• Convergence Security Journal
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• v.7 no.3
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• pp.39-44
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• 2007

In order to detect and locate edge features precisely in real images we have developed an algorithm by introducing a nonlocal differentiation of intensity profiles called adaptive directional derivative (ADD), which is evaluated independently of varying ramp widths. In this paper, we first develop the edge detector system employing the ADD and then, the performance of the algorithm is illustrated by comparing the results to those from the Canny's edge detector.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1329-1336
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• 2005

We propose an optimal edge detection algorithm adaptive to the various widths of edges. In order to construct this algorithm, we introduce an alternative definition of edge point and generalize the directional derivatives in the pixel space to obtain an extended directional derivatives beyond scaling. The result from applying our algorithm to a 2D image is analyzed comparing to that from the other algorithm.

• Convergence Security Journal
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• v.6 no.3
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• pp.29-35
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• 2006

In order to detect and locate edge features precisely in real images we have developed an algorithm by introducing a nonlocal differentiation of intensity profiles called adaptive directional derivative (ADD), which is evaluated independently of varying ramp widths. In this paper, we first develop the edge detector system employing the ADD and then, the performance of the algorithm is illustrated by comparing the results to those from the Canny's edge detector.

• Convergence Security Journal
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• v.8 no.4
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• pp.127-134
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• 2008

In this paper, we try to improve the accuracy of automatic measurement for analysis equipment by detecting efficiently the edge of a waterdrop with transparency. In order to detect the edge of a waterdrop with transparency, we use an edge detecting technique, MADD (Modified Adaptive Directional Derivative), which can identify the ramp edges with various widths as the perfectly sharp edges and respond effectively regardless of enlarging or reducing the image. The proposed edge detecting technique by means of perfect sharpening of ramp edges employs the modified adaptive directional derivatives instead of the usual local differential operators in order to detect the edges of image. The modified adaptive directional derivatives are defined by introducing the perfect sharpening map into the adaptive directional derivatives. Finally we apply the proposed method to contact angle arithmetic and show the effiency and validity of the proposed method.

• Journal of the Korea Computer Industry Society
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• v.3 no.12
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• pp.1725-1736
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• 2002

We describe a characteristic behavior of edge signal intensity, the strictly monotonic variation of intensity across edges and propose a new algorithm for edge detection based on it. We define an extended directional derivatives, which is nonlocal and beyond scaling in the pixel space, to describe that the algorithm is adaptive to the various widths of edges and relevant as an optimal edge detection algorithm. As an industrial application of the algorithm, we discuss a simple computer vision procedure for an example of visual inspection.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.186-191
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• 2019

In this paper, we propose a implementation approach of the high-speed feature extraction algorithm. The proposed method is based on the block type classification algorithm which reduces the computation time when target macro block is divided to smooth block type that has no image features. It is quantitatively identified that occurs at 29.5% of the total image using 200 standard test images with $64{\times}64$ macro block size. This means that within a standard test image containing various image information, 29.5% can reduce the complexity of the operation. When the proposed approach is applied to the Canny edge detection, the required latency of the edge detection can be completely eliminated, such as 2D derivative filter, gradient magnitude/direction computation, non-maximal suppression, adaptive threshold calculation, hysteresis thresholding. Also, it is expected that operation time of the feature detection can be reduced by applying block type classification algorithm to various feature extraction algorithms in this way.