, and larger size circular patches. And then, the cirque track division strategy and ordinal measure concept are introduced to generate a cirque-based...
In this paper we present our research work in traffic sign detection and classification. Specifically we present a set of asymmetric Haar-like features that will be shown to be effective in reducing false alarm rates for traffic sign detection, and a robust multi-class traffic sign detection and classification system built based upon the stage-by-stage performance analysis of individual traffic sign...
Robust tracking is a challenging problem, due to intrinsic appearance variability of objects caused by in-plane or out-plane rotation and extrinsic factors change such as illumination, occlusion, background clutter and local blur. In this paper, we present a novel framework which uses an on-line AdaBoost algorithm as PSO's fitness function to search object and attain the efficiency and robustness...
We successfully develop a defect inspection method based on a robust method for matching the distance between points in three dimensions. The three-dimensional distance data of an object is measured by means of a laser range finder. The data is compared with the measured data of a high-quality item. Then, we examine the differences between two sets of data in order to detect defects in the target...
Background modeling has been widely researched to detect moving objects from image sequences. It is necessary to adapt the background model various changes of illumination condition. A hybrid type of background model which consists of more than one background model has been used for object detection since it is very robust for illumination changes. In this paper, we also propose a new hybrid type...
Object tracking is important for video analysis applications. However, tracking through occlusions is a difficult task due to significant appearance changes of the objects. Approaches based on either global features or one kind of local features can not solve the problem completely. In this paper, a multi-cue based tracking approach is introduced. It combines a corner tracking with a color and a shape...
To develop better image change detection algorithms, new models able to capture all the spatio-temporal regularities and geometries seen in an image pair are needed. In contrast to the usual pixel-wise methods, we propose a patch-based formulation for modeling semi-local interactions and detecting occlusions and other local or regional changes in an image pair. To this end, the image redundancy property...
SSD-based object tracking has shown its improved performance compared with mean-shift and many people have made further improvements based on it. However, how kernels should be designed to better cooperate with SSD metric and Newton-style iteration remains unsolved. Our work is to find out the underlying principles for SSD kernel design, which can help make the tracker more sensitive to the object...
This paper address issues that arise in copyright protection systems of digital images, which employ blind watermark verification structures in the curvelet domain. First, we observe that statistical distribution with heavy algebraic tails, such as the alpha-stable family, are in many cases more accurate modeling tools for the curvelet coefficients than families with exponential tails such as generalized...
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