This paper proposes a method for image smoothing which is invariant under general coordinate transformation. This method is based on a position dependent metric tensor which transforms appropriately against the general coordinate transformation. Using this metric tensor, a method for invariant image smoothing against the general coordinate transformation is constructed. Effectiveness of the proposed method is confirmed by computer experiments.
The realization of color constancy on computer vision is important to recognize objects in varying light sources. This paper proposes a method to estimate the illuminant under the “Minimum Brightness Variance Assumption” which states that the variation of the brightness of the objects is as small as possible. In this method, the illuminant is estimated to be red when the red part of the object in the scene is bright. In detail, we define an evaluation function to calculate the variance of the brightness in the scene and we minimize the evaluation function to estimate the color of the illuminant and the color of the object. We conducted experiments with synthetic images and confirmed that the proposed method works well to reduce the influence of the illuminant for the objects in the scene.
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.
This paper proposes a new curve smoothing method invariant to affine transformation. Curve smoothing is one of the
important challenges in computer vision as a procedure for noise suppression in shape analysis such as Curvature Scale
Space (CSS). Currently, Gaussian filtering is widely used among a lot of smoothing methods. However Gaussian
filtering is not affine invariant. This paper proposes a new method for curve smoothing that is invariant under affine
transformation such that area of any region in the image does not change. Specifically, we introduce an affine invariant
evaluate function with a metric tensor. The original curve is smoothed by minimizing the evaluation function. We
mathematically prove that this method is affine invariant. Further, experimental results show that the proposed method is
almost never affected by affine transformation different from usual Gaussian filtering. In the proposed method,
processing results are expected to be not affected much by variation of the viewpoint.