2,601 research outputs found
Automatic Objects Removal for Scene Completion
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as 3D scene reconstruction and
other big data applications. However, this is not an easy task due to the fact
the retrieved photos are neither aligned nor calibrated. Furthermore, with the
occlusion of unexpected foreground objects like people, vehicles, it is even
more challenging to find feature correspondences and reconstruct realistic
scenes. In this paper, we propose a structure based image completion algorithm
for object removal that produces visually plausible content with consistent
structure and scene texture. We use an edge matching technique to infer the
potential structure of the unknown region. Driven by the estimated structure,
texture synthesis is performed automatically along the estimated curves. We
evaluate the proposed method on different types of images: from highly
structured indoor environment to the natural scenes. Our experimental results
demonstrate satisfactory performance that can be potentially used for
subsequent big data processing: 3D scene reconstruction and location
recognition.Comment: 6 pages, IEEE International Conference on Computer Communications
(INFOCOM 14), Workshop on Security and Privacy in Big Data, Toronto, Canada,
201
Vision-Based Localization Algorithm Based on Landmark Matching, Triangulation, Reconstruction, and Comparison
Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRC) global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data
High-Precision Fruit Localization Using Active Laser-Camera Scanning: Robust Laser Line Extraction for 2D-3D Transformation
Recent advancements in deep learning-based approaches have led to remarkable
progress in fruit detection, enabling robust fruit identification in complex
environments. However, much less progress has been made on fruit 3D
localization, which is equally crucial for robotic harvesting. Complex fruit
shape/orientation, fruit clustering, varying lighting conditions, and
occlusions by leaves and branches have greatly restricted existing sensors from
achieving accurate fruit localization in the natural orchard environment. In
this paper, we report on the design of a novel localization technique, called
Active Laser-Camera Scanning (ALACS), to achieve accurate and robust fruit 3D
localization. The ALACS hardware setup comprises a red line laser, an RGB color
camera, a linear motion slide, and an external RGB-D camera. Leveraging the
principles of dynamic-targeting laser-triangulation, ALACS enables precise
transformation of the projected 2D laser line from the surface of apples to the
3D positions. To facilitate laser pattern acquisitions, a Laser Line Extraction
(LLE) method is proposed for robust and high-precision feature extraction on
apples. Comprehensive evaluations of LLE demonstrated its ability to extract
precise patterns under variable lighting and occlusion conditions. The ALACS
system achieved average apple localization accuracies of 6.9 11.2 mm at
distances ranging from 1.0 m to 1.6 m, compared to 21.5 mm by a commercial
RealSense RGB-D camera, in an indoor experiment. Orchard evaluations
demonstrated that ALACS has achieved a 95% fruit detachment rate versus a 71%
rate by the RealSense camera. By overcoming the challenges of apple 3D
localization, this research contributes to the advancement of robotic fruit
harvesting technology
Structure Preserving Large Imagery Reconstruction
With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as image clustering, 3D scene
reconstruction, and other big data applications. However, such tasks are not
easy due to the fact the retrieved photos can have large variations in their
view perspectives, resolutions, lighting, noises, and distortions.
Fur-thermore, with the occlusion of unexpected objects like people, vehicles,
it is even more challenging to find feature correspondences and reconstruct
re-alistic scenes. In this paper, we propose a structure-based image completion
algorithm for object removal that produces visually plausible content with
consistent structure and scene texture. We use an edge matching technique to
infer the potential structure of the unknown region. Driven by the estimated
structure, texture synthesis is performed automatically along the estimated
curves. We evaluate the proposed method on different types of images: from
highly structured indoor environment to natural scenes. Our experimental
results demonstrate satisfactory performance that can be potentially used for
subsequent big data processing, such as image localization, object retrieval,
and scene reconstruction. Our experiments show that this approach achieves
favorable results that outperform existing state-of-the-art techniques
Performance Analysis of Low-Cost Tracking System for Mobile Robots
This paper proposes a reliable and straightforward approach to mobile robots (or moving objects in general) indoor tracking, in order to perform a preliminary study on their dynamics. The main features of this approach are its minimal and low-cost setup and a user-friendly interpretation of the data generated by the ArUco library. By using a commonly available camera, such as a smartphone one or a webcam, and at least one marker for each object that has to be tracked, it is possible to estimate the pose of these markers, with respect to a reference conveniently placed in the environment, in order to produce results that are easily interpretable by a user. This paper presents a simple extension to the ArUco library to generate such user-friendly data, and it provides a performance analysis of this application with static and moving objects, using a smartphone camera to highlight the most notable feature of this solution, but also its limitations
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