International Journal of Pattern Recognition and Artificial Intelligence

A visual secret sharing (VSS) scheme is intended to share secret information in a group to avoid potential treat of interruption and modification. In this paper, we present a novel VSS scheme based on the improved local binary pattern (LBP) operator. It makes full use of local contrast features of LBP for concealing secret image data into different image shares, which can be used to recover the secret easily and exactly. By varying LBP extensions, we can design various kinds of VSS schemes for sharing secret information. Compared to the currently available VSS algorithms, the proposed scheme demonstrates better randomness in shares with less pixel expansion and exact determination in reconstruction with lower computational cost.

In the sign language fingerspelling scheme, letters in the alphabet are presented by a distinctive finger shape or movement. The presented work is conducted for autokinetic translating fingerspelling signs to text. A recognition framework by using intensity and depth information is proposed and compared with some distinguished works. Histogram of Oriented Gradients (HOG) and Zernike moments are used as discriminative features due to their simplicity and good performance. A Deep Belief Network (DBN) composed of three Restricted Boltzmann Machines (RBMs) is used as a classifier. Experiments are executed on a challenging database, which consists of 120,000 pictures representing 24 alphabet letters over five different users. The proposed approach obtained higher average accuracy, outperforming all other methods. This indicates the effectiveness and the abilities of the proposed framework.

Belief propagation (BP) algorithm still exists some shortages, such as inaccurate edge preservation and ambiguous detail information in the foreground, while self-adapting dissimilarity measure (SDM) also exists some shortages, such as ill textureless and occluded information in the background. To address these problems, we present a novel stereo matching algorithm fusing BP and SDM with an excellent background and foreground information. Lots of experiments show that BP and SDM can complement each other. BP algorithm can hold the better background information due to message propagation inference, whereas SDM can possess the better foreground information due to detail treatment. Therefore, a piecewise function is proposed, which can combine BP algorithm in an excellent background information and SDM in the foreground information, and greatly improve the disparity effect as a whole. We also expect that this work can attract more attention on combination of local methods and global methods, due to its simplicity, efficiency, and accuracy. Experimental results show that the proposed method can keep the superior performance and hold better background and foreground on the Middlebury datasets, compared to BP and SDM.

Top-K subgraph matching is one of the hot research issues in graph data management, which is to find, from the data graph, K subgraphs isomorphic to the query graph with the largest sum of weights. The existing methods of Top-K subgraph matching on large graphs usually use the filter-and-verify strategy. However, they all suffer from inefficiency in both stages. In the filtering stage, there exists repeated enumeration of vertices and the excessive memory cost of the filtering. In the verification stage, there exists redundant verification. Regarding to the above problems, we propose to use the preprocessing of the graph compression based on equivalent vertices to reduce the enumeration. In the filtering stage, we propose to reduce the memory cost by only considering the direct neighbors. In the verification stage, we take the vertex with the minimum number of candidate vertices in the query graph as the start vertex of the matching order, and use the idea of Ranking While Matching (RWM) to terminate the execution of the algorithm as early as possible by estimating the upper bound of the weights, so as to reduce redundant verification and improve the overall performance. Finally, the experimental results show that our method is much more efficient than existing methods in compression and the processing time.

Building structure and other factors lead to the performance deterioration of global postioning system (GPS) positioning systems indoors. An adaptive model for Bluetooth-based indoor positioning is proposed in this paper, targeting at the complex indoor environment, to improve the performance of Bluetooth-oriented indoor positioning systems. More accurate Received Signal Strength Indicator (RSSI) calibration which is optimized via Gaussian filtering, together with the environment-dependent attenuation coefficient optimization, results in a more precise hybrid model in the complicated indoor environment. Experiment results show that the difference between the estimated results and the measured samples is less than 0.25m as the target node and reference node is less than 1.5m far from each other. As the distance increases to more than 1.5m, the relative difference between the estimated values and the measured ones decreases to 7.8% at most, satisfying the requirements for indoor positioning applications.

A novel hybrid-fitting energy-based active contours model in the level set framework is proposed. The method fuses the local image fitting term and the global image fitting term to drive the contour evolution, and a special extra term that penalizes the deviation of the level set function from a signed distance function is also included in our method, so the complex and costly reinitialization procedure is completely eliminated. Our model can efficiently segment the images with intensity inhomogeneity no matter where the initial curve is located in the image. In its numerical implementation, two efficient numerical schemes are used to ensure the sufficient efficiency of the evolution process, one is the Lattice Boltzmann Model (LBM), which is used for breaking the restrictions on time step, the other is the Sparse Field Method (SFM), which is introduced for fast local computation. Compared with the traditional schemes, these two strategies can further shorten the time consumption of the evolution process, this allows the level set to quickly reach the true target location. The extensive and promising experimental results on numerous synthetic and real images have shown that our method can efficiently improve the image segmentation performance, in terms of accuracy, efficiency, and robustness.

General machine vision algorithms are difficult to detect LCD sub-pixel level defects. By studying the LCD screen images, we found that the pixels in the LCD screen are regularly arranged. The spectrum distribution of LCD images, which is obtained by the Fourier transform, is relatively consistent. According to this feature, a method of sub-pixel defect detection based on notch filter and image registration is proposed. First, we take a defect-free template image to establish registration template and notch-filtering template; then we take the defect images for image registration with registration template, and solve the offset problem. After the notch-filter template filtering the background texture, the defect is more obvious; Finally the defects are obtained by the threshold segmentation method. The experiment results show that the proposed method can detect sub-pixel defects accurately and quickly.

With regard to the numerous complex holes that accompany a three-dimensional (3D) model reconstruction, a hole-repair method is proposed in this paper. First, signed distance function (SDF) is constructed, and the curved surface where the hole is positioned is expressed by a static SDF. Another dynamic SDF is used to express the initial curved surface. Using a variation level set on implicit surfaces, we introduce a global convex optimization energy model to perform minimum inducement, thus turning the problem of extracting the hole edge into a problem of spherical evolution of a hidden surface. Finally, we consider the extracted hole-edge surface as the initial observation surface and complete the hole repair using convolution and synthesis. The experiment shows that this algorithm can effectively restore notable geometrical features of areas with complex holes and can repair the holes in a model with many grids.

In this paper, the three-dimensional (3D) reconstruction of target self-calibrating system for the guidance system of air-to-air missile is researched. The basic ideology of self-calibrating theory is studied in depth and also the advantages and disadvantages of traditional calibration method, which is based on active vision and target self-calibrating method, are listed for comparison. The mathematical model of the perspective camera is established, and on this basis, the camera parameters are figured out combining with LM optimization algorithm. The reconstruction is conducted by the method of stratified calibrating. It is proved that the theory of 3D reconstruction of target self-calibrating system in air to air missile is available according to the experimental results. It puts forward a new research approach for the guidance system of air to air missile to identify the target characteristic information in different azimuths.

In recent decades, magnetic resonance imaging (MRI) has attracted attention in radiation therapy as the only modality. This nontrivial task requires the application of pseudo computed tomography (PCT) generation methods. On the one hand, the electron density information provided by the CT scan is critical for calculating the 3D dose distribution of tissues. On the other hand, the bone image provided by the CT is precise enough for the construction of a radiograph. Lately, the use of MRI/CT has combined all of the soft tissue contrast merits which are contributed by the MRI and the virtue of CT imaging. However, owing to the unbalance of voxel-intensities in the MRI and CT scan, the MRI/CT workflow also has shortcomings. Inspired by the random forest-based PCT estimation, this paper investigated the potential of the 3D group feature as the input of the random forest regression, which is based on the 3D block-matching method, taking the correlated central voxel as the target. Four types of features including the voxel level, sub-regional level, whole cubic level with adaptive weighted conjunction and compressed level were introduced to attain the robust features. The group-based random forest regression was then utilized to obtain the approximated PCT only from corresponding MRI, and the feature is extracted from the 3D cubic MRI patches and mapped to the 3D cubic CT patch, which helps in decreasing the computation difficulty, representing the MR patches into an anatomical feature space. The alternative regression forest was used in solving the regression task for enhancing the prediction power compared with the random forest. The proposed method could efficiently capture the correlation that is observable between the CT as well as the MR images on the basis of the alternative random forest (ARF) with cubic features, and the experimental results show the performance and effectiveness of the proposed method compared with the recent learning-based and atlas-based (AB) methods

Due to the nonlinear deformation of nonrigid and nonuniform tissues, it is challenging to accurately measure the displacements of feature points distributed on the inner parts, boundaries, and separatrices of tissue layers. To address this challenge, we propose a feature point matching technique called RAPID to measure MR 2D slice deformation of nonuniform and nonrigid biological tissues. We propose to use the covariance of several neighboring point statistics computed around a keypoint, as the keypoint descriptor. Inspired by the kernel methods, we advocate adopting a Riemannian pseudo kernel to map SPD matrices to a high dimensional Hilbert space, where the Euclidean geometry applies. We compare our RAPID with two existing schemes (i.e., SIFT and SURF). Our experimental results show that our RAPID is superior to SIFT and SURF, because the benefits offered by RAPID are two-fold. First, our RAPID increases the number of matched data points. Second, RAPID substantially improves the key-point matching accuracy of SIFT and SURF.

Path planning is the essential aspect of autonomous flight system for unmanned aerial vehicles (UAVs). An improved particle swarm optimization (PSO) algorithm, named GBPSO, is proposed to enhance the performance of three-dimensional path planning for fixed-wing UAVs in this paper. In order to improve the convergence speed and the search ability of the particles, the competition strategy is introduced into the standard PSO to optimize the global best solution during the process of particle evolution. More specifically, according to a set of segment evaluation functions, the optimal path found by single waypoint selection way is adopted as one of the candidate global best paths. Meanwhile, based on the particle as an integrated individual, an optimal trajectory from the start point to the flight target is generated as another global best candidate path. Subsequently, the global best path is determined by considering the pre-specified elevation function values of two candidate paths. Finally, to verify the performance of the proposed method, GBPSO is compared with some existing path-planning methods in two simulation scenarios with different obstacles. The results demonstrate that GBPSO is more effective, robust and feasible for UAV path planning.

In reinforcement learning (RL), the exploration/exploitation (E/E) dilemma is a very crucial issue, which can be described as searching between the exploration of the environment to find more profitable actions, and the exploitation of the best empirical actions for the current state. We focus on the single trajectory RL problem where an agent is interacting with a partially unknown MDP over single trajectories, and try to deal with the E/E in this setting. Given the reward function, we try to find a good E/E strategy to address the MDPs under some MDP distribution. This is achieved by selecting the best strategy in mean over a potential MDP distribution from a large set of candidate strategies, which is done by exploiting single trajectories drawn from plenty of MDPs. In this paper, we mainly make the following contributions: (1) We discuss the strategy-selector algorithm based on formula set and polynomial function. (2) We provide the theoretical and experimental regret analysis of the learned strategy under an given MDP distribution. (3) We compare these methods with the “state-of-the-art” Bayesian RL method experimentally.

The traditional Gaussian mixture background model failed to build a reasonable background in complex scenarios, so this paper proposes an improved self-adaptive Gaussian mixture background modeling which integrates the difference method and adaptive threshold segmentation to improve the traditional one. In the proposed model, the difference method is applied to achieve the segmentation of changing area and background area, and different weight update policies are used for different areas. Background area updates background model with a fixed update rate. The changing region is divided into moving target area and background show area with the fusion of adaptive threshold; the background show area has a large update rate, allowing the previously obscured parts to recover rapidly; the moving target area won’t build new Gaussian components for the Gaussian mixture model. Experiments show that the video sequence algorithm with uncertainties of building background model has good adaptability. It helps to improve computing speed to a large extent and it can also respond to the change of the actual scene quickly.