A Coloring Method of Gray-Level Image using Neural Networks
نویسنده
چکیده
In this paper, we describe a coloring method of gray-level images in a restricted area based on neural networks. The coloring method employs color clustering and classiication algorithms to images in an application area. In this research, the self-organizing feature map algorithm for clustering is applied to construction of a codebook. Variations of intensity in the gray-level image are classiied into corresponding codevectors using the back-propagation algorithm. The coloring is accomplished by clustering the classiied codevectors of a gray-level image into colorvectors of the constructed codebook. Also, the proposed method is demonstrated in experiments with portrait images.
منابع مشابه
Cystoscopy Image Classication Using Deep Convolutional Neural Networks
In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...
متن کاملAn Automated MR Image Segmentation System Using Multi-layer Perceptron Neural Network
Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image arti...
متن کاملImage Backlight Compensation Using Recurrent Functional Neural Fuzzy Networks Based on Modified Differential Evolution
In this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.The proposed method combines the fuzzy C-means clustering method, a recurrent functional neural fuzzy network (RFNFN), and a modified differential evolution.The proposed RFNFN is based on the two backlight factors that can accurately detect the compensat...
متن کاملA comparative performance of gray level image thresholding using normalized graph cut based standard S membership function
In this research paper, we use a normalized graph cut measure as a thresholding principle to separate an object from the background based on the standard S membership function. The implementation of the proposed algorithm known as fuzzy normalized graph cut method. This proposed algorithm compared with the fuzzy entropy method [25], Kittler [11], Rosin [21], Sauvola [23] and Wolf [33] method. M...
متن کاملDiagnosis of brain tumor using image processing and determination of its type with RVM neural networks
Typically, the diagnosis of a tumor is done through surgical sampling, which is more precise with existing methods. The difference is that this is an aggressive, time consuming and expensive way. In the statistical method, due to the complexity of the brain tissues and the similarity between the cancerous cells and the natural tissues, even a radiologist or an expert physician may also be in er...
متن کامل