239,607 research outputs found
Context-Based classification of objects in topographic data
Large-scale topographic databases model real world features as vector data objects. These can be point, line or area features. Each of these map objects is assigned to a
descriptive class; for example, an area feature might be classed as a building, a garden or a road. Topographic data is subject to continual updates from cartographic surveys
and ongoing quality improvement. One of the most important aspects of this is assignment and verification of class descriptions to each area feature. These attributes
can be added manually, but, due to the vast volume of data involved, automated techniques are desirable to classify these polygons.
Analogy is a key thought process that underpins learning and has been the subject of much research in the field of artificial intelligence (AI). An analogy identifies
structural similarity between a well-known source domain and a less familiar target domain. In many cases, information present in the source can then be mapped to the
target, yielding a better understanding of the latter. The solution of geometric analogy problems has been a fruitful area of AI research. We observe that there is a correlation
between objects in geometric analogy problem domains and map features in topographic data. We describe two topographic area feature classification tools that use
descriptions of neighbouring features to identify analogies between polygons: content vector matching (CVM) and context structure matching (CSM). CVM and CSM classify an area feature by matching its neighbourhood context against those of analogous polygons whose class is known.
Both classifiers were implemented and then tested on high quality topographic polygon data supplied by Ordnance Survey (Great Britain). Area features were found to exhibit a high degree of variation in their neighbourhoods. CVM correctly classified 85.38% of the 79.03% of features it attempted to classify. The accuracy for CSM was 85.96% of the 62.96% of features it tried to identify. Thus, CVM can classify 25.53% more features than CSM, but is slightly less accurate. Both techniques excelled at identifying the feature classes that predominate in suburban data. Our structure-based classification approach may also benefit other types of spatial data, such as topographic line data, small-scale topographic data, raster data, architectural plans and circuit diagrams
A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition
Visual Place Recognition is an essential component of systems for camera
localization and loop closure detection, and it has attracted widespread
interest in multiple domains such as computer vision, robotics and AR/VR. In
this work, we propose a faster, lighter and stronger approach that can generate
models with fewer parameters and can spend less time in the inference stage. We
designed RepVGG-lite as the backbone network in our architecture, it is more
discriminative than other general networks in the Place Recognition task.
RepVGG-lite has more speed advantages while achieving higher performance. We
extract only one scale patch-level descriptors from global descriptors in the
feature extraction stage. Then we design a trainable feature matcher to exploit
both spatial relationships of the features and their visual appearance, which
is based on the attention mechanism. Comprehensive experiments on challenging
benchmark datasets demonstrate the proposed method outperforming recent other
state-of-the-art learned approaches, and achieving even higher inference speed.
Our system has 14 times less params than Patch-NetVLAD, 6.8 times lower
theoretical FLOPs, and run faster 21 and 33 times in feature extraction and
feature matching. Moreover, the performance of our approach is 0.5\% better
than Patch-NetVLAD in Recall@1. We used subsets of Mapillary Street Level
Sequences dataset to conduct experiments for all other challenging conditions.Comment: CCF Conference on Computer Supported Cooperative Work and Social
Computing (ChineseCSCW
Video Registration in Egocentric Vision under Day and Night Illumination Changes
With the spread of wearable devices and head mounted cameras, a wide range of
application requiring precise user localization is now possible. In this paper
we propose to treat the problem of obtaining the user position with respect to
a known environment as a video registration problem. Video registration, i.e.
the task of aligning an input video sequence to a pre-built 3D model, relies on
a matching process of local keypoints extracted on the query sequence to a 3D
point cloud. The overall registration performance is strictly tied to the
actual quality of this 2D-3D matching, and can degrade if environmental
conditions such as steep changes in lighting like the ones between day and
night occur. To effectively register an egocentric video sequence under these
conditions, we propose to tackle the source of the problem: the matching
process. To overcome the shortcomings of standard matching techniques, we
introduce a novel embedding space that allows us to obtain robust matches by
jointly taking into account local descriptors, their spatial arrangement and
their temporal robustness. The proposal is evaluated using unconstrained
egocentric video sequences both in terms of matching quality and resulting
registration performance using different 3D models of historical landmarks. The
results show that the proposed method can outperform state of the art
registration algorithms, in particular when dealing with the challenges of
night and day sequences
Investigation on the automatic geo-referencing of archaeological UAV photographs by correlation with pre-existing ortho-photos
We present a method for the automatic geo-referencing of archaeological photographs captured aboard unmanned aerial vehicles (UAVs), termed UPs. We do so by help of pre-existing ortho-photo maps (OPMs) and digital surface models (DSMs). Typically, these pre-existing data sets are based on data that were captured at a widely different point in time. This renders the detection (and hence the matching) of homologous feature points in the UPs and OPMs infeasible mainly due to temporal variations of vegetation and illumination. Facing this difficulty, we opt for the normalized cross correlation coefficient of perspectively transformed image patches as the measure of image similarity. Applying a threshold to this measure, we detect candidates for homologous image points, resulting in a distinctive, but computationally intensive method. In order to lower computation times, we reduce the dimensionality and extents of the search space by making use of a priori knowledge of the data sets. By assigning terrain heights interpolated in the DSM to the image points found in the OPM, we generate control points. We introduce respective observations into a bundle block, from which gross errors i.e. false matches are eliminated during its robust adjustment. A test of our approach on a UAV image data set demonstrates its potential and raises hope to successfully process large image archives
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