Eye typing using Markov and active appearance models

Hansen, Dan Witzner; Hansen, John Paulin; Nielsen, Mads; Johansen, Anders; Stegmann, Mikkel Bille

doi:10.1109/acv.2002.1182170

Cited by 67 publications

(56 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hansen's Method: The gaze tracking method specific for eye typing application is presented [10]. The extraction component consists of shape, texture of eye, pixel intensity information of eye corners and pupil position obtained by active appearance model.…”

Section: Appearance Based Methodsmentioning

confidence: 99%

Mapping Functions in Gaze Tracking

Sheela¹,

Vijaya²

2011

IJCA

View full text Add to dashboard Cite

Eye gaze is one of the powerful modes for interaction with the computer. Gaze estimation systems compute the direction of eye gaze. The gaze region on the screen determines the focus and attention of an individual. The gaze tracking systems consists of two major functionalities, feature extraction and determination of gaze mapping functions. The features are the parameters that characterize the pupil and the iris. The gaze mapping functions determine the coordinates on the screen based on interpolation or approximation. In this paper, an in depth analysis of mapping functions is dealt. An attempt has been made to understand the literature of gaze tracking systems.

show abstract

Section: Appearance Based Methodsmentioning

confidence: 99%

Mapping Functions in Gaze Tracking

Sheela¹,

Vijaya²

2011

IJCA

View full text Add to dashboard Cite

show abstract

“…We use the class of Haar feature here; this rectangular feature is more sensitive to the edge and the line segments and the Eigen value of the class Haar is calculated very quickly. [10] The number of sample Features. We add a line feature (4) to the features of the original four rectangular; the original line feature (3) is rotated 90 degrees to form a new line feature.…”

Section: Calculation Of the Pedestrian Sample Eigen Valuesmentioning

confidence: 99%

Research on Pedestrian’s Detection Based on the Integration of AdaBoost Algorithm and Shape Features

Ying

Guo

et al. 2011

AEF

View full text Add to dashboard Cite

Abstract. In this paper, propose a pedestrian detection method that based on AdaBoost algorithm and pedestrian shape features integration. First according to the collected pedestrian true, false sample, selected the characteristics of the extended class Haar, adopt AdaBoost algorithm training get pedestrian classifier to split the initial candidate region of all pedestrians in the image. In this paper, propose an adaptive threshold weight update method, significantly reduced the number of the characteristics of strong classifier, optimize the classifier structure, reduce the complexity of the algorithm; meanwhile, the online update detector, improving the reliability of the detector. Pedestrian leg have strong vertical edge symmetry characteristic so that extracted the vertical edge detection in the initial candidate region, According to the symmetry determine the vertical axis of symmetry, combined with the morphological characteristics of pedestrians to determine the width and height characteristics of the pedestrian, to determine the pedestrian candidate region, Finally, put a further validation to the pedestrian candidate region. IntroductionWith China's rapid increase in car ownership, frequency of road traffic accidents, especially the traffic accidents caused by vehicle and pedestrian collision, is the main reason for pedestrian casualties. It makes the application of safety driver assistance systems in our country with greater urgency and relevance. Detection of the pedestrian in front of the vehicle is one the Indispensable functions of the safety driver assistance systems. It can effectively help the driver timely respond to the external environment in the urban to avoid pedestrian collisions. For a pedestrian detection system, it should have a good real-time, low false alarm rate, good environment adaptability. Currently, the commonly used pedestrian segmentation method is edge detection, is mainly to split the pedestrian in road. This paper presents a pedestrian candidate region segmentation method based on the integration of the shape characteristics of the pedestrian and AdaBoost algorithm, extract the possible position of the pedestrians in the image, so that provide input for the effective identification of pedestrian. [1,2]

show abstract

“…One of the most successful model-based approaches is the Active Appearance Model by Cootes et al [6] which applies learned shape and texture (intensity) priors from a training set. The method has been applied to medical image segmentation [3,9,16], eye tracking [11,14], object tracking [22] and facial recognition [1,7,10] with success. AAM creates a shape model from set of training shapes by modeling them in a linear subspace.…”

Section: Introductionmentioning

confidence: 99%

Elastic appearance models

Hansen¹,

Fagertun²,

Larsen³

2011

Procedings of the British Machine Vision Conference 2011

View full text Add to dashboard Cite

This paper presents a fusion of the active appearance model (AAM) and the Riemannian elasticity framework which yields a non-linear shape model and a linear texture model -the active elastic appearance model (EAM). The non-linear elasticity shape model is more flexible than the usual linear subspace model, and it is therefore able to capture more complex shape variations. Local rotation and translation invariance are the primary explanation for the additional flexibility. In addition, we introduce global scale invariance into the Riemannian elasticity framework which together with the local translation and rotation invariances eliminate the need for separate pose estimation. The new approach was tested against AAM in three experiments; face labeling, face labeling with poor initialization and corpus callosum segmentation. In all the examples the EAM performed significantly better than AAM. Our Matlab implementation can be downloaded through svn from https://svn.imm.dtu.dk/AAMLab/svn/AAMLab/trunk/ .

show abstract

Eye typing using Markov and active appearance models

Cited by 67 publications

References 7 publications

Mapping Functions in Gaze Tracking

Mapping Functions in Gaze Tracking

Research on Pedestrian’s Detection Based on the Integration of AdaBoost Algorithm and Shape Features

Elastic appearance models

Contact Info

Product

Resources

About