Head detection and tracking by 2-D and 3-D ellipsoid fitting

Grammalidis, Nikos; Strintzis, Michael G.

doi:10.1109/cgi.2000.852337

Cited by 35 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This technique has been successfully applied to head detection and tracking in head and shoulder video-conferencing sequences [17,18], is extremely fast, and yields very accurate results. The 3D points used for the fitting of the ellipse are derived from the constructed 3D face model, while knowledge of the natural human face symmetry is also exploited.…”

Section: I3 Hair Modelingmentioning

confidence: 99%

Building Three Dimensional Head Models

2001

View full text Add to dashboard Cite

Section: I3 Hair Modelingmentioning

confidence: 99%

Building Three Dimensional Head Models

2001

View full text Add to dashboard Cite

“…Medical images present a challenge to most segmentation algorithms due to clutter from the surrounding structures and noise inherent to medical imaging equipment, therefore shape priors are usually incorporated. Specifically, an ellipse is a powerful parametric form used in many computer vision tasks [4,5]. A parametric maximum likelihood fit to the medical data, particularly cardiac scintigrams, using an ellipse parameterization was developed in [6].…”

Section: Fig 1 Mr Images Obtained After the Contrast Agent Administra-mentioning

confidence: 99%

Semi-Automatic Lymph Node Segmentation in LN-MRI

Ünal

Slabaugh

Ess

et al. 2006

2006 International Conference on Image Processing

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link ABSTRACTAccurate staging of nodal cancer still relies on surgical exploration because many primary malignancies spread via lymphatic dissemination. The purpose of this study was to utilize nanoparticle-enhanced lymphotropic magnetic resonance imaging (LN-MRI) to explore semi-automated noninvasive nodal cancer staging. We present a joint image segmentation and registration approach, which makes use of the problem specific information to increase the robustness of the algorithm to noise and weak contrast often observed in medical imaging applications. The effectiveness of the approach is demonstrated with a given lymph node segmentation problem in post-contrast pelvic MRI sequences.

show abstract

“…Some authors have proposed to use 3D information for head detection [3,4,6,8]. All these approaches either use a pre-selected set of data points that are known to lie at the person, or they use a very dense data set coming from stereo/disparity imaging.…”

Section: Introductionmentioning

confidence: 99%

Head Detection and Localization from Sparse 3D Data

Clabian¹,

Rötzer²,

Bischof

et al. 2002

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.Head detection is an important, but difficult task, if no restrictions such as static illumination, frontal face appearance or uniform background can be assumed. We present a system that is able to perform head detection under very general conditions by employing a 3D measurement system namely a structured light distance measurement. An algorithm of head detection from sparse 3D data (19x19 data points) is developed that reconstructs a 3D surface over the image plane and detects head hypotheses of ellipsoidal shape. We demonstrate that detection and rough localization is possible in up to 90% of the images.

show abstract

Head detection and tracking by 2-D and 3-D ellipsoid fitting

Abstract: Abstract

Cited by 35 publications

References 14 publications

Building Three Dimensional Head Models

Building Three Dimensional Head Models

Semi-Automatic Lymph Node Segmentation in LN-MRI

Head Detection and Localization from Sparse 3D Data

Contact Info

Product

Resources

About