International Journal of Pattern Recognition and Artificial Intelligence

Copy number variation (CNV) is a prevalent kind of genetic structural variation which leads to an abnormal number of copies of large genomic regions, such as gain or loss of DNA segments larger than 1kb. CNV exists not only in human genome but also in plant genome. Current researches have testified that CNV is associated with many complex diseases. In this paper, guanine-cytosine (GC) bias, mappability and their effect on read depth signals in sequencing data are discussed first. Subsequently, a new correction method for GC bias and an improved combinatorial detection algorithm for CNV using high-throughput sequencing reads based on hidden Markov model (CNV-HMM) are proposed. The corrected read depth signals have lower correlation with GC content, mappability of reads and the width of analysis window. Then we create a hidden Markov model which maps the reads onto the reference genome and records the unmapped reads. The unmapped reads are counted and normalized. The CNV-HMM detects the abnormal signal of read count and gains the candidate CNVs using the expectation maximization (EM) algorithm. Finally, we filter the candidate CNVs using split reads to promote the performance of our algorithm. The experiment result indicates that the CNV-HMM algorithm has higher accuracy and sensitivity for CNVs detection than most current detection algorithms.

Ordinary projection screen is not sensitive to interaction, it cannot meet the demands of teaching, virtual reality, and other applications. Due to the fact that people always use hands to complete a variety of human–computer interaction, the finger-based interactive projection technology is worth being researched. In this paper, an ordinary monocular camera is used to acquire video frame on projection screen, and the touch signal of finger in frame is used as the input of interactive projection system. Because the differences between spatial frequency of common digital camera and the projection screen is small, the frame obtained from camera will contain moire fringe, which needs to be filtered in image frequency domain. Then the difference between current frame edge and previous frame edge is calculated to obtain moving object edge clues. According to these clues, the most possible contour curve is searched in current frame edge, and the curve is fitted by polynomial approximation method. Its curvature integration is used to match with the curvature integration of finger template curve. After that the fingers in the curve are recognized. Because color information is not needed, this method can be used to recognize gloved fingers. Finally, finger shadow is used to judge whether the finger touches projection screen to complete interactive process. The experiments of writing and collaboratively rotating picture on projector screen show that this method can effectively complete interactive operation with the projection screen and can realize the multi-user operation.

Coronary optical coherence tomography (OCT) is a new high-resolution intravascular imaging technology that clearly depicts coronary artery stenosis and plaque information. Study of coronary OCT images is of significance in the diagnosis of coronary atherosclerotic heart disease (CAD). We introduce a new method based on the convolutional neural network (CNN) and an improved random walk (RW) algorithm for the recognition and segmentation of calcified, lipid and fibrotic plaque in coronary OCT images. First, we design CNN with three different depths (2, 4 or 6 convolutional layers) to perform the automatic recognition and select the optimal CNN model. Then, we device an improved RW algorithm. According to the gray-level distribution characteristics of coronary OCT images, the weights of intensity and texture term in the weight function of RW algorithm are adjusted by an adaptive weight. Finally, we apply mathematical morphology in combination with two RWs to accurately segment the plaque area. Compared with the ground truth of clinical segmentation results, the Jaccard similarity coefficient (JSC) of calcified and lipid plaque segmentation results is 0.864, the average symmetric contour distance (ASCD) is 0.375mm, the JSC and ASCD reliabilities are 88.33% and 92.50% respectively. The JSC of fibrotic plaque is 0.876, the ASCD is 0.349mm, the JSC and ASCD reliabilities are 90.83% and 95.83% respectively. In addition, the average segmentation time (AST) does not exceed 5 s. Reliable and significantly improved results have been achieved in this study. Compared with the CNN, traditional RW algorithm and other methods. The proposed method has the advantages of fast segmentation, high accuracy and reliability, and holds promise as an aid to doctors in the diagnosis of CAD.

In this paper, we present a robust multibit image watermarking scheme to undertake the common image-processing attacks as well as affine distortions. This scheme combines contrast modulation and effective synchronization for large payload and high robustness. We analyze the robustness, payload, and the lower bound of fidelity. Regarding watermark resynchronization under affine distortions, we develop a self-referencing rectification method to detect the distortion parameters for reconstruction by the center of mass in affine covariant regions. The effectiveness and advantages of the proposed scheme are confirmed by experimental results, which show the superior performance as comparing against several state-of-the-art watermarking methods.

This paper proposes a novel sparsity-based error concealment (EC) algorithm which integrates the Gauss Bayes model and singular value decomposition for high efficiency video coding (HEVC). Under the sequential recovery framework, pixels in missing blocks are successively reconstructed in Gauss Bayes mode. We find that the estimation error follows the Gaussian distribution in HEVC, so the error pixel estimation problem can be transferred to a Bayesian estimation. We utilize the singular value decomposition (SVD) technique to select sample pixels, which yields high estimation accuracy and reduces estimation error. A new recovery order based on confidence is established to resolve the error propagation problem. Compared to other state-of-the-art EC algorithms, experimental results show that the proposed method gives better reconstruction performance in terms of objective and subjective evaluations. It also has significantly lower complexity.

It is common that historical Tibetan documents belonging to historical collections are poorly preserved and are prone to degradation processes. This causes many challenges that can be addressed by image binarization, the most common of which is stains. A lack of uniform standard datasets makes it difficult to evaluate binarization effects. Motivated by the poor effects and difficulty of evaluating the binarization of historical Tibetan document images, a combined approach is proposed that aims to improve overall performance. The method includes the following parts: first, image generation through standard binarization and the background extraction of color images, which are both used for image synthesis in preparing for an evaluation. Then, preliminary binarization processing is implemented through channel combination in the lab color space and through local binarization. The synthetic images are used to select the coefficient when the channels are combined. Furthermore, Local Binary Pattern (hereafter LBP) and image smoothing is carried out after the combination of the channels to obtain the outline of the text area. Finally, the final binarization image is obtained by combining the preliminary binarization image and the text area contour image. Our method achieved top performance compared to other methods after a large number of synthetic image tests with a variety of background types.

This paper proposes an information hiding algorithm using matrix embedding with Hamming codes and histogram preservation in order to keep the histogram of the image unchanged before and after hiding information in digital media. First, the algorithm uses matrix embedding with Hamming codes to determine the rewriting bits of the original image, rewrite and flip them, and successfully embed the secret information. Then, according to the idea of a break-even point, a balanced pixel frequency adaptive algorithm is proposed and each embedded bit of secret information is detected and compensated by the adjacent bit of histogram data, so that the histogram change of the image before and after information hiding is minimized. At present, most of the histogram distortion values after steganography are generally over 1000 or even higher. As a contrast, the method proposed in this paper can keep the histogram distortion values to be less than 1000. The feasibility and effectiveness of the algorithm are verified by relative entropy analysis as well. The experimental results also show that the algorithm performs well in steganographic analyses of images.

When the network is subject to intrusion and attack, the node output channel equalization will be affected, resulting in bit error and distortion in the output of network transmission symbols. In order to improve the anti-attack ability and equalization of network node, a network intrusion feature map node equalization algorithm based on modified variable step-size constant modulus blind equalization algorithm (MISO-VSS-MCMA) is proposed. In this algorithm, the node transmission channel model after network intrusion is constructed, and sequential processing is performed to intruded nodes with the variable structure feedback link control method. With diversity spread spectrum technology, the channel loss after network intrusion is compensated and the network intrusion map feature is extracted. According to the extracted feature amount, channel equalization processing is performed for the cost function with the MISO-VSS-MCMA method to reduce the damage of network intrusion to the channel. Simulation results show that in node transmission channel equalization after network intrusion, this algorithm can reduce the error bit rate of signal transmission in network, and provide a good ability of correcting phase deflection in the output constellation, thus avoiding the error bit distortion and channel damage caused by network intrusion to the signal with a good equalization effect. This algorithm provides stronger convergence and map concentration, which demonstrates that its anti-interference and signal recovery capabilities are better, so it improves the anti-attack ability of the network.

Generally, the blades of wind turbine installed in cold regions often encounter the situation of icing on their surface in winter, which affects the performance of wind turbine greatly and reduces power generation. Blade icing of wind turbine is a typical alarm related to multivariates, such as environment temperature, humidity, blade rotation and so on. To successfully achieve a supervised classification, it is necessary to extract the correlated features with blade icing. This paper proposes an extraction algorithm using reward extremum which can extract a group of variables correlated to blade icing from many monitoring variables of wind turbine, and they affect each other. This algorithm, using reward function which can measure the reward or value of alarm problem, can effectively distinguish the variables correlated to blade icing from the uncorrelated ones without knowing the mean of variables and the relations between them. Whether the blade of wind turbine is iced or not is a binary logic, so logistic regression algorithm is better choice to detect blade icing. Hence, this paper proposes a hybrid model using logistic regression algorithm and extraction algorithm using reward extremum, which can be built by machine learning with historic monitoring data. This model can real-time detect blade icing by real-time monitoring data. A correlation variable set can be obtained by the application of extraction algorithm using reward extremum on the historic monitoring data; and then the weights of the correlation variables in the set can be gained from the historic data by using logistic regression algorithm, so a blade icing detection pattern can be found using logistic regression algorithm; finally, whether the blade of wind turbine is iced in delay time or not can be real-time detected with the help of the hybrid model by real-time monitoring data. It is proved by wind turbine data that this hybrid model can work well not only to get correlation variable set but also to improve the generalization performance of logistic regression and achieve better prediction results. The performance of the model on blade icing alarm of wind turbine provides a new way to find out multivariate correlation to alarm from the mass historic monitoring data.

Support vector machine (SVM) is always used for face recognition. However, kernel function selection is a key problem for SVM. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function to improve the accuracy of classification and recognition of SVM. Firstly, an improved artificial fish swarm optimization algorithm (IAFSA) is proposed to optimize the parameters in SVM. In the improved version of artificial fish swarm optimization algorithm, the visual distance and the step size of artificial fish are adjusted adaptively. In the early stage of convergence, artificial fish are widely distributed, and the visual distance and step size take larger values to accelerate the convergence of the algorithm. In the later stage of convergence, artificial fish gathered gradually, and the visual distance and the step size were given small values to prevent oscillation. Then the optimized SVM is used to recognize face images. Simultaneously, in order to improve the accuracy rate of face recognition, an improved local binary pattern (ILBP) is proposed to extract features of face images. Numerical results show the advantage of our new algorithm over a range of existing algorithms.

With the increase in the morbidity of liver cancer and its high mortality rate, liver segmentation in abdominal computed tomography (CT) scan images has received extensive attention. Segmentation results play an important role in computer-assisted diagnosis and therapy. However, it remains a challenging task because of the complexity of the liver’s anatomy, low contrast between the liver and its adjacent organs, and presence of lesions. This study presents an automatic method for liver segmentation from CT scan images based on the convex–concave point for tree structured part model (CCP–TSPM). First, TSPM is utilized as a coarse segmentation tool for capturing the topological shape variation. Then, the proposed CCP is implemented to adjust the position between adjacent points dynamically. As a result, the CCP–TSPM can locate the liver boundary adaptively. Furthermore, color space data provide abundant feature information, which can further improve the method’s effectiveness and efficiency. Finally, the curve is evolved by an iteration level set function to obtain the fine segmentation results. The experimental results show that the proposed method can extract the liver boundary successfully. Furthermore, a comparison of the results with those of the state-of-the-art methods demonstrates the superior performance of the proposed method.

This paper presents the method and findings of a visual knowledge domain map used to explore the development and evolution of Building information modeling (BIM) technology. Metadata taken from 806 literature records is used to visualize the state of BIM research. The records were published between 1998 and 2017, and Citespace software is employed to visualize key data about the research contained on the Web of Science platform, including nationality of research institutions, research themes, co-citations of periodicals, authorship, literature sources, and the development of the discipline over time. The distribution of power in this field is also examined by interrogating the BIM knowledge base, identifying research hotspots to provide a reference for assessing the current state-of-the-art.

In the original compression tracking algorithm, the size of the tracking box is fixed. There should be better tracking results for scale-invariant objects, but worse tracking results for scale-variant objects. To overcome this defect, a scale-adaptive compressive tracking (CT) algorithm is proposed. First of all, the imbalance of the gray and texture features in the original CT algorithm is balanced by the multi-feature method, which makes the algorithm more robust. Then, searching different candidate regions by using the method of multi-scale search along with feature normalization makes the features extracted from different scales comparable. Finally, the candidate region with the maximum discriminate degree is selected as the object region. Thus, the tracking-box size is adaptive. The experimental results show that when the object scale changes, the improving CT algorithm has higher accuracy and robustness than the original CT algorithm.

Sentiment analysis, including aspect-level sentiment classification, is an important basic natural language processing (NLP) task. Aspect-level sentiment can provide complete and in-depth results. Words with different contexts variably influence the aspect-level sentiment polarity of sentences, and polarity varies based on different aspects of a sentence. Recurrent neural networks (RNNs) are regarded as effective models for handling NLP and have performed well in aspect-level sentiment classification. Extensive literature exists on sentiment classification that utilizes convolutional neural networks (CNNs); however, no literature on aspect-level sentiment classification that uses CNNs is available. In the present study, we develop a CNN model for handling aspect-level sentiment classification. In our model, attention-based input layers are incorporated into CNN to introduce aspect information. In our experiment, in which a benchmark dataset from Twitter is compared with other models, incorporating aspect information into CNN improves aspect-level sentiment classification performance without using syntactic parser or other language features.