Stitching IC Images

Image stitching software is used in many areas such as photogrammetry, biomedical imaging, and even amateur digital photography. However, these algorithms require relatively large image overlap, and for this reason they cannot be used to stitch the integrated circuit (IC) images, whose overlap is typically less than 60 pixels for a 4096 by 4096 pixel image. In this paper, we begin by using algorithmic graph theory to study optimal patterns for adding IC images one at a time to a grid. In the remaining sections we study ways of stitching all the images simultaneously using different optimisation approaches: least squares methods, simulated annealing, and nonlinear programming

Optical quality assurance of GEM foils

An analysis software was developed for the high aspect ratio optical scanning system in the Detec- tor Laboratory of the University of Helsinki and the Helsinki Institute of Physics. The system is used e.g. in the quality assurance of the GEM-TPC detectors being developed for the beam diagnostics system of the SuperFRS at future FAIR facility. The software was tested by analyzing five CERN standard GEM foils scanned with the optical scanning system. The measurement uncertainty of the diameter of the GEM holes and the pitch of the hole pattern was found to be 0.5 {\mu}m and 0.3 {\mu}m, respectively. The software design and the performance are discussed. The correlation between the GEM hole size distribution and the corresponding gain variation was studied by comparing them against a detailed gain mapping of a foil and a set of six lower precision control measurements. It can be seen that a qualitative estimation of the behavior of the local variation in gain across the GEM foil can be made based on the measured sizes of the outer and inner holes.Comment: 12 pages, 29 figure

Image mosaicing of panoramic images

Image mosaicing is combining or stitching several images of a scene or object taken from different angles into a single image with a greater angle of view. This is practised a developing field. Recent years have seen quite a lot of advancement in the field. Many algorithms have been developed over the years. Our work is based on feature based approach of image mosaicing. The steps in image mosaic consist of feature point detection, feature point descriptor extraction and feature point matching. RANSAC algorithm is applied to eliminate variety of mismatches and acquire transformation matrix between the images. The input image is transformed with the right mapping model for image stitching. Therefore, this paper proposes an algorithm for mosaicing two images efficiently using Harris-corner feature detection method, RANSAC feature matching method and then image transformation, warping and by blending methods

Disparity map generation based on trapezoidal camera architecture for multiview video

Visual content acquisition is a strategic functional block of any visual system. Despite its wide possibilities, the arrangement of cameras for the acquisition of good quality visual content for use in multi-view video remains a huge challenge. This paper presents the mathematical description of trapezoidal camera architecture and relationships which facilitate the determination of camera position for visual content acquisition in multi-view video, and depth map generation. The strong point of Trapezoidal Camera Architecture is that it allows for adaptive camera topology by which points within the scene, especially the occluded ones can be optically and geometrically viewed from several different viewpoints either on the edge of the trapezoid or inside it. The concept of maximum independent set, trapezoid characteristics, and the fact that the positions of cameras (with the exception of few) differ in their vertical coordinate description could very well be used to address the issue of occlusion which continues to be a major problem in computer vision with regards to the generation of depth map

Automated 3D object modeling from aerial video imagery

Research in physically accurate 3D modeling of a scene is gaining momentum because of its far reaching applications in civilian and defense sectors. The modeled 3D scene must conform both geometrically and spectrally to the real world for all the applications. Geometric modeling of a scene can be achieved in many ways of which the two most popular methods are - a) using multiple 2D passive images of the scene also called as stereo vision and b) using 3D point clouds like Lidar (Light detection and ranging) data. In this research work, we derive the 3D models of objects in a scene using passive aerial video imagery. At present, this geometric modeling requires a lot of manual intervention due to a variety of factors like sensor noise, low contrast conditions during image capture, etc. Hence long time periods, in the order of weeks and months, are required to model even a small scene. This thesis focuses on automating the process of geometric modeling of objects in a scene from passive aerial video imagery. The aerial video frames are stitched into stereo mosaics. These stereo mosaics not only provide the elevation information of a scene but also act as good 3D visualization tools. The 3D information obtained from the stereo mosaics is used to identify the various 3D objects, especially man-made buildings using probabilistic inference provided by Bayesian Networks. The initial 3D building models are further optimized by projecting them on to the individual video frames. The limitations of the state-of-art technology in attaining these goals are presented along with the techniques to overcome them. The improvement that can be achieved in the accuracy of the 3D models when Lidar data is fused with aerial video during the object identification process is also examined

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