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Multi-label learning is the term used to express a type of supervised learning that requires classification algorithms to learn from a set of examples; each example can belong to one or multiple labels. The learning task consists of breaking the multi-label classification problem into several single label classification problems. This learning process results in the prediction of new class labels...
Classification of Videos based on their content is becoming more and more essential everyday because of the vast amount of video data becoming available. Various Feature Extraction and data mining techniques can be used to perform Video Classification. This paper uses edge detection techniques such as Object Extraction and Canny Edge Detection (using Sobel, Prewitt and Robert's operator) to extract...
Brain-computer interfaces (BCIs) aim to provide a non-muscular channel to communicate with the external world through the use of the brain Electroencephalograph (EEG) activity. A crucial step in such an operation is brain signal processing methods. BCI systems use EEG as it is practical, noninvasive, cheap and has real time capability imaging technology. BCI's efficiency is dependent on brain signal...
Tire marks printed correctly can ensure the tire used properly. Tire mark regions in a tire image being extracted correctly are the first guarantee in a tire mark recognition system. In this paper, extraction algorithm based on radial scan method is discussed. Experiment results show that half character be extracted to tire mark regions occasionally via radial scan method. Hence, an improved extraction...
This article presents a new method for texture description suitable to be used as a solution to the retrieval problem in large image collections. The proposed approach combines multiscalegray-level co-occurrence matrices (GLCM) with Symbolic Data Analysis. A benchmark data set is used to demonstrate the usefulness of the proposed methodology. The experimental results demonstrate that the proposed...
Semi-supervised classification from pairwise constraints is a challenge in pattern recognition, since the constraints just represent the relationships between data pairs rather than the definite labels. In the last few years, several methods have been proposed, however, they still utilize either the discriminability within the constraints or the abundant unlabeled data insufficiently. In this paper,...
Automated splitting of clustered nuclei from images of tissue sections is essential to many biomedical studies. Many existing image segmentation methods tend to produce over-segmented or under-segmented results for clustered nuclei images. In this paper, a new curvature information based image segmentation algorithm is proposed. Through combining curvature information with a distance map, our algorithm...
This paper provides a short review of various association rule classifiers (ARC) that have been developed over the past decade and the common structure behind most ARCs. Furthermore, different pruning and classification schemes used in various ARCs are reviewed and two ARCs are discussed which break with the standard structure behind ARC.
The inevitable skew of the fabric images during scanning has brought great difficulties for the subsequent image processing operations. In the paper, a new skew detection method of fabric images based on the multi-threshold analysis is proposed: First, Sobel operator gradient is used to extract the weft information, and then improve the algorithm's computational speed by introducing the aberration...
Manual alert detection on modern high performance clusters (HPC) is cumbersome given their increasing complexity and size of their logs. The ability to automatically detect such alerts quickly and accurately with little or no human intervention is therefore desirable. The entropy-based approach of the Nodeinfo framework, which is in production use at Sandia National Laboratories, is one approach to...
In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm...
Map building is very important to accomplish autonomous navigation and other complex intelligent tasks. A novel method of feature map building was proposed to process the uncertainty data and noise data of sonar. This method has introduced sonar arc to describe detectable region for sonar, and extracted line by a novel Hough transform based on randomized Hough transform and multi-resolution Hough...
Process Neural Network (PNN) has an important significance in solving industry modeling problems which are related to time, but long time is cost on high dimension inputs nonlinear modeling problems. A new Improved Process Neural Networks based on KPCA and Walsh (IPNN-KPW) are proposed in this paper. KPCA method and discrete Walsh transform are used to reduce process neural network's time cost. Momentum...
The existing iris location algorithms are of low executing speed and poor robustness. In order to improve the accuracy of iris location, an iris location algorithm based on gradient direction information is proposed according to both the ring structural characteristics of iris and the gray distribution features of eye image in this paper. Experiments shows that the algorithm is effective and can improve...
According to the drawback of the traditional circle target extraction algorithm used by Hough Transform, such as computation complexity, low efficiency and etc, an new circle target extraction algorithm is proposed in this paper which can extract multiple circle targets with different radius at one time. First, the Average Absolute Difference is implemented to enhance the edge of the circle targets...
The end sifting method has been proposed to solve the end effect problem of empirical mode decomposition (EMD). During the intrinsic mode function (IMF) sifting process by EMD method, a procedure including end effect judgment and end sifting, which is different from the traditional idea dealing with the problem by dictating or predicting an end point, is added. This method has greatly improved the...
To solve the skew situation of scanned fabric image, a method based on Hough transform for skew detection and correction in fabric images was presented. By combining the characteristics of fabric images and the weft direction information extracted by Sobel operator, this method performed hierarchical Hough transform on the weft boundary to detect the skew angle of fabric image. Finally, a rotation...
Intelligent vehicles are one of the enlightening ideas that will shape our future by providing enhanced safety and improved mobility. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. However, lane detection is a challenging task because of the varying road conditions that one can encounter while driving. In this paper,...
Biometric authentication has attracted attention because of its high security and convenience. However, biometric feature such as fingerprint can not be revoked like passwords. Thus once the biometric data of a user stored in the system has been compromised, it can not be used for authentication securely for his/her whole life long. To address this issue, an authentication scheme called cancelable...
Hard exudates in retinal images are one of the most prevalent earliest signs of diabetic retinopathy. The accurate identification of hard exudates is of increasing importance in the early detection of diabetic retinopathy. In this paper, we present a novel method to identify hard exudates from digital retinal images. A feature combination based on stationary wavelet transform (SWT) and gray level...
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