A new edge detection operator based on image features is proposed, which analyzes edges in images for edge features in two dimensions. The local extreme of the operator is created at the edge location and a low value ...
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A new edge detection operator based on image features is proposed, which analyzes edges in images for edge features in two dimensions. The local extreme of the operator is created at the edge location and a low value is created at the smooth region. Edges can be located by obtaining the local extreme and a threshold of the operator response. The detection operator is shown to be better than the Canny operator in terms of signal-to-noise ratio and edge location accuracy.
Two-dimensional gel electrophoresis (2DE) images are often corrupted by impulse noise in broad sense (including various artifacts, such as fingerprints, hairs, gel cracks, strips, water stains, dust and so on). In thi...
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Analyzing notor imagery electrocardiogram (ECoG) signal is very challenging for it is hard to set up a classifier based on the labeled ECoG obtained in the first session and apply it to the unlabeled test data obtaine...
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Analyzing notor imagery electrocardiogram (ECoG) signal is very challenging for it is hard to set up a classifier based on the labeled ECoG obtained in the first session and apply it to the unlabeled test data obtained in the second session. Here we propose a new approach to analyze ECoG trails in the case of session-to-session transfer exists. In our approach, firstly, dimension reduction is performed with independent component analysis (ICA) decomposition. Secondly, ECoG trials are clustered by an unsupervised learning algorithm called affinity propagation. Primary experimental results show that the proposed approach gives the reasonable result than that using the classical K-means clustering algorithm.
This paper presents a deep learning method application to the extraction of emotions included in Chinese speech with a deep belief network (DBN) structure. Eight proper features such as pitch, mel frequency cepstrum c...
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ISBN:
(纸本)9781479974351
This paper presents a deep learning method application to the extraction of emotions included in Chinese speech with a deep belief network (DBN) structure. Eight proper features such as pitch, mel frequency cepstrum coefficient (MFCC) are chosen from Mandarin speech used as network inputs, and a DBN classifier is used instead of traditional shallow learning methods to recognition of emotions. Experiment studies have proven that its recognition rate is higher than that of the traditional back propagation (BP) method and support vector machine (SVM) classifier.
Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar...
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We propose a novel method, the complete two-dimensional principal component analysis (complete 2DPCA), for image features extraction. Compared to the original 2DPCA, complete 2DPCA not only gain a higher recognition r...
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We propose a novel method, the complete two-dimensional principal component analysis (complete 2DPCA), for image features extraction. Compared to the original 2DPCA, complete 2DPCA not only gain a higher recognition rate, but also reduce the feature coefficients needed for face recognition. Complete 2DPCA is based on 2D image matrices. Two image covariance matrices are constructed directly using the original image matrix and theirs eigenvectors are derived for image feature extraction. Our experiments were performed on ORL face database, and experimental results show that the proposed method has an encouraging performance
There are two key problems in efficient large scale texture mapping for terrain rendering-efficient data organization and real time data updating in memory. In order to solve these problems, in this paper we propose a...
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There are two key problems in efficient large scale texture mapping for terrain rendering-efficient data organization and real time data updating in memory. In order to solve these problems, in this paper we propose a quadtree based indexing method to organize multi-resolution images and to fast retrieve data from disk; For memory updating, we present a real time dual-cache structure based updating method, which effectively reduces the frequency of data refresh. We also innovatively use a wavelet image enhancement algorithm to enhance original terrain texture, which obtain richer edge information and give us a more realistic effect in terrain rendering. Through the analysis of storage efficiency and rendering speed of our experiment, this dual-cache structure based method solves rendering speed and memory limit problems perfectly.
Dimension reduction methods are often used to analyzing high dimensional data, linear dimension methods are commonly used due to their simple geometric interpretations and for effective computational cost. Dimension r...
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Dimension reduction methods are often used to analyzing high dimensional data, linear dimension methods are commonly used due to their simple geometric interpretations and for effective computational cost. Dimension reduction plays an important role for feature selection. In this paper, we have given a detailed comparison of state-of-the-art linear dimension reduction methods like principal component analysis (PCA), random projections (RP), and locality preserving projections (LPP). We have determined which dimension reduction method performs better under the FastTag image annotation framework. Experiments are conducted on three standard bench mark image datasets such as CorelSk, IAPRTC-12 and ESP game to compare the efficiency, effectiveness and also memory usage. A detailed comparison among the aforementioned dimension reduction method is given.
Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. When combining PACT with Local difference Magnitu...
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Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. When combining PACT with Local difference Magnitude Binary pattern (LMBP), a new representation called Local Difference Binary pattern (LDBP) was proposed and performed better. LDBP is based on the comparisons between center pixel and its neighboring pixels. However, the relationship among neighbor pixels is not considered. In this paper we proposed Local Neighbor Binary pattern (LNBP) to utilize the relationship among neighboring pixels. LNBP provides complementary information regarding neighboring pixels for LDBP. We propose to combine LDBP with LNBP, and used a spatial representation for scene recognition. Experiments on two widely used dataset demonstrate the proposed method can improve the performance of recognition.
Segmentation becomes a difficult task if the objects are not homogeneous and have overlapping characteristics. The Graph Cuts methods combined with Gaussian Mixture Model (GMM) for initialization label has been adopte...
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Segmentation becomes a difficult task if the objects are not homogeneous and have overlapping characteristics. The Graph Cuts methods combined with Gaussian Mixture Model (GMM) for initialization label has been adopted to detect cattle object in an image with complex background. The RGB colors and Gray Level Co-occurrence Matrix (GLCM) textures are used as the features set. This method can robustly segment the cattle beef image from its background. This segmentation method produces the average of accuracy value up to 90%.
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