تشخیص نواحی مزاحم بصری در تصاویر به وسیله روش نمایش تنک دومرحله‌ای و وزن‌دار نمونه آزمون
محورهای موضوعی : مهندسی برق و کامپیوتر
فردین صبوری
1
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فرزین یغمایی
2
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1 - دانشگاه سمنان
2 - دانشگاه سمنان
کلید واژه: سیستم بینایی انسانتوجه بصرینواحی برجستهمزاحمت بصریسیستم کدگذاری و بازنمایی تنک,
چکیده مقاله :
مخاطب یک تصویر مایل است که در کوتاه‌ترین زمان، پیام اصلی تصویر را دریافت کند. از این رو سیستم بینایی انسان توجه بصری را ناخودآگاه به سمت نواحی برجسته، با فرض وجود اطلاعات مفید در آنها هدایت می‌کند. عملاً این فرض همواره صادق نبوده و در مواردی، نواحی برجسته صرفاً موجب مزاحمت بصری می‌گردند. از این رو در کاربردهای مختلف نیاز به ساز و کاری جهت تشخیص این نواحی می‌باشد تا با حذف این نواحی، حواس مخاطب از سوژه اصلی تصویر پرت نشود. همچنین نادیده‌گرفتن این نواحی، کمک شایانی است به روش‌هایی که بر پایه تشخیص نواحی برجسته و مهم عمل می‌کنند. بدین منظور در این مقاله، بر اساس روش‌های منطبق بر چالش عدم توازن دسته‌ها، هر قطعه از تصاویر آموزشی با توجه به ماسک آنها به 9 دسته افراز می‌شود که شماره هر دسته متناسب با شدت مزاحمت است. سپس ویژگی‌های مبتنی بر قطعه استخراج و دسته هر قطعه بر اساس روش نمایش تنک دومرحله‌ای و وزن‌دار نمونه آزمون که بر مبنای سیستم کدگذاری و بازنمایی تنک است، تعیین می‌شود. به منظور ارزیابی دقیق روش پیشنهادی و مقایسه آن با سایر روش‌ها، 4 معیار ارزیابی با رویکردهای مختلف معرفی و پیشنهاد می‌شود. با ارزیابی و سنجش نتایج نشان داده می‌شود که روش پیشنهادی علی‌رغم زمان‌بر بودن، نسبت به کارهای پیشین دارای دقت بیشتری است.
The image observer usually wants to receive the message and the main subject of the image in the shortest time. Hence, assuming there is useful information in the salient regions, the human vision system unconsciously guides visual attention towards them. This assumption is not always correct in practice, and in some cases, salient regions merely cause visual distractions. Therefore, in different applications, a mechanism is needed to identify these regions. To prevent from distracting observer’s attention from the main subject, these regions are eliminated. Furthermore, neglecting these regions could be of considerable assistance to the methods that function base on salient regions recognition. So, in this paper, Based on the methods of the class imbalance challenge each segment of training images in the dataset is a partition to 9 classes according to the relevant mask in the dataset, that the number of each class is proportional to its disturbance intensity. Then, segment-based features are extracted and determining the class of each segment is determined according to WTPTSSR method, which is based on the Sparse Coding and Representation system.Finally, in order to precisely analyzing the proposed method and comparing it to other approaches, four analysis criteria with different performances are presented. According to results, despite being time-consuming, the proposed method has a higher accuracy than the previous ones.
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