Image stitching, or known as image mosaic, is the process that combines images with overlapped areas to form an image with wide view and high resolution. Image stitching technique has been quickly developed these years. It has become an important branch in digital image processing and has wide applications. Many image stitching methods have been proposed. This article takes investigation on some image stitching techniques, including image registration, seam removal and quality assessment. Most existing related methods are introduced. Experiments are done to show the result of some main methods. At last, the advantages and disadvantages of some existing methods are discussed and some future potential work are pointed out.

Review on image-stitching techniques

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

Optimization of DV-hop localization algorithm in hybrid optical wireless sensor networks

Localization is one of the key concepts in wireless sensor networks. Different techniques and measures to calculate the location of unknown nodes were introduced in recent past. But the issue of nodes’ mobility requires more attention. The algorithms introduced earlier to support mobility lack the utilization of the anchor nodes’ privileges. Therefore, in this article, an improved DV-Hop localization algorithm is introduced that supports the mobility of anchor nodes as well as unknown nodes. Coordination of anchor nodes creates a minimum connected dominating set that works as a backbone in the proposed algorithm. The focus of the research paper is to locate unknown nodes with the help of anchor nodes by utilizing the network resources efficiently. The simulated results in network simulator-2 and the statistical analysis of the data provide a clear impression that our novel algorithm improves the error rate and the time consumption.

https://journals.sagepub.com/doi/pdf/10.1177/1550147718755636

An improved DV-Hop localization with minimum connected dominating set for mobile nodes in wireless sensor networks:

Mobile terminal gesture recognition is an extreme challenge, not only because its limited computing resource make it complicated to identify feature points but also the complex background can easily affect the recognition result. This study proposes a gesture recognition method based on improved features from accelerated segment test (FAST) corner detection. First, in order to eliminate the effects of complex background and light, the intersection of the two frame images is obtained through background subtraction and the multi-colour space to realise the detection of the hand. Second, in order to improve the performance of the algorithm, an improved FAST corner detection method combined with the back propagation neural network (BPNN) is proposed in accordance with the characteristics of fingertips. Subsequently, the feature points are screened by method of non-maximum suppression. Finally, gesture recognition is realised by matching feature points. Experimental results illustrate that this method has strong anti-interference ability in complex background, and it is good at performance.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2018.5959

Mobile terminal gesture recognition based on improved FAST corner detection

In order to reduce energy-consumption and hardware investment in wireless sensor networks, an improved DV-Hop localization algorithm based on energy-saving non-ranging optimization is proposed. In this algorithm, only one anchor node broadcasts its position coordinate information to other node, thus greatly reducing the energy consumption of the node. Non-ranging energy-saving optimization algorithm is first adopted, where the size of the anchor node’s average hop and the distance of average hop between anchor nodes are used to estimate the distance between anchor node and unknown node so as to obtain the location coordinates of unknown node. In order to improve the localization accuracy, the ratio of position is introduced to reduce the localization error after obtaining the estimation of coordinates. Simulation experiment results show that compared with the DV-Hop algorithm based on least squares and the DV-Hop algorithm based on improved particle swarm optimization, our proposed localization algorithm can significantly reduce the localization error and the number of anchor nodes, which has better results in the localization accuracy and the energy-saving performance

An Improved DV-Hop Localization Algorithm Based on Energy-saving Non-ranging Optimization

The localization of the unknown nodes is essential for many applications in wireless sensor networks (WSN). But the traditional DV-Hop algorithm has not high accuracy of the localization of the nodes. In this paper, we proposed an improved DV-Hop location algorithm based on reduce the accumulation errors of the average hop distance of the unknown nodes in wireless sensor networks. We have made detailed analysis of the DV-Hop algorithm, and have introduced the weighted averaged method for calculate to the average hop distance in the second stages. Then the estimates regional based on two beacon nodes is built, and the centroid of the region is the coordinates of the unknown node. Simulation experiments are conducted to compare the traditional DV-Hop algorithm and improved DV-Hop algorithm. The simulation results show that the proposed algorithm has better localization accuracy based on the same or different number of the sensor nodes and the communication radius.

DV-Hop localization algorithms based on centroid in wireless sensor networks

A novel mask with a filled circle and outer ring is proposed to detect corners from images, based on the adaptive threshold of a local region. First, the inner filled circle is used in a response function to filter four non-corner regions: image noise, object edges, corner neighbourhoods, and flat regions. Second, corner candidates are detected using a complex response function, by considering the margin of inner circle and the outer ring together. Finally, related algorithms are developed to determine and remove the false corners lying on thin-band, noisy, and salient pixels. The authors' approach has been tested on artificial, noisy, fuzzy, and real images, and its performance is evaluated, analysed, and compared with the existing grey-level-based corner detection methods of Harris, SUSAN, FAST, and Lan and Zhang. The presented corner detector has better detection accuracy, less sensitivity to noisy and fuzzy images, high computational efficiency, and good repeatability.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2014.0952

Robust fast corner detector based on filled circle and outer ring mask

The invention relates to a method of extraction of a corner of a blurred image in a mine shaft environment The method of the extraction of the corner of the blurred image in the mine shaft environment comprises the steps of carrying out area judgment on the blurred image by using an inner template, rapidly recognizing a flat domain and a corner domain, and avoiding further judgment of the flat domain; dividing an image in the corner domain into a background area and a foreground area by using a maximum entropy algorithm and combining area characteristics so as to select corresponding corner domain values according to different areas to carry out corner extraction; extracting a candidate corner by using an outer template according to a corner domain state of a detection point; carrying out a fake-removing operation on the candidate corner, removing burr points on the edges and faked corners which satisfy a corner response function and are arranged on a narrow band, and obtaining a final real corner According to the experimental results, compared with an existing corner extraction algorithm, the method of the extraction of the corner of the blurred image in the mine shaft environment has a high extraction precision and good robustness under the premise of guaranteeing instantaneity when the method of the extraction of the corner of the blurred image in the mine shaft environment is applied to blurred mine shaft images with uneven illumination

Method of extraction of corner of blurred image in mine shaft environment

We propose an adaptive and fast corner detector based on a double-circle mask to meet real-time applications. First, we calculate the variance of the nucleus of every pixel in the mask. Second, we present an adaptive threshold of a flat area to detect whether the nucleus is on a flat area to avoid the furthermore detecting. Then a corner response function is used to detect the corner candidates. Finally, we propose a false corner removal algorithm to ensure the quality of the corners. The experimental results show that the proposed method performs better in time consuming and corner detecting, and also is robust to noises.

An adaptive corner detecting for real-time applications

Abstract We propose a fast and reliable corner detector that can detect corners under non-uniform illumination and fuzzy mineshaft images effectively. First, we presented an inner mask that used only four pixels to determine the flat and corner regions of an image, which could eliminate unnecessary computation of flat regions, thus reducing computing cost. Second, we separated the corner regions into background and foreground and computed the separate corner threshold to settle non-uniform illumination. Third, we proposed a fast corner-detection algorithm to compute the nucleus continuous contributive segment based on the corner state. Finally, we proposed two effective methods to remove the false corners. Experimental results showed that our approach has a better detection quality and is less time consuming than three other algorithms on an artificial image, a noisy image, and non-uniform images and could meet the real-time requirement of mineshaft applications.

Yuanxiu Xing

Papers

DV-Hop localization algorithms based on centroid in wireless sensor networks

Robust fast corner detector based on filled circle and outer ring mask

Method of extraction of corner of blurred image in mine shaft environment

An adaptive corner detecting for real-time applications

A Fast and Reliable Corner Detector for Non-Uniform Illumination Mineshaft Images