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International Journal of Signal and Imaging Systems Engineering最新文献

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Hue preserving colour image enhancement models in RGB colour space without gamut problem 无色域问题的RGB颜色空间中保色调彩色图像增强模型
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2018-05-22 DOI: 10.1504/IJSISE.2018.10013068
K. G. Dhal, Sanjoy Das
{"title":"Hue preserving colour image enhancement models in RGB colour space without gamut problem","authors":"K. G. Dhal, Sanjoy Das","doi":"10.1504/IJSISE.2018.10013068","DOIUrl":"https://doi.org/10.1504/IJSISE.2018.10013068","url":null,"abstract":"All hue preserving colour image enhancement techniques are associated with the change of colour space, such as RGB to hue saturation intensity (HSI), hue saturation value (HSV), lightness-hue-saturation (LHS), and YUV etc. In these colour spaces intensity or saturation or both have been processed then recombined with unchanged hue component to build a hue preserving method. But all the above techniques are time-consuming for colour space conversion and also introduce gamut problem for which the values of enhanced pixels may not lie within their respective range. In this study, three hue preserving models corresponding to three colour spaces viz. HSI, HSV and YUV, have been proposed by considering only RGB and CMY colour spaces. Any grey level contrast enhancement method can be successfully employed for a colour image through those models. In this paper, a novel variant of Histogram equalisation (HE) has been proposed based on entropy based segmentation to enhance the contrast. The proposed variant called entropy based brightness preserved dynamic histogram equalisation (EBBPDHE) is a modification of brightness preserved dynamic histogram equalisation (BPDHE).","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"102"},"PeriodicalIF":0.6,"publicationDate":"2018-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47737227","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: 6
An alphabet reduction algorithm for lossless compression of images with sparse histograms 一种用于稀疏直方图图像无损压缩的字母约简算法
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2018-05-22 DOI: 10.1504/IJSISE.2018.10013067
S. Chaoui, Atef Masmoudi
{"title":"An alphabet reduction algorithm for lossless compression of images with sparse histograms","authors":"S. Chaoui, Atef Masmoudi","doi":"10.1504/IJSISE.2018.10013067","DOIUrl":"https://doi.org/10.1504/IJSISE.2018.10013067","url":null,"abstract":"In this paper, we propose a new adaptive arithmetic coding for lossless image compression applying an alphabet reduction algorithm. The algorithm is a reduction mechanism of the alphabet set within each block by assigning to each one an as small as possible symbol set including all the really present symbols called active symbols, instead of using the nominal alphabet set. The method can be considered as away to address the well-known zero-frequency problem which appears especially for images with sparse and locally sparse histograms. The analytical expression of the expected gain in terms of compression efficiency when using the block active symbol sets is derived. We show experimentally that the proposed method, in conjunction with adaptive arithmetic coding order-0 model applied for images with sparse and locally sparse histograms, provides promising compression ratios and outperforms several state-of-the-art lossless image compression standards such as JPEG2000, JPEG-LS and CALIC.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"85"},"PeriodicalIF":0.6,"publicationDate":"2018-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42007058","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
Fusion features for robust speaker identification 用于鲁棒说话人识别的融合特征
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2018-05-22 DOI: 10.1504/IJSISE.2018.10013027
I. Fredj, Youssef Zouhir, K. Ouni
{"title":"Fusion features for robust speaker identification","authors":"I. Fredj, Youssef Zouhir, K. Ouni","doi":"10.1504/IJSISE.2018.10013027","DOIUrl":"https://doi.org/10.1504/IJSISE.2018.10013027","url":null,"abstract":"Speaker's identification systems aim to identify, through a set of speech parameters, the speaker's identity. Thus, a relevant speech representation is required. For this purpose, we suggest to combine spectral parameters as the Mel frequency Cepstral coefficients (MFCC) and the perceptual linear predictive (PLP) coefficients and prosodic parameter such as the signal fundamental frequency (F0). There are two main classes for F0 estimation divided into temporal and spectral methods. We employ the sawtooth waveform inspired pitch estimator (SWIPE) algorithm for F0 estimation. It is based on the pitch estimation in the frequency domain. In addition, we evaluate the Gaussian mixture model-universal background model (GMM-UBM) for the modelling purpose. Experiments are involved in Timit database. Identification rates are promising and prove the benefit of the combination for MFCC and PLP rather than using each feature separately and this mainly for noisy data.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"65"},"PeriodicalIF":0.6,"publicationDate":"2018-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41921495","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
An automated vision-based algorithm for out of context detection in images 一种基于视觉的图像上下文外自动检测算法
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2018-03-26 DOI: 10.1504/IJSISE.2018.10011685
R. Karthika, L. Parameswaran
{"title":"An automated vision-based algorithm for out of context detection in images","authors":"R. Karthika, L. Parameswaran","doi":"10.1504/IJSISE.2018.10011685","DOIUrl":"https://doi.org/10.1504/IJSISE.2018.10011685","url":null,"abstract":"Vehicular traffic on highways is a major concern relating to safety and security. Violation of traffic rules results in fatal incidents to a very large extent. In this work, an attempt has been made to detect violation of traffic rules namely vehicles in no parking and no stopping zones. Dataset consisting of cars in these zones has been used for experimentation. The proposed algorithm used histograms of oriented gradient (HOG) and Adaboost cascaded classifier for training. The traffic signs have been identified using Hough transform, Circlet transform and colour analysis. Experimental results are promising with an accuracy in the range of 90–97% with recognising no parking and no stopping sign.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"1"},"PeriodicalIF":0.6,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45551185","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: 9
Modelling the nonlinear oscillations due to vertical bouncing using a multi-scale restoring force system identification method 采用多尺度恢复力系统辨识方法对垂直弹跳引起的非线性振动进行建模
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2018-03-26 DOI: 10.1504/IJSISE.2018.10011744
Yuzhu Guo, Lingzhong Guo, V. Racic, Shu Wang, S. Billings
{"title":"Modelling the nonlinear oscillations due to vertical bouncing using a multi-scale restoring force system identification method","authors":"Yuzhu Guo, Lingzhong Guo, V. Racic, Shu Wang, S. Billings","doi":"10.1504/IJSISE.2018.10011744","DOIUrl":"https://doi.org/10.1504/IJSISE.2018.10011744","url":null,"abstract":"Human vertical bouncing motion is studied using a system identification method. A multi-scale mathematical model is identified directly from real experimental data to characterise the nonlinear oscillation associated with the vertical bouncing. A new method which combines the restoring force surface (RFS) method and the iterative orthogonal forward regression (iOFR) algorithm is proposed to determine the model structure and estimate the associated parameters. Two types of sub-models are used to study the nonlinear oscillations in different scales. Results show that the model predicted outputs provide excellent predictions of the experimental data and the models are capable of reproducing the nonlinear oscillations in both time and frequency domain.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"52"},"PeriodicalIF":0.6,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46657874","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 model based approach for tennis court tracking in real time 一种新的基于模型的网球场实时跟踪方法
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2018-03-26 DOI: 10.1504/IJSISE.2018.10011677
Manel Farhat, A. Khalfallah, M. Bouhlel
{"title":"A new model based approach for tennis court tracking in real time","authors":"Manel Farhat, A. Khalfallah, M. Bouhlel","doi":"10.1504/IJSISE.2018.10011677","DOIUrl":"https://doi.org/10.1504/IJSISE.2018.10011677","url":null,"abstract":"The detection and the tracking of the tennis court is a primordial step to analyse a tennis video at higher semantic level. In this context, a new approach for tennis court tracking in real time is proposed in this paper. Our proposed system is based on model based approach allows to compute the homography between the court detected in the scene and the court model presenting the real world coordinate. For this aim, the first step is to detect the tennis court by detecting the court line and determining some interest points. We check then the motion of the camera. In case of camera motion, the court is tracked by tracking the interest points using the Lucas-Kanade algorithm. After that, these points are used by a RANSAC algorithm to estimate the homography. However, in case of a fixed camera, we need only the model based correction system.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"9"},"PeriodicalIF":0.6,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48397597","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
Early detection of Parkinson's disease through multimodal features using machine learning approaches 使用机器学习方法通过多模式特征早期检测帕金森病
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2018-03-26 DOI: 10.1504/IJSISE.2018.10011741
G. Pahuja, T. N. Nagabhushan, B. Prasad, R. Pushkarna
{"title":"Early detection of Parkinson's disease through multimodal features using machine learning approaches","authors":"G. Pahuja, T. N. Nagabhushan, B. Prasad, R. Pushkarna","doi":"10.1504/IJSISE.2018.10011741","DOIUrl":"https://doi.org/10.1504/IJSISE.2018.10011741","url":null,"abstract":"This research establishes a relation between objective biomarkers of Parkinson's disease (PD) based on T1-weighted MRI scans and other clinical biomarkers. It shall aid doctors in identifying the onset and progression of PD among the patients. Voxel-based morphometry has been used for feature extraction from MRI scans. These extracted features are combined with biochemical biomarkers for dataset enrichment. A genetic algorithm is applied to this dataset to remove the redundancies and to obtain an optimal set of features. Subsequently, we used Self-adaptive resource allocation network (SRAN), extreme learning machine (ELM) and support vector machines (SVM) to classify different subjects. It is observed that SRAN classifier gave the best performance when compared with ELM and SVM. Finally, it is found that a variation of grey matter in Thalamus is responsible for PD. The obtained results corroborate the earlier findings from the literature.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"31"},"PeriodicalIF":0.6,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46106528","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: 5
Comparative analysis of two leading evolutionary intelligence approaches for multilevel thresholding 两种领先的进化智能多级阈值方法的比较分析
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2018-03-26 DOI: 10.1504/IJSISE.2018.10011687
Z. Ye, Hang Yin, Yongmao Ye
{"title":"Comparative analysis of two leading evolutionary intelligence approaches for multilevel thresholding","authors":"Z. Ye, Hang Yin, Yongmao Ye","doi":"10.1504/IJSISE.2018.10011687","DOIUrl":"https://doi.org/10.1504/IJSISE.2018.10011687","url":null,"abstract":"The rapid advance of artificial intelligence has made complex image processing in real time possible. Multilevel thresholding has become a feasible way for image segmentation, even in the presence of poor contrast and external artefacts. Genetic algorithms (GAs) and particle swarm optimisation (PSO) are broadly recognised by far to be two dominating schemes which outperform classical ones on multilevel thresholding. Qualitative analysis can usually be applied to observe their superiority to all classical approaches. However, no convincing result is reached with respect to differences in performance between GAs and PSO. The existing segmentation practices are either examined by visual appeals exclusively, or evaluated quantitatively assuming perfect statistical distributions. To make thorough comparisons, comparative analysis of two leading multilevel thresholding approaches is conducted for true colour image segmentation. The information theory is also employed to analyse the outcomes of systematic approaches using diverse quantitative metrics from various aspects.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"20"},"PeriodicalIF":0.6,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44561982","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: 3
Copy-move image forgery detection using direct fuzzy transform and ring projection 基于直接模糊变换和环投影的复制运动图像伪造检测
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2018-03-26 DOI: 10.1504/IJSISE.2018.10011742
Mohd Dilshad Ansari, S. P. Ghrera
{"title":"Copy-move image forgery detection using direct fuzzy transform and ring projection","authors":"Mohd Dilshad Ansari, S. P. Ghrera","doi":"10.1504/IJSISE.2018.10011742","DOIUrl":"https://doi.org/10.1504/IJSISE.2018.10011742","url":null,"abstract":"Cloning (copy-move) image forgery detection (CMFD) is a pure image processing method without any support of embedded security information. Fuzzy transform (F-Transform) is a powerful tool that encompasses both classical transforms as well as approximation technique using fuzzy IF-THEN rules studied in fuzzy modelling. Ring projection transform (RPT) for features extraction is a very effective tool as it transforms two-dimensional data into one-dimensional with a very few component which significantly reduces the computational complexity. We propose a new and comprise scheme of fuzzy transform and RPT for CMFD. Firstly, the F-transform is employed on the input image to yield highly reduced dimension representation, which is split into fixed size overlapping blocks. Further, RPT is applied to every block for calculating their features. These feature vectors are lexicographically sorted. Finally, the duplicated blocks are filtered out using correlation coefficient. The proposed algorithm is faster and efficient in terms of execution time and accuracy.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"11 1","pages":"44"},"PeriodicalIF":0.6,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47377019","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: 7
Compressed fixed-point data formats with non-standard compression factors 具有非标准压缩因子的压缩定点数据格式
IF 0.6
International Journal of Signal and Imaging Systems Engineering Pub Date : 2017-11-15 DOI: 10.1504/IJSISE.2017.10008791
M. Richey, H. Saiedian
{"title":"Compressed fixed-point data formats with non-standard compression factors","authors":"M. Richey, H. Saiedian","doi":"10.1504/IJSISE.2017.10008791","DOIUrl":"https://doi.org/10.1504/IJSISE.2017.10008791","url":null,"abstract":"Sign bit compression in fixed-point numbering systems can improve the dynamic range and round-off noise for signal processing algorithms. This paper analyses non-standard compression factors (CF) for compressed fixed-point data formats, where sign bit compression is performed on each individual fixed-point number. Although these compression techniques are applicable to other fixed-point formats, the compressed two's complement data format is selected for illustration. A brief background on compressed two's complement is provided. Obvious compression factors are powers of two due to binary formatting, but compression factors other than standard powers of two are presented. Compression factors of 3 and 5 are analysed in greater detail. Motivation for and advantages of non-power-of-two compression factors are identified.","PeriodicalId":56359,"journal":{"name":"International Journal of Signal and Imaging Systems Engineering","volume":"10 1","pages":"301"},"PeriodicalIF":0.6,"publicationDate":"2017-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49500535","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
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