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Image Segmentation for Skin Detection
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Human skin detection, which usually performed before image processing, is the method of discovering skin-colored pixels and regions that may be of human faces or limbs in videos or photos. Many computer vision approaches have been developed for skin detection. A skin detector usually transforms a given pixel into a suitable color space and then uses a skin classifier to mark the pixel as a skin or a non-skin pixel. A skin classifier explains the decision boundary of the class of a skin color in the color space based on skin-colored pixels. The purpose of this research is to build a skin detection system that will distinguish between skin and non-skin pixels in colored still pictures. This performed by introducing a metric that measures the distances of pixel colors to skin tones. Results showed that the YCbCr color space performed better skin pixel detection than regular Red Green Blue images due to its isolation of the overall energy of an image in the luminance band. The RGB color space poorly classified images with wooden backgrounds or objects. Then, a histogram-based image segmentation scheme utilized to distinguish between the skin and non-skin pixels. The need for a compact skin model representation should stimulate the development of parametric models of skin detection, which is a future research direction.

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Publication Date
Thu Feb 28 2019
Journal Name
Journal Of Engineering
Digital Color Image Watermarking Using Encoded Frequent Mark
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With the increased development in digital media and communication, the need for methods to protection and security became very important factor, where the exchange and transmit date over communication channel led to make effort to protect these data from unauthentication access.

This paper present a new method to protect color image from unauthentication access using watermarking. The watermarking algorithm hide the encoded mark image in frequency domain using Discrete Cosine Transform. The main principle of the algorithm is encode frequent mark in cover color image. The watermark image bits are spread by repeat the mark and arrange in encoded method that provide algorithm more robustness and security. The propos

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Publication Date
Wed Jul 06 2022
Journal Name
Journal Of Al-qadisiyah For Computer Science And Mathematics
Image Compression using Polynomial Coding Techniques: A review
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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Self-Localization of Guide Robots Through Image Classification
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The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura

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Publication Date
Tue Jun 23 2020
Journal Name
Baghdad Science Journal
Content Based Image Retrieval (CBIR) by Statistical Methods
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            An image retrieval system is a computer system for browsing, looking and recovering pictures from a huge database of advanced pictures. The objective of Content-Based Image Retrieval (CBIR) methods is essentially to extract, from large (image) databases, a specified number of images similar in visual and semantic content to a so-called query image. The researchers were developing a new mechanism to retrieval systems which is mainly based on two procedures. The first procedure relies on extract the statistical feature of both original, traditional image by using the histogram and statistical characteristics (mean, standard deviation). The second procedure relies on the T-

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Publication Date
Sat Dec 01 2018
Journal Name
Al-nahrain Journal Of Science
Image Classification Using Bag of Visual Words (BoVW)
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In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.

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Publication Date
Fri Jun 01 2018
Journal Name
Journal Of Physics: Conference Series
Hiding text in gray image using mapping technique
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Publication Date
Sun Jan 01 2023
Journal Name
Proceedings Of International Conference On Data Science And Applications
Very Low Illumination Image Enhancement via Lightness Mapping
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Publication Date
Tue Jun 01 2021
Journal Name
Al-nahrain Journal Of Science
Medical Image Denoising Via Matrix Norm Minimization Problems
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This paper presents the matrix completion problem for image denoising. Three problems based on matrix norm are performing: Spectral norm minimization problem (SNP), Nuclear norm minimization problem (NNP), and Weighted nuclear norm minimization problem (WNNP). In general, images representing by a matrix this matrix contains the information of the image, some information is irrelevant or unfavorable, so to overcome this unwanted information in the image matrix, information completion is used to comperes the matrix and remove this unwanted information. The unwanted information is handled by defining {0,1}-operator under some threshold. Applying this operator on a given ma

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Publication Date
Sat Jun 01 2019
Journal Name
International Journal Of Computer Science And Mobile Computing
Hierarchal Polynomial Coding of Grayscale Lossless Image Compression
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Publication Date
Tue Jan 01 2013
Journal Name
International Journal Of Computer Applications
Content-based Image Retrieval (CBIR) using Hybrid Technique
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Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level co- occurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is conclud

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