International Journal of Pattern Recognition and Artificial Intelligence

This paper introduces mind evolutionary algorithm (MEA) into the application of short-term traffic flow prediction, and proposes a short-term traffic flow prediction model of wavelet neural network based on mind evolutionary algorithm (MEA-WNN). The optimal connection weight and wavelet parameters of wavelet neural network (WNN) are searched globally by MEA, and the convergence capacity of wavelet neural network is improved. The experimental data show that, compared with the prediction model of the traditional WNN and the WNN based on genetic algorithm (GA-WNN), the prediction model of MEA-WNN has higher global prediction accuracy.

Homology Continuity is a fundamental property of the nature, but few of the traditional pattern recognition algorithms were aware of it. Firstly, this paper gives a brief description to the Principle of Homology Continuity (PHC), and tries to mathematically redefine it. Then, we introduce a PHC-based pattern learning method — Geometrical Covering Learning (GCL), following the Hyper sausage neural network as an instance of GCL. Lastly, we propose a GCL solution to the “two-spirals” pattern recognition problem. The final experimental results show that the new method is feasible and efficient.

This paper proposed a high-performance image retrieval framework, which combines the improved feature extraction algorithm SIFT (Scale Invariant Feature Transform), improved feature matching, improved feature coding Fisher and improved Gaussian Mixture Model (GMM) for image retrieval. Aiming at the problem of slow convergence of traditional GMM algorithm, an improved GMM is proposed. This algorithm initializes the GMM by using on-line $K$-means clustering method, which improves the convergence speed of the algorithm. At the same time, when the model is updated, the storage space is saved through the improvement of the criteria for matching rules and generating new Gaussian distributions. Aiming at the problem that the dimension of SIFT (Scale Invariant Feature Transform) algorithm is too high, the matching speed is too slow and the matching rate is low, an improved SIFT algorithm is proposed, which preserves the advantages of SIFT algorithm in fuzzy, compression, rotation and scaling invariance advantages, and improves the matching speed, the correct match rate is increased by an average of 40% to 55%. Experiments on a recently released VOC 2012 database and a database of 20 category objects containing 230,800 images showed that the framework had high precision and recall rates and less query time. Compared with the standard image retrieval framework, the improved image retrieval framework can detect the moving target quickly and effectively and has better robustness.

For the detection of a moving target position in video monitoring images, the existing locating tracking systems mainly adopt binocular or structured light stereoscopic technology, which has drawbacks such as system design complexity and slow detection speed. In light of these limitations, a tracking method for monocular sequence moving targets is presented, with the introduction of ground constraints into monocular visual monitoring; the principle and process of the method are introduced in detail in this paper. This method uses camera installation information and geometric imaging principles combined with nonlinear compensation to derive the calculation formula for the actual position of the ground moving target in monocular asymmetric nonlinear imaging. The footprint location of a walker is searched in the sequence imaging of a monitoring test platform that is built indoors. Because of the shadow of the walker in the image, the multi-threshold OTSU method based on test target background subtraction is used here to segment the images. The experimental results verify the effectiveness of the proposed method.

Green plant species identification plays an important role in so many aspects, such as ecological environment protection, Chinese medicine preparation, agricultural and horticultural application, etc. A method on green plants recognition based on wavelet transform and variable local edge patterns (VLEP) is proposed in this paper. Firstly, the original image is decomposed by wavelet transformation. Then texture features are extracted using VLEPs. At the same time, block-based and multi-resolution ideas are considered together to extract features after images are transformed by wavelet. Finally, the fused texture features are classified by the nearest neighbor method. The experimental results show that the proposed method is a promising method for recognizing the common green plants with the natural and complex background compared with the other state-of-the-art methods, and combination of block-based and multi-resolution ideas can further improve the accuracy rate effectively.

School bullying is a common social problem, which affects children both mentally and physically, making the prevention of bullying a timeless topic all over the world. This paper proposes a method for detecting bullying in school based on activity recognition and speech emotion recognition. In this method, motion and voice data are gathered by movement sensors and a microphone, followed by extraction of a set of motion and audio features to distinguish bullying incidents from daily life events. Among extracted motion features are both time-domain and frequency-domain features, while audio features are computed with classical MFCCs. Feature selection is implemented using the wrapper approach. At the next stage, these motion and audio features are merged to form combined feature vectors for classification, and LDA is used for further dimension reduction. A BPNN is trained to recognize bullying activities and distinguish them from normal daily life activities. The authors also propose an action transition detection method to reduce computational complexity for practical use. Thus, the bullying detection algorithm will only run, when an action transition event has been detected. Simulation results show that the combined motion-audio feature vector outperforms separate motion features and acoustic features, achieving an accuracy of 82.4% and a precision of 92.2%. Moreover, with the action transition method, the computation cost can be reduced by half.

The FITS is widely used as a common file format in astronomy. However, how to detect an effective celestial event from large-scale astronomical images with complex background is a thorny issue. This paper proposes a square grid structure for target detection (GSTD) of solar activities, inncluding the separation of the target area and the deletion of the background area. This kind of processing can greatly reduce the storage cost of the data set, while the solar activity area can be well preserved. The solar image is operated through an equally sized square grid to separate the solar activity target area and the background area to speed up the processing of images, improve processing accuracy, and effectively prevent image noise interference. The experiment results have shown that this method delivers satisfactory performance in accuracy and time-cost. Through the anti-jamming processing of image noise, the accurate positioning and effective recognition of solar activities are realized. This method has been proved to achieve good image segmentation recognition of solar phenomenon in the research of various types of solar activities. Moreover, it can provide a feasible way to reduce the storage occupancy of Content-Based Image Retrieval (CBIR) systems.

Image recognition and feature extraction play an important role in precision agriculture. In this paper, a manifold learning algorithm was used for dimension reduction of gray and RGB color images. To clarify the boundaries of disease spots and leaf background, three clustering algorithms were applied in experiments to realize clearer maize leaf disease images. Locally, linear embedding (LLE) and Gustafson–Kessel (GK) algorithms were selected to realize image feature extraction. It was shown that the recognition rate of feature extraction for gray and color images were 95% and 99%, respectively.

A thermal light correlated longitudinal imaging experiment is proposed. The quasi-thermal light beam is split into two beams, a test beam and a reference beam, respectively. The light in the test beam is scattered by two amplitude objects with a specific longitudinal distance between them, while the light of the reference beam travels uninterrupted. At the end of the test and reference beams, two charge-coupled detectors (CCDs) are used to measure the intensity of the optical field. Through intensity correlation measurement the images of the two detected objects can be achieved simultaneously, only if the distance between the objects is less than the longitudinal coherent length. The theoretical analysis shows that the longitudinal coherent length is determined by both the transverse size of the incoherent thermal light source and the length of the optical path. The quality of the correlated images of the two objects is improved greatly by making use of the orthogonal matching pursuit (OMP) and the proposed variant random orthogonal matching pursuit (Random-OMP) algorithms. The experimental results show that the Random-OMP algorithm is more effective than the OMP algorithm for increasing both the visibility and continuity of the images. The experimental scenario can mimic an optical tomography imaging system, and the two objects with longitudinal distance can be taken as the two transverse layers of a three-dimensional object. The proposed Random-OMP algorithm is effective for improving the quality of the tomography image and has potential value in optical tomography imaging technology using incoherent light sources.

This paper presented 3D reconstruction method for road scene with the help of obstacle detection. 3D reconstruction for road scene can be used in autonomous driving, driver assistance system, car navigation systems. However, some errors often rose when 3D reconstructing due to the shade from the moving object in the road scene. The presented 3D reconstruction method with obstacle detection feedback can avoid this problem. Firstly, this paper offers a framework for the 3D reconstruction of road scene by laser scanning and vision. A calibration method based on the location of horizon is proposed, and a method of attitude angle measuring based on vanishing point is proposed to revise the 3D reconstruction result. Secondly, the reconstruction framework is extended by integrating with an object recognition that can automatically detect and discriminate obstacles in the input video streams by a RANSAC approach and threshold filter, and localizes them in the 3D model. 3D reconstruction and obstacle detection are tightly integrated and benefit from each other. The experiment result verified the feasibility and practicability of the proposed method.

Augmented reality (AR) by analyzing the characteristics of the scene, the computer-generated geometric information which can be added to the real environment in the way of visual fusion, reinforces the perception of the world. Three-dimensional (3D) registration is one of the core issues of in AR. The key issue is to estimate the visual sensor’s posture in the 3D environment and figure out the objects in the scene. Recently, computer vision has made significant progress, but the registration based on natural feature points in 3D space for AR system is still a severe problem. There is the difficulty of working out the mobile camera’s posture in the 3D scene precisely due to the unstable factors, such as the image noise, changing light and the complex background pattern. Therefore, to design a stable, reliable and efficient scene recognition algorithm is still very challenging work. In this paper, we propose an algorithm which combines Visual Simultaneous Localization and Mapping (SLAM) and Deep Convolutional Neural Networks (DCNNS) to boost the performance of AR registration. Semantic segmentation is a dense prediction task which aims to predict categories for each pixel in an image when applying to AR registration, and it will be able to narrow the searching range of the feature point between the two frames thus enhancing the stability of the system. Comparative experiments in this paper show that the semantic scene information will bring a revolutionary breakthrough to the AR interaction.

This paper proposes a novel algorithm for feature extraction for face recognition, namely the rearranged modular two-dimensional locality preserving projection (Rm2DLPP). In the proposed algorithm, the original images are first divided into modular blocks, then the subblocks are rearranged to form two-dimensional matrices and finally the two-dimensional locality preserving projection algorithm is applied directly on the arranged matrices. The advantage of the Rm2DLPP algorithm is that it can utilize the local block features and global spatial structures of 2D face images simultaneously. The performance of the proposed method is evaluated and compared with other face recognition methods on the ORL, AR and FERET databases. The experimental results demonstrate the effectiveness and superiority of the proposed approach.

Face tracking in surveillance videos is one of the important issues in the field of computer vision and has realistic significance. In this paper, a new face tracking framework in videos based on convolutional neural networks (CNNs) and Kalman filter algorithm is proposed. The framework uses a rough-to-fine CNN to detect faces in each frame of the video. The rough-to-fine CNN method has a higher accuracy in complex scenes such as face rotation, light change and occlusion. When face tracking fails due to severe occlusion or significant rotation, the framework uses Kalman filter to predict face position. The experimental results show that the proposed method has high precision and fast processing speed.

In order to reduce the serious problems caused by the operators’ fatigue, we propose a novel network model Convolutional Neural Network and Long Short-Term Memory Network (CNN-LSTM) — for fatigue detection in the inter-frame images of video sequences, which mainly consists of CNN and LSTM network. Firstly, in order to improve the accuracy of the deep network structure, the Viola–Jones detection algorithm and the Kernelized Correlation Filter (KCF) tracking algorithm are used in the face detection to normalize the size of the inter-frame images of video sequences. Secondly, we use the CNN and the LSTM network to detect the fatigue state in real time and efficiently. The fatigue-related facial features are extracted by the CNN. Then, the temporal symptoms of the whole fatigue process can be extracted by LSTM networks, the input data which is the facial feature vector can be obtained by the CNN. Thirdly, we train and test the network in a step-by-step approach. Finally, we experiment with the proposed network model. The experimental results demonstrate that the network structure can effectively detect the fatigue state, and the overall accuracy rate can rise to 82.8%.