Object recognition is challenging problem in computer vision due to appearance variation and presence of visual clutter and occlusions. Recently manifolds are thought to be fundamental for visual perception, and manif...
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pattern Mining is a popular issue in biological sequence analysis. With the introduction of wildcard gaps, more interesting patterns can be mined. In this paper, we propose a new definition related to pattern frequenc...
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In this paper a system is developed for face recognition processes. Preprocessing and face localization is necessary to obtain a high classification rate in face recognition tasks. In this study after preprocessing of...
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In this paper a system is developed for face recognition processes. Preprocessing and face localization is necessary to obtain a high classification rate in face recognition tasks. In this study after preprocessing of face images, for omitting the redundant information such as background and hair, the oval shape of face is approximated by an ellipse using shape information. Then the parameters (orientation and center coordinates) of this ellipse are optimized using genetic algorithm (GA). High order pseudo Zernike moment invariant (PZMI) which has useful properties is utilized to produce feature vectors. Also radial basis function neural network (RBFNN) with HLA learning rule has been used as a classifier. Simulation results on ORL database indicate that the error rate of proposed system which uses genetic algorithm for optimizing the face localization step is lower than an older system which described in (H. Haddadnia et al., 2003)
Focus on the image compressing problem of unmanned aerial vehicle with high compression ratio, fixed compressing ratio and low computational complexity requirement, a low-complexity image-sequence compressing algorith...
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Part-based models have become the mainstream approach for visual object classification and detection. The key tools adopted by the most methods are interest point detectors and descriptors, shared codes for object par...
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ISBN:
(纸本)9781479952106
Part-based models have become the mainstream approach for visual object classification and detection. The key tools adopted by the most methods are interest point detectors and descriptors, shared codes for object parts (visual codebook) and discriminative learning using positive and negative class examples. Distinction of our method from the existing part-based methods for object detection is the use of sparse class-specific landmarks with semantic meaning. The landmarks are the additional distinguished information of object location in the proposed framework. Additionally, localising semantic and discriminative landmarks (object parts) is significant in other related applications of computer vision, such as facial expression recognition and pose/orientation estimation of objects. Therefore, we propose a model which deviates from the mainstream by the fact that the object parts' appearance and spatial variation, constellation, are explicitly modelled in a generative probabilistic manner. With using only positive examples our method can achieve object detection accuracy comparable to state-of-the-art discriminative method.
Fault tolerance is a central issue in the design and implementation of interconnection networks for large parallel systems. Connection probability of a network is a good network fault tolerance measure. For a mesh of ...
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Learning in nonstationary environments, also called learning concept drift, has been receiving increasing attention due to increasingly large number of applications that generate data with drifting distributions. Thes...
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Learning in nonstationary environments, also called learning concept drift, has been receiving increasing attention due to increasingly large number of applications that generate data with drifting distributions. These applications are usually associated with streaming data, either online or in batches, and concept drift algorithms are trained to detect and track the drifting concepts. While concept drift itself is a significantly more complex problem than the traditional machine learning paradigm of data coming from a fixed distribution, the problem is further complicated when obtaining labeled data is expensive, and training must rely, in part, on unlabelled data. Independently from concept drift research, semi-supervised approaches have been developed for learning from (limited) labeled and (abundant) unlabeled data; however, such approaches have been largely absent in concept drift literature. In this contribution, we describe an ensemble of classifiers based approach that takes advantage of both labeled and unlabeled data in addressing concept drift: available labeled data are used to generate classifiers, whose voting weights are determined based on the distances between Gaussian mixture model components trained on both labeled and unlabeled data in a drifting environment.
This paper proposes a novel fast architecture for two-dimensional discrete wavelet transform by using lifting scheme. The parallel and embedded decimation techniques are employed to optimize the architecture, which is...
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In this paper, the knowledge modeling, architecture design and detailed implementation of an ontology-based knowledge base for target recognition in remote sensing images is presented. Knowledge base is a critical com...
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Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing ...
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ISBN:
(纸本)9781467397155
Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing the global optimal method in consecutive frames assignment and local optimal approach in spatial trajectory *** the process,the detection errors were recognized and cell moving trajectories were completed *** also introduced the concept of clustering to measure the correlation between established short trajectories and reduce the tracking errors caused by fast *** rare information of cells was used in the linkage,the system can work well with *** experimental results show the effectiveness of our approach with cells having different density and activity.
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