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The paper proposes CONSIFT descriptors which are the rotation-variant modification of SIFT (primarily for affine-invariant keypoints). CONSIFT of a keypoint K is its SIFT computed relatively to the orientation defined by the location of another keypoint L (and concatenated with similarly computed SIFT for keypoint L relatively to the location of K). It is additionally recommended that K and L are...
In this paper we focus on the evaluation of the deformable part model (DPM) proposed by Felzenszwalb et al. [IO] in the context of vision-based people detection in heavy machines applications. The proposed system uses a single fisheye camera to provide a wide field-of-view (FOV) at low cost. However, the fisheye optical distortions present several difficulties for image processing and object recognition...
The online anomaly detection has been propounded as the the key idea of monitoring fault of large-scale sensor nodes in Internet of Things. Although the exciting progresses of research have been made in online anomaly detection area, the highly dynamic distribution makes the anomaly detection scheme difficult to be used in online manner. This paper presents an online anomaly learning and forecasting...
Detection and recognition of collective human activities are important modules of any system devoted to high level social behavior analysis. In this paper, we present a novel semantic-based spatio-temporal descriptor which can cope with several interacting people at different scales and multiple activities in a video. Our descriptor is suitable for modelling the human motion interaction in crowded...
In this paper we consider adaptive detection of a signal embedded in additive disturbance whose multivariate distribution belongs to a very general class, including many statistical models commonly adopted for radar disturbance. We introduce the concept of generalized Constant False Alarm Rate (CFAR) and show that a class of receivers sharing some invariances complies with the quoted property. Then,...
In many statistical signal processing applications, the quality of the estimation of parameters of interest plays an important role. We focus in this paper, on the estimation of the covariance matrix. In the classical Gaussian context, the Sample Covariance Matrix (SCM) is the most often used, since it is the Maximum Likelihood estimate. It is easy to manage and has a lot of well-known statistical...
In this paper, we present a novel object matching approach using the method considering both the similarity on regions and structure in its feature space. The previous works [1], [2] and [3] show that it's possible to formulate the object matching problem as a linear programming problem. However, it remains an open problem how to better use the feature similarity and structure similarity at a same...
Matching local salient points is limited in some computer vision problems (e.g., wide baseline point matching and its applications), since local features vary dramatically under large view changes. In this paper, we propose a new definition on salient points which includes context information around the given point. The proposed contextual salient points are extracted based on the geometry structure...
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