Precision edge contrast and orientation estimation

Lyvers, E.P.; Mitchell, O. Robert

doi:10.1109/34.9114

Cited by 126 publications

(48 citation statements)

References 9 publications

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“…Edge orientation estimated from the Sobel operator response is not accurate for angles within arctan(1/3) and π/4 in each quadrant (Lyvers and Mitchell, 1998). Within the high gradient regions, if the angle ∠e i,j = tan…”

Section: Boundary Extractionmentioning

confidence: 99%

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Design and implementation of a graphic-haptic display system

Janabi–Sharifi¹,

Hayward²,

Wang³

2007

Displays

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Despite current advances in multimedia environments, tracing the geometrical structures of graphical images using force feedback remains an important research issue.In this paper, the development and implementation of a Multi-Modal Display System (MMDs) for tracing 2D boundaries in graphic images are discussed. A method is proposed that provides a type of haptic feedback designed to assist a user to trace the contours of objects seen in images. This method is an example of a family of haptic synthesis methods whereby the force field explored by the user is dynamic in the sense that it depends both on movement as well as on the object being haptically represented. The proposed performance-based method provides users with a movement guidance through an active haptic sense rather than common impedance technique. The tracing effectiveness of the proposed method is verified experimentally.

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Section: Boundary Extractionmentioning

confidence: 99%

“…This is used to isolate out the regions of highest gradient. These high gradient regions are then reprocessed using convolution mask optimized for orientation accuracy (Lyvers and Mitchell, 1998 …”

Section: Boundary Extractionmentioning

confidence: 99%

Design and implementation of a graphic-haptic display system

Janabi–Sharifi¹,

Hayward²,

Wang³

2007

Displays

View full text Add to dashboard Cite

show abstract

“…Useful surveys of these techniques can be found in [9] and in [19]. In an object-oriented framework, we can foresee any number of edge detection techniques being used successfully.…”

Section: Edge Detection Imagesmentioning

confidence: 99%

“…The orientation of the axes is given by (14) where the matrix A is given in (6) and where a(l), b(l), and h(l) are given in (9). See Fig.…”

Section: The Range Of L For Which the Conic Is An Ellipsementioning

confidence: 99%

A method to detect and characterize ellipses using the Hough transform

Bennett

Burridge

Saito

1999

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-In this paper we describe a new technique for detecting and characterizing ellipsoidal shapes automatically from any type of image. This technique is a single pass algorithm which can extract any group of ellipse parameters or characteristics which can be computed from those parameters without having to detect all five parameters for each ellipsoidal shape. Moreover, the method can explicitly incorporate any a priori knowledge the user may have concerning ellipse parameters. The method is based on techniques from Projective Geometry and on the Hough Transform. This technique can significantly reduce interpretation and computation time by automatically extracting only those features or geometric parameters of interest from images and making exact use of a priori information.

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“…With these constant gray-level values and with the iid property their sample mean and variance are (3) By the law of large numbers and . In [9], a comparison of structure detectors is given where it is shown that precise structure and edge detection is computationally expensive, and computationally less-expensive detectors either need some manual tuning, are designed for specific edge models, or are not sufficiently precise. To reject structured blocks our method first divides the image into blocks, , of the same size .…”

Section: A Detection Of Intensity-homogeneous Blocksmentioning

confidence: 99%

Fast and reliable structure-oriented video noise estimation

Amer

Dubois

2005

IEEE Trans. Circuits Syst. Video Technol.

171

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Abstract-Noise can significantly impact the effectiveness of video processing algorithms. This paper proposes a fast white-noise variance estimation that is reliable even in images with large textured areas. This method finds intensity-homogeneous blocks first and then estimates the noise variance in these blocks, taking image structure into account. This paper proposes a new measure to determine homogeneous blocks and a new structure analyzer for rejecting blocks with structure. This analyzer is based on high-pass operators and special masks for corners to stabilize the homogeneity estimation. For typical video quality (PSNR of 20-40 dB), the proposed method outperforms other methods significantly and the worst-case estimation error is 3 dB, which is suitable for real applications such as video broadcasts. The method performs well both in highly noisy and good-quality images. It also works well in images including few uniform blocks.Index Terms-Adaptive variance averaging, homogeneous regions, second-order operators, structure analyzers, textured regions, video enhancement, video noise estimation, white noise.

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Precision edge contrast and orientation estimation

Cited by 126 publications

References 9 publications

Design and implementation of a graphic-haptic display system

Design and implementation of a graphic-haptic display system

A method to detect and characterize ellipses using the Hough transform

Fast and reliable structure-oriented video noise estimation

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