For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a high...
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We present a system to automatically estimate the body pose of a reclined patient, based on measurement data from a pressure sensing mattress. It can be used to replace or reduce manual input in clinical imaging proce...
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Faraway engineers are able to sketch direct the shape of engineering components by the browser, and the recognition system will proceed with search for the component database of company by the Internet. In this paper,...
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When software developers modify one or more files in a large code base, they must also identify and update other related files. Many file dependencies can be detected by mining the development history of the code base...
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ISBN:
(纸本)9781617823800
When software developers modify one or more files in a large code base, they must also identify and update other related files. Many file dependencies can be detected by mining the development history of the code base: in essence, groups of related files are revealed by the logs of previous workflows. From data of this form, we show how to detect dependent files by solving a problem in binary matrix completion. We explore different latent variable models (LVMs) for this problem, including Bernoulli mixture models, exponential family PCA, restricted Boltzmann machines, and fully Bayesian approaches. We evaluate these models on the development histories of three large, open-source software systems: Mozilla Firefox, Eclipse Subversive, and Gimp. In all of these applications, we find that LVMs improve the performance of related file prediction over current leading methods.
Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement. However, most of them still suffer from two main problems: expensive computational ...
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Embedding data into vector spaces is a very popular strategy of patternrecognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis...
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Because of variable dependence, high dimensional data typically have much lower intrinsic dimensionality than the number of its variables. Hence high dimensional data can be expected to lie in (nonlinear) lower dimens...
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Automatic vehicle Make and Model recognition (MMR) system offers a competent way to vehicle classification and recognition systems. This paper proposes a real time while robust vehicle make and model recognition syste...
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Automatic vehicle Make and Model recognition (MMR) system offers a competent way to vehicle classification and recognition systems. This paper proposes a real time while robust vehicle make and model recognition system extracting the vehicle sub-image from the background and studies some sparse feature coding methods such as Orthogonal Matching Pursuit (OMP), some variation of Sparse Coding (SC) methods and compares them to choose the best one. Our method employs the sparse feature coding methods on dense Scale-Invariant Feature Transform (SIFT) features and Support Vector Machine (SVM) for classification. The proposed system is examined by an Iranian on road vehicles dataset, which its samples are in different point of views, various weather conditions and illuminations.
Human action recognition is the process of lab.ling videos contain human motion with action classes. The run time complexity is one of the most important challenges in action recognition. In this paper, we address thi...
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Human action recognition is the process of lab.ling videos contain human motion with action classes. The run time complexity is one of the most important challenges in action recognition. In this paper, we address this problem using video abstraction techniques including key-frame extraction and video skimming. At first we extract key-frames and then skim the video clip by concatenating excerpts around the selected key-frames. This shorter sequence is used as input for classifier. Our proposed approach not only reduces the space complexity but also reduces the run time in both train and test steps. The experimental results provided on KTH action datasets show that the proposed method achieves good performance without losing considerable classification accuracy.
Limited-angle computed tomography suffers from missing data in the projection domain, which results in intensity inhomogeneities and streaking artifacts in the image domain. We address both challenges by a two-step de...
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