• Proceedings of the KSRS Conference
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• v.2
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• pp.736-739
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• 2006

While hyperspectral data are very rich in information, their processing poses several challenges such as computational requirements, noise removal and relevant information extraction. In this paper, the application of advanced scale-space filtering to selected hyperspectral bands was investigated. In particular, a pre-processing tool, consisting of anisotropic diffusion and morphological leveling filtering, has been developed, aiming to an edge-preserving smoothing and simplification of hyperspectral data, procedures which are of fundamental importance during feature extraction and object detection. Two scale space parameters define the extent of image smoothing (anisotropic diffusion iterations) and image simplification (scale of morphological levelings). Experimental results demonstrated the effectiveness of the developed scale space filtering for the enhancement and smoothing of hyperspectral remote sensing data and their advantage against watershed over-segmentation problems and edge detection.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.433-438
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• 2019

This paper proposes a method of improving deep learning based numbers and characters recognition performance on parts of drawing through image preprocessing. The proposed character recognition system consists of image preprocessing and 7 layer deep learning model. Mathematical morphological filtering is used as preprocessing to remove the lines and shapes which causes false recognition of numbers and characters on parts drawing. Further.. Further, the used deep learning model is a 7 layer deep learning model instead of VGG-16 model. As a result of the proposed OCR method, the recognition rate of characters is 92.57% and the precision is 92.82%.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.29-33
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• 1998

In this paper, we propose a hierarchical segmentation method for tracking a semantic video object using a watershed algorithm based on morphological filtering. In the proposed method, each hierarchy consists of three steps: First, markers are extracted on the simplified current frame. Second, region growing by a modified watershed algorithm is performed for segmentation. Finally, the segmented regions are classified into 3 categories, i.e., inside, outside, and uncertain regions according to region probability values, which are acquired by the probability map calculated from a estimated motion field. Then, for the remaining uncertain regions, the above three steps are repeated at lower hierarchies with less simplified frames until every region is decided to a certain region. The proposed algorithm provides prospective results in video sequences such as Miss America, Clair, and Akiyo.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.7
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• pp.116-126
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• 1996

In this paper, we propose a new termination rule for the iterative restoration of degraded images, by which the number of iterations can be dramatically reduced. This rule uses a parameter, called noise suppression factor (NSF), to appropriately teminate the iteration process, reduces a lot the computational load, and avoids the amplification of noise for the better quality. We also propose a method using the morphological filters, when applied to the resulting image, that will significantly reduce the ringing effect which would otherwise exist in the boundary of the image. Simulation with the blurred lena image with gaussian noise showns that the proposed termination rule combined with the morphological filtering gives the restored image with much improved quality.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1424-1436
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• 2020

Although huge progress has been made in current image segmentation work, there are still no efficient segmentation strategies for tree image which is taken from natural environment and contains complex background. To improve those problems, we propose a method for tree image segmentation combining adaptive mean shifting with image abstraction. Our approach perform better than others because it focuses mainly on the background of image and characteristics of the tree itself. First, we abstract the original tree image using bilateral filtering and image pyramid from multiple perspectives, which can reduce the influence of the background and tree canopy gaps on clustering. Spatial location and gray scale features are obtained by step detection and the insertion rule method, respectively. Bandwidths calculated by spatial location and gray scale features are then used to determine the size of the Gaussian kernel function and in the mean shift clustering. Furthermore, the flood fill method is employed to fill the results of clustering and highlight the region of interest. To prove the effectiveness of tree image abstractions on image clustering, we compared different abstraction levels and achieved the optimal clustering results. For our algorithm, the average segmentation accuracy (SA), over-segmentation rate (OR), and under-segmentation rate (UR) of the crown are 91.21%, 3.54%, and 9.85%, respectively. The average values of the trunk are 92.78%, 8.16%, and 7.93%, respectively. Comparing the results of our method experimentally with other popular tree image segmentation methods, our segmentation method get rid of human interaction and shows higher SA. Meanwhile, this work shows a promising application prospect on visual reconstruction and factors measurement of tree.

• Journal of radiological science and technology
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• v.45 no.5
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• pp.397-405
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• 2022

Computed radiography (CR) systems, which convert an analog signal recorded on a cassette into a digital image, combine the characteristics of analog and digital imaging systems. Compared to digital radiography (DR) systems, CR systems have presented difficulties in evaluating system performance because of their lower detective quantum efficiency, their lower signal-to-noise ratio (SNR), and lower modulation transfer function (MTF). During the step of energy-storing and reading out, a baseline offset occurs in the edge area and makes low-frequency overestimation. The low-frequency offset component in the line spread function (LSF) critically affects the MTF and other image-analysis or qualification processes. In this study, we developed the method of baseline correction using mathematical morphology to determine the LSF and MTF of CR systems accurately. We presented a baseline correction that used a morphological filter to effectively remove the low-frequency offset from the LSF. We also tried an MTF evaluation of the CR system to demonstrate the effectiveness of the baseline correction. The MTF with a 3-pixel structuring element (SE) fluctuated since it overestimated the low-frequency component. This overestimation led the algorithm to over-compensate in the low-frequency region so that high-frequency components appeared relatively strong. The MTFs with between 11- and 15-pixel SEs showed little variation. Compared to spatial or frequency filtering that eliminated baseline effects in the edge spread function, our algorithm performed better at precisely locating the edge position and the averaged LSF was narrower.