توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Objective Evaluation of Image Segmentation Algorithms Using Neural Network
اطلاعات انتشار: بیستمین کنفرانس مهندسی برق ایران، سال
تعداد صفحات: ۶
Image segmentation is an important research area in computer vision and many image segmentation methods have been proposed, therefore it is necessary to be able to evaluate theperformance of image segmentation algorithms objectively. In this paper we present a new metric to evaluate the accuracy ofimage segmentation algorithms, based on the most important feature of each segments using neural networks. The neural network after training can assess the similarity or dissimilarity of each pairs of segments, based on the most important feature of two segments that can be distinguished from each other andfinally the segmentation algorithms accuracy have been computed by novel presented metric. Our proposed method donot require a manually–segmented reference image for comparison, therefore can be used for real–time evaluation and is sensitive to over–segmentation. Experimental results were obtained for a selection of images from Berkeley segmentation data set and demonstrated that it’s a proper measure for comparing image segmentation algorithms<\div>

۲Fuzzy Evaluation of Image Segmentation Algorithms Using Neural Networks
اطلاعات انتشار: اولین همایش ملی کاربرد سیستم های هوشمند (محاسبات نرم) در علوم و صنایع، سال
تعداد صفحات: ۱۰
The color and texture features are very complex in natural images, usually the segmentation algorithms cannot segments these images well and better algorithms must be chosen from among the other algorithms. In this paper we present a fuzzy novel metric to evaluate the complex images using neural network and boundary accuracy, segment–by–segment comparisons of a segmented image and a groundtruth based on fuzzy Gaussian function. The neural network after training can assess the similarity or dissimilarity of each pairs of segments and finally the segmentation algorithms accuracy have been computed by novel presented metric. Our proposed method is sensitive to over–segmentation and undersegmentation. Experimental results were obtained for a selection of images from Berkeley segmentation data set and demonstrated that it s a proper measure for comparing image segmentation algorithms.<\div>

۳Unsupervised Objective Evaluation of Segmentation Algorithms for IR Images
نویسنده(ها): ، ،
اطلاعات انتشار: اولین همایش ملی کاربرد سیستم های هوشمند (محاسبات نرم) در علوم و صنایع، سال
تعداد صفحات: ۱۰
Image segmentation is an important research area in computer vision and many image segmentation methods have been proposed, therefore it is necessary to be able to evaluate the performance of image segmentation algorithms objectively. To date, the most common method for evaluating the effectiveness of a segmentation method is supervised, in which a segmented image is compared quantitatively against a manually segmented image. The evaluation methods that require user assistance are impractical in many vision applications and decrease the depth of evaluation, so unsupervised methods have been proposed. This paper have been presented a new unsupervised metric to evaluate the accuracy of IR image segmentation algorithms based on difference gray value of each pixel from mean gray value density of its region. We enumerate some suitable segmentation algorithms for IR images and then we evaluated them. Experimental results were obtained for a selection of IR images from OTCBVS Data Set and demonstrated that our metric is a proper measure for comparing IR image segmentation algorithms.<\div>
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