Primitive features by steering, quadrature, and scale

Folsom, Tyler C.; Pintér, Róbert

doi:10.1109/34.730552

Cited by 27 publications

(14 citation statements)

References 21 publications

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“…Certain image analysis algorithms use Gabor functions as the impulse response of a set of 2-D filters [40]. By varying the parameters ω x and ω y , the orientation of the Gabor function can be adjusted; by varying σ x and σ y the spatial extent and aspect ratio of the function can be adjusted.…”

Section: Links With the 2-d Gabor Transformmentioning

confidence: 99%

The dual-tree complex wavelet transform

Selesnick

Baraniuk

Kingsbury³

2005

IEEE Signal Process. Mag.

2,156

1,202

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[ A coherent framework for multiscale signal and image processing ] T he dual-tree complex wavelet transform (CWT) is a relatively recent enhancement to the discrete wavelet transform (DWT), with important additional properties: It is nearly shift invariant and directionally selective in two and higher dimensions. It achieves this with a redundancy factor of only 2 d for d-dimensional signals, which is substantially lower than the undecimated DWT. The multidimensional (M-D) dual-tree CWT is nonseparable but is based on a computationally efficient, separable filter bank (FB). This tutorial discusses the theory behind the dual-tree transform, shows how complex wavelets with good properties can be designed, and illustrates a range of applications in signal and image processing. We use the complex number symbol C in CWT to avoid confusion with the often-used acronym CWT for the (different) continuous wavelet transform. BACKGROUNDThis article aims to reach two different audiences. The first is the wavelet community, many members of which are unfamiliar with the utility, convenience, and unique properties of complex wavelets. The second is the broader class of signal processing folk who work with applications where the DWT has proven somewhat disappointing, such as those involving complex or modulat-

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Section: Links With the 2-d Gabor Transformmentioning

confidence: 99%

The dual-tree complex wavelet transform

Selesnick

Baraniuk

Kingsbury³

2005

IEEE Signal Process. Mag.

2,156

1,202

View full text Add to dashboard Cite

show abstract

“…In the context of this paper, the term 'edge' stands for a local luminance change for which a gradient can be defined and which is of sufficient strength to be considered important in a given task. Examples of edge detectors are operators that incorporate linear filtering [6,13,25,35,41], local orientation analysis [17,36,59], fitting of analytical models to the image data [8,16,22,40] and local energy [12,24,30,39]. Some of these methods were biologically motivated [24,25,35,39].…”

Section: Introductionmentioning

confidence: 99%

Contour and boundary detection improved by surround suppression of texture edges

Grigorescu

Petkov

Westenberg

2004

Image and Vision Computing

201

202

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Recently, many image processing applications have taken advantage of a psychophysical and neurophysiological mechanism, called "surround suppression" to extract object contour from a natural scene. However, these traditional methods often adopt a single suppression model and a fixed input parameter called "inhibition level", which needs to be manually specified. To overcome these drawbacks, we propose a novel model, called "context-adaptive surround suppression", which can automatically control the effect of surround suppression according to image local contextual features measured by a surface estimator based on a local linear kernel. Moreover, a dynamic suppression method and its stopping mechanism are introduced to avoid manual intervention. The proposed algorithm is demonstrated and validated by a broad range of experimental results.

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“…Well known performance indicators are recall and precision , which relate the number of correctly detected contour pixels to the number of ground truth (GT) pixels and the number of contour pixels that are actually detected by an algorithm (DC), respectively (12) Following [19], we evaluate the performance in terms of the so-called -measure, defined as the harmonic mean of and , . Fig.…”

Section: B Quantitative Comparisonmentioning

confidence: 99%

“…Different techniques have been proposed for edge detection: examples are linear filtering [1]- [5], local orientation analysis [6], [7], fitting of analytical models to the image data [8]- [11], local energy [12]- [15], statistical analysis of the gradient field [16]- [19], anisotropic diffusion [20]- [22], complementary analysis of boundaries and regions [23]- [25], and biologically motivated surround suppression [26]- [29]. The main limitation of these operators is that they detect contours by only analysing a small neighborhood around each pixel.…”

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confidence: 99%

Adaptive Pseudo Dilation for Gestalt Edge Grouping and Contour Detection

Papari

Petkov

2008

IEEE Trans. on Image Process.

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Abstract-We consider the problem of detecting object contours in natural images. In many cases, local luminance changes turn out to be stronger in textured areas than on object contours. Therefore, local edge features, which only look at a small neighborhood of each pixel, cannot be reliable indicators of the presence of a contour, and some global analysis is needed. We introduce a new morphological operator, called adaptive pseudo-dilation (APD), which uses context dependent structuring elements in order to identify long curvilinear structure in the edge map. We show that grouping edge pixels as the connected components of the output of APD results in a good agreement with the gestalt law of good continuation. The novelty of this operator is that dilation is limited to the Voronoi cell of each edge pixel. An efficient implementation of APD is presented. The grouping algorithm is then embedded in a multithreshold contour detector. At each threshold level, small groups of edges are removed, and contours are completed by means of a generalized reconstruction from markers. The use of different thresholds makes the algorithm much less sensitive to the values of the input parameters. Both qualitative and quantitative comparison with existing approaches prove the superiority of the proposed contour detector in terms of larger amount of suppressed texture and more effective detection of low-contrast contours.

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Primitive features by steering, quadrature, and scale

Cited by 27 publications

References 21 publications

The dual-tree complex wavelet transform

The dual-tree complex wavelet transform

Contour and boundary detection improved by surround suppression of texture edges

Adaptive Pseudo Dilation for Gestalt Edge Grouping and Contour Detection

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