Extraction of salient contours from cluttered scenes

Tang, Qiling; Sang, Nong

doi:10.1016/j.patcog.2007.02.009

Cited by 59 publications

(81 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been used extensively for testing other biologically-inspired boundary detection algorithms, and the results shown in the last column of Fig. 5 can be compared directly to: Fig.15a in Papari and Petkov (2008) Tang et al (2007b). The sparsity of the representations found by PC/BC are not strongly influenced by the proposed extensions to this algorithm: the inclusion lateral connections, and subsequently the inclusion of two sets of prediction neurons to represent boundary edges and texture edges.…”

Section: Resultsmentioning

confidence: 99%

Image Segmentation Using a Sparse Coding Model of Cortical Area V1

Spratling

2013

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Algorithms that encode images using a sparse set of basis functions have previously been shown to explain aspects of the physiology of primary visual cortex (V1), and have been used for applications such as image compression, restoration, and classification. Here, a sparse coding algorithm, that has previously been used to account of the response properties of orientation tuned cells in primary visual cortex, is applied to the task of perceptually salient boundary detection. The proposed algorithm is currently limited to using only intensity information at a single scale. However, it is shown to out-perform the current state-of-the-art image segmentation method (Pb) when this method is also restricted to using the same information.

show abstract

Section: Resultsmentioning

confidence: 99%

Image Segmentation Using a Sparse Coding Model of Cortical Area V1

Spratling

2013

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…In particular, it has been shown that response properties of neurons in area V1 are modeled by convolution of the input image with a family of Gabor functions (Sanger, 1988). Further research has demonstrated that the upper layers of area V1 are modeled well by a bank of Gabor filters (Grigorescu et al, 2003); (Huang et al, 2008); (Lee & Choe, 2003); (Ursine et al, 2004); (Tang et al, 2007). A related, but alternative, approach to the Gabor response functions to model simple and complex cells of V1 is the use of Gaussian derivatives (Huang et al, 2009).…”

Section: 001mentioning

confidence: 99%

Cortical Specification of a Fast Fourier Transform Supports a Convolution Model of Visual Perception

Sheridan¹

2012

Fourier Transform Applications

View full text Add to dashboard Cite

“…The algorithm initializes D so that the first three vertices of W form a CCW triangle (lines 2-9). Next it sequentially processes each of the remaining vertices v i in order (lines [10][11][12][13][14][15][16][17][18][19][20][21][22]. Each of these vertices will satisfy one of two conditions.…”

Section: Convex Hull Extractionmentioning

confidence: 99%

“…Such characteristics include circularity [12], rectangularity, triangularity [13], rectilinearity [21] and convexity [14,22,23]. In machine vision research object contours are commonly extracted from images using an edge detection and linking strategy [19]. Due to occlusion, scene complexity and image noise, contour extraction techniques do not necessarily always return a closed object contour.…”

Section: Introductionmentioning

confidence: 99%

A Convexity Measure for Open and Closed Contours

Corcoran

Mooney

Winstanley

2011

Procedings of the British Machine Vision Conference 2011

View full text Add to dashboard Cite

Convexity represents a fundamental descriptor of object shape. This paper presents a new convexity measure for both open and closed simple contours. Given such a contour this measure extracts two corresponding open convex hulls. The shape similarity between these two hulls and the original contour is then computed and normalized to give a measure of convexity. The time complexity of the proposed technique is O (n). The authors believe this technique represents the first measure of convexity which uses shape similarity and which can be applied to both open and closed contours. The proposed technique is shown to provide similar or greater performance relative to two other state of the art techniques.

show abstract

Extraction of salient contours from cluttered scenes

Cited by 59 publications

References 39 publications

Image Segmentation Using a Sparse Coding Model of Cortical Area V1

Image Segmentation Using a Sparse Coding Model of Cortical Area V1

Cortical Specification of a Fast Fourier Transform Supports a Convolution Model of Visual Perception

A Convexity Measure for Open and Closed Contours

Contact Info

Product

Resources

About