• Journal of Korea Multimedia Society
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• v.25 no.7
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• pp.895-902
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• 2022

Building segmentation using satellite imagery such as EO (Electro-Optical) and SAR (Synthetic-Aperture Radar) images are widely used due to their various uses. EO images have the advantage of having color information, and they are noise-free. In contrast, SAR images can identify the physical characteristics and geometrical information that the EO image cannot capture. This paper proposes a learning framework for efficient building segmentation that consists of a teacher-student-based privileged knowledge distillation and deformable convolution block. The teacher network utilizes EO and SAR images simultaneously to produce richer features and provide them to the student network, while the student network only uses EO images. To do this, we present objective functions that consist of Kullback-Leibler divergence loss and knowledge distillation loss. Furthermore, we introduce deformable convolution to avoid pixel-level noise and efficiently capture hard samples such as small and thin buildings at the global level. Experimental result shows that our method outperforms other methods and efficiently captures complex samples such as a small or narrow building. Moreover, Since our method can be applied to various methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.225-232
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• 2009

In this paper, a new face detection method based on pupil color distribution maps with the frequency under the illumination variance is proposed. Face-like regions are first extracted by applying skin color distribution maps to a color image and then, they are reduced by using the standard deviation of chrominance components. In order to search for eye candidates effectively, the proposed method extracts eye-like regions from face-like regions by using pupil color distribution maps. Furthermore, the proposed method is able to detect eyes very well by segmenting the eye-like regions, based on a lighting compensation technique and a segmentation algorithm even though face regions are changed into dark-tone due to varying illumination conditions. Eye candidates are then detected by means of template matching method. Finally, face regions are detected by using the evaluation values of two eye candidates and a mouth. Experimental results show that the proposed method can achieve a high performance.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.401-404
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• 2006

Mean shift procedure is applied for the data points in the joint spatial-range domain and achieves a high quality. However, a color image is segmented differently according to the inputted spatial parameter or range parameter and the demerit is that the image is broken into many small regions in case of the small parameter. In this paper, to improve this demerit, we propose the method that groups similar regions using region merging method for over-segmented images. The proposed method converts a over-segmented image in RGB color space into in HSI color space and merges similar regions by hue information. Here, to preserve edge information, the proposed method use by merging constraints to decide whether regions is merged or not. After then, we merge the regions in RGB color space for non-processed regions in HSI color space. Experimental results show the superiority in region's segmentation results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.1
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• pp.1-12
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• 2004

In this paper, we present an identificable face (n face) detection method robust to color degradation and partial veiling. This method is composed of three parts: segmentation of face candidate regions, extraction of face candidate windows, and decision of veiling. In the segmentation of face candidate regions, face candidate regions are detected by finding skin color regions and facial components such as eyes, a nose and a mouth, which may have degraded colors, from an input image. In the extraction of face candidate windows, face candidate windows which have high potentials of faces are extracted in face candidate regions. In the decision of veiling, using an eigenface method, a face candidate window whose similarity with eigenfaces is maximum is determined and whether facial components of the face candidate window are veiled or not is determined in the similar way. Experimental results show that the proposed method yields better the detection rate by about $11.4\%$ in test DB containing color-degraded faces and veiled ones than a conventional method without considering color degradation and partial veiling.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.692-704
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• 2002

In this paper, we present am image retrieval method based on color-spatial features using quadtree and texture information extracted from object MBRs in an image. Tile proposed method consists of creating a DC image from an original image, changing a color coordinate system, and decomposing regions using quadtree. As such, conditions are present to decompose the DC image, then the system extracts representative colors from each region. And, image segmentation is used to search for object MBRs, including object themselves, object included in the background, or certain background region, then the wavelet coefficients are calculated to provide texture information. Experiments were conducted using the proposed similarity method based on color-spatial and texture features. Our method was able to refute the amount of feature vector storage by about 53%, but was similar to the original image as regards precision and recall. Furthermore, to make up for the deficiency in using only color-spatial features, texture information was added and the results showed images that included objects from the query images.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2197-2207
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• 1997

In this paper, we propose a video data segmentation method using edge and color distribution of video frames and also develop a video browser by using the proposed algorithm. To segment a video, we use a 644-bin HSV color histogram and the edge information which generated with automatic threshold method. We consider scene's characteristics by using positions and colo distributions of object in each frame. We develop a hierarchical and a shot-based browser for video browsing. We also show that our proposed method is less sensitive to light effects and more robust to motion effects than previous ones like a histogram-based method by testing with various video data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.1
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• pp.68-80
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• 2013

Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.257-262
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• 2009

A method of the region extraction and recognition of a speed sign in the real road environment is proposed. The region of speed sign is extracted by using color information and then numbers are segmented in the region. We improve the recognition rate by performing an incline compensation of the speed sign for directions clockwise and counterclockwise. In image sequences of the real road environment, a robust recognition results are achieved with speed signs at normal condition as well as inclined.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.141-156
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• 2013

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non-face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions.

• Journal of Surface Science and Engineering
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• v.54 no.5
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• pp.278-284
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• 2021

Corrosion of steel plate is common phenomenon which results in the gradual destruction caused by a wide variety of environments. Corrosion monitoring is the tracking of the degradation progress for a long period of time. Corrosion on steel plate appears as a discoloration and any irregularities on the surface. In this study, we developed a quantitative evaluation method of the rust formed on steel plate by using k-means clustering from the corroded area in a given image. The k-means clustering for automated corrosion detection was based on the GrabCut segmentation and Gaussian mixture model(GMM). Image color of the corroded surface at cut-edge area was analyzed quantitatively based on HSV(Hue, Saturation, Value) color space.