山脉识别算法
Mountain range identification algorithm
如果我有一张显示山脉的照片，是否有算法或方法可以尝试搜索并找到该山脉？例如，假设我有一张这样的旧照片（片段）：
所以在这里我们可以在背景中看到 3 座不同的山脉，我们可以用手勾勒出它们在天空或背后山脉中的轮廓。
将这些轮廓线作为输入，是否有一种算法可以将其与 DEM 相匹配？总体目标是找出照片的拍摄地点。
这样的算法确实存在，至少对于受限区域而言。例如，参见论文：
User-Driven 使用数字高程模型对未标记沙漠图像进行地理定位，Tzeng, E. 等人，计算机视觉和模式识别研讨会 (CVPRW)，23-28 2013 年 6 月，俄勒冈州波特兰。
(Abstract): We propose a system for user-aided visual
localization of desert imagery without the use of any metadata such as
GPS readings, camera focal length, or field-of-view. The system makes
use only of publicly available digital elevation models (DEMs) to
rapidly and accurately locate photographs in non-urban environments
such as deserts. Our system generates synthetic skyline views from a
DEM and extracts stable concavity-based features from these skylines
to form a database. To localize queries, a user manually traces the
skyline on an input photograph. The skyline is automatically refined
based on this estimate, and the same concavity-based features are
extracted. We then apply a variety of geometrically constrained
matching techniques to efficiently and accurately match the query
skyline to a database skyline, thereby localizing the query image. We
evaluate our system using a test set of 44 ground-truthed images over
a 10, 000 km2 region of interest in a desert and show that in many
cases, queries can be localized with precision as fine as 100 m2.
全文也是available。
当然，这项技术的规模（例如全球范围）是另一回事...
另一个relevant paper是：
山地地形图像的大比例视觉 Geo-Localization，Georges Baatz 等人，Proc。 2012 年欧洲计算机视觉会议
Abstract. Given a picture taken somewhere in the world, automatic
geo-localization of that image is a task that would be extremely
useful e.g. for historical and forensic sciences, documentation purposes, organization of the world’s photo material and also intelligence
applications. While tremendous progress has been made over the last
years in visual location recognition within a single city,
localization in natural environments is much more difficult, since
vegetation, illumination, seasonal changes make appearance-only
approaches impractical. In this work, we target mountainous terrain
and use digital elevation models to extract representations for fast
visual database lookup. We propose an automated approach for very
large scale visual localization that can efficiently exploit visual
information (contours) and geometric constraints (consistent
orientation) at the same time. We validate the system on the scale of
a whole country (Switzerland, 40000km 2 ) using a new dataset of more
than 200 landscape query pictures with ground truth.
如果我有一张显示山脉的照片，是否有算法或方法可以尝试搜索并找到该山脉？例如，假设我有一张这样的旧照片（片段）：
所以在这里我们可以在背景中看到 3 座不同的山脉，我们可以用手勾勒出它们在天空或背后山脉中的轮廓。
将这些轮廓线作为输入，是否有一种算法可以将其与 DEM 相匹配？总体目标是找出照片的拍摄地点。
这样的算法确实存在，至少对于受限区域而言。例如，参见论文：
User-Driven 使用数字高程模型对未标记沙漠图像进行地理定位，Tzeng, E. 等人，计算机视觉和模式识别研讨会 (CVPRW)，23-28 2013 年 6 月，俄勒冈州波特兰。
(Abstract): We propose a system for user-aided visual localization of desert imagery without the use of any metadata such as GPS readings, camera focal length, or field-of-view. The system makes use only of publicly available digital elevation models (DEMs) to rapidly and accurately locate photographs in non-urban environments such as deserts. Our system generates synthetic skyline views from a DEM and extracts stable concavity-based features from these skylines to form a database. To localize queries, a user manually traces the skyline on an input photograph. The skyline is automatically refined based on this estimate, and the same concavity-based features are extracted. We then apply a variety of geometrically constrained matching techniques to efficiently and accurately match the query skyline to a database skyline, thereby localizing the query image. We evaluate our system using a test set of 44 ground-truthed images over a 10, 000 km2 region of interest in a desert and show that in many cases, queries can be localized with precision as fine as 100 m2.
全文也是available。
当然，这项技术的规模（例如全球范围）是另一回事...
另一个relevant paper是：
山地地形图像的大比例视觉 Geo-Localization，Georges Baatz 等人，Proc。 2012 年欧洲计算机视觉会议
Abstract. Given a picture taken somewhere in the world, automatic geo-localization of that image is a task that would be extremely useful e.g. for historical and forensic sciences, documentation purposes, organization of the world’s photo material and also intelligence applications. While tremendous progress has been made over the last years in visual location recognition within a single city, localization in natural environments is much more difficult, since vegetation, illumination, seasonal changes make appearance-only approaches impractical. In this work, we target mountainous terrain and use digital elevation models to extract representations for fast visual database lookup. We propose an automated approach for very large scale visual localization that can efficiently exploit visual information (contours) and geometric constraints (consistent orientation) at the same time. We validate the system on the scale of a whole country (Switzerland, 40000km 2 ) using a new dataset of more than 200 landscape query pictures with ground truth.