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This paper proposes a key motion spotting method which is designed to locate the motion of interest (or key motion) from a database of motion sequences. As a key motion could be just a subsequence of the stored motion sequence, the proposed method differs much from the general methods of content-based retrieval of motion sequences, which searches from the database the individual sequences similar...
In this paper we introduce a novel method for movement recognition in motion capture data. A movement is regarded as a combination of basic movement patterns, the so-called dynemes. Initially a K-means variant that takes into account the periodic nature of angular data is applied on training data to discover the most discriminative dynemes. Each frame is then assigned to one of these dynemes and a...
This paper addresses the challenging problem of recognition and classification of textured surfaces under illumination variation, geometric transformations and noisy sensor measurements. We propose a new texture operator, Adaptive Median Binary Patterns (AMBP) that extends our previous Median Binary Patterns (MBP) texture feature. The principal idea of AMBP is to hash small local image patches into...
Dynamic time warping algorithm is a pattern matching algorithm that allows a nonlinear stretching of the data. In a recognition system using a matching algorithm, data clustering methods are used to reduce the number of gesture templates in the database, and thus reduce the computational cost; however, the recognition rate is degraded. In this paper, we proposed a DTW gesture recognition system that...
The development of Affective Computing has witnessed tremendous number of studies about facial and vocal expression, while bodily expression only comprises the minority. However, with the emerging of social signal processing, people have paid more attention to the significant role of body language in social communication and emotional expression. One situation when body language shows its importance...
Investigating potential dependencies in data and their effect on future business developments can help experts to prevent misestimations of risks and chances. This makes correlation a highly important factor in risk analysis tasks. Previous research on correlation in uncertain data management addressed foremost the handling of dependencies between discrete rather than continuous distributions. Also,...
Texture analysis algorithms are employed in many computer vision applications. A group of high performing texture algorithms are based on the concept of local binary patterns (LBP) which describe the relationship of pixels to their local neighbourhood. LBP descriptors are invariant to intensity changes and rotation invariance is simple to derive. In addition, LBP features can be calculated for different...
In Liquid Chromatography/Mass Spectrometry (LC-MS), identifying corresponding peptide features (LC peaks) in multiple replicate datasets plays a crucial role in the differential analysis of complex peptide or protein samples for biomarker discovery. Given a peptide sequence, we aim at identifying its LC peak intervals in all datasets simultaneously. Generally, features are first identified in each...
This paper proposes a hierarchical visual motion retrieval system on the web. To make it possible for the user to retrieve motion data interactively and visually on a computer screen from coarse level to fine level about motion similarity, the proposed system employs tree based visualization method for the hierarchical structure of motion data. The hierarchical structure of motion data is constructed...
Robotics research tends to focus upon either non-contact sensing or machine manipulation, but not both. This paper explores the benefits of combining the two by addressing the problem of classifying unknown objects, such as found in service robot applications. In the proposed approach, an object lies on a flat background, and the goal of the robot is to interact with and classify each object so that...
We present an approach for unsupervised alignment of an ensemble of images called congealing. Our algorithm is based on image registration using the mutual information measure as a cost function. The cost function is optimized by a standard gradient descent method in a multiresolution scheme. As opposed to other congealing methods, which use the SSD measure, the mutual information measure is better...
This paper proposes a method of feature co-occurrence representation based on boosting for object detection. A previously proposed method that combines multiple binary-classified codes by AdaBoost to represent the co-occurrence of features has been shown to be effective in face detection. However, if an input feature is difficult to be assigned to a correct binary code due to occlusion or other factors,...
An inherent property to the texture patterns is that they are only meaningful in an appropriate range of scales. Taking this into account, the description of the texture patterns should be limited to its meaningful scales. This assumption motivates the research on local scale texture description. In this paper a new method for the extraction and description of texture features using local scale is...
In this paper, we propose an object detection method that uses Joint features combined from multiple Histograms of Oriented Gradients (HOG) feature using two-stage boosting. There has been much research in recent years on statistical training methods and object detection methods that combine low-level features obtained from local areas. In our approach, multiple low-level HOG features are combined...
This paper deals with human body pose recovery from multiple cameras, which is a key task in monitoring of human activity. This regression-based approach relies on a 3D description of a body voxel reconstruction, combined with a decomposition of the estimation, which allows to recover a wide range of poses using synthetic training data. The precision of the proposed shape descriptor is quantitatively...
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