مجله ماشین بینایی و پردازش تصویر

مجله ماشین بینایی و پردازش تصویر

دسته‌بندی ابرنقاط سه‌بعدی با استفاده از یک شبکه عصبی عمیق توسعه‌یافته

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مهندسی کامپیوتر- دانشگاه بوعلی سینا- همدان- ایران
2 گروه مهندسی کامپیوتر، دانشگاه بوعلی سینا، همدان، ایران
3 گروه مهندسی کامپیوتر، دانشگاه بوعلی‌سینا ، همدان
چکیده
با گذر زمان و هرچه بیشتر شدن ابزارهای دریافتگر داده‌های سه‌بعدی ، میزان دسترسی به داده‌هایی همچون ابرنقاط افزایش یافت. ابرنقاط سه‌بعدی از جمله داده‌هایی است که اجسام را در فضای سه‌بعد بازنمایی می کند. این افزایش داده‌ها درکنار پیشرفت فراوان شبکه‌های عصبی عمیق امکان حل مسائل بسیاری را فراهم کرد. دسته‌بندی اجسام سه‌بعدی، قطعه‌بندی آن‌ها و تخمین جریان حرکتی اجسام از جمله مسائلی هستند که با فراهم آمدن شرایطی که پیشتر گفته شد، توجه پژوهشگران بسیاری را به خود جلب کرده‌اند. در این مقاله به منظور بهبود دقت دسته‌بندی ابرنقاط، روشی ارائه شده که با استفاده از نسخه‌ای تغییریافته از تابع KNN لایه‌هایی از همسایگی شکل می‌گیرد . ویژگی‌های هر یک از این لایه‌های همسایگی استخراج می‌شود و پس از آن با الحاق شدن به ویژگی‌های سراسری برآمده از نقاط منحصر به فرد، از بردار تجمیع شده برای عمل دسته‌بندی استفاده می‌گردد. نتایج به دست آمده از اجرای روش ارائه‌شده بر روی مجموعه‌های دادگان ModelNet40 و ScanObjectNN گواهی می‌دهد که این سازوکار توانسته در مقایسه با دیگر روش‌ها به نتایج و کارایی قابل توجهی دست یابد.
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