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利用预捕获特征检测和方向传感器为安卓设备实现高分辨率全景图的自动连续拼接。

Automatic Sequential Stitching of High-Resolution Panorama for Android Devices Using Precapture Feature Detection and the Orientation Sensor.

机构信息

Department of Electronics Engineering, Sejong University, Seoul 05006, Republic of Korea.

College of Semiconductor System, Yonsei University, Wonju-si 26493, Republic of Korea.

出版信息

Sensors (Basel). 2023 Jan 12;23(2):879. doi: 10.3390/s23020879.

DOI:10.3390/s23020879
PMID:36679674
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9863381/
Abstract

Image processing on smartphones, which are resource-limited devices, is challenging. Panorama generation on modern mobile phones is a requirement of most mobile phone users. This paper presents an automatic sequential image stitching algorithm with high-resolution panorama generation and addresses the issue of stitching failure on smartphone devices. A robust method is used to automatically control the events involved in panorama generation from image capture to image stitching on Android operating systems. The image frames are taken in a firm spatial interval using the orientation sensor included in smartphone devices. The features-based stitching algorithm is used for panorama generation, with a novel modification to address the issue of stitching failure (inability to find local features causes this issue) when performing sequential stitching over mobile devices. We also address the issue of distortion in sequential stitching. Ultimately, in this study, we built an Android application that can construct a high-resolution panorama sequentially with automatic frame capture based on an orientation sensor and device rotation. We present a novel research methodology (called "Sense-Panorama") for panorama construction along with a development guide for smartphone developers. Based on our experiments, performed by Samsung Galaxy SM-N960N, which carries system on chip (SoC) as Qualcomm Snapdragon 845 and a CPU of 4 × 2.8 GHz Kyro 385, our method can generate a high-resolution panorama. Compared to the existing methods, the results show improvement in visual quality for both subjective and objective evaluation.

摘要

在资源有限的智能手机上进行图像处理具有挑战性。现代手机全景生成是大多数手机用户的需求。本文提出了一种自动顺序图像拼接算法,具有高分辨率全景生成功能,并解决了智能手机设备上拼接失败的问题。在 Android 操作系统中,使用稳健的方法自动控制从图像捕获到图像拼接的全景生成过程中的事件。使用智能手机设备中包含的方向传感器以固定的空间间隔拍摄图像帧。基于特征的拼接算法用于全景生成,并进行了新颖的修改,以解决在移动设备上进行顺序拼接时出现的拼接失败问题(无法找到局部特征会导致此问题)。我们还解决了顺序拼接中的失真问题。最终,在本研究中,我们构建了一个 Android 应用程序,该应用程序可以基于方向传感器和设备旋转自动捕获帧,顺序构建高分辨率全景。我们提出了一种新的研究方法(称为“Sense-Panorama”)用于全景构建,同时为智能手机开发人员提供了开发指南。基于我们在搭载系统芯片(SoC)为高通骁龙 845 和 4×2.8GHz Kyro 385 CPU 的三星 Galaxy SM-N960N 上进行的实验,我们的方法可以生成高分辨率全景。与现有方法相比,结果表明,无论是主观评估还是客观评估,视觉质量都有所提高。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/0f50fa50ace1/sensors-23-00879-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/c8ad97aaa7c1/sensors-23-00879-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/4a00e2f73d9a/sensors-23-00879-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/dda751a3a326/sensors-23-00879-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/46b2cf5bec0f/sensors-23-00879-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/0803478d7487/sensors-23-00879-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/32bc1f9d9de5/sensors-23-00879-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/93ad5c25e5e2/sensors-23-00879-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/0f50fa50ace1/sensors-23-00879-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/c8ad97aaa7c1/sensors-23-00879-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/c5474dba9fa9/sensors-23-00879-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/e2615fcee19f/sensors-23-00879-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/fa48689ff948/sensors-23-00879-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/fd1ae4c82ca6/sensors-23-00879-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/4a74a2622c25/sensors-23-00879-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/4a00e2f73d9a/sensors-23-00879-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/dda751a3a326/sensors-23-00879-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/46b2cf5bec0f/sensors-23-00879-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/0803478d7487/sensors-23-00879-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/32bc1f9d9de5/sensors-23-00879-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/93ad5c25e5e2/sensors-23-00879-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e70/9863381/0f50fa50ace1/sensors-23-00879-g013.jpg

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