13,627 research outputs found
Graph-Based Classification of Omnidirectional Images
Omnidirectional cameras are widely used in such areas as robotics and virtual
reality as they provide a wide field of view. Their images are often processed
with classical methods, which might unfortunately lead to non-optimal solutions
as these methods are designed for planar images that have different geometrical
properties than omnidirectional ones. In this paper we study image
classification task by taking into account the specific geometry of
omnidirectional cameras with graph-based representations. In particular, we
extend deep learning architectures to data on graphs; we propose a principled
way of graph construction such that convolutional filters respond similarly for
the same pattern on different positions of the image regardless of lens
distortions. Our experiments show that the proposed method outperforms current
techniques for the omnidirectional image classification problem
Vision-Based Navigation III: Pose and Motion from Omnidirectional Optical Flow and a Digital Terrain Map
An algorithm for pose and motion estimation using corresponding features in
omnidirectional images and a digital terrain map is proposed. In previous
paper, such algorithm for regular camera was considered. Using a Digital
Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables
recovering the absolute position and orientation of the camera. In order to do
this, the DTM is used to formulate a constraint between corresponding features
in two consecutive frames. In this paper, these constraints are extended to
handle non-central projection, as is the case with many omnidirectional
systems. The utilization of omnidirectional data is shown to improve the
robustness and accuracy of the navigation algorithm. The feasibility of this
algorithm is established through lab experimentation with two kinds of
omnidirectional acquisition systems. The first one is polydioptric cameras
while the second is catadioptric camera.Comment: 6 pages, 9 figure
OMNIDIRECTIONAL IMAGE PROCESSING USING GEODESIC METRIC
International audienceDue to distorsions of catadioptric sensors, omnidirectional images can not be treated as classical images. If the equivalence between central catadioptric images and spherical images is now well known and used, spherical analysis often leads to complex methods particularly tricky to employ. In this paper, we propose to derive omnidirectional image treatments by using geodesic metric. We demonstrate that this approach allows to adapt efficiently classical image processing to omnidirectional images
Scale Invariant Feature Transform on the Sphere: Theory and Applications
A SIFT algorithm in spherical coordinates for omnidirectional images is proposed. This algorithm can generate two types of local descriptors, Local Spherical Descriptors and Local Planar Descriptors. With the first ones, point matching between two omnidirectional images can be performed, and with the second ones, the same matching process can be done but between omnidirectional and planar images. Furthermore, a planar to spherical mapping is introduced and an algorithm for its estimation is given. This mapping allows to extract objects from an omnidirectional image given their SIFT descriptors in a planar image. Several experiments, confirming the promising and accurate performance of the system, are conducte
Segmentation-Based Bounding Box Generation for Omnidirectional Pedestrian Detection
We propose a segmentation-based bounding box generation method for
omnidirectional pedestrian detection that enables detectors to tightly fit
bounding boxes to pedestrians without omnidirectional images for training. Due
to the wide angle of view, omnidirectional cameras are more cost-effective than
standard cameras and hence suitable for large-scale monitoring. The problem of
using omnidirectional cameras for pedestrian detection is that the performance
of standard pedestrian detectors is likely to be substantially degraded because
pedestrians' appearance in omnidirectional images may be rotated to any angle.
Existing methods mitigate this issue by transforming images during inference.
However, the transformation substantially degrades the detection accuracy and
speed. A recently proposed method obviates the transformation by training
detectors with omnidirectional images, which instead incurs huge annotation
costs. To obviate both the transformation and annotation works, we leverage an
existing large-scale object detection dataset. We train a detector with rotated
images and tightly fitted bounding box annotations generated from the
segmentation annotations in the dataset, resulting in detecting pedestrians in
omnidirectional images with tightly fitted bounding boxes. We also develop
pseudo-fisheye distortion augmentation, which further enhances the performance.
Extensive analysis shows that our detector successfully fits bounding boxes to
pedestrians and demonstrates substantial performance improvement.Comment: Pre-print submitted to Journal of Multimedia Tools and Application
Face tracking using a hyperbolic catadioptric omnidirectional system
In the first part of this paper, we present a brief review on catadioptric omnidirectional
systems. The special case of the hyperbolic omnidirectional system is analysed in depth.
The literature shows that a hyperboloidal mirror has two clear advantages over alternative
geometries. Firstly, a hyperboloidal mirror has a single projection centre [1]. Secondly, the
image resolution is uniformly distributed along the mirror’s radius [2].
In the second part of this paper we show empirical results for the detection and tracking
of faces from the omnidirectional images using Viola-Jones method. Both panoramic and
perspective projections, extracted from the omnidirectional image, were used for that purpose.
The omnidirectional image size was 480x480 pixels, in greyscale. The tracking method used
regions of interest (ROIs) set as the result of the detections of faces from a panoramic projection
of the image. In order to avoid losing or duplicating detections, the panoramic projection was
extended horizontally. Duplications were eliminated based on the ROIs established by previous
detections. After a confirmed detection, faces were tracked from perspective projections (which
are called virtual cameras), each one associated with a particular face. The zoom, pan and tilt
of each virtual camera was determined by the ROIs previously computed on the panoramic
image.
The results show that, when using a careful combination of the two projections, good frame
rates can be achieved in the task of tracking faces reliably
Stereoscopic Omnidirectional Image Quality Assessment Based on Predictive Coding Theory
Objective quality assessment of stereoscopic omnidirectional images is a
challenging problem since it is influenced by multiple aspects such as
projection deformation, field of view (FoV) range, binocular vision, visual
comfort, etc. Existing studies show that classic 2D or 3D image quality
assessment (IQA) metrics are not able to perform well for stereoscopic
omnidirectional images. However, very few research works have focused on
evaluating the perceptual visual quality of omnidirectional images, especially
for stereoscopic omnidirectional images. In this paper, based on the predictive
coding theory of the human vision system (HVS), we propose a stereoscopic
omnidirectional image quality evaluator (SOIQE) to cope with the
characteristics of 3D 360-degree images. Two modules are involved in SOIQE:
predictive coding theory based binocular rivalry module and multi-view fusion
module. In the binocular rivalry module, we introduce predictive coding theory
to simulate the competition between high-level patterns and calculate the
similarity and rivalry dominance to obtain the quality scores of viewport
images. Moreover, we develop the multi-view fusion module to aggregate the
quality scores of viewport images with the help of both content weight and
location weight. The proposed SOIQE is a parametric model without necessary of
regression learning, which ensures its interpretability and generalization
performance. Experimental results on our published stereoscopic omnidirectional
image quality assessment database (SOLID) demonstrate that our proposed SOIQE
method outperforms state-of-the-art metrics. Furthermore, we also verify the
effectiveness of each proposed module on both public stereoscopic image
datasets and panoramic image datasets
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