کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6864422 | 1439541 | 2018 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A fast hybrid retargeting scheme with seam context and content aware strip partition
ترجمه فارسی عنوان
یک طرح انتقالی ترکیبی سریع با چارچوب درز و محتوا پارتیشن نوار آگاه
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کلمات کلیدی
حکاکی بدون درز پیوسته، متن شانه، محتوا پارتیشن نوار آگاه، توزیع سیم، محتوا سریع تصویر فاصله تصویر،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Image retargeting as a basic trick has been successfully applied to various computer vision problems. In this work, we propose a fast non-continuous seam carving with seam context (FN2SC) for robust and efficient image retargeting. FN2SC first conducts content aware image partition to separate an image into several strips with different target sizes from their content importance. It helps the removed seams in key objects distribute in a relatively uniform manner and prevents distortions to key objects. Then FN2SC performs fast non-continuous seam carving controlled by seam context of both neighboring relationship and touch bound relationship of seams. The seam context makes the removed seams distribute scattered and relieves artifacts caused by seam carving. During seam carving, image distortion is monitored by fast content aware image distance. Finally, FN2SC switches seam carving to scaling when the distortion meets the tolerance, which resizes the strips to the target sizes for image retargeting. Specifically, fast seam searching and image distortion based switching make FN2SC a fast and effective hybrid scheme. Experimental results demonstrate that the proposed FN2SC approach achieves good performance in terms of image quality and efficiency comprehensively.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 286, 19 April 2018, Pages 198-213
Journal: Neurocomputing - Volume 286, 19 April 2018, Pages 198-213
نویسندگان
Lifang Wu, Chuncan Yan, Meng Jian, Shuang Liu, Weiming Dong, Chang Wen Chen,