International Journal of Multimedia and Image Processing最新文献

Kernel-Induced Fuzzy C-Means with Adaptive Mean Filter for SAR Image Segmentation 基于自适应均值滤波的核诱导模糊c均值SAR图像分割

International Journal of Multimedia and Image Processing Pub Date : 2022-06-30 DOI: 10.20533/ijmip.2042.4647.2022.0065

Sicong Li

引用次数: 0

A Systematic Review of the Literature in Dynamic Video Summarization 动态视频摘要的文献系统综述

International Journal of Multimedia and Image Processing Pub Date : 2022-06-30 DOI: 10.20533/ijmip.2042.4647.2022.0064

Mahsa Rahimi Resketi, H. Motameni, Ebrahim Akbari, H. Nematzadeh

引用次数: 0

Modified Finite Difference and Linear Iterative PDE Methods in Digital Image Inpainting 数字图像修补中的修正有限差分和线性迭代PDE方法

International Journal of Multimedia and Image Processing Pub Date : 2019-03-30 DOI: 10.20533/ijmip.2042.4647.2019.0055

Sumudu S Kalubowila

引用次数: 0

A Study on Histogram Moments and their Application to Image Thresholding 直方图矩及其在图像阈值分割中的应用研究

International Journal of Multimedia and Image Processing Pub Date : 2019-03-30 DOI: 10.20533/ijmip.2042.4647.2019.0058

S. Ameer

{"title":"A Study on Histogram Moments and their Application to Image Thresholding","authors":"S. Ameer","doi":"10.20533/ijmip.2042.4647.2019.0058","DOIUrl":"https://doi.org/10.20533/ijmip.2042.4647.2019.0058","url":null,"abstract":"Histogram moments have been widely investigated for image thresholding. This paper proposes several novel schemes in this area. In some of the proposals, the idea is simply to match a moment from the original image (calculated from the histogram) to a corresponding moment of the thresholded image. In other proposals, the threshold is the one optimizing a specific moment. Comparative results with Otsu shows the effectiveness of the proposed schemes. 1.Introduction Image thresholding has been widely investigated due to its vital role in many applications and one of the effective methods for image segmentation. Various schemes have been proposed in the literature, a good review can be found in [1]. The histogram plays a crucial role in many of these schemes. In general, the histogram is used as an approximation to the probability density function [2 – 3]. In these cases and their extensions, the threshold is selected as a solution to an optimization problem for some objective function dependent on features extracted from the histogram. The aforementioned schemes can be generalized to multi-level thresholding as in [4]. However, the computational price is too high. An interesting scheme to preserve the moments was proposed by [5]. For binary thresholding, the first three moments of the thresholded image have to be equal to those of the original image. A higher dimensional histogram can be constructed using the local variance [6]. This research proposes few formulations that exploit the use of different moments deducted from the 1D histogram. In general, the optimum threshold(s) are the ones producing a moment match (to that of the original) or the moment attains its optimum value at these threshold(s).","PeriodicalId":342220,"journal":{"name":"International Journal of Multimedia and Image Processing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127695321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image-Based Gender Prediction Model Using Multilayer Feed-Forward Neural Networks 基于图像的多层前馈神经网络性别预测模型

International Journal of Multimedia and Image Processing Pub Date : 2019-03-30 DOI: 10.20533/ijmip.2042.4647.2019.0056

Mohamed Yousif Elmahi, E. I. M. Zayid

{"title":"Image-Based Gender Prediction Model Using Multilayer Feed-Forward Neural Networks","authors":"Mohamed Yousif Elmahi, E. I. M. Zayid","doi":"10.20533/ijmip.2042.4647.2019.0056","DOIUrl":"https://doi.org/10.20533/ijmip.2042.4647.2019.0056","url":null,"abstract":"In this study, we develop a reliable and highperformance multi-layer feed-forward artificial neural networks (MFANNs) model for predicting gender classification. The study used features for a set of 450 images randomly chosen from the FERET dataset. We extract the only high-merit candidate parameters form the FERET dataset. A discrete cosine transformation (DCT) is employed to facilitate an image description and conversion. To reach the final gender estimation model, authors examined three artificial neural classifiers and each extremely performs deep computation processes. In addition to the MFANNs, artificial neural networks (ANNs) classifiers include support vector regression with radial-basis function (SVR-RBF) and k-Nearest Neighbor (k-NN). A 10-folds cross-validation technique (CV) is used to prove the integrity of the dataset inputs and enhance the calculation process of the model. In this model, the performance criteria for accuracy rate and mean squared error (MSE) are carried out. Results of the MFANNs models are compared with the ones that obtained by SVR-RBF and k-NN. It is shown that the MFANNs model performs better (i.e. lowest MSE = 0.0789, and highest accuracy rate = 96.9%) than SVR-based and k-NN models. Linked the study findings with the results obtained in the literature review, we conclude that our method achieves a recommended calculation for gender prediction.","PeriodicalId":342220,"journal":{"name":"International Journal of Multimedia and Image Processing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126895089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Face Photograph Recognition via Generation from Sketches using Convolutional Neural Networks 基于卷积神经网络的草图生成人脸图像识别

International Journal of Multimedia and Image Processing Pub Date : 2019-03-30 DOI: 10.20533/ijmip.2042.4647.2019.0057

Mustafa Karasolak, Roya Chopani

{"title":"Face Photograph Recognition via Generation from Sketches using Convolutional Neural Networks","authors":"Mustafa Karasolak, Roya Chopani","doi":"10.20533/ijmip.2042.4647.2019.0057","DOIUrl":"https://doi.org/10.20533/ijmip.2042.4647.2019.0057","url":null,"abstract":"Face photo-sketch matching is an important problem for law enforcement agencies in terms of identifying suspects. In this study, a new sketch-photo generation and recognition technique is proposed by using residual convolutional neural network architecture. The suggested RCNN architecture consists of 6 convolutions, 6 ReLU, 4 poolings, 2 deconvolution layers. The proposed architecture is trained with face photos and sketches. Sketches are supplied as an input to the RCNN architecture and, generated face photos are obtained as the output. Then, the generated face photos are compared with the photos of the people in the database. Structural Similarity Index (SSIM) is used to measure the pairwise similarity and the photo with the highest index score is matched. CUHK Face Sketch Database containing 188 images is tested. In the experiments, 148, 20, and 20 images are used for training, validation, and testing, respectively. Data augmentation applied to 148 training images produced 444 images. Experimental results show that the success of the training curve is 90.55% and the validation success is 91.1%. True face recognition success from generated face images with SSIM is 93.89% for CUHK Face Sketch database (CUFS) and 84.55% AR database.","PeriodicalId":342220,"journal":{"name":"International Journal of Multimedia and Image Processing","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127044393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Automated Extraction of Parasite in the Microscopic Images by Distance Regularized Level Set Evolution initialized with Hough Transform Hough变换初始化的距离正则化水平集进化自动提取显微图像中的寄生虫

International Journal of Multimedia and Image Processing Pub Date : 2019-03-30 DOI: 10.20533/ijmip.2042.4647.2019.0059

Oscar Takam Nkamgang, D. Tchiotsop, Beaudelaire Saha Tchinda, H. Fotsin

{"title":"Automated Extraction of Parasite in the Microscopic Images by Distance Regularized Level Set Evolution initialized with Hough Transform","authors":"Oscar Takam Nkamgang, D. Tchiotsop, Beaudelaire Saha Tchinda, H. Fotsin","doi":"10.20533/ijmip.2042.4647.2019.0059","DOIUrl":"https://doi.org/10.20533/ijmip.2042.4647.2019.0059","url":null,"abstract":"The analysis of biomedical microscopic images is carried out manually in medical laboratories. The manual analysis of clinical images lets to both repetitive tasks and management of huge amounts of data. This is tedious and times consuming for laboratory technicians. Inevitably, it is also prone to human errors. Our objective in this work is to contribute to the automation of the analysis of microscopic images of stools using Distance Regularized Level Set Evolution (DRLSE) automatically initialized by Hough transform. We firstly converted the microscopic images to edge maps using canny algorithm. Next, we located the parasite through circular Hough transform and draw circles around them. Those circles stand as initial contours of DRLSE. The contours evolve until they fit the boundaries of the parasites. The final extraction is performed using a complementary method based on the signed distance character of the level set function. The Distance Regularized Level Set Evolution (DRLSE) has been automatically initialized. We applied our method to the detection of intestinal parasites in microscopic images. Experimental results show accurate, efficient and less time consuming of our scheme compared to others recently proposed in the literature. This is a notable contribution to the automation of stools examination in the medical laboratories. In forthcoming works, we plan to include this segmentation process in an expert system of parasitic diseases diagnosis.","PeriodicalId":342220,"journal":{"name":"International Journal of Multimedia and Image Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125985662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Based Image Subtraction Algorithm for Bare PCB Defect Detection 基于图像减法的裸PCB缺陷检测新算法

International Journal of Multimedia and Image Processing Pub Date : 2018-09-30 DOI: 10.20533/ijmip.2042.4647.2018.0054

Daniel Katz Bonello, Y. Iano, U. B. Neto

引用次数: 3

The Threat and Vulnerabilities of Submarine Cables in Information Security and Telecommunication 信息安全与电信领域海底电缆的威胁与漏洞

International Journal of Multimedia and Image Processing Pub Date : 2018-09-30 DOI: 10.20533/ijmip.2042.4647.2018.0053

Aisha Suliaman Alazri

引用次数: 0

Comparison of Electronic Signature between Europe and Japan: Possibiltiy of Mutual Recognition 欧洲与日本电子签名的比较:互认的可能性

International Journal of Multimedia and Image Processing Pub Date : 2018-06-30 DOI: 10.20533/ijmip.2042.4647.2018.0052

Soshi Hamaguchi, T. Kinoshita, S. Tezuka

引用次数: 0