Accurate Dense Stereo Matching Based on Image Segmentation Using an Adaptive Multi-Cost Approach

This paper presents a segmentation-based stereo matching algorithm using an adaptive multi-cost approach, which is exploited for obtaining accuracy disparity maps. The main contribution is to integrate the appealing properties of multi-cost approach into the segmentation-based framework. Firstly, the reference image is segmented by using the mean-shift algorithm. Secondly, the initial disparity of each segment is estimated by an adaptive multi-cost method, which consists of a novel multi-cost function and an adaptive support window cost aggregation strategy. The multi-cost function increases the robustness of the initial raw matching costs calculation and the adaptive window reduces the matching ambiguity effectively. Thirdly, an iterative outlier suppression and disparity plane parameters fitting algorithm is designed to estimate the disparity plane parameters. Lastly, an energy function is formulated in segment domain, and the optimal plane label is approximated by belief propagation. The experimental results with the Middlebury stereo datasets, along with synthesized and real-world stereo images, demonstrate the effectiveness of the proposed approach

A Credit-Based Congestion-Aware Incentive Scheme for DTNs

In Delay-Tolerant Networks (DTNs), nodes may be selfish and reluctant to expend their precious resources on forwarding messages for others. Therefore, an incentive scheme is necessary to motivate selfish nodes to cooperatively forward messages. However, the current incentive schemes mainly focus on encouraging nodes to participate in message forwarding, without considering the node congestion problem. When many messages are forwarded to the nodes with high connection degree, these nodes will become congested and deliberately discard messages, which will seriously degrade the routing performance and reduce the benefits of other nodes. To address this problem, we propose a credit-based congestion-aware incentive scheme (CBCAIS) for DTNs. In CBCAIS, a check and punishment mechanism is proposed to prevent forwarding nodes from deliberately discarding message. In addition, a message acceptance selection mechanism is proposed to allow the nodes to decide whether to accept other messages, according to self congestion degree. The experimental results show that CBCAIS can effectively stimulate selfish nodes to cooperatively forward messages, and achieve a higher message delivery ratio with lower overhead ratio, compared with other schemes

High-capacity reversible watermarking scheme of 2D-vector data

The application of two-dimensional (2D) vector map faces the security issues of copyright protection, which limit the usage of vector data in many scenarios. Reversible watermarking is a more feasible solution, which aims to restore the original data after watermark extraction. In this paper, high-capacity reversible watermarking scheme of 2D-vector data is proposed based on the idea of iterative embedding. It groups vertices as units for each polyline and selects highly correlated vertex units as cover data. Then the reversible embedding is carried out by iteratively modifying the median vertex coordinates of each selected embedding unit. This scheme can strictly recover the original vector data with watermark accurate extraction. Meanwhile, both higher payload capacity and perception invisibility are validated through theoretical analysis and comprehensive experiments. The experimental results show that the proposed reversible watermarking scheme is suitable for 2D-vector data applications where high-precision data are required. ? 2014 Springer-Verlag London

Research on OpenCL optimization for FPGA deep learning application.

In recent years, with the development of computer science, deep learning is held as competent enough to solve the problem of inference and learning in high dimensional space. Therefore, it has received unprecedented attention from both the academia and the business community. Compared with CPU/GPU, FPGA has attracted much attention for its high-energy efficiency, short development cycle and reconfigurability in the aspect of deep learning algorithm. However, because of the limited research on OpenCL optimization on FPGA of deep learning algorithms, OpenCL tools and models applied to CPU/GPU cannot be directly used on FPGA. This makes it difficult for software programmers to use FPGA when implementing deep learning algorithms for a rewarding performance. To solve this problem, this paper proposed an OpenCL computational model based on FPGA template architecture to optimize the time-consuming convolution layer in deep learning. The comparison between the program applying the computational model and the corresponding optimization program provided by Xilinx indicates that the former is 8-40 times higher than the latter in terms of performance

Channel Compression Optimization Oriented Bus Passenger Object Detection

Bus passenger flow information can facilitate scientific dispatching plans, which is essential to decision making and operation performance evaluation. Real-time acquisition of bus passenger flow information is an indispensable part for bus intellectualization. The method of passenger flow statistics in bus video monitoring scene based on deep convolution neural network can provide rich information for passenger flow statistics. In order to adapt to the real scenario of mobile and embedded devices on buses, and to consider the bandwidth limitation, this paper uses a lightweight network model M7, which is suitable for the vehicle system. Based on the classic network model tiny YOLO, the model is optimized by a depthwise separable convolution method. The optimized network model M7 reduces the number of parameters and improves the detection speed, while maintaining a low loss in detection accuracy. As such, the network model M7 is compressed and further optimized by removing redundant channels. The experimental results show that the detection speed of the network model target recognition after channel compression is 40%, which is faster than the precious channel compression on the premise of ensuring detection

Access-Pattern Aware Checkpointing Data Storage Scheme for Mobile Computing Environment

AbstractThe rapid development of communication technology and mobile devices has facilitated the mobile computing applications such as location-based services, and information sharing. Characteristics of the mobile computing system, such as the space limitation, mobility handling, low bandwidth and limited battery life, make mobile computing applications more prone to failures. Checkpoint and rollback recovery technology, as a fault tolerance method for continuing services in mobile computing environment, is researched in this paper. Based on user access patterns, mainly considering the visit time of the mobile host(MH) to the sojourn mobile support stations (MSSs), a checkpointing data storage scheme is proposed. Only if the stay time in the current mobile support station is long enough, the mobile host should do the checkpoint and store the checkpoint data on the sojourn mobile support station. Based on the visited time to the MSS, the scheme manages checkpointing data high efficiently which is more cognitively flexible and adapt to the nomadic and mobile computing environmental conditions