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This paper presents a new algorithm for colorizing gray scale natural still images. The algorithm uses artificial neural network (ANN) to predict the low frequency discrete cosine transform (DCT) components of the RGB channels. A set of natural color images are used to train three ANNs. The trained networks estimates the RGB layers of the gray scale image that best match a set of training colored...
Machine vision is still a challenging topic and attracts researchers to carry out researches in this field. Efforts have been placed to design machine vision systems (MVS) that are inspired by human vision system (HVS). Attention is one of the important properties of HVS, with which the human can focus only on part of the scene at a time; regions with more abrupt features attract human attention more...
Skin color is a robust cue in human skin detection. It has been widely used in various human-related image processing applications. Although many researches have been carried out for skin color detection, there is no consensus on which color space is the most appropriate for skin color detection because many researchers do not provide strict justification of their color space choice. In this paper,...
This paper discusses the effort of discriminating plaque psoriasis skin lesions using Artificial Neural Network (ANN) for dermatological early diagnosis based on color representations. For any digital acquired images, colors can be identified numerically, for example, with respect to the unique RGB, HSV and YCbCr pixel indices. Previous work have produced intelligent identification models for selected...
We propose and evaluate a framework for detection of plant leaf/stem diseases. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively expensive, especially in developing countries. Providing fast, automatic, cheap and accurate image-processing-based solutions for that task can be of great realistic significance. The proposed framework is image-processing-based...
In this paper we propose a coin recognition system using a statistical approach and apply it to the recognition of Jordanian coins. The proposed method depends on two features in the recognition process: the color of the coin, and its area. The recognition process consists of several steps. Firstly, a gray-level image is extracted from the original colored image. The image is then segmented into two...
Research in content-based image retrieval (CBIR) shows that high-level semantic concepts in image cannot be constantly depicted using low-level image features. So the process of designing a CBIR system should take into account diminishing the existing gap between low-level visual image features and the high-level semantic concepts. In this paper, we propose a new architecture for a CBIR system named...
Image classification problem is one of the most challenges of computer vision. In this paper, a robust image classification approach using multilevel neural networks is proposed. In this approach, each image is fixedly divided into five regions each equal to half of the original image. Then these regions are classified by the multilevel neural classifier into five categories, i.e., ??sky??, ??water??,...
In the following paper we present a complete analytical model that predicts the band-gap (Eg) of single-walled carbon nanotubes (SWCNTs) directly from their diameter (d) and chiral angle (thetas). The proposed analytical model is based on two mathematical expressions that have been derived by curve-fitting the outcome generated from the third-nearest-neighbor tight-binding (TB) method in conjunction...
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