International Journal of Pattern Recognition and Artificial Intelligence

To improve the accuracy, reduce the time consumption and obtain the number of faults, a fault detection method based on AP (affinity propagation) clustering and PCA (principal component analysis) was proposed. Firstly, discontinuous points in seismic horizons were searched out by the connected component labeling method. Secondly, the AP clustering algorithm was used to cluster the discontinuous points and the points of the same cluster were used to determine a fault, meanwhile, the faults existing in a seismic section were quantified. Finally, the PCA was adopted to calculate the principal direction of the discontinuous points contained in the same cluster. As a result, the corresponding cluster center and the principal direction determined a straight line, and the part that intercepted by the clustered edge was the fault we wanted. In the proposed method, the time consumption of correlation calculation of the traditional method was reduced; the computing work was simplified and the number of the faults in the seismic section was obtained. To confirm the feasibility and advancement of the proposed method, comparative experiments were done on the seismic model data and the real seismic section. The results show that the accuracy of the proposed method was better and the time cost was greatly reduced.

The continuous image sequence recognition is more difficult than the single image recognition because the classification of continuous image sequences and the image edge recognition must be very accurate. Hence, a method based on sequence alignment for action segmentation and classification is proposed to reconstruct a template sequence by estimating the mean action of a class category, which calculates the distance between a single image and a template sequence by sparse coding in Dynamic Time Warping. The proposed method, the methods of Kulkarni et al. [Continuous action recognition based on sequence alignment, Int. J. Comput. Vis. pp. 1–26.] and Hoai et al. [Joint segmentation and classification of human actions in video, IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 108–119.] are compared on the recognition accuracy of the continuous recognition and isolated recognition, which clearly shows that the proposed method outperforms the other methods. When applied to continuous gesture classification, it not only can recognize the gesture categories more quickly and accurately, but is more realistic in solving continuous action recognition problems in a video than the other existing methods.

The data are rapidly expanding nowadays, which makes it very difficult to analyze valuable information from big data. Most of the existing data mining algorithms deal with big data problems at large time and space costs. This paper focuses on the sampling problem of big data and puts forward an efficient heuristic Cluster Sampling Arithmetic, called CSA. Many of the former researchers adopted random method to extract early sample set from the original data and then made a variety of different processing of the sample in order to obtain the corresponding minimum sample set, which is regarded as a representation of the original big data set. However, the final processing results of big data will be severely affected by the random sampling process at the beginning, resulting in lower comprehensiveness and quality of the final data results and longer processing time. Based on this view, CSA introduces the idea of clustering to obtain minimum sample set of big data, which is in contrast to the random sampling method in the current literature. CSA makes cluster analysis of the original data set and selects the center of each class as centralized members of the minimum sample set. It aims at ensuring that the sample distribution accords with the characteristics of the original data, guarantees the data integrity and reduces the processing time. The max–min distance means that the pattern recognition has been integrated into the clustering process in order to get the clustering center and prevent algorithm from local optimum. The final experimental results show that, compared with the existing work, CSA algorithm can efficiently reflect the characteristics of the original data and reduce the time of data processing. The obtained minimum sample set has also achieved good effects in the classification algorithm.

The surface dynamometer cards are important working condition data of sucker-rod pumping system. It has a very important practical significance for the analysis of transmission system and the diagnosis of oil production condition of sucker-rod pumping system. The pump dynamometer cards are important reference for the diagnosis of oil production condition, and its key technology is the identification of pump dynamometer cards. A new similar pattern recognition algorithm based on root-mean-square error (RMSE) is proposed, a theoretical model of the similarity matching algorithm based on RMSE is established, and the algorithm is studied and analyzed. The three-dimensional vibration mathematical models for the surface dynamometer cards are created, by which the surface dynamometer cards can be transformed to the pump dynamometer cards. The accuracy, reliability and stability between the algorithm of RMSE similarity matching and the classical algorithms of similarity pattern matching are studied. The research shows that the resistance to the graphics deformation of RMSE algorithm is the highest among all algorithms. The application of RMSE algorithm and classic similarity matching algorithms to the identification of real pump dynamometer cards and the fault diagnosis of oil wells indicates that the RMSE algorithm has very high identification reliability and accuracy. The remarkable feature of the RMSE algorithm is that it has very high identification accuracy for small difference, while the classical similarity matching algorithms do not have this feature.

From the last decade, Sentiment Analysis of languages such as English and Chinese are particularly the focus of attention but resource poor languages such as Urdu are mostly ignored by the research community, which is focused in this research. After acquiring data from various blogs of about 14 different genres, the data is being annotated with the help of human annotators. Three well-known classifiers, that is, Support Vector Machine, Decision tree and $k$-Nearest Neighbor ($k$-NN) are tested, their outputs are compared and their results are ultimately improved in several iterations after taking a number of steps that include stop words removal, feature extraction, identification and extraction of important features. extraction. Initially, the performance of the classifiers is not satisfactory as the accuracy achieved by all the three is below 50%. Ensemble of classifiers is also tried but the results are not fruitful (in terms of high accuracy). The results are analyzed carefully and improvements are made including feature extraction that raised the performance of these classifiers to a satisfactory level. It is further concluded that $k$-NN is performing better than Support Vector Machine and Decision tree in terms of accuracy, precision, recall and $f$-measure.

The physical properties of water lead to attenuation of light that travels through the water channel. The attenuation is dependent on the color spectrum wavelength, that results in low contrast and color cast in image acquisition. Several methods have been proposed to handle these problems, such as Linear Stretching, Histogram Equalization (HE) and their variants. Considering the advantages of HE and Linear Stretching, this paper presents a new Adaptive Linear Stretch method (ALS) which can efficiently improve the subjective impression of the traditional Linear Stretching and keep the computational cost low at the same time. To achieve adaptability, the adaptable threshold is deduced from the histogram of image. Performance analysis reveals that the proposed method significantly enhances the image contrast, reduces the color cast and meanwhile, keeps the computational consumption low.

Image enhancement processing is a very important operation during image preprocessing. Compared with to enhancc the overall contrast level of image, enhancing the local contrast of image can improve the level of such contrast directly as well as the quality and effect of image enhancement. In this paper, the gray prediction model is applied to the process of enhancing image local contrast, so as to measure the change range of image local contrast and adaptively adjust the scale of enhancing image local contrast. The simulation results show that, in addition to enhancing the contrast of gray level on the edge of image, the proposed algorithm can inhibit roughened nonedge region and improve the quality of local enhancement processing, which create a more favorable condition for the further image edge detection.

With the rapid development of digital cameras and smart phones, the image identification system in current times will be of a great impact. This will cause the form of image information to increase serious security issues. Especially, the emergence of the recaptured image makes conventional digital image forensics algorithm invalid. Therefore, a new image forensics algorithm is urgently needed to identify the recaptured image. In this paper, a new recaptured image identifying algorithm is put forward based on wavelet transformation and noise analysis by analyzing the differences between the real and recaptured images generated in the imaging process. First, the proposed algorithm extracts mean value, variance and skewness as wavelet characteristic from the high-frequency images and low-frequency images by wavelet transformation. Meanwhile, the proposed algorithm analyzes the noise image by means of local binary pattern to extract noise characteristic. Finally, the support vector machine is applied to classify the recaptured image with wavelet characteristics and noise characteristics. The results show the presented method can not only identify the recaptured image obtained from different media but also have better identification rate, and the dimension of the characteristic vector is also lower than those obtained by other algorithms.

Fiducial marker systems are composed of a number of patterns that are mounted in the environment and automatically detected by computer vision algorithms using digital techniques. Thus, these systems are valuable in augmented reality (AR), robot navigation, and other applications. This paper proposes a new AR marker called CH-marker, which uses Hamming check codes to encode multiple kinds of colors and restore binary codes in the squares occluded in the markers. The marker solves the registration failure, which occurs when the markers are partially occluded in dynamic scenes. Experiments showed that the proposed marker is effective, reliable, and can meet the application demand of AR.

Object localization is one of the inherent tasks of computer vision. It plays an intrinsic role in object detection tasks that initiate with a recognition procedure of figuring out the presence of single/multiple instances of objects of interest in a given image. It involves determination of precise locations of object instances. This paper presents an overview of some of the popularly used approaches to the object localization problem, involving efficient branch-and-bound strategy for sub-window search, super-pixel neighborhood information based approach, boosted local structured Histogram of Oriented Gradients-Local Binary Patterns (HOG-LBP) based strategy, multi-instance learning based weakly supervised object localization, object localization by utilizing deep networks and image tag based object localization. The performance of the mentioned approaches have been compared on the basis of their results on PASCAL-VOC 2007 dataset.

The signal phase differences of Coriolis sensor and the mass flow are proportional. To improve the measurement accuracy of flow signal processing for Coriolis mass flowmeter, a novel method based on Hilbert Transform algorithm was proposed. The main method is as follows, two signals enhanced by LZ-filter with noise-canceling were considered as Hilbert Transform, then, the phase difference of the two filtered signals was calculated by a triangle characteristic of sin function. Synchronously, the instantaneous frequency was estimated by the structure of the analytical signal. Finally, the mass flow value was obtained by calculating the phase difference. The simulation experiments and Digital Signal Processing system (DSP) verification testing demonstrate that the new signal processing method, which is the LZ algorithm, has better characteristics of real-time and high-precision than the others. The experimental results show that the accuracy of phase difference measurement is 0.02% and the tracking accuracy of frequency is better than 0.07%.

Jigsaw puzzle algorithm is important as it can be applied to many areas such as biology, image editing, archaeology and incomplete crime-scene reconstruction. But, still, some problems exist in the process of practical application, for example, when there are a large number of similar objects in the puzzle fragments, the error rate will reach 30%–50%. When some fragments are missing, most algorithms fail to restore the images accurately. When the number of fragments of the jigsaw puzzle is large, efficiency is reduced. During the intelligent puzzle, mainly the Sum of Squared Distance Scoring (SSD), Mahalanobis Gradient Compatibility (MGC) and other metrics are used to calculate the similarity between the fragments. On the basis of these two measures, we put forward some new methods: 1. MGC is one of the most effective measures, but using MGC to reassemble the puzzle can cause an error image every 30 or 50 times, so we combine the Jaccard and MGC metric measure to compute the similarity between the image fragments, and reassemble the puzzle with a greedy algorithm. This algorithm not only reduces the error rate, but can also maintain a high accuracy in the case of a large number of fragments of similar objects. 2. For the lack of fragmentation and low efficiency, this paper uses a new method of SSD measurement and mark matrix, it is general in the sense that it can handle puzzles of unknown size, with fragments of unknown orientation, and even puzzles with missing fragments. The algorithm does not require any preset conditions and is more practical in real life. Finally, experiments show that the algorithm proposed in this paper improves not only the accuracy but also the efficiency of the operation.

Existing robots only try to give reasonable answers to users, not considering if the answer can make user happy or not. This paper proposed a new kind of robot, lie robot. Lie robot does not like existing robots which always select answers for users according to the truth, lie robot select answers for users mainly according to the user preferences in order to make user happier. The main idea of lie robot is to predict the user feedbacks for answers based on the big data of the user past feedbacks detected by the lie robot.

In order to solve the dynamic optimization problem (DOP), this paper proposes to use a heterogeneous differential evolution (HDE) algorithm framework with memory enhanced Brownian and quantum (MEBQ) individual scheme. The proposed HDE/MEBQ algorithm has the following two advantages when solving DOP. First and foremost, the HDE optimization framework can satisfy the problem requirement of different characteristics. DOP is actually a continuous process to solve different kinds of optimization problems to meet new requirements when the search environmental change occurs. Therefore, HDE/MEBQ is able to fastly respond to the environmental changes of DOP as the HDE framework uses multiple populations with heterogeneous parameters and operators to meet different search requirements in various search environments. Secondly, according to the phenomenon that most of the environmental changes may not be too drastic in real-world applications, historical information in the past may be useful for finding the optimum solution in the new environment. Thus, the MEBQ scheme used in HDE/MEBQ provides helpful historical evolutionary information from the elite ancestors for guiding individuals to evolve in a new environment strongly and to obtain faster convergence and a more precise solution. We evaluated HDE/MEBQ on several DOPs from CEC 2009 and compared with several state-of-the-art dynamic evolutionary algorithms. The results show that HDE/MEBQ performs superior in statistics and gets very competitive results in most of the test conditions, especially in complex DOPs.