Segmentation Methods for Hand Written Character Recognition

Namrata Dave 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.4

Hand written Character Recognition is area of research since many years. Automation of existing manual system is need of most industries as well as government areas. Recognition of hand written characters is a demand for many fields. In this paper we have discussed our approach for hand written character segmentation. This paper discusses various methodologies to segment a text based image at various levels of segmentation. This paper serves as a guide for people working on the text based image segmentation area of Computer Vision. First, the need for segmentation is justified in the context of text based information retrieval. Then, the various factors affecting the segmentation process are discussed. Followed by the levels of text segmentation are explored. Also, the available techniques with their advantages and weaknesses are reviewed, along with directions for quick referral are suggested. At last, we have given our approach to text segmentation in brief.

The Role of Market Segmentation and Fences under the Revenue Management Settings

김상원 한국항공경영학회 2009 한국항공경영학회지 Vol.7 No.2

Market segmentation is one of the key strategic elements in revenue management (RM). Market segments should be kept separate to prevent demand leakage and the associated revenue loss. Tools to restrict customer migration (demand leakage) across segments are referred to as fences. However, most fences are not perfect and allow some degree of demand leakage. This paper represents the characteristics of market segmentation and fences under general revenue management settings. We present a general picture of market segmentation and fences in the world of revenue management. We develop mathematical models for the perfect fence and the imperfect fence. We propose cost functions with respect to implementing fences, and establish the connection between the costs and revenue gain created from market segmentation. We also discuss segmentation process, maintaining of segmentation and the implementation issues of fencing. Market segmentation is one of the key strategic elements in revenue management (RM). Market segments should be kept separate to prevent demand leakage and the associated revenue loss. Tools to restrict customer migration (demand leakage) across segments are referred to as fences. However, most fences are not perfect and allow some degree of demand leakage. This paper represents the characteristics of market segmentation and fences under general revenue management settings. We present a general picture of market segmentation and fences in the world of revenue management. We develop mathematical models for the perfect fence and the imperfect fence. We propose cost functions with respect to implementing fences, and establish the connection between the costs and revenue gain created from market segmentation. We also discuss segmentation process, maintaining of segmentation and the implementation issues of fencing.

식재료 인식을 위한 연속된 이미지에 적용된 시간차학습 기반의 가중치 객체 영역 검출

민현정 제어·로봇·시스템학회 2023 제어·로봇·시스템학회 논문지 Vol.29 No.12

This paper presents a TD (Temporal difference) based weighted instance segmentation algorithm for consecutive images. The motivation behind this study is to enhance the segmentation capabilities of service robots, specifically those involved in restaurant cooking assistance. The autonomy of robots heavily relies on visual information through their sensors, and deep neural networks have shown promise in object segmentation. The proposed method employs a weighted segmentation method based on combined probabilities and segmentation history across consecutive images. It accumulates segmentation results in each frame and uses them in subsequent segmentation to reduce segmentation errors. The temporal difference method is based upon a probability map derived from instance segmentation, specifically the mask region-based convolutional neural network (Mask R-CNN) method. The experimental results focus on the segmentation of raw chicken parts for cooking materials, comparing the proposed method with instance segmentation. The experiments demonstrated that 29% of the images exhibited improved segmentation of target objects compared with the existing methods.

Research on Image Segmentation Algorithm and Performance of Power Insulator Based on Adaptive Region Growing

Liu Xingmou,Tian Hao,Wang Yan,Jiang Fan,Zhang Chenyang 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.6

With the widespread application of power inspections, the problem of insulator segmentation in complex environments has become a current challenge. An insulator image segmentation method based on adaptive region growing and the adaptive Otsu algorithm is proposed. The 8 neighborhood pixels are used for region growth, and the segmentation results are obtained through morphological processing. Finally, the original segmented image, dynamic threshold segmentation, global threshold segmentation, and adaptive region growth are quantitatively analyzed. For the result of natural lighting image segmentation, the accuracy of adaptive region growth segmentation is improved by 14.23% for the original segmentation. For the results of infrared image segmentation, the accuracy of adaptive region growing segmentation is improved by 8.13% compared with the original segmentation. Experimental results show that adaptive region growth threshold segmentation can extract contour information more completely, which has certain advantages compared with traditional threshold segmentation. It provides an important basis for the study of insulator fault diagnosis and infrared insulator temperature fi eld feature extraction.

ESRGAN과 Semantic Soft Segmentation을 이용한 객체 분할

윤동식,곽노윤 한국사물인터넷학회 2023 한국사물인터넷학회 논문지 Vol.9 No.1

This paper is related to object segmentation using ESRGAN(Enhanced Super Resolution GAN) and SSS(Semantic Soft Segmentation). The segmentation performance of the object segmentation method using Mask R-CNN and SSS proposed by the research team in this paper is generally good, but the segmentation performance is poor when the size of the objects is relatively small. This paper is to solve these problems. The proposed method aims to improve segmentation performance of small objects by performing super-resolution through ESRGAN and then performing SSS when the size of an object detected through Mask R-CNN is below a certain threshold. According to the proposed method, it was confirmed that the segmentation characteristics of small-sized objects can be improved more effectively than the previous method. 본 논문은 ESRGAN(Enhanced Super Resolution GAN)과 SSS(Semantic Soft Segmentation)을 이용한객체 분할에 관한 것이다. 본 논문의 연구진이 앞서 제안한 Mask R-CNN과 SSS를 이용한 객체 분할 방법의 분할 성능은 전반적으로 양호하지만 객체의 크기가 상대적으로 작은 경우 분할 성능이 저조해지는 문제점이 있었다. 본 논문은이러한 문제점을 해소하기 위한 것이다. 제안된 방법은 Mask R-CNN을 통해 검출된 객체의 크기가 일정 기준치 이하인 경우, ESRGAN을 통해 초해상화를 수행한 후, SSS을 수행함으로써 소형 객체의 분할 성능을 개선하고자 한다. 제안된 방법에 따르면, 기존의 방법에 비해 크기가 작은 객체의 분할 특성을 좀 더 효과적으로 개선할 수 있음을 확인할수 있었다.

Scalable joint segmentation and registration framework for infant brain images

Dong, Pei,Wang, Li,Lin, Weili,Shen, Dinggang,Wu, Guorong Elsevier 2017 Neurocomputing Vol.229 No.-

<P><B>Abstract</B></P> <P>The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We developed an efficient approach to deal with the tissue segmentation and registration for the infant brain MR images. </LI> <LI> Our proposed framework is scalable to various registration tasks in early brain development studies. </LI> <LI> Promising results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old. </LI> <LI> The proposed technique can be very useful for many ongoing early brain development studies. </LI> </UL> </P>

동질도 평가를 통한 실버세대 세분군 분류 및 평가

배재호 한국유통과학회 2010 유통과학연구 Vol.8 No.3

As the population, buying power, and intensity of self-expression of the elderly generation increase, its importance as a market segment is also growing. Therefore, the mass marketing strategy for the elderly generation must be changed to a micro-marketing strategy based on the results of sub-segmentation that suitably captures the characteristics of this generation. Furthermore, as a customer access strategy is decided by sub-segmentation, proper segmentation is one of the key success factors for micro-marketing. Segments or sub-segments are different from sectors, because segmentation or sub-segmentation for micro-marketing is based on the homogeneity of customer needs. Theoretically, complete segmentation would reveal a single voice. However, it is impossible to achieve complete segmentation because of economic factors, factors that affect effectiveness, etc. To obtain a single voice from a segment, we sometimes need to divide it into many individual cases. In such a case, there would be a many segments to deal with. On the other hand, to maximize market access performance, fewer segments are preferred. In this paper, we use the term "sub-segmentation" instead of "segmentation," because we divide a specific segment into more detailed segments. To sub-segment the elderly generation, this paper takes their lifestyles and life stages into consideration. In order to reflect these aspects, various surveys and several rounds of expert interviews and focused group interviews (FGIs) were performed. Using the results of these qualitative surveys, we can define six sub-segments of the elderly generation. This paper uses five rules to divide the elderly generation. The five rules are (1) mutually exclusive and collectively exhaustive (MECE) sub-segmentation, (2) important life stages, (3) notable lifestyles, (4) minimum number of and easy classifiable sub-segments, and (5) significant difference in voices among the sub-segments. The most critical point for dividing the elderly market is whether children are married. The other points are source of income, gender, and occupation. In this paper, the elderly market is divided into six sub-segments. As mentioned, the number of sub-segments is a very key point for a successful marketing approach. Too many sub-segments would lead to narrow substantiality or lack of actionability. On the other hand, too few sub-segments would have no effects. Therefore, the creation of the optimum number of sub-segments is a critical problem faced by marketers. This paper presents a method of evaluating the fitness of sub-segments that was deduced from the preceding surveys. The presented method uses the degree of homogeneity (DoH) to measure the adequacy of sub-segments. This measure uses quantitative survey questions to calculate adequacy. The ratio of significantly homogeneous questions to the total numbers of survey questions indicates the DoH. A significantly homogeneous question is defined as a question in which one case is selected significantly more often than others. To show whether a case is selected significantly more often than others, we use a hypothesis test. In this case, the null hypothesis (H0) would be that there is no significant difference between the selection of one case and that of the others. Thus, the total number of significantly homogeneous questions is the total number of cases in which the null hypothesis is rejected. To calculate the DoH, we conducted a quantitative survey (total sample size was 400, 60 questions, 4~5 cases for each question). The sample size of the first sub-segment-has no unmarried offspring and earns a living independently-is 113. The sample size of the second sub-segment-has no unmarried offspring and is economically supported by its offspring-is 57. The sample size of the third sub-segment-has unmarried offspring and is employed and male-is 70. The sample size of the fourth sub-segment-has unmarried offspring and is not employed and male-is 45. The s...

An Applied Research on Improved Watershed Algorithm in Medical Image Segmentation

BenZhai Hai,RuiYun Xie,PeiYan Yuan 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.11

The image segmentation technology is of great significance to the target identification. The watershed segmentation algorithm has wide application in image segmentation. The traditional watershed segmentation often causes the problems of over segmentation and noise sensitivity. Therefore, a medical image segmentation algorithm is proposed based on K-means clustering algorithm and improved watershed algorithm. First, K - means clustering algorithm is used for initial segmentation, and then the concept of similarity is put forward to improve the original watershed algorithm. Finally, the adjacent tiles of the initial segmentation is merged. The magnetic resonance image is regarded as the segmentation object. The experimental result shows that the proposed algorithm effectively solves the problem of the over-segmentation of traditional watershed algorithm, and achieves a satisfactory effect for the image segmentation.

Target Seg : A GUI for Image Segmentation using Morphogical Watershed and Graph cut Techniques

Anuradha.S.G,K.Karibasappa,B.Eswar Reddy 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.3

The aim of this paper is to develop an efficient and a powerful Matlab based graphical user interface to address the problem of image segmentation. We propose two approaches for segmenting images: An automatic marker controlled watershed segmentation for segmenting an entire image or a scene and a semiautomatic graph cut based segmentation using fixation points. Automatic Watershed segmentation with a Sobel edge detector is used to detect the gradient of an input image resulting in an image less sensitive to noise. To deal with the usual problem of over segmentation using watershed, marker controlled watershed transformation is applied further for segmenting an image. Fixation based graph cut segmentation allows the user to analyze the input image displayed on the screen and specify some hard constraints indicating the object of interest or target object by using the mouse interaction. Experiments are done on the publically available dataset and the results of the supervised evaluation methods are observed to be satisfactory and are demonstrated along with the manually segmented reference image or a ground truth image obtained from segmentation evaluation database

Inter-observer and intra-observer reliability between manual segmentation and semi-automated segmentation for carotid vessel wall volume measurements on three-dimensional ultrasonography

Chun Wai Chan,Sze Chai Christy Chow,Man Hei Kwok,Ka Ching Tiffany Ngan,Tsun Hei Or,Simon Takadiyi Gunda,Michael Ying 대한초음파의학회 2023 ULTRASONOGRAPHY Vol.42 No.2

Purpose: Carotid vessel wall volume (VWV) measurement on three-dimensional ultrasonography (3DUS) outperforms conventional two-dimensional ultrasonography for carotid atherosclerosis evaluation. Although time-saving semi-automated algorithms have been introduced, their clinical availability remains limited due to a lack of validation, particularly an extensive reliability analysis. This study compared inter-observer and intra-observer reliability between manual segmentation and semi-automated segmentation for carotid VWV measurements on 3DUS. Methods: Thirty-one 3DUS volume datasets were prospectively acquired from 20 healthy subjects, aged >18 years, without previous stroke, transient ischemic attack, or cardiovascular disease. Five observers segmented all volume datasets both manually and semi-automatically. The process was repeated five times. Reliability was expressed by the intraclass correlation coefficient, supplemented by the coefficient of variation. Results: Carotid VWV measurements using the common carotid artery (CCA) were more reliable than those using the internal carotid artery (ICA) or external carotid artery (ECA) for both manual and semiautomated segmentation (manual segmentation, CCA: inter-observer, 0.935; intra-observer, 0.934 to 0.966; ICA: inter-observer, 0.784; intra-observer, 0.756 to 0.878; ECA: inter-observer, 0.732; intraobserver, 0.919 to 0.962; semi-automated segmentation, CCA: inter-observer, 0.986; intra-observer, 0.954 to 0.993; ICA: inter-observer, 0.977; intra-observer, 0.958 to 0.978; ECA: inter-observer, 0.966; intra-observer, 0.884 to 0.937). Total carotid VWV measurements by manual (inter-observer, 0.922; intra-observer, 0.927 to 0.961) and semi-automated segmentation (inter-observer, 0.987; intra-observer, 0.968 to 0.989) were highly reliable. Semi-automated segmentation showed higher reliability than manual segmentation for both individual and total carotid VWV measurements. Conclusion: 3DUS carotid VWV measurements of the CCA are more reliable than measurements of the ICA and ECA. Total carotid VWV measurements are highly reliable. Semi-automated segmentation has higher reliability than manual segmentation.