## 4 High-Resolution Images in Camera-Based WMSNs
### Image Stitching Technique  
As Sink has no resource limitation, it performs image stitching process on the low-resolution images of a desired cluster head initially, extracts some necessary information and sends them to that cluster head. After receiving this information by the cluster head, it would be capable of stitching images by a simple process. Considering the nature of WMSNs, it is necessary for their topology to be reconfigured at periods which depend on the network application. The information transmitted by Sink is valid as long as the network topology has not been changed. On the other hand, after reconfiguration of the network topology, if it is the first time that Sink sends a query to a cluster head, it should stitch the received low-resolution images to send necessary information to the cluster head. Now, having this information, the cluster head stitches images with low computational overhead. Image stitching process that is used in extracting this information is explained as follows.  
Almost all image stitching methods can be put in one of the two broad categories: Direct Methods [31; 32; 33; 34] and Feature-based methods [35; 36; 37; 38]. Direct methods shift or rotate images related to each other and search for the best pixel-to-pixel match by minimizing a predefined error metric. But, Feature-Based methods extract features of each image at first and use them to register images in the next steps. So, these methods are faster whereas they suffer from low accuracy. However, after the presentation of SIFT feature extraction method [39], the accuracy problem has been solved. Recently, SIFT is utilized in many applications, especially in image stitching. Therefore, today, Feature-based methods are very popular in image stitching techniques.  
Nearly all image stitching methods need an initialization which must be typically done by the user such as approximately aligning the images or fixing image ordering. For example, REALVIZ Stitcher version 4 [40] has a user interface to roughly position the images with a mouse before automatic registration proceeds. Also, the PhotoStitch software bundled with Canon digital cameras requires a horizontal or vertical sweep, or a square matrix of images [38]. But the method presented by [PERSON] and [PERSON] [37; 38] is a fully automated panoramic image stitching which does not need any human input. This method is insensitive to the ordering, orientation, scale, and illumination of the input images.  
Consequently, according to the camera-based WMSNs characteristics including: variant scale, orientation and illumination of camera sensor images and the fact that WMSNs are autonomous systems, [PERSON] and [PERSON]'s method is suitable for image stitching in these types of networks. However, in this paper a simplified [PERSON] and [PERSON]'s stitching method is proposed to satisfy camera-based WMSNs limitations. Feature matching, Image matching and Blending are steps of this simplified stitching method.  
Feature matching and image matching construct image registration procedure whose aim is to find one transformation matrix for each image. This matrix is used to fix the image in its proper position [41] and the blending step blends registered images especially in borders and removes seams [37], consequently. The steps of this simplified stitching method are explained in details in the following subsections.