• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.37-47
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• 2020

Today, the volume of international trade by airplanes and ships is rapidly increasing, and the volume of trade over land is expected to increase as inter-Korean relations change. In customs processes, humans inspect using the naked eye; therefore, computer vision technology can be used to assist customs inspectors responsible for X-ray security screening. In particular, because of recent advances in deep learning technology, algorithms for image understanding and object detection performance are improving, and studies on their application to X-ray screening have been published. This manuscript describes trends in artificial intelligence X-ray image-reading technology to detect prohibited items. X-ray inspection AI technology is similar to the Sphinx, which was the guardian of the pyramids in ancient Egyptian mythology.

• Journal of the Ergonomics Society of Korea
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• v.32 no.2
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• pp.207-215
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• 2013

Objective: This study aimed to investigate important human factors that should be considered when developing ultra-high definition TVs by reviewing measurement methods and main characteristics of ultra-high definition displays. Background: Although much attention has been paid to high-definition displays, there have been few studies for systematically evaluating human factors. Method: In order to determine human factors to be considered in developing human-friendly displays, we reviewed subjective and objective measurement methods to figure out the current limitations and establish a guideline for developing human-centered ultra-high definition TVs. In doing so, pros and cons of both subjective and objective measurement methods for assessing humans factors were discussed and specific aspects of ultra-high definition displays were also investigated in the literature. Results: Hazardous effects such as visually-induced motion sickness, visual fatigue, and mental fatigue in the brain caused by undesirable TV viewing are induced by not only temporal decay of visual function but also cognitive load in processing sophisticated external information. There has been a growing evidence that individual differences in visual and cognitive ability to process external information can make contrary responses after exposing to the same viewing situation. A wide vision, ultra-high definition TVs provide, can has positive and negative influences on viewers depending on their individual characteristics. Conclusion: Integrated measurement methods capable of considering individual differences in human visual system are required to clearly determine potential effects of super-high vision displays with a wide view on humans. All of brainwaves, autonomic responses, eye functions, and psychological responses should be simultaneously examined and correlated. Application: The results obtained in this review are expected to be a guideline for determining optimized viewing factors of ultra-high definition displays and accelerating successful penetration of the next-generation displays into our daily life.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.1
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• pp.94-98
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• 2015

Recently, with the rapid advancement in information and computer vision technology, a CCTV system using object recognition and tracking has been studied in a variety of fields. However, it is difficult to recognize a precise object outdoors due to varying pixel values by moving background elements such as shadows, lighting change, and moving elements of the scene. In order to adapt the background outdoors, this paper presents to analyze a variety of background models and proposed background update algorithms based on the weight factor. The experimental results show that the accuracy of object detection is maintained, and the number of misrecognized objects are reduced compared to previous study by using the proposed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.81-89
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• 2022

This study aims to analyze the images of Instagram posts and to draw implcations regarding the exhibition of . This study collects and crawl 24,295 images from Instagram posts as a dataset. We use the Google Cloud Vision API for labeling the images and a total of 212,567 clusters of labels are finally classified into 9 categories using Word2Vec. The categories of museum spaces, photo zone, architecture category are dominant along with people category. In conclusion, visitors curate their experiences and memories of physical places and spaces while they are experiencing with the exhibition. This result reproves the results of previous studies which emphasize a sense of social presence and place making. The convergent approach of art management and art technology used in this study help museum professionals have an insight on big data based visitor research on a practical level.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• International Journal of Knowledge Content Development & Technology
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• v.10 no.2
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• pp.51-69
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• 2020

This paper describes global research in the field of augmented reality (22078) as indexed in Scopus database during 1992-2019, using a series of bibliometric indicators. The augmented reality (AR) research registered high 54.23% growth, averaged citation impact of 8.90 citations per paper. Nearly 1% of global output in the subject (226 papers) registered high-end citations (100+) per paper. The top 15 countries accounted for 87.05% of global publications output in the subject. The USA is in leadership position for its highest publications productivity (19.25% global share). The U.K. leads the world on relative citation index (2.05). International collaboration has been a major driver of AR research pursuits; between 11.89% and 44.04% of national share of top 15 countries in AR research appeared as international collaborative publications. AR research productivity by application types was the largest across sectors, such as education, industry and medical. Computer science has emerged as the most popular areas in AR research pursuits. Technical University of Munich, Germany and Osaka University, Japan have been the most productive organizations and Nara Institute of S&T, Japan (66.55 and 7.48) and Imperial College, London, U.K. (57.14 and 6.42) have been the most impactful organizations. M. Billinghurst and N. Navab have been the most productive authors and S. Feiner and B. MacIntyre have been the most impactful authors. IEEE Transactions on Visualization & Computer Graphics, Multimedia Tools & Applications and Virtual Reality topped the list of most productive journals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• ETRI Journal
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• v.35 no.6
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.