Automatic Seamless of Image Stitching

The objective of this paper is to implement image stitching by adopting feature-based alignment algorithm and blending algorithm to produce a high quality image, the images for stitching to create panorama are captured in a fixed linear spatial interval. The processing method involves feature extraction, image matching based on Harris corner detectors method as the feature detection and neighboring pairs alignment using RANSAC (RANdom Sample Consensus) algorithm. Linear blending is applied to remove the transition between the aligned images. The presented image stitching algorithm is successfully able to create panorama image. Keywords: Image stitching, Harris detectors, RANSAC algorithm, Linear blending

Hardware Acceleration in Image Stitching: GPU vs FPGA

Image stitching is a process where two or more images with an overlapping field of view are combined. This process is commonly used to increase the field of view or image quality of a system. While this process is not particularly difficult for modern personal computers, hardware acceleration is often required to achieve real-time performance in low-power image stitching solutions. In this thesis, two separate hardware accelerated image stitching solutions are developed and compared. One solution is accelerated using a Xilinx Zynq UltraScale+ ZU3EG FPGA and the other solution is accelerated using an Nvidia RTX 2070 Super GPU. The image stitching solutions implemented in this paper increase the system’s field of view and involve the end-to-end process of feature detection, image registration, and image mixing. The latency, resource utilization, and power consumption for the accelerated portions of each system are compared and each systems tradeoffs and use cases are considered

Laryngoscopic Image Stitching for View Enhancement and Documentation - First Experiences

One known problem within laryngoscopy is the spatially limited view onto the hypopharynx and the larynx through the endoscope. To examine the complete larynx and hypopharynx, the laryngoscope can be rotated about its main axis, and hence the physician obtains a complete view. If such examinations are captured using endoscopic video, the examination can be reviewed in detail at a later time. Nevertheless, in order to document the examination with a single representative image, a panorama image can be computed for archiving and enhanced documentation. Twenty patients with various clinical findings were examined with a 70 rigid laryngoscope, and the video sequences were digitally stored. The image sequence for each patient was then post-processed using an image stitching tool based on SIFT features, the RANSAC approach and blending. As a result, endoscopic panorama images of the larynx and pharynx were obtained for each video sequence. The proposed approach of image stitching for laryngoscopic video sequences offers a new tool for enhanced visual examination and documentation of morphologic characteristics of the larynx and the hypopharynx

Image Stitching Based on Corner Detection

An image stitching is a method of combining multiple images which are overlapping images of the same scene into a larger image. Mostly used methods are Harris corner detection method and SIFTS (Scale Invariant Feature Transform) method. In this paper, a study of Harris corner detection algorithm and SIFT algorithm is done by comparatively in image stitching using similarity matrix matching scheme. Total 30 pairs of different images have been used for their simulation and comparison. The algorithms have been compared with more number of corners detected in images, number of matching pairs and number of matching time. From the results of simulation it has been observed that SIFT corner detection method is most efficient in image stitching