• Title/Summary/Keyword: Segmentation Processing

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Efficient Sign Language Recognition and Classification Using African Buffalo Optimization Using Support Vector Machine System

  • Karthikeyan M. P.;Vu Cao Lam;Dac-Nhuong Le
    • International Journal of Computer Science & Network Security
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    • v.24 no.6
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    • pp.8-16
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    • 2024
  • Communication with the deaf has always been crucial. Deaf and hard-of-hearing persons can now express their thoughts and opinions to teachers through sign language, which has become a universal language and a very effective tool. This helps to improve their education. This facilitates and simplifies the referral procedure between them and the teachers. There are various bodily movements used in sign language, including those of arms, legs, and face. Pure expressiveness, proximity, and shared interests are examples of nonverbal physical communication that is distinct from gestures that convey a particular message. The meanings of gestures vary depending on your social or cultural background and are quite unique. Sign language prediction recognition is a highly popular and Research is ongoing in this area, and the SVM has shown value. Research in a number of fields where SVMs struggle has encouraged the development of numerous applications, such as SVM for enormous data sets, SVM for multi-classification, and SVM for unbalanced data sets.Without a precise diagnosis of the signs, right control measures cannot be applied when they are needed. One of the methods that is frequently utilized for the identification and categorization of sign languages is image processing. African Buffalo Optimization using Support Vector Machine (ABO+SVM) classification technology is used in this work to help identify and categorize peoples' sign languages. Segmentation by K-means clustering is used to first identify the sign region, after which color and texture features are extracted. The accuracy, sensitivity, Precision, specificity, and F1-score of the proposed system African Buffalo Optimization using Support Vector Machine (ABOSVM) are validated against the existing classifiers SVM, CNN, and PSO+ANN.

The Effect of Common Features on Consumer Preference for a No-Choice Option: The Moderating Role of Regulatory Focus (재몰유선택적정황하공동특성대우고객희호적영향(在没有选择的情况下共同特性对于顾客喜好的影响): 조절초점적조절작용(调节焦点的调节作用))

  • Park, Jong-Chul;Kim, Kyung-Jin
    • Journal of Global Scholars of Marketing Science
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    • v.20 no.1
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    • pp.89-97
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    • 2010
  • This study researches the effects of common features on a no-choice option with respect to regulatory focus theory. The primary interest is in three factors and their interrelationship: common features, no-choice option, and regulatory focus. Prior studies have compiled vast body of research in these areas. First, the "common features effect" has been observed bymany noted marketing researchers. Tversky (1972) proposed the seminal theory, the EBA model: elimination by aspect. According to this theory, consumers are prone to focus only on unique features during comparison processing, thereby dismissing any common features as redundant information. Recently, however, more provocative ideas have attacked the EBA model by asserting that common features really do affect consumer judgment. Chernev (1997) first reported that adding common features mitigates the choice gap because of the increasing perception of similarity among alternatives. Later, however, Chernev (2001) published a critically developed study against his prior perspective with the proposition that common features may be a cognitive load to consumers, and thus consumers are possible that they are prone to prefer the heuristic processing to the systematic processing. This tends to bring one question to the forefront: Do "common features" affect consumer choice? If so, what are the concrete effects? This study tries to answer the question with respect to the "no-choice" option and regulatory focus. Second, some researchers hold that the no-choice option is another best alternative of consumers, who are likely to avoid having to choose in the context of knotty trade-off settings or mental conflicts. Hope for the future also may increase the no-choice option in the context of optimism or the expectancy of a more satisfactory alternative appearing later. Other issues reported in this domain are time pressure, consumer confidence, and alternative numbers (Dhar and Nowlis 1999; Lin and Wu 2005; Zakay and Tsal 1993). This study casts the no-choice option in yet another perspective: the interactive effects between common features and regulatory focus. Third, "regulatory focus theory" is a very popular theme in recent marketing research. It suggests that consumers have two focal goals facing each other: promotion vs. prevention. A promotion focus deals with the concepts of hope, inspiration, achievement, or gain, whereas prevention focus involves duty, responsibility, safety, or loss-aversion. Thus, while consumers with a promotion focus tend to take risks for gain, the same does not hold true for a prevention focus. Regulatory focus theory predicts consumers' emotions, creativity, attitudes, memory, performance, and judgment, as documented in a vast field of marketing and psychology articles. The perspective of the current study in exploring consumer choice and common features is a somewhat creative viewpoint in the area of regulatory focus. These reviews inspire this study of the interaction possibility between regulatory focus and common features with a no-choice option. Specifically, adding common features rather than omitting them may increase the no-choice option ratio in the choice setting only to prevention-focused consumers, but vice versa to promotion-focused consumers. The reasoning is that when prevention-focused consumers come in contact with common features, they may perceive higher similarity among the alternatives. This conflict among similar options would increase the no-choice ratio. Promotion-focused consumers, however, are possible that they perceive common features as a cue of confirmation bias. And thus their confirmation processing would make their prior preference more robust, then the no-choice ratio may shrink. This logic is verified in two experiments. The first is a $2{\times}2$ between-subject design (whether common features or not X regulatory focus) using a digital cameras as the relevant stimulus-a product very familiar to young subjects. Specifically, the regulatory focus variable is median split through a measure of eleven items. Common features included zoom, weight, memory, and battery, whereas the other two attributes (pixel and price) were unique features. Results supported our hypothesis that adding common features enhanced the no-choice ratio only to prevention-focus consumers, not to those with a promotion focus. These results confirm our hypothesis - the interactive effects between a regulatory focus and the common features. Prior research had suggested that including common features had a effect on consumer choice, but this study shows that common features affect choice by consumer segmentation. The second experiment was used to replicate the results of the first experiment. This experimental study is equal to the prior except only two - priming manipulation and another stimulus. For the promotion focus condition, subjects had to write an essay using words such as profit, inspiration, pleasure, achievement, development, hedonic, change, pursuit, etc. For prevention, however, they had to use the words persistence, safety, protection, aversion, loss, responsibility, stability etc. The room for rent had common features (sunshine, facility, ventilation) and unique features (distance time and building state). These attributes implied various levels and valence for replication of the prior experiment. Our hypothesis was supported repeatedly in the results, and the interaction effects were significant between regulatory focus and common features. Thus, these studies showed the dual effects of common features on consumer choice for a no-choice option. Adding common features may enhance or mitigate no-choice, contradictory as it may sound. Under a prevention focus, adding common features is likely to enhance the no-choice ratio because of increasing mental conflict; under the promotion focus, it is prone to shrink the ratio perhaps because of a "confirmation bias." The research has practical and theoretical implications for marketers, who may need to consider common features carefully in a practical display context according to consumer segmentation (i.e., promotion vs. prevention focus.) Theoretically, the results suggest some meaningful moderator variable between common features and no-choice in that the effect on no-choice option is partly dependent on a regulatory focus. This variable corresponds not only to a chronic perspective but also a situational perspective in our hypothesis domain. Finally, in light of some shortcomings in the research, such as overlooked attribute importance, low ratio of no-choice, or the external validity issue, we hope it influences future studies to explore the little-known world of the "no-choice option."

Study on Structure Visual Inspection Technology using Drones and Image Analysis Techniques (드론과 이미지 분석기법을 활용한 구조물 외관점검 기술 연구)

  • Kim, Jong-Woo;Jung, Young-Woo;Rhim, Hong-Chul
    • Journal of the Korea Institute of Building Construction
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    • v.17 no.6
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    • pp.545-557
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    • 2017
  • The study is about the efficient alternative to concrete surface in the field of visual inspection technology for deteriorated infrastructure. By combining industrial drones and deep learning based image analysis techniques with traditional visual inspection and research, we tried to reduce manpowers, time requirements and costs, and to overcome the height and dome structures. On board device mounted on drones is consisting of a high resolution camera for detecting cracks of more than 0.3 mm, a lidar sensor and a embeded image processor module. It was mounted on an industrial drones, took sample images of damage from the site specimen through automatic flight navigation. In addition, the damege parts of the site specimen was used to measure not only the width and length of cracks but white rust also, and tried up compare them with the final image analysis detected results. Using the image analysis techniques, the damages of 54ea sample images were analyzed by the segmentation - feature extraction - decision making process, and extracted the analysis parameters using supervised mode of the deep learning platform. The image analysis of newly added non-supervised 60ea image samples was performed based on the extracted parameters. The result presented in 90.5 % of the damage detection rate.

Objective and Quantitative Evaluation of Image Quality Using Fuzzy Integral: Phantom Study (퍼지적분을 이용한 영상품질의 객관적이고 정량적 평가: 팬톰 연구)

  • Kim, Sung-Hyun;Suh, Tae-Suk;Choe, Bo-Young;Lee, Hyoung-Koo
    • Progress in Medical Physics
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    • v.19 no.4
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    • pp.201-208
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    • 2008
  • Physical evaluations provide the basis for an objective and quantitative analysis of the image quality. Nonetheless, there are limitations in using physical evaluations to judge the utility of the image quality if the observer's subjectivity plays a key role despite its imprecise and variable nature. This study proposes a new method for objective and quantitative evaluation of image quality to compensate for the demerits of both physical and subjective image quality and combine the merits of them. The images of chest phantom were acquired from four digital radiography systems on clinic sites. The physical image quality was derived from an image analysis algorithm in terms of the contrast-to-noise ratio (CNR) of the low-contrast objects in three regions (lung, heart, and diaphragm) of a digital chest phantom radiograph. For image analysis, various image processing techniques were used such as segmentation, and registration, etc. The subjective image quality was assessed by the ability of the human observer to detect low-contrast objects. Fuzzy integral was used to integrate them. The findings of this study showed that the physical evaluation did not agree with the subjective evaluation. The system with the better performance in physical measurement showed the worse result in subjective evaluation compared to the other system. The proposed protocol is an integral evaluation method of image quality, which includes the properties of both physical and subjective measurement. It may be used as a useful tool in image evaluation of various modalities.

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Automatic Segmentation of Trabecular Bone Based on Sphere Fitting for Micro-CT Bone Analysis (마이크로-CT 뼈 영상 분석을 위한 구 정합 기반 해면뼈의 자동 분할)

  • Kang, Sun Kyung;Kim, Young Un;Jung, Sung Tae
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.8
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    • pp.329-334
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    • 2014
  • In this study, a new method that automatically segments trabecular bone for its morphological analysis using micro-computed tomography imaging was proposed. In the proposed method, the bone region was extracted using a threshold value, and the outer boundary of the bone was detected. The sphere of maximum size with the corresponding voxel as the center was obtained by applying the sphere-fitting method to each voxel of the bone region. If this sphere includes the outer boundary of the bone, the voxels included in the sphere are classified as cortical bone; otherwise, they are classified as trabecular bone. The proposed method was applied to images of the distal femurs of 15 mice, and comparative experiments, with results manually divided by a person, were performed. Four morphological parameters-BV/TV, Tb.Th, Tb.Sp, and Tb.N-for the segmented trabecular bone were measured. The results were compared by regression analysis and the Bland-Altman method; BV/TV, Tb.Th, Tb.Sp, and Tb.N were all in the credible range. In addition, not only can the sphere-fitting method be simply implemented, but trabecular bone can also be divided precisely by using the three-dimensional information.

Integration of Component Image Information and Design Information by Graph to Support Product Design Information Reuse (제품 설계 정보 재사용을 위한 그래프 기반의 부품 영상 정보와 설계 정보의 병합)

  • Lee, Hyung-Jae;Yang, Hyung-Jeong;Kim, Kyoung-Yun;Kim, Soo-Hyung;Kim, Sun-Hee
    • The KIPS Transactions:PartD
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    • v.13D no.7 s.110
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    • pp.1017-1026
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    • 2006
  • Recently, distributed collaborative development environment has been recognized an alternative environment for product development in which multidisciplinary participants are naturally involving. Reuse of Product design information has long been recognized as one of core requirements for efficient product development. This paper addresses an image-based retrieval system to support product design information reuse. In the system, product images obtained from multi-modal devices are utilized to reuse design information. The proposed system conducts the segmentation of a product image by using a labeling method and generates an attributed relational graph (ARG) that represents properties of segmented regions and their relationships. The generated ARG is extended by integrating corresponding part/assembly information. In this manner, the reuse of assembly design information using a product image has been realized. The main advantages of the presented system are following. First, the system is not dependent to specific design tools, because it utilizes multimedia images that can be obtained easily from peripheral devices. Second ratio-based features extracted from images enable image retrievals that contain various sizes of parts. Third, the system has shown outstanding search performance, because we applied various information of segmented part regions and their relationships between parts.

Implementation of a TCP/IP Offload Engine Using High Performance Lightweight TCP/IP (고성능 경량 TCP/IP를 이용한 소프트웨어 기반 TCP/IP 오프로드 엔진 구현)

  • Jun, Yong-Tae;Chung, Sang-Hwa;Yoon, In-Su
    • Journal of KIISE:Computing Practices and Letters
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    • v.14 no.4
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    • pp.369-377
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    • 2008
  • Today, Ethernet technology is rapidly developing to have a bandwidth of 10Gbps beyond 1Gbps. In such high-speed networks, the existing method that host CPU processes TCP/IP in the operating system causes numerous overheads. As a result of the overheads, user applications cannot get the enough computing power from the host CPU. To solve this problem, the TCP/IP Offload Engine(TOE) technology was emerged. TOE is a specialized NIC which processes the TCP/IP instead of the host CPU. In this paper, we implemented a high-performance, lightweight TCP/IP(HL-TCP) for the TOE and applied it to an embedded system. The HL-TCP supports existing fundamental TCP/IP functions; flow control, congestion control, retransmission, delayed ACK, processing out-of-order packets. And it was implemented to utilize Ethernet MAC's hardware features such as TCP segmentation offload(TSO), checksum offload(CSO) and interrupt coalescing. Also we eliminated the copy overhead from the host memory to the NIC memory when sending data and we implemented an efficient DMA mechanism for the TCP retransmission. The TOE using the HL-TCP has the CPU utilization of less than 6% and the bandwidth of 453Mbps.

Extracting Flooded Areas in Southeast Asia Using SegNet and U-Net (SegNet과 U-Net을 활용한 동남아시아 지역 홍수탐지)

  • Kim, Junwoo;Jeon, Hyungyun;Kim, Duk-jin
    • Korean Journal of Remote Sensing
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    • v.36 no.5_3
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    • pp.1095-1107
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    • 2020
  • Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.

Negative Transition of Smart Device Utility: Empirical Study on IT-enabled Work Flexibility, After Hours Work Connectivity, and Work-Life Conflict (스마트기기 효용의 부정적 전이: IT기반 업무 유연성, 근무시간 외 업무 연결성, 일-삶 갈등에 관한 실증 연구)

  • Kim, Hyung-Jin;Lee, Yoon-ji;Lee, Ho-Geun
    • Informatization Policy
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    • v.26 no.4
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    • pp.36-61
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    • 2019
  • While smart devices can have a positive impact on work efficiency and productivity by reducing time-space constraints and enabling rapid processing of tasks, side effects can arise from the imbalances between work and personal life. In recent years, as smart devices are increasingly used in work environments, it is more necessary than ever to understand the related phenomenon, find the cause of negative effects, and search for appropriate solutions. This study has developed and verified a theoretical model that shows how the technical characteristics known as the utility of smart devices are converted into negative results such as work-life conflict. As a result of analyzing the collected data from the employees, our study provides significant implications for the researchers, as well as the practitioners and policy makers, regarding various relationships among IT-enabled work flexibility, after-hours work connectivity and work-life conflict, and the new knowledge about the important role of segmentation supplies from the organization.

A Histogram Matching Scheme for Color Pattern Classification (컬러패턴분류를 위한 히스토그램 매칭기법)

  • Park, Young-Min;Yoon, Young-Woo
    • The KIPS Transactions:PartB
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    • v.13B no.7 s.110
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    • pp.689-698
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    • 2006
  • Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. Color image consists of various color patterns. And most pattern recognition methods use the information of color which has been trained and extract the feature of the color. This thesis extracts adaptively specific color feature from images with several limited colors. Because the number of the color patterns is limited, the distribution of the color in the image is similar. But, when there are some noises and distortions in the image, its distribution can be various. Therefore we cannot extract specific color regions in the standard image that is well expressed in special color patterns to extract, and special color regions of the image to test. We suggest new method to reduce the error of recognition by extracting the specific color feature adaptively for images with the low distortion, and six test images with some degree of noises and distortion. We consequently found that proposed method shouws more accurate results than those of statistical pattern recognition.