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RISS 인기검색어
- 검색키워드
- 저자 : ( Tahmina Khanam ) (검색결과 2 건)
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Baggage Recognition in Occluded Environment using Boosting Technique
( Tahmina Khanam ),( Kaushik Deb ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.11
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.
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Feasibility Study of Case-Finding for Breast Cancer by Community Health Workers in Rural Bangladesh
Chowdhury, Touhidul Imran,Love, Richard Reed,Chowdhury, Mohammad Touhidul Imran,Artif, Abu Saeem,Ahsan, Hasib,Mamun, Anwarul,Khanam, Tahmina,Woods, James,Salim, Reza Asian Pacific Journal of Cancer Prevention 2015 Asian Pacific journal of cancer prevention Vol.16 No.17
Background: Mortality from breast cancer is high in low- and middle-income countries, in part because most patients have advanced stage disease when first diagnosed. Case-finding may be one approach to changing this situation. Materials and Methods: We conducted a pilot study to explore the feasibility of population-based case finding for breast cancer by community health workers (CHWs) using different data collection methods and approaches to management of women found to have breast abnormalities. After training 8 CHWs in breast problem recognition, manual paper data collection and operation of a cell-phone software platform for reporting demographic, history and physical finding information, these CHWs visited 3150 women >age 18 and over they could find-- from 2356 households in 8 villages in rural Bangladesh. By 4 random assignments of villages, data were collected manually (Group 1), or with the cell-phone program alone (Group 2) or with management algorithms (Groups 3 and 4), and women adjudged to have a serious breast problem were shown a motivational video (Group 3), or navigated/accompanied to a breast problem center for evaluation (Group 4). Results: Only three visited women refused evaluation. The manual data acquisition group (1) had missing data in 80% of cases, and took an average of 5 minutes longer to acquire, versus no missing data in the cell phone-reporting groups (2,3 and 4). One woman was identified with stage III breast cancer, and was appropriately treated. Conclusions: Among very poor rural Bangladeshi women, there was very limited reluctance to undergo breast evaluation. The estimated rarity of clinical breast cancer is supported by these population-based findings. The feasibility and efficient use of mobile technology in this setting is supported. Successor studies may most appropriately be trials focusing on improving the suggested benefits of motivation and navigation, on increasing the numbers of cases found, and on stage of disease at diagnosis as the primary endpoint.
ScienceON
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