Mean shift object tracking with occlusion handling
- De Villiers, B.Z., Clarke, W.A., Robinson, P.E.
- Authors: De Villiers, B.Z. , Clarke, W.A. , Robinson, P.E.
- Date: 2012
- Subjects: Object tracking , Target objects
- Type: Article
- Identifier: uj:6283 , ISBN 978-0-620-54601-0 , http://hdl.handle.net/10210/9889
- Description: An object tracking algorithm using the Mean Shift framework is presented which is largely invariant to both partial and full occlusions, complex backgrounds and change in scale. Multiple features are used to gain a descriptive representation of the target object. Image moments are used to determine the scale of the target object. A kalman filter is used to successfully track the target object through partial and full occlusions, the Bhattacharyya coefficient is used to determine the measurement noise estimation.
- Full Text:
- Authors: De Villiers, B.Z. , Clarke, W.A. , Robinson, P.E.
- Date: 2012
- Subjects: Object tracking , Target objects
- Type: Article
- Identifier: uj:6283 , ISBN 978-0-620-54601-0 , http://hdl.handle.net/10210/9889
- Description: An object tracking algorithm using the Mean Shift framework is presented which is largely invariant to both partial and full occlusions, complex backgrounds and change in scale. Multiple features are used to gain a descriptive representation of the target object. Image moments are used to determine the scale of the target object. A kalman filter is used to successfully track the target object through partial and full occlusions, the Bhattacharyya coefficient is used to determine the measurement noise estimation.
- Full Text:
Gaussian blur identification using scale-space theory
- Robinson, Philip, Roodt, Yuko, Nel, Andre
- Authors: Robinson, Philip , Roodt, Yuko , Nel, Andre
- Date: 2012
- Subjects: Blur identification , Blur estimation , Gaussian blur , Image deblurring algorithms , Scale-space theory
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/366248 , uj:6060 , ISBN 978-0-620-54601-0 , http://hdl.handle.net/10210/10475
- Description: Image deblurring algorithms generally assume that the nature of the blurring function that degraded an image is known before an image can be deblurred. In the case of most naturally captured images the strength of the blur present in the image is not known. This paper proposes a method to identify the standard deviation of a Gaussian blur that has been applied to a single image with no a priori information about the conditions under which the image was captured. This simple method makes use of a property of the Gaussian function and the Gaussian scale space representation of an image to identify the amount of blur. This is in contrast to the majority of statistical techniques that require extensive training or complex statistical models of the blur for identification.
- Full Text:
- Authors: Robinson, Philip , Roodt, Yuko , Nel, Andre
- Date: 2012
- Subjects: Blur identification , Blur estimation , Gaussian blur , Image deblurring algorithms , Scale-space theory
- Type: Article
- Identifier: http://ujcontent.uj.ac.za8080/10210/366248 , uj:6060 , ISBN 978-0-620-54601-0 , http://hdl.handle.net/10210/10475
- Description: Image deblurring algorithms generally assume that the nature of the blurring function that degraded an image is known before an image can be deblurred. In the case of most naturally captured images the strength of the blur present in the image is not known. This paper proposes a method to identify the standard deviation of a Gaussian blur that has been applied to a single image with no a priori information about the conditions under which the image was captured. This simple method makes use of a property of the Gaussian function and the Gaussian scale space representation of an image to identify the amount of blur. This is in contrast to the majority of statistical techniques that require extensive training or complex statistical models of the blur for identification.
- Full Text:
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