توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Robust Watershed Segmentation of Moving Shadows using Wavelets
اطلاعات انتشار: هشتمین کنفرانس ماشین بینایی و پردازش تصویر، سال
تعداد صفحات: ۶
Segmentation of moving objects in a video sequenceis a primary mission of many computer vision tasks. However,shadows extracted along with the objects can result in largeerrors in object localization and recognition. We propose a novelmethod of moving shadow detection using wavelets andwatershed segmentation algorithm, which can effectivelyseparate the cast shadow of moving objects in a scene obtainedfrom a video sequence. The wavelet transform is used to de–noiseand enhance edges of foreground image, and to obtain anenhanced version of gradient image. Then, the watershedtransform is applied to the gradient image to segment differentparts of object including shadows. Finally a post–processingexertion is accommodated to mark segmented parts withchromacity close to the background reference as shadows.Experimental results on two datasets prove the efficiency androbustness of the proposed approach.<\div>

۲LSAP: A New Lexicon–based Method for Sentiment Analysis in Persian
اطلاعات انتشار: اولین کنفرانس بین المللی دستاوردهای نوین پژوهشی در مهندسی برق و کامپیوتر، سال
تعداد صفحات: ۱۱
Sentiment analysis is a subfield of data mining and natural language processing that deals with the extraction of people’s opinion from their writings on the Web. Although there are numerous studies addressing the problem of sentiment analysis in English, very few works considered the problem of Persian sentiment analysis. Persian is spoken by more than a hundred million speakers around the world and is the official language of Iran, Tajikistan, and Afghanistan. From a computational point of view, Persian is a challenging language due to its derivational nature and the use of Arabic words, informal style of writing, and different forms of writing for compound words. In this paper, we present a lexicon–based method for sentiment analysis in Persian, LSAP. Several problems of sentiment analysis in Persian such as misspelling, word spacing, and stemming are addressed in LSAP. In order to show the effectiveness of LSAP, it has been applied to the problem of polarity detection and sentiment strength detection of online cell–phone reviews. The results establish the superiority of LSAP in comparison with six well–known supervised machine learning methods.<\div>
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