• Title/Summary/Keyword: SIFT

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Quality Assessment of Images Projected Using Multiple Projectors

  • Kakli, Muhammad Umer;Qureshi, Hassaan Saadat;Khan, Muhammad Murtaza;Hafiz, Rehan;Cho, Yongju;Park, Unsang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.6
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    • pp.2230-2250
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    • 2015
  • Multiple projectors with partially overlapping regions can be used to project a seamless image on a large projection surface. With the advent of high-resolution photography, such systems are gaining popularity. Experts set up such projection systems by subjectively identifying the types of errors induced by the system in the projected images and rectifying them by optimizing (correcting) the parameters associated with the system. This requires substantial time and effort, thus making it difficult to set up such systems. Moreover, comparing the performance of different multi-projector display (MPD) systems becomes difficult because of the subjective nature of evaluation. In this work, we present a framework to quantitatively determine the quality of an MPD system and any image projected using such a system. We have divided the quality assessment into geometric and photometric qualities. For geometric quality assessment, we use Feature Similarity Index (FSIM) and distance-based Scale Invariant Feature Transform (SIFT). For photometric quality assessment, we propose to use a measure incorporating Spectral Angle Mapper (SAM), Intensity Magnitude Ratio (IMR) and Perceptual Color Difference (ΔE). We have tested the proposed framework and demonstrated that it provides an acceptable method for both quantitative evaluation of MPD systems and estimation of the perceptual quality of any image projected by them.

A Hybrid Feature Selection Method using Univariate Analysis and LVF Algorithm (단변량 분석과 LVF 알고리즘을 결합한 하이브리드 속성선정 방법)

  • Lee, Jae-Sik;Jeong, Mi-Kyoung
    • Journal of Intelligence and Information Systems
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    • v.14 no.4
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    • pp.179-200
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    • 2008
  • We develop a feature selection method that can improve both the efficiency and the effectiveness of classification technique. In this research, we employ case-based reasoning as a classification technique. Basically, this research integrates the two existing feature selection methods, i.e., the univariate analysis and the LVF algorithm. First, we sift some predictive features from the whole set of features using the univariate analysis. Then, we generate all possible subsets of features from these predictive features and measure the inconsistency rate of each subset using the LVF algorithm. Finally, the subset having the lowest inconsistency rate is selected as the best subset of features. We measure the performances of our feature selection method using the data obtained from UCI Machine Learning Repository, and compare them with those of existing methods. The number of selected features and the accuracy of our feature selection method are so satisfactory that the improvements both in efficiency and effectiveness are achieved.

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A Hybrid Proposed Framework for Object Detection and Classification

  • Aamir, Muhammad;Pu, Yi-Fei;Rahman, Ziaur;Abro, Waheed Ahmed;Naeem, Hamad;Ullah, Farhan;Badr, Aymen Mudheher
    • Journal of Information Processing Systems
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    • v.14 no.5
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    • pp.1176-1194
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    • 2018
  • The object classification using the images' contents is a big challenge in computer vision. The superpixels' information can be used to detect and classify objects in an image based on locations. In this paper, we proposed a methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW). It calculates the initial positions of each segment of an image using superpixels and then ranks it according to the region score. Further, this information is used to extract local and global features using a hybrid approach of Scale Invariant Feature Transform (SIFT) and GIST, respectively. To enhance the classification accuracy, the feature fusion technique is applied to combine local and global features vectors through weight parameter. The support vector machine classifier is a supervised algorithm is used for classification in order to analyze the proposed methodology. The Pascal Visual Object Classes Challenge 2007 (VOC2007) dataset is used in the experiment to test the results. The proposed approach gave the results in high-quality class for independent objects' locations with a mean average best overlap (MABO) of 0.833 at 1,500 locations resulting in a better detection rate. The results are compared with previous approaches and it is proved that it gave the better classification results for the non-rigid classes.

Tooth selectivity on venus clam (Gomphina melanaegis) dredge (민들조개 (Gomphina melanaegis) 형망의 갈퀴에 의한 어획선택성)

  • 박해훈;김승환
    • Journal of the Korean Society of Fisheries and Ocean Technology
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    • v.36 no.4
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    • pp.267-273
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    • 2000
  • The tooth selectivity of the dredge for catching venus clam (Gomphina melanaegis) was described in which the teeth penetrated the bottom and lifted the shell into the bag. Some factors affecting the selection action of the teeth of the dredge were analyzed related to shell length and shell height. The retention probability of venus clam not sifting through the gaps between the teeth was calculated for various shell lengths and was fitted to two parameter logistic selection curve. The formula obtained is as follows: $P= \frac{1}{1+exp[8.24](\frac{d}{L_1}-0.649)}$, where d is distance between teeth and $L_1$ is a shell length. For biological minimum size(25mm) of venus clam to be catchability 50% the distance between teeth was estimated 16.2mm from the logistic curve. Therefore it is desirable to extend that current spacing between teeth from 12mm to 16mm for the venus clam dredge. That space increasing enables fuel oil of vessel to drag a dredge to be reduced and also man power to sift through sifter smaller venus clams on boards to be reduced.

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Image Retrieval Method Based on IPDSH and SRIP

  • Zhang, Xu;Guo, Baolong;Yan, Yunyi;Sun, Wei;Yi, Meng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.5
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    • pp.1676-1689
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    • 2014
  • At present, the Content-Based Image Retrieval (CBIR) system has become a hot research topic in the computer vision field. In the CBIR system, the accurate extractions of low-level features can reduce the gaps between high-level semantics and improve retrieval precision. This paper puts forward a new retrieval method aiming at the problems of high computational complexities and low precision of global feature extraction algorithms. The establishment of the new retrieval method is on the basis of the SIFT and Harris (APISH) algorithm, and the salient region of interest points (SRIP) algorithm to satisfy users' interests in the specific targets of images. In the first place, by using the IPDSH and SRIP algorithms, we tested stable interest points and found salient regions. The interest points in the salient region were named as salient interest points. Secondary, we extracted the pseudo-Zernike moments of the salient interest points' neighborhood as the feature vectors. Finally, we calculated the similarities between query and database images. Finally, We conducted this experiment based on the Caltech-101 database. By studying the experiment, the results have shown that this new retrieval method can decrease the interference of unstable interest points in the regions of non-interests and improve the ratios of accuracy and recall.

Studies of vision monitoring system using a background separation algorithm during radiotherapy (방사선 치료시 배경분리알고리즘을 이용한 비젼모니터링 시스템에 대한 연구)

  • Park, Kiyong;Choi, Jaehyun;Park, Jeawon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.2
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    • pp.359-366
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    • 2016
  • The normal tissue in radiation therapy, to minimize radiation, it is most important to maximize local tumor control rates in intensive research the exact dose to the tumor sites. Therefore, the initial, therapist accuracy of detecting movement of the patient fatigue therapist has been a problem that is weighted down directly. Also, by using a web camera, a difference value between the image to be updated to the reference image is calculated, if the result exceeds the reference value, using the system for determining the motion has occurred. However, this system, it is not possible to quantitatively analyze the movement of the patient, the background is changed when moving the treatment bed in the co-therapeutic device was not able to sift the patient. In this paper, using a alpah(${\alpha}$) filter index is an attempt to solve these limitations points, quantifies the movement of the patient, by separating a background image of the patient and treatment environment, and movement of the patient during treatment It senses only, it was possible to reduce the problems due to patient movement.

A Strategy for improving Performance of Q-learning with Prediction Information (예측 정보를 이용한 Q-학습의 성능 개선 기법)

  • Lee, Choong-Hyeon;Um, Ky-Hyun;Cho, Kyung-Eun
    • Journal of Korea Game Society
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    • v.7 no.4
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    • pp.105-116
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    • 2007
  • Nowadays, learning of agents gets more and more useful in game environments. But it takes a long learning time to produce satisfactory results in game. So, we need a good method to shorten the learning time. In this paper, we present a strategy for improving the learning performance of Q-learning with prediction information. It refers to the chosen action at each status in the Q-learning algorithm, It stores the referred value at the P-table of prediction module, and then it searches some values with high frequency at the table. The values are used to renew second compensation value from the Q-table. Our experiments show that our approach gets the efficiency improvement of average 9% after the middle point of learning experiments, and that the more actions in a status space, the higher performance.

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A Convergence Investigation on Nursing Task Performance, Appropriate Performer, and Job Satisfaction of Nurses with Shift Work in General Ward (교대근무를 하는 일반병동 간호사의 간호업무 수행실태, 수행주체와 직무만족에 대한 융복합적 조사연구)

  • Park, Kyongok;Yi, Yeojin;An, Jiwon
    • Journal of the Korea Convergence Society
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    • v.12 no.7
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    • pp.295-304
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    • 2021
  • This study is a secondary data analysis to investigate the nursing tasks performance, appropriate performer, and job satisfaction in the general wards with sift work. We found that nurses performed direct (45%) and indirect nursing (55%). The workload per shift was 37.2% in the day, 35.6% in the evening, and 27.2% at night. The tasks performed after handover were 'direct nursing (34.5%)' and 'documentation and notification (25.8%)'. Nurses responded that there were some tasks that could be delegated to nursing assistants or had ambiguous boundaries with other medical personnel. There was a significant correlation between compliance to the job description of the night shift and job satisfaction (rs=.43, p=.01). These results imply that it is necessary to establish strategies that will enhance work efficiency based on job analysis by shift work, reduce handover time using EMR system, stmart devices, and clarify appropriate performers.

Genome analysis of Yucatan miniature pigs to assess their potential as biomedical model animals

  • Kwon, Dae-Jin;Lee, Yeong-Sup;Shin, Donghyun;Won, Kyeong-Hye;Song, Ki-Duk
    • Asian-Australasian Journal of Animal Sciences
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    • v.32 no.2
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    • pp.290-296
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    • 2019
  • Objective: Pigs share many physiological, anatomical and genomic similarities with humans, which make them suitable models for biomedical researches. Understanding the genetic status of Yucatan miniature pigs (YMPs) and their association with human diseases will help to assess their potential as biomedical model animals. This study was performed to identify non-synonymous single nucleotide polymorphisms (nsSNPs) in selective sweep regions of the genome of YMPs and present the genetic nsSNP distributions that are potentially associated with disease occurrence in humans. Methods: nsSNPs in whole genome resequencing data from 12 YMPs were identified and annotated to predict their possible effects on protein function. Sorting intolerant from tolerant (SIFT) and polymorphism phenotyping v2 analyses were used, and gene ontology (GO) network and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed. Results: The results showed that 8,462 genes, encompassing 72,067 nsSNPs were identified, and 118 nsSNPs in 46 genes were predicted as deleterious. GO network analysis classified 13 genes into 5 GO terms (p<0.05) that were associated with kidney development and metabolic processes. Seven genes encompassing nsSNPs were classified into the term associated with Alzheimer's disease by referencing the genetic association database. The KEGG pathway analysis identified only one significantly enriched pathway (p<0.05), hsa04080: Neuroactive ligand-receptor interaction, among the transcripts. Conclusion: The number of deleterious nsSNPs in YMPs was identified and then these variants-containing genes in YMPs data were adopted as the putative human diseases-related genes. The results revealed that many genes encompassing nsSNPs in YMPs were related to the various human genes which are potentially associated with kidney development and metabolic processes as well as human disease occurrence.

Study on the Methods of Detection and Analysis for Responding Inorganic Acids Spill (무기산 누출 사고 대응을 위한 탐지·분석 방법 연구)

  • Lee, Jin Seon;Jung, Mi Suk;Kim, Ki Joon;Ahn, Sung Young;Yoon, Young Sam;Yoon, Junheon
    • Korean Journal of Hazardous Materials
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    • v.2 no.1
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    • pp.6-11
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    • 2014
  • There have been frequent chemical leaks over the past 10 years. Particularly, inorganic acids like sulfuric acid, nitric acid, and hydrogen chloride take up 37 % of the total chemical accidents which took place for the past 10 years. When an acid chemical leak happens, fume is generated, diffusing into the air, which might cause serious damage to health of local residents and the environment. However, most of the acid-based chemicals, detecting and analysis methods have not been settled considering the frequency of accidents. In this study, we investigated detection and analysis methods to quickly analyze accident sites and evaluate the impacts on environments. Reviewing local and international test analysis methods of acids suggested that nitric acid, sulfuric acid, hydrogen chloride and hydrogen fluoride can be analyzed with IC. It was also found that UV is better for the analysis of hydrogen fluoride and GC/MS for acrylic acid. The analytical methods suggested in the official test methods basically have limitations of consuming much time at stages of preparation and analysis. Considering prompt responses to chemical accidents, further studies should be done to compare the applicability of rapid monitoring methods such as FT-IR, IMR-MS and SIFT-MS.