13,640 research outputs found
A Survey on Joint Object Detection and Pose Estimation using Monocular Vision
In this survey we present a complete landscape of joint object detection and
pose estimation methods that use monocular vision. Descriptions of traditional
approaches that involve descriptors or models and various estimation methods
have been provided. These descriptors or models include chordiograms,
shape-aware deformable parts model, bag of boundaries, distance transform
templates, natural 3D markers and facet features whereas the estimation methods
include iterative clustering estimation, probabilistic networks and iterative
genetic matching. Hybrid approaches that use handcrafted feature extraction
followed by estimation by deep learning methods have been outlined. We have
investigated and compared, wherever possible, pure deep learning based
approaches (single stage and multi stage) for this problem. Comprehensive
details of the various accuracy measures and metrics have been illustrated. For
the purpose of giving a clear overview, the characteristics of relevant
datasets are discussed. The trends that prevailed from the infancy of this
problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and
Pattern Recognition (CCVPR) 201
Towards multiple 3D bone surface identification and reconstruction using few 2D X-ray images for intraoperative applications
This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone’s edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems
Facial Asymmetry Analysis Based on 3-D Dynamic Scans
Facial dysfunction is a fundamental symptom which often relates to many neurological illnesses, such as stroke, Bell’s palsy, Parkinson’s disease, etc. The current methods for detecting and assessing facial dysfunctions mainly rely on the trained practitioners which have significant limitations as they are often subjective. This paper presents a computer-based methodology of facial asymmetry analysis which aims for automatically detecting facial dysfunctions. The method is based on dynamic 3-D scans of human faces. The preliminary evaluation results testing on facial sequences from Hi4D-ADSIP database suggest that the proposed method is able to assist in the quantification and diagnosis of facial dysfunctions for neurological patients
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
This paper proposes a segmentation-free, automatic and efficient procedure to
detect general geometric quadric forms in point clouds, where clutter and
occlusions are inevitable. Our everyday world is dominated by man-made objects
which are designed using 3D primitives (such as planes, cones, spheres,
cylinders, etc.). These objects are also omnipresent in industrial
environments. This gives rise to the possibility of abstracting 3D scenes
through primitives, thereby positions these geometric forms as an integral part
of perception and high level 3D scene understanding.
As opposed to state-of-the-art, where a tailored algorithm treats each
primitive type separately, we propose to encapsulate all types in a single
robust detection procedure. At the center of our approach lies a closed form 3D
quadric fit, operating in both primal & dual spaces and requiring as low as 4
oriented-points. Around this fit, we design a novel, local null-space voting
strategy to reduce the 4-point case to 3. Voting is coupled with the famous
RANSAC and makes our algorithm orders of magnitude faster than its conventional
counterparts. This is the first method capable of performing a generic
cross-type multi-object primitive detection in difficult scenes. Results on
synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201
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