استخراج ویژگی‌ها جهت بازشناسی اشیا با الهام از بینایی انسان

نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی دکترای مهندسی برق، گروه الکترونیک، دانشکده‌ی مهندسی برق، دانشگاه علم وصنعت ایران، تهران

2 استاد، گروه الکترونیک، دانشکده‌ی مهندسی برق، دانشگاه علم وصنعت ایران، تهران

10.22041/ijbme.2018.85614.1343

چکیده

در این مقاله سعی شده است تا با الگو برداری از سامانه‌ی بینایی انسان، یک روش مقاوم و تکرارپذیر برای بازشناسی اشیا ارائه شود. یکی از معروف­ترین مدل­های ارائه شده مبتنی بر بینایی انسان، مدل HMAX می­باشد که عمل‌کرد مناسبی در بازشناسی اشیا از خود نشان داده است. اما تفاوت­هایی نیز بین این مدل و بینایی انسان وجود دارد، به طوری که رویه‌ی مغز به طور کامل مدل نشده است. از جمله نواقص این مدل می­توان به تکرارناپذیری (حتی در شرایط ثابت)، وجود افزونگی بسیار زیاد و در نتیجه حجم بالای محاسبات و کند بودن اشاره کرد. در این مقاله، سعی شده است تا با مدل کردن عمل‌کرد بخش ثانویه‌ی قشر بینایی و اضافه کردن آن به HMAX، مدل کامل­تری از بینایی انسان ارائه گشته و نقاط ضعف مدل HMAX ، پوشش داده شود. بخش اضافه شده، مانند بخش ثانویه‌ی قشر بینایی، با تمرکز روی ویژگی­های سطح بالاتر و انتخاب ویژگی­های متمایزکننده و البته تکرارپذیر، باعث بهبود یافتن عمل‌کرد مدل خواهد شد. بخش اضافه شده، بار محاسباتی بسیار اندکی داشته به طوری که نه‌تنها باعث کند شدن مدل نمی­شود، بلکه با انتخاب ویژگی­های مختصر و مفید، باعث افزایش سرعت نیز خواهد شد. روش پیشنهادی از لحاظ دقت و زمان پردازش با روش استاندارد مقایسه شده و برتری مدل پیشنهادی نشان داده شده است. علاوه بر آن، تاثیر تعداد ویژگی­های استخراج شده و تعداد تصاویر مورد استفاده جهت آموزش، مورد بررسی قرار گرفته است تا برتری روش پیشنهادی، به ویژه در زمانی که تعداد تصاویر اندکی در دست می‌باشد، نشان داده شود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Feature Extraction for Object Recognition Inspired by Human Visual System

نویسندگان [English]

  • Hiwa Sufikarimi 1
  • Karim Mohammadi 2
1 Ph.D Student, Electronic School, Electrical Engineering Faculty, Iran University of Science and Technology, Tehran, Iran
2 Professor, Electronic School, Electrical Engineering Faculty, Iran University of Science and Technology, Tehran, Iran
چکیده [English]

In this paper, we tried to present a robust and reliable approach to object recognition by inspiring human visual system. A famous model, inspiring mammalian visual system, is HMAX (Hierarchical Model and X). It shows significant accuracy rates on object recognition tasks. However, there are some differences between this model and human visual system. Indeed cortex's functions are not properly modeled. Unrepeatability under fixed conditions, redundancy, high computing load and being slow are some drawbacks of HMAX. By modeling the secondary visual cortex and adding to the HMAX, we tried to introduce a more accurate model of the human visual system and cover the drawbacks of the previous models. The proposed approach functionally mimics the secondary visual cortex. Attending to high-level features, selecting discriminative and repeatable features, it has higher performance than standard HMAX. The added parts have negligible computation load. Therefore, it does not slow down this model. On the contrary, by selecting brief and useful features, the speed of the model is increased. The proposed approach is compared to the standard HMAX in terms of speed and accuracy rate. The results showed the advantage of proposed approach rather than the standard HMAX. In addition, the effect of the number of features and training images on their performance was shown. It is shown that the proposed approach has a better performance than the standard HMAX especially when the number of feature and training images is small.

کلیدواژه‌ها [English]

  • Object Recognition
  • Feature Extraction
  • Biologically Inspired
  • Repeatability
  • HMAX

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