• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• Proceedings of the CALSEC Conference
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• 2001.08a
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• pp.625-629
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• 2001

A cyber shopping mall is a place where consumers acquire product information, and make purchase decisions in the cyber space. Even though it offers many advantages over traditional malls, there are still several limitations to do shopping in an existing cyber mall. One of these limitations is the absence of an efficient shopping aid to compare multiple items from multiple malls. Existing search systems usually support a keyword search with limited conditions. Consumers spend lots of their time to compare multiple alternatives from search results. In this paper, we propose an intelligent product search system. There are two main features in our system. The first one is a full support of comparison shopping with multiple perspectives based on commercial search engines. The second one is an enhancement to the shopping aid based on a new concept of Shopping AssistanT. Our system is implemented in Visual Basic and PERL, and experimental results show a satisfactory performance.

• Journal of the Ergonomics Society of Korea
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• v.30 no.2
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• pp.365-373
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• 2011

The existing GOMS model overestimates the performance time of mouse activities because it describes them in a serial sequence. However, parallel movements of eye and hand(eye-hand coordination) have been dominant in mouse activities and this eye-hand coordination is the main factor for the overestimation of performance time. In this study, therefore, the revised CGOMSL model was developed to implement eye-hand coordination to the mouse activity to overcome one of the limitations of GOMS model, the lack of capability for parallel processing. The suggested revised CGOMSL model for drag activity, as an example for one of mouse activities in this study, begins visual search processing before a hand movement but ends the visual search processing with the hand movement in the same time. The results show that the revised CGOMSL model made the prediction of human performance more accurately than the existing GOMS model. In other words, one of the limitations of GOMS model, the incapability of parallel processing, could be overcome with the revised CGOMSL model so that the performance time should be more accurately predicted.

• Korean Journal of Cognitive Science
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• v.25 no.3
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• pp.211-232
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• 2014

People search a certain object or a person so many time in a day. Besides the information about what the target is, perceptual information of the target can influence on the search process. In the current study, using an eye-tracker we aimed to examine whether the perceptual information of previewing target stimuli on the visual search process of the target and the task performance. Participants had to identify the previewing target stimulus presented in the middle of the screen, and then had to search the target among 8 items presented in a circle array, and had to decide whether the size of the target in the search display was same as that of the previewing stimulus. The experimental conditions were divided into 8 within-subject conditions by whether the search display was consisted of all the same size items or different size items (homogeneous search display vs. inhomogeneous search display), by the size of the preview target stimulus, and by the size of the target stimulus in the search display. Research hypothesis is that the size information of the previewing influence on the visual search process of the target and task performance when the items in the search display are in different sizes. In the results of behavioral data analysis, the reaction time showed the main effect of the search display, and the size of the target stimulus in the search display. and the interaction between the size consistency effect of target stimulus and the search display condition. In the results of analysis of eye-movement information, the Initial Saccade to Target Ratio measurement showed the interaction between the size consistency effect of target stimulus and the search display condition as the reaction time measurement did. That is, the size consistency effect of target stimulus only in the inhomogeneous search display condition indicated that participants searched the items in the same size as that of preview target stimulus. Post-hoc analyses revealed that the search and task performance in the inhomogeneous display condition were faster when the target size was consistent, but rather slower when the target size was inconsistent.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.327-336
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• 2004

For visual inspection system in real industrial environment, it is one of most important tasks to design fast and stable pattern matching algorithm. This paper presents an edge-based point correlation algorithm avoiding full search in visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties for applying to automated inspection system in factory environment. First of all, NGC algorithms need high time complexity and thus high performance hardware to satisfy real-time process. In addition, lighting condition in realistic factory environments if not stable and therefore intensity variation from uncontrolled lights gives many roubles for applying directly NGC as pattern matching algorithm in this paper, we propose an algorithm to solve these problems from using thinned and binarized edge data and skipping full point search with edge-map analysis. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the time complexity. Matching edges instead of using original gray-level pixel data overcomes NGC problems and pyramid of edges also provides fast and stable processing. All proposed methods are preyed from experiments using real images.

• Journal of information and communication convergence engineering
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• v.12 no.1
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• pp.53-59
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• 2014

Motion estimation is a core part of most video compression systems since it directly affects the output video quality and the encoding time. The full search (FS) technique gives the highest visual quality but has the problem of a significant computational load. To solve this problem, we present in this paper a modified median (MMED) operation and advanced search strategies for fast motion estimation. The proposed MMED operation includes a temporally co-located motion vector (MV) to select an appropriate initial candidate. Moreover, we introduce a search procedure that reduces the number of thresholds and simplifies the early termination conditions for the determination of a final MV. The experimental results show that the proposed approach achieves substantial speedup compared with the conventional methods including the motion vector field adaptive search technique (MVFAST) and predictive MVFAST (PMVFAST). The proposed algorithm also improves the PSNR values by increasing the correlation between the MVs, compared with the FS method.

• Genomics & Informatics
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• v.7 no.3
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• pp.178-180
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• 2009

A few bioinformatics tools have been used to find out conserved regions as probes. We have developed a system based on a heuristic method with web interfaces to find out conserved regions against microbial genomes. The system runs in real time by using relative entropy in limited narrow regions and detecting similar regions between pair regions with local alignment. The system could be useful to find out conserved regions as genome-wide scale.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1289-1301
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• 2015

Pedestrian detection (PD) is an essential task in various applications and sliding window-based methods utilizing HOG (Histogram of Oriented Gradients) or HOG-like descriptors have been shown to be very effective for accurate PD. However, due to exhaustive search across images, PD methods based on sliding window usually require heavy computational time. In this paper, we propose a real-time PD method for embedded visual surveillance with fixed backgrounds. The proposed PD method employs HOG descriptors as many PD methods does, but utilizes selective search so that it can save processing time significantly. The proposed selective search is guided by restricting searching to candidate regions extracted from Adaptive Gaussian Mixture Model (AGMM)-based background subtraction technique. Moreover, approximate computation of HOG descriptor and implementation in fixed-point arithmetic mode contributes to reduction of processing time further. Possible accuracy degradation due to approximate computation is compensated by applying an appropriate one among three offline trained SVM classifiers according to sizes of candidate regions. The experimental results show that the proposed PD method significantly improves processing speed without noticeable accuracy degradation compared to the original HOG-based PD and HOG with cascade SVM so that it is a suitable real-time PD implementation for embedded surveillance systems.

• Archives of design research
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• v.20 no.3 s.71
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• pp.203-214
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• 2007

This study proposes an information retrieval method reflecting the user's preferences based on the fuzzy set theory to develop information contents which support pedestrian's navigation. Firstly, the research evaluated subjects' preferences on commercial spaces set to a hypothetical destination. Also it surveyed the causal relationship between the visual characteristics and the emotional characteristics to propose methods of Navigation Knowledge Base (NKB). The NKB was composed of three elements; 1. the correlation model between emotional characteristics, 2. the causal relationship between visual characteristics and emotional characteristics, 3. the transformation model between visual characteristics and the physical characteristics. Secondly, this study classified the pedestrian's destination search into 4 types with his or her preferences and the time conditions limited during navigation. For each type it presented the Destination Search Algorithm (DSA). Finally, the research simulated the destination search in 4 navigation types using NKB and DSA and verified the availability of the information retrieval method reflecting pedestrian's preferences. In conclusion, the proposed information search method will be applied to reflect the user's preferences to develop information appliances.

• Journal of the Semiconductor & Display Technology
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• v.5 no.2 s.15
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• pp.27-31
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• 2006

As more and more digital images are made by various applications, image retrieval becomes a primary concern in technology of multimedia. This paper presents color based descriptor that uses information of color distribution in color images which is the most basic element for image search and performance of proposed visual feature is evaluated through the simulation. In designing the image search descriptor used color histogram, HSV, Daubechies 9/7 and 2 level wavelet decomposition provide better results than other parameters in terms of computational time and performances. Also histogram quadratic matrix outperforms the sum of absolute difference in similarity measurements, but spends more than 60 computational times.