This paper presents an orientation operator to extract image local orientation features. We show that a proper employment of image integration leads to an unbiased orientation estimate, based on which an orientation operator is proposed. The resulting discrete operator has flexibility in the scale selection as the scale change does not violate the bias minimization criteria. An analytical formula is developed to compare orientation biases of various discrete operators. The proposed operator shows lower bias than eight well-known gradient operators. Experiments further demonstrate higher orientation accuracy of the proposed operator than these gradient operators
Estimation of local orientation in images is often posed as the task of finding the minimum variance...
This paper examines how observers estimate the overall orientation of spatially disorganised texture...
The aim of this paper is to present a study where we evaluate the optimal inclusion of the texture o...
Abstract. We investigate the suitability of different local feature detectors for the task of automa...
Orientation estimation is considered as an important task in many subsequent pattern recognition and...
Filtering of an image with rotated versions of an orientation selective filter yields a set of image...
This paper describes a new algorithm for local orientation estimation. The proposed algorithm detect...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
... this paper is based on a signal-theoretic and statistical analysis of the notion of orientation....
This paper describes a new algorithm for local orientation estimation. The proposed algorithm detect...
This paper describes a new algorithm for local orientation estimation. The proposed algorithm detect...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
Estimation of local orientation in images is often posed as the task of finding the minimum variance...
This paper examines how observers estimate the overall orientation of spatially disorganised texture...
The aim of this paper is to present a study where we evaluate the optimal inclusion of the texture o...
Abstract. We investigate the suitability of different local feature detectors for the task of automa...
Orientation estimation is considered as an important task in many subsequent pattern recognition and...
Filtering of an image with rotated versions of an orientation selective filter yields a set of image...
This paper describes a new algorithm for local orientation estimation. The proposed algorithm detect...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
... this paper is based on a signal-theoretic and statistical analysis of the notion of orientation....
This paper describes a new algorithm for local orientation estimation. The proposed algorithm detect...
This paper describes a new algorithm for local orientation estimation. The proposed algorithm detect...
Image intensity gradients can be encoded in a 2-dimensional channel representation. This report disc...
Estimation of local orientation in images is often posed as the task of finding the minimum variance...
This paper examines how observers estimate the overall orientation of spatially disorganised texture...
The aim of this paper is to present a study where we evaluate the optimal inclusion of the texture o...