Earticle

목적: 본 연구는 두상영역분할을 변동해 가면서 슬라이스 라인에 따른 디자인 형태변화를 파악하고, 이를 통해 커트디자인의 표본 제시와 헤어커트 기초자료를 제공하는데 목적이 있다. 방법: 센터 포인트- 골든 백 미디엄 포인트- 이어 백 포인트, 센터 포인트- 백 포인트-이어 백 포인트, 센터 포인트-백 네이프 미디엄 포인트-이어 백 포인트를 기점으로 두상 위를 두상시술각도 45°와 A라 인, 평행라인, V라인으로 커트하였다. 두상하단은 두상시술각도 110°, 130°, 150°로 하였다. 결과: A라인과 V라인은 얼굴라인으로 갈수록 뚜렷해지고 평행라인은 반듯한 형태로 나타났다. 인크리스 레이어 커트는 무게선이 없고, 오목하게 늘어진 형태로 각도에 따른 변화는 크게 차이가 없었다. 두상영역분할에서는 상단의 출발점을 위로 두면 길이가 짧아지고 아래로 두면 길이가 길어졌다. 미디엄 그래쥬에이션 커트는 동일선상에 모발이 떨어지지 않고, 무게감 가장자리 위에 나타났다. 결론: A라인은 부피감, 무게감, 팽창감이 증가하고 V라인은 감소하며 반면 볼륨감, 리듬감, 생동감은 증가하였다. A라인은 성숙함과 가름한 형태와 V라인은 귀여 움과 발랄한 형태 그리고, 평행라인은 평범함과 단정한 형태로 관찰되었다. 똑같은 슬라이스 라인이라고 해도 두상영역분할에 따라 길이가 다르게 나타났다. 고객 두상형태와 얼굴형태 및 취향과 함께 두상분할과 슬라이스 라인 및 각도를 선정해야 된다고 사료된다.

Purpose: This research investigated how changing the division of a head area affects design. Furthermore, this research aimed to understand the variations in design form based on the slice line, present a sample of the cut design, and provide basic data for haircuts. Methods: The upper head was divided in to three areas by from the center point to the golden back medium point-then ear back point, from the center point to the back point-then ear back point, and from the center point to the back nape medium point- then ear back point. The upper head angle was 45° and the slice line adopts A line, parallel line, and V line. The upper and lower head angles were 110°, 130°, and 150°. Results: A line and V line became more distinct toward the face line, while the parallel line appeared flatter. There was no weight line in the incremental layer cut, and there was no noticeable difference in the change according to the angle in the concave stretched shape. When dividing the head area, the length was shortened when the starting point of the upper part was placed upward and lengthened when it was placed downward. The medium gradient cut appeared on the edge of the weight without falling hair on the same line. Conclusion: A line exhibited increased volume, weight, and expansion, while V line showed a decrease in these factors but an increase in volume, rhythm, and vitality. A line was observed to be mature and thin, while V line was cute and sporty, and the parallel line was plain and neat. Even with the same slice line, the length varied based on the segmentation of the head region. The head division, slice line, and angle should be selected based on the customer's head shape, face shape, and taste.

目的: 这项研究调查了改变头部区域的划分如何影响设计。此外，本研究旨在了解基于切片线的设计形式的变 化，提供剪发设计的样本，并为理发提供基础数据。方法: 以从中点到金背中点-再耳背点，从中点到背点-再耳 背点，从中点到后颈中点-再耳背点为起点将头顶上半部分按上头角45°采用A线、平行线、V线剪发。下头角分 别采用110°、130°、150°。结果: A 线和 V 线朝向面部线条变得更加清晰，而平行线则显得更平坦。增量层切中 没有重量线，在凹拉伸形状中根据角度的变化没有明显差异。划分头部区域时，上部起点向上则缩短长度，向 下则延长长度。中等渐变剪发出现在重量的边缘，没有落在同一直线上的头发。结论: A线增加了体积感、重量 感、膨胀感，相反V线减少了，而层次感、韵律感、活泼感增加了。观察A线为成熟纤细型，V线为可爱活泼型， 平行线为素净利落型。即使对于相同的剪发线，长度也因头部区域的分割而不同。认为需要根据顾客的头型、 脸型和口味来选择头型、切片线和角度。

다중 레이어 영상 모델인 Mixed Raster Content 모델 (MRC) 기반의 영상 부호화는 스크린 이미지와 같은 혼합 영상을 전경 레이어, 이진 마스크 레이어, 배경 레이어로 재구성한 뒤, 각 레이어마다 그 레이어의 신호 특성에 적합한 부호화기를 이용하여 영상을 압축하는 기법이다. 문자와 같은 계단 형태의 강한 에지를 갖는 영역의 위치 정보를 마스크 레이어에 저장하고, 그 위치의 색상 신호는 전경 레이어에 저장한다. 그리고 나머지 영역인 배경 영역의 색상 신호는 배경 레이어에 저장한다. 따라서 마스크 레이어가 전경과 배경의 분할 정보를 담게 되며, 이 분할 정보의 정확도에 따라 전체 부호화기의 압축 효율이 직접적인 영향을 받는다. 본 논문은 MRC 기반의 영상 부호화를 위한 새로운 레이어 분할 알고리즘을 제안한다. 제안 방법은 형태학적 필터인 top hat 변환을 이용하여 문자를 배경신호로부터 분할한다. 이때 문자의 경계를 에지 맵으로부터 추정하여 문자 색상과 배경과의 상대적 밝기를 결정하고 이를 통해 형태학적 필터링에 필요한 top hat 변환의 종류를 정확히 선택하도록 하였다. 실험을 통해 제안 방법이 비교 대상 알고리즘에 비해 우수한 분할 성능을 가짐을 보인다.

An image coding based on MRC model, a kind of multi-layer image model, first segments a screen image into foreground, mask, and background layers, and then compresses each layer using a codec that is suitable to the layer. The mask layer defines the position of foreground regions such as textual and graphical contents. The colour signal of the foreground (background) region is saved in the foreground (background) layer. The mask layer which contains the segmentation result of foreground and background regions is of importance since its accuracy directly affects the overall coding performance of the codec. This paper proposes a new layer segmentation algorithm for the MRC based image coding. The proposed method extracts text pixels from the background using morphological top hat filtering. The application of white or black top hat transformation to local blocks is controlled by the information of relative brightness of text compared to the background. In the proposed method, the boundary information of text that is extracted from the edge map of the block is used for the robust decision on the relative brightness of text. Simulation results show that the proposed method is superior to the conventional methods.

Video storytelling has become a popular technology to let users design and plan their films. As the development of multimedia devices and network, more and more people will share their productions on the Internet. This approach is not only used in movie industry, but e-learning, digital archive and art. With the development of cloud computing, it can provide various computing and storage services over the Internet. Instead of using 3-D model generation system, we proposed a system composed of several video technologies to let teachers plan materials with existing avatars and scene from the cloud data base in this paper. Users can design the background by cloud panorama generation at first. Then, drug a trajectory line on the scene. The authoring tool will select an avatar from the database and make it with several kinds of behaviors. Users can select several behaviors during the motion trajectory; our tool will search the most suitable avatar and insert it into the panorama. This tool equips with special functions to integrate different motion tracks for the generation of video narratives is also presented in this paper.

Spectral segmentation is a major trend in image segmentation. Specially, constrained spectral segmentation, inspired by the user-given inputs, remains its challenging task. Since it makes use of the spectrum of the affinity matrix of a given image, its overall quality depends mainly on how to design the graphical model. In this work, we propose a sparse, multi-layer graphical model, where the pixels and the over-segmented regions are the graph nodes. Here, the graph affinities are computed by using the must-link and cannot-link constraints as well as the likelihoods that each node has a specific label. They are then used to simultaneously cluster all pixels and regions into visually coherent groups across all layers in a single multi-layer framework of Normalized Cuts. Although we incorporate only the adjacent connections in the multi-layer graph, the foreground object can be efficiently extracted in the spectral framework. The experimental results demonstrate the relevance of our algorithm as compared to existing popular algorithms.

The multi layer perceptron for segmenting objects in images only uses the input windows that are made from a image in a fixed size. These windows are recognized so each independent learning data that they make the performance of the multi layer perceptron poor. The poor performance is caused by not considering the position information and effect of input windows in input images. So we propose a new approach to add the position information and effect of input windows to the multi layer perceptron#s input layer. Our new approach improves the performance as well as the learning time in the multi layer perceptron. In our experiment, we can find our new algorithm good.

Detecting defective multi-layer ceramic capacitors (MLCCs) during the inspection stage is a crucial production task to effectively manage production yield and maintain quality. However, this task presents two challenges: the necessity of pixel-level segmentation in high-resolution images and unexplored defect patterns. To address these challenges, this paper introduces an MLCC defect-detection framework based on deep learning with an MLCC dataset we constructed and a comprehensive analysis of MLCC images. Our framework employs an object-detection model to identify dielectric regions in input MLCC images, followed by a semantic segmentation model to create dielectric masks for calculating the margin ratio. This approach follows the traditional inspection process but can be performed without specialized personnel. Furthermore, we generated pseudo-defect images using generative adversarial networks to obtain sufficient training data. Experiments demonstrate the effectiveness of our framework, which achieved a defect-detection accuracy of 93.1%, as revealed by an in-depth error analysis.

In medical image analysis, segmentation is considered as a vital process since it partitions an image into coherent parts and extracts interesting objects from the image. In this paper, we consider automatic segmentations of OCT retinal images to find six layer boundaries using convolutional neural networks. Segmenting retinal images by layer boundaries is very important in diagnosing and predicting progress of eye diseases including diabetic retinopathy, glaucoma, and AMD (age-related macular degeneration). We applied well-known CNN architecture for general image segmentation, called Segnet, U-net, and CNN-S into this problem. We also proposed a shortest path-based algorithm for finding the layer boundaries from the outputs of Segnet and U-net. We analysed their performance on public OCT image data set. The experimental results show that the Segnet combined with the proposed shortest path-based boundary finding algorithm outperforms other two networks.

This paper proposes a motion segmentation algorithm for layer decomposition of image sequences. The proposed algorithm segments an image into initial regions by using its color and texture and computes a motion model of each initial region. Each pixel assigns one of the motion represented by the models or a motion except them, which segments the image into the motion regions. The proposed algorithm is app]ied image sequences and the segmented motion is shown.

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.