AbstractThe main purpose of this paper is to lay the foundations of a general theory which encompasses the features of the classical Hough transform and extend them to general algebraic objects such as affine schemes. The main motivation comes from problems of detection of special shapes in medical and astronomical images. The classical Hough transform has been used mainly to detect simple curves such as lines and circles. We generalize this notion using reduced Gröbner bases of flat families of affine schemes. To this end we introduce and develop the theory of Hough regularity. The theory is highly effective and we give some examples computed with CoCoA (see [1])
We develop a new formulation for including invariance in a general form of the Hough transform. We f...
In this work we present a modification of the arbitrary shape detection process based on the pairing...
This paper contains a brief literature survey of applications and improvements of the Hough transfor...
The Hough transform is a standard pattern recognition technique introduced between the 1960s and the...
The Hough transform is a standard pattern recognition technique introduced between the 1960s and the...
This paper introduces a two-steps adaptive generalized Hough transform (GHT) for the detection of no...
The Hough Transform is a method for detecting curves by exploiting the duality between points on a c...
The Hough transform (HT) is an established technique which evidences a shape by mapping image edge p...
We develop a formal procedure for the automated recognition of rational and elliptic curves in medic...
This paper describes techniques to perform fast and accurate curve detection using a variant of the ...
The Hough transform for detecting parameterised shapes in images is still today mostly applied on bi...
The Hough Transform is a widely researched area of computer vision due to its unique promise for sha...
The Hough Transform is a widely researched area of computer vision due to its unique promise for sha...
National audienceThe Hough transform for detecting parameterised shapes in images is still today mos...
The Hough Transform is a widely researched area of computer vision due to its unique promise for sha...
We develop a new formulation for including invariance in a general form of the Hough transform. We f...
In this work we present a modification of the arbitrary shape detection process based on the pairing...
This paper contains a brief literature survey of applications and improvements of the Hough transfor...
The Hough transform is a standard pattern recognition technique introduced between the 1960s and the...
The Hough transform is a standard pattern recognition technique introduced between the 1960s and the...
This paper introduces a two-steps adaptive generalized Hough transform (GHT) for the detection of no...
The Hough Transform is a method for detecting curves by exploiting the duality between points on a c...
The Hough transform (HT) is an established technique which evidences a shape by mapping image edge p...
We develop a formal procedure for the automated recognition of rational and elliptic curves in medic...
This paper describes techniques to perform fast and accurate curve detection using a variant of the ...
The Hough transform for detecting parameterised shapes in images is still today mostly applied on bi...
The Hough Transform is a widely researched area of computer vision due to its unique promise for sha...
The Hough Transform is a widely researched area of computer vision due to its unique promise for sha...
National audienceThe Hough transform for detecting parameterised shapes in images is still today mos...
The Hough Transform is a widely researched area of computer vision due to its unique promise for sha...
We develop a new formulation for including invariance in a general form of the Hough transform. We f...
In this work we present a modification of the arbitrary shape detection process based on the pairing...
This paper contains a brief literature survey of applications and improvements of the Hough transfor...