• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.24-31
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• 2017

Face-based video retrieval has become an active and important branch of intelligent video analysis. Face profiling and matching is a fundamental step and is crucial to the effectiveness of video retrieval. Although many algorithms have been developed for processing static face images, their effectiveness in face-based video retrieval is still unknown, simply because videos have different resolutions, faces vary in scale, and different lighting conditions and angles are used. In this paper, we combined content-based and semantic-based image analysis techniques, and systematically evaluated four mainstream local features to represent face images in the video retrieval task: Harris operators, SIFT and SURF descriptors, and eigenfaces. Results of ten independent runs of 10-fold cross-validation on datasets consisting of TED (Technology Entertainment Design) talk videos showed the effectiveness of our approach, where the SIFT descriptors achieved an average F-score of 0.725 in video retrieval and thus were the most effective, while the SURF descriptors were computed in 0.3 seconds per image on average and were the most efficient in most cases.

• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2014.10a
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• pp.245-246
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• 2014

This paper suggests the navigation information display system which is based on augmented reality technology and especially focuses on image analysis technology. Navigator has to always confirm the information from marine electronic navigation devices and then they compare with the view of outside targets of the windows. During this 'head down' posture, they feel uncomfortable and sometimes it cause near-accidents such as collision or missing objects, because he or she cannot keep an eye on the front view of windows. Augmented reality can display both of information of virtual and real in a single display. Therefore we tried to adapt the AR technology to help navigators and have been studied and developed image pre-processing module as a previous research already. To analysis the outside view of the bridge window, we have extracted navigational information from the camera image by using image processing. This paper mainly describes about recognizing ship feature by haar-like feature and filtering region of interest area by AIS data, which are to improve accuracy of the image analysis.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.503-508
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• 2007

In order to understand information system post-adoption phenomena, the expectation-confirmation model (ECM) was proposed. Past studies based on the ECM focus on a referent centered on the target IS being studied. The effect of this reference, captured through confirmation, has been strongly shown. However, the saliency of two additional reference effects, captured through self-image congruity and regret, has not been explored. In order to fill this knowledge gap, this paper attempts to develop a research model that extends the ECM by incorporating self-image congruity and regret as well as perceived enjoyment. For this extension, we synthesize the extant literature on continued IS use, self-image congruity, and regret. The analysis results tell us that self-image congruity plays a key role in forming two post-adoption beliefs, perceived usefulness and perceived enjoyment. It is also found that the absolute effect of regret on continuance intention is larger than those of other antecedents identified in IS. Overall, this study preliminarily confirms the saliency of self-image congruity and regret in post-adoption phenomena. Our study results is likely to help the IS community systematically address unexplored effects of self-image congruity and regret.

• The Journal of Information Systems
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• v.32 no.4
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• pp.149-162
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• 2023

Purpose In the textile industry, fabric defects significantly impact product quality and consumer satisfaction. This research seeks to enhance defect detection by developing a transformer-based deep learning image segmentation model for learning high-dimensional image features, overcoming the limitations of traditional image classification methods. Design/methodology/approach This study utilizes the ZJU-Leaper dataset to develop a model for detecting defects in fabrics. The ZJU-Leaper dataset includes defects such as presses, stains, warps, and scratches across various fabric patterns. The dataset was built using the defect labeling and image files from ZJU-Leaper, and experiments were conducted with deep learning image segmentation models including Deeplabv3, SegformerB0, SegformerB1, and Dinov2. Findings The experimental results of this study indicate that the SegformerB1 model achieved the highest performance with an mIOU of 83.61% and a Pixel F1 Score of 81.84%. The SegformerB1 model excelled in sensitivity for detecting fabric defect areas compared to other models. Detailed analysis of its inferences showed accurate predictions of diverse defects, such as stains and fine scratches, within intricated fabric designs.

• Journal of Korean Academy of Nursing Administration
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• v.2 no.2
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• pp.17-42
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• 1996

Most human behaviors are based on self-perceptual image. Subjectivity in professional nursing image is shown in their opinions, beliefs, values, and attitudes of professional nursing and it helps to understand individual's behavior. This study was initiated to find the characteristics and patterns in subjectivity of hospital nurses' professional nursing image. The Data were collected from Apr. 20 to Aug. 22, 1996. The research method employed Q-methodology which is based on self-psychology and abductive logics. Analysis of Q-type obtained by QUANL pc program. The characteristics of professional nursing image was analyzed based on the typal array, extreme comments, and the subject's demographic information. The results revealed that there are three different types on the professional nursing image. The three types were named as follows : The first type, the Improvable, consisting of 6 subjects, preferentially perceived nursing is human behavior as life process, coordinating with other health personnel for the patients as nurses' important role. On the other hand, they are taking a little dissatisfied view of professional nursing image, which can be estimated to advance for the construction of the professional nursing image. The second type, the Self-conflicted, consisting of 13 subjects, who have the subjectivity of the image by focusing on external and environmental factors rather than developing positive individual nurses' image for their profession. They have very conflic-ting and self-degrading traits. The third type, the Affirmative, consisting of 10 subjects, who appreciate the essence of nursing, and that they highly perceived nurse' positive attitude, devotion, mature interrelationship and self-developing efforts etc. In conclusion, this study discovers three types on the professional nursing image and their relationship. By identifying the nature of three types, this study suggests that the results should be useful reinforcement tool in educating nursing students as well as in continuing education for hospital nurses.

• Journal of Korean Public Health Nursing
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• v.14 no.1
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• pp.80-99
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• 2000

This study was designed to find the characteristics and patterns in subjectivity of an attitude on the degree of importance of professional nursing for advancing nursing image. Q-methodology was used as a research design and the research procedures were as follows. Q-sampling has been derived from the literature review and interview. Its credibility and validity were also tested by nursing professors. Total of 34 statements were selected. P-sampling has been drawn and 32 samples were selected. Based on 9 point scale. the selected respondents rated their operant definition on the degree of importance of professional nursing for advancing nursing image. The results of above procedures were analyzed by PCQ program. The attitude about the degree of importance of professional nursing for advancing nursing image were analyzed based on the typical array. extreme comments. and the demographic information of study subjects. The results revealed that there were four types of attitude about the degree of importance of professional nursing for advancing nursing image. The four types were named as follows; 1) The first type. agree of problem-solving perspectives. was consisted of 8 subjects. They emphasized that the nurse should solve a patient's problem promptly and precisely for advancing nursing image. 2) The second type. agree of kindness perspectives. was consisted of 5 subjects. They insisted that the nurse should be kind to patient and his family for advancing nursing image. 3) The third type. agree of love and service perspectives. was consisted of 4 subjects. They emphasized nursing spirit based on love and service for advancing nursing image. 4) The fourth type. agree of professional knowledge perspectives. was consisted of 6 subjects. They emphasized that the nurse should construct of professional nursing knowledge for advancing nursing image. As a result. this study discovered three types of the degree of importance of professional nursing for advancing nursing image. By identifying the nature of each of four types. this study can be useful to develop efficient strategies for advancing nursing image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.1078-1099
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• 2021

Traditional image steganography hides secret information by embedding, which inevitably leaves modification traces and is easy to be detected by steganography analysis tools. Since coverless steganography can effectively resist steganalysis, it has become a hotspot in information hiding research recently. Most coverless image steganography (CIS) methods are based on mapping rules, which not only exposes the vulnerability to geometric attacks, but also are less secure due to the revelation of mapping rules. To address the above issues, we introduced camouflage images for steganography instead of directly sending stego-image, which further improves the security performance and information hiding ability of steganography scheme. In particular, based on the different sub-features of stego-image and potential camouflage images, we try to find a larger similarity between them so as to achieve the reversible steganography. Specifically, based on the existing CIS mapping algorithm, we first can establish the correlation between stego-image and secret information and then transmit the camouflage images, which are obtained by reversible sub-feature retrieval algorithm. The received camouflage image can be used to reverse retrieve the stego-image in a public image database. Finally, we can use the same mapping rules to restore secret information. Extensive experimental results demonstrate the better robustness and security of the proposed approach in comparison to state-of-art CIS methods, especially in the robustness of geometric attacks.

• Science of Emotion and Sensibility
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• v.20 no.1
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• pp.57-66
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• 2017

Emotional image quality optimization methodologies are investigated using technological image quality controls based on the eye tests of various image samples. The images are evaluated based on various contrast, lightness and saturation image quality metric tone curves. The order of importance to image quality enhancements is contrast, saturation and brightness. The slopes of emotional image qualities with respect to technical image quality metric changes are found to be composed of mathematical function modelling with nearly zero, intermediate and maximum slope regions in general, which can reflect well known log and saturated as well as conventional reverse U shape natures. Image quality improvements are analyzed not only with just single but also with multiple image quality metrics. To ease the unified image quality metric analysis and control, a new function is presented to utilize both the newly found and conventional emotional image quality behaviors. It is found that the overall image quality enhancement can be realized only in a few limited cases of multiple image quality metric controls. It is also found that the kinds of image quality enhancement methodologies are not strongly dependent on image contents (genre).