Detecting Wide Lines Using Isotropic Nonlinear Filtering

Liu, L.; Zhang, David; You, Jane

doi:10.1109/tip.2007.894288

Cited by 74 publications

(45 citation statements)

References 33 publications

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“…Since the proposed method detects the whole line, we selected two methods among the related literature on wide line detectors: an active contour based approach proposed in [37], and a wide line detection technique based on an isotropic nonlinear filtering (proposed in [31]). Since lines can be extracted by applying methods that use edge information, the results provided by the Canny method [17] are also reported, as it is considered by the related literature [44] one of the methods that produces optimal solutions.…”

Section: Real Imagesmentioning

confidence: 99%

“…[31] for further explanations). In order to avoid small regions, all results reported in Figure 10 are obtained after a morphological closing of one pixel, and the suppression of connected regions with a small area (under 50 pixels).…”

Section: Real Imagesmentioning

confidence: 99%

“…A line is a 1D figure without thickness, but in image processing and pattern recognition applications, line thickness or a line full cross section is important in recognizing traces, roads, railways, or rivers from satellite or aerial imagery. In [31] a wide line detection approach applies a non linear filter to extract lines. Isotropic responses are obtained by using circular masks, so as to ensure that lines with different widths can be extracted in their entirety.…”

Section: Introductionmentioning

confidence: 99%

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Automated Extraction of Archaeological Traces by a Modified Variance Analysis

et al. 2015

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This paper considers the problem of detecting archaeological traces in digital aerial images by analyzing the pixel variance over regions around selected points. In order to decide if a point belongs to an archaeological trace or not, its surrounding regions are considered. The one-way ANalysis Of VAriance (ANOVA) is applied several times to detect the differences among these regions; in particular the expected shape of the mark to be detected is used in each region. Furthermore, an effect size parameter is defined by comparing the statistics of these regions with the statistics of the entire population in order to measure how strongly the trace is appreciable. Experiments on synthetic and real images demonstrate the effectiveness of the proposed approach with respect to some state-of-the-art methodologies.

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Section: Real Imagesmentioning

confidence: 99%

Section: Real Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automated Extraction of Archaeological Traces by a Modified Variance Analysis

et al. 2015

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“…Liu and Zhang extract tongue cracks to some degree using a wide line detector (WLD) [24,25], which has been considered quite an effective method until now. However, Liu and Zhang only concentrate on identifying tagged pixels of cracks with certain thresholds.…”

Section: Introductionmentioning

confidence: 99%

WLDF: Effective Statistical Shape Feature for Cracked Tongue Recognition

Li¹,

Wang²,

Cui³

2017

Journal of Electrical Engineering and Technology

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-This paper proposes a new method using Wide Line Detector based statistical shape Feature (WLDF) to identify whether or not a tongue is cracked; a cracked tongue is one of the most frequently used visible features for diagnosis in traditional Chinese Medicine (TCM). We first detected a wide line in the tongue image, and then extracted WLDF, such as the maximum length of each detected region, and the ratio between maximum length and the area of the detected region. We trained a binary support vector machine (SVM) based on the WLDF to build a classifier for cracked tongues. We conducted an experiment based on our proposed scheme, using 196 samples of cracked tongues and 245 samples of non-cracked tongues. The results of the experiment indicate that the recognition accuracy of the proposed method is greater than 95%. In addition, we provide an analysis of the results of this experiment with different parameters, demonstrating the feasibility and effectiveness of the proposed scheme.

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“…Several imaging systems have been developed for the acquisition of tongue images [13][14][15]. A number of feature extraction methods have been proposed for extracting features of tongue color [16,17], coating, and texture [18][19][20][21], fissures [22][23][24][25], shape [26,27], tooth marks [28], petechia spots [29], and the sublingual vein [30,31]. Pattern recognition approaches such as the Bayesian network and support vector machine have been used for syndromes and disease analysis [9,[32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Fast marching over the 2D Gabor magnitude domain for tongue body segmentation

Cui

Zhang

et al. 2013

EURASIP J. Adv. Signal Process.

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Tongue body segmentation is a prerequisite to tongue image analysis and has recently received considerable attention. The existing tongue body segmentation methods usually involve two key steps: edge detection and active contour model (ACM)-based segmentation. However, conventional edge detectors cannot faithfully detect the contour of the tongue body, and the initialization of ACM suffers from the edge discontinuity problem. To address these issues, we proposed a novel tongue body segmentation method, GaborFM, which initializes ACM by performing fast marching over the two-dimensional (2D) Gabor magnitude domain of the tongue images. For the enhancement of the contour of the tongue body, we used the 2D Gabor magnitude-based detector. To cope with the edge discontinuity problem, the fast marching method was utilized to connect the discontinuous contour segments, resulting in a closed and continuous tongue body contour for subsequent ACM-based segmentation. Qualitative and quantitative results showed that GaborFM is superior to the other methods for tongue body segmentation.

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Detecting Wide Lines Using Isotropic Nonlinear Filtering

Cited by 74 publications

References 33 publications

Automated Extraction of Archaeological Traces by a Modified Variance Analysis

Automated Extraction of Archaeological Traces by a Modified Variance Analysis

WLDF: Effective Statistical Shape Feature for Cracked Tongue Recognition

Fast marching over the 2D Gabor magnitude domain for tongue body segmentation

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