Computer Vision, Imaging and Computer Graphics. Theory and Application

Kraus, Martin; Laramee, Robert S.; Richard, Paul; Braz, José

doi:10.1007/978-3-642-38241-3

Cited by 7 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reason is simple-negative parallax should only be used if an object is freely floating in the user's view because the moment it touches the screen's border, the object is cut and immersion is disturbed. 44 Moreover, there are new methodologies for analyzing the quality of interactive stereoscopic environments based on visual distortions caused by near/far plane violation or the manipulation of the eye separation. This problem is sometimes also referred to as the stereoscopic frame (or boundary) violation.…”

Section: Grade Excellent Goodmentioning

confidence: 99%

Stereoscopic cell visualization: from mesoscopic to molecular scale

et al. 2014

View full text Add to dashboard Cite

Cell visualization is an important area of scientific and educational visualization. There is already a number of astonishing animations illustrating the structural and functional properties of biological cells available in the Internet. However, these visualizations usually do not take advantage of three-dimensional (3-D) stereoscopic techniques. The stereoscopic visualization of the microcosmos cell-invisible to the human eye-bears high potential for educational as well as scientific approaches. Using open source tools it will be shown that it is possible to generate statically rendered as well as interactive stereoscopic cell visualizations. First, the 3-D modeling software Blender in conjunction with Schneider's stereoscopic camera plug-in will be used to generate a stereoscopic cell animation. While static renderings have an advantage in that the stereoscopic effect can be optimized for spectators, interactive stereoscopic visualizations always have to adjust and optimize the stereoscopic effect for users who can freely navigate through space. Cell visualization is paradigmatic for this problem because the scale differences from the mesoscopic to the molecular level account for a factor of 100,000. Therefore, two stereoscopic approaches of the CELLmicrocosmos project will be introduced, which address the stereoscopic scaling problem. The stereoscopic quality was positively evaluated by 20 students. Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms

show abstract

Section: Grade Excellent Goodmentioning

confidence: 99%

Stereoscopic cell visualization: from mesoscopic to molecular scale

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Detection of two mouth corners and nose tip from the detected face: The automatically detected single face region is given as input to a detector of facial landmarks learned by the structured output SVM based on the Deformable Part Models proposed in [10]. It gives us eight landmarks from the face which includes face center, right eye inner corner, left eye inner corner, mouth right corner, mouth left corner, right eye outer corner, left eye outer corner and nose tip.…”

Section: Determination Of Pose Free Image Mouth Regionmentioning

confidence: 99%

Facial Expression based Person Authentication

Saravanan¹,

Palanivel²,

Balasubramanian³

2014

IJCA

View full text Add to dashboard Cite

High quality automatic facial expression based person authentication system is practically difficult mainly due to poses in face. This work paves way to develop a more perfect automatic person authentication system using facial expressions. In this work, ways to extract automatically pose free face images from video taken in normal room condition, determining mouth region, extracting features along with performance comparison in person authentication during normal and smile facial expressions is explained. The system contains two stages. In first stage, automatic pose free image selector is used to collect pose free face images from videos of ten persons taken in two sessions each with normal and smile facial expressions with poses. Testing on images taken from forty videos of resolution 640 x 480 the system identified and extracted pose free face images automatically which are 100% perfect pose free face images. The rejected images may have pose free images, but it will not affect the working accuracy of the system even though may reduce its speed, but not significantly. In stage second, automatically selected pose free images of mouth during normal and smile facial expression from the twenty videos of first session is used for training an auto associative neural network. Images from the second session of twenty videos are used to test for person authentication. The results clearly show that normal face gives more performance than smile facial expression for person authentication by accepting authentic persons and rejecting impostors. Equal error rate is used to calculate the performance of the person authentication system. Equal error rate for person authentication using normal face is 0.32% whereas with smile facial expression is 0.4%. The person authentication system is considered more efficient if the equal error rate value is lower.

show abstract