Background subtraction is one of commonly used techniques for many applications such as human detection in images.
For background estimation, principal component analysis (PCA) is an available method. Since the background
sometimes changes according to illumination change or due to a newly appeared stationary article, the eigenspace should
be updated momentarily. A naïve algorithm for eigenspace updating is to update the covariance matrix. Then, the
eigenspace is updated by solving the eigenvalue problem for the covariance matrix. But this procedure is very time
consuming because covariance matrix is a very large size matrix. In this paper we propose a novel method to update the
eigenspace approximately with exceedingly low computational cost. Main idea to decrees computational cost is to
approximate the covariance matrix by low dimensional matrix. Thus, computational cost to solve eigenvalue problem
becomes exceedingly decrease. A merit of the proposed method is discussed.
In this paper, we propose an integrated framework for detecting suspicious behaviors in video surveillance systems which are established in public places such as railway stations, airports, shopping malls and etc. Especially, people loitering in suspicion, unattended objects left behind and exchanging suspicious objects between persons are common security concerns in airports and other transit scenarios. These involve understanding scene/event, analyzing human movements, recognizing controllable objects, and observing the effect of the human movement on those objects. In the proposed framework, multiple background modeling technique, high level motion feature extraction method and embedded Markov chain models are integrated for detecting suspicious behaviors in real time video surveillance systems. Specifically, the proposed framework employs probability based multiple backgrounds modeling technique to detect moving objects. Then the velocity and distance measures are computed as the high level motion features of the interests. By using an integration of the computed features and the first passage time probabilities of the embedded Markov chain, the suspicious behaviors in video surveillance are analyzed for detecting loitering persons, objects left behind and human interactions such as fighting. The proposed framework has been tested by using standard public datasets and our own video surveillance scenarios.
In today world, different kinds of networks such as social, technological, business and etc. exist. All of the networks are
similar in terms of distributions, continuously growing and expanding in large scale. Among them, many social networks
such as Facebook, Twitter, Flickr and many others provides a powerful abstraction of the structure and dynamics of
diverse kinds of inter personal connection and interaction. Generally, the social network contents are created and
consumed by the influences of all different social navigation paths that lead to the contents. Therefore, identifying
important and user relevant refined structures such as visual information or communities become major factors in
modern decision making world. Moreover, the traditional method of information ranking systems cannot be successful
due to their lack of taking into account the properties of navigation paths driven by social connections. In this paper, we
propose a novel image ranking system in social networks by using the social data relational graphs from social media
platform jointly with visual data to improve the relevance between returned images and user intentions (i.e., social
relevance). Specifically, we propose a Markov chain based Social-Visual Ranking algorithm by taking social relevance
into account. By using some extensive experiments, we demonstrated the significant and effectiveness of the proposed
social-visual ranking method.
In this paper, we propose a convenient system to control a remote camera according to the eye-gazing direction of the operator, which is approximately obtained through calculating the face direction by means of image processing. The operator put a marker attached cap on his head, and the system takes an image of the operator from above with only one video camera. Three markers are set up on the cap, and 'three' is the minimum number to calculate the tilt angle of the head. The more markers are used, the robuster system may be made to occlusion, and the wider moving range of the head is tolerated. It is supposed that the markers must not exist on any three dimensional straight line. To compensate the marker's color change due to illumination conditions, the threshold for the marker extraction is adaptively decided using a k-means clustering method. The system was implemented with MATLAB on a personal computer, and the real-time operation was realized. Through the experimental results, robustness of the system was confirmed and tilt and pan angles of the head could be calculated with enough accuracy to use.
This paper proposes a new method called a 3D box method for estimating the shape of an object from multi-views. This method is useful in those fields, which are needed to recover the 3D information from their images, such as in the field of industries, medical sciences, etc. Concept of voting and counting votes from different image views is used to solve the inverse problems of 3D reconstruction of an object from 2D image. The straight line drawn from the lens center of the calibrated camera to an image point of object's silhouettes casts a single vote in each 3D box on its way if it is extended in space. Images are taken from thirty-six positions of a calibrated camera dividing them into four groups as 3 X 3 cameras in each group, which are positioned on each side of the object. The technique of camera position grouping is used to solve the concavity problems by the detection of occlusion using stereo method and acquiring occlusion free depth. To reduce the large memory size needed to solve in a single-stage, a multi-stage algorithm is developed for getting the accurate shape of the 3D object. Computer simulations are conducted to demonstrate the performance of the algorithm on images of various shapes of objects.
KEYWORDS: Image processing, Curium, Visual communications, Signal processing, 3D image processing, Video processing, Video, Computer simulations, Image quality, Color and brightness control algorithms
A new method for video signal processing is described in this paper. The purpose is real-time image transformations at low cost, low power, and small size hardware. This is impossible without special hardware. Here generalized digital differential analyzer (DDA) and control memory (CM) play a very important role. Then indentation, which is called jaggy, is caused on the boundary of a background and a foreground accompanied with the processing. Jaggy does not occur inside the transformed image because of adopting linear interpretation. But it does occur inherently on the boundary of the background and the transformed images. It causes deterioration of image quality, and must be avoided. There are two well-know ways to improve image quality, blurring and supersampling. The former does not have much effect, and the latter has the much higher cost of computing. As a means of settling such a trouble, a method is proposed, which searches for positions that may arise jaggy and smooths such points. Computer simulations based on the real data from VTR, one scene of a movie, are presented to demonstrate our proposed scheme using DDA and CMs and to confirm the effectiveness on various transformations.
A new approach for the detection of motions of three-dimensional rigid bodies from two-dimensional images is presented. The approach is based on two main stages. In the first stage, the positions and velocities of feature points are detected from two-dimensional images. We state the idea in this paper but the detail. In the second stage, the rotation and the tranlation velocity of each bodies are detected from the positions and velocities of the set of feature points. We employ Hough transform method in the both stages. We describe the details of the second stage and the way of computation reduction in Hough transform. The effectiveness of our method are confirmed through computer experiment.
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