Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6707998
S. Veenadevi, A. Ananth
Fractal image compressions of Standard Lena and Satellite imageries have been carried out for the variable size range block method. The image is partitioned by considering maximum and minimum size of the range block and matching it with the domain block. The domain block size of window K*K are sliding over the entire image in steps of K/2. Affine transformation and entropy coding are applied to achieve fractal compression. The Matlab simulation for the standard Lena, and Satellite imageries have been carried out for three different cases of variable range block sizes. The image is reconstructed using iterative functions and inverse transforms. The results of the fractal compression scheme indicate that for the case Rmax = 16 and Rmin = 8, it is possible to achieve higher Compression Ratio (CR) ~ 16 and good Peak Signal to Noise Ratios (PSNR) ~21 dB for satellite imageries. The fractal compression scheme with variable range methods are found to be better than the fixed range methods for achieving higher compression ratios and PSNR values for satellite imageries. The results are presented and discussed in the paper.
{"title":"Fractal image compression of Satellite imageries using variable size of range block","authors":"S. Veenadevi, A. Ananth","doi":"10.1109/ICSIPA.2013.6707998","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6707998","url":null,"abstract":"Fractal image compressions of Standard Lena and Satellite imageries have been carried out for the variable size range block method. The image is partitioned by considering maximum and minimum size of the range block and matching it with the domain block. The domain block size of window K*K are sliding over the entire image in steps of K/2. Affine transformation and entropy coding are applied to achieve fractal compression. The Matlab simulation for the standard Lena, and Satellite imageries have been carried out for three different cases of variable range block sizes. The image is reconstructed using iterative functions and inverse transforms. The results of the fractal compression scheme indicate that for the case Rmax = 16 and Rmin = 8, it is possible to achieve higher Compression Ratio (CR) ~ 16 and good Peak Signal to Noise Ratios (PSNR) ~21 dB for satellite imageries. The fractal compression scheme with variable range methods are found to be better than the fixed range methods for achieving higher compression ratios and PSNR values for satellite imageries. The results are presented and discussed in the paper.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121551080","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}
Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6707973
Muhammad Shahin Uddin, M. Tahtali, A. Lambert, M. Pickering
Speckle noise is a major shortcoming of any type of ultrasound imaging. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle reduction algorithm is to attain a speckle free image, whilst preserving the important anatomical features. In this paper, we introduce a nonlinear multi-scale complex wavelet diffusion based algorithm for speckle reduction and sharp edge preservation of 2D ultrasound images. The proposed method exploits some useful features of the dual tree complex wavelet transform and nonlinear diffusion. Simulated experimental results demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the previous existing approaches in both qualitative and quantitative measures.
{"title":"Speckle reduction for ultrasound images using nonlinear multi-scale complex wavelet diffusion","authors":"Muhammad Shahin Uddin, M. Tahtali, A. Lambert, M. Pickering","doi":"10.1109/ICSIPA.2013.6707973","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6707973","url":null,"abstract":"Speckle noise is a major shortcoming of any type of ultrasound imaging. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle reduction algorithm is to attain a speckle free image, whilst preserving the important anatomical features. In this paper, we introduce a nonlinear multi-scale complex wavelet diffusion based algorithm for speckle reduction and sharp edge preservation of 2D ultrasound images. The proposed method exploits some useful features of the dual tree complex wavelet transform and nonlinear diffusion. Simulated experimental results demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the previous existing approaches in both qualitative and quantitative measures.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"24 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114136263","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}
Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6708037
A. Babiker, I. Faye, A. Malik
Emotion recognition process is proven to be an essential tool to increase human-computer reaction, interpret social relations, investigate mental health, and study human behavior. Pupil diameter (PD) has been addressed as the most reliable approach to identify emotions. This is because it is controlled by Automatic Nervous System (ANS) and is easy to detect. The goal of this paper is to identify the difference in PD due to individual's positive and negative emotional states. The paper introduces experimental results obtained using eye-tracking system with 30 participants. Initial results obtained by applying differently paired-sample t-test suggested a significant increase in pupil dilation during negative emotions compared to positive ones. It also shows steeper, higher, more sustained and longer dilation in high arousal negative stimuli.
{"title":"Pupillary behavior in positive and negative emotions","authors":"A. Babiker, I. Faye, A. Malik","doi":"10.1109/ICSIPA.2013.6708037","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708037","url":null,"abstract":"Emotion recognition process is proven to be an essential tool to increase human-computer reaction, interpret social relations, investigate mental health, and study human behavior. Pupil diameter (PD) has been addressed as the most reliable approach to identify emotions. This is because it is controlled by Automatic Nervous System (ANS) and is easy to detect. The goal of this paper is to identify the difference in PD due to individual's positive and negative emotional states. The paper introduces experimental results obtained using eye-tracking system with 30 participants. Initial results obtained by applying differently paired-sample t-test suggested a significant increase in pupil dilation during negative emotions compared to positive ones. It also shows steeper, higher, more sustained and longer dilation in high arousal negative stimuli.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131286053","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}
Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6708010
Rabiu Habibu, M. Saripan, M. Marhaban, S. Mashohor
Face detection and segmentation is an important prerequisite step for many face related processes such as face recognition and facial expression recognition. A method that automatically segments the given faces images irrespective of their different sizes and orientations will not only ease the subsequent face analysis task but will as well enhances its performance. In this work, an adaptive radius based face segmentation method is presented. We utilised the face's intrinsic properties derived from Gaussian and mean curvature of the face surface to segment each face. The UPM-3DFE and Gavab 3D face databases were used in testing the new method. Visual inspection of the result indicated that the novel method can attained up to 99.23% segmentation accuracy.
{"title":"3d-based face segmentation using adaptive radius","authors":"Rabiu Habibu, M. Saripan, M. Marhaban, S. Mashohor","doi":"10.1109/ICSIPA.2013.6708010","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708010","url":null,"abstract":"Face detection and segmentation is an important prerequisite step for many face related processes such as face recognition and facial expression recognition. A method that automatically segments the given faces images irrespective of their different sizes and orientations will not only ease the subsequent face analysis task but will as well enhances its performance. In this work, an adaptive radius based face segmentation method is presented. We utilised the face's intrinsic properties derived from Gaussian and mean curvature of the face surface to segment each face. The UPM-3DFE and Gavab 3D face databases were used in testing the new method. Visual inspection of the result indicated that the novel method can attained up to 99.23% segmentation accuracy.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133715141","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}
Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6708004
Olfa Ben Rhaiem, L. Chaari
The H.264/SVC was developed as an extension of H.264/AVC; the latest scalable H.264 codec (SVC) provides combined temporal, quality and spatial scalability. This paper provides theoretical concepts of these different scalability approaches and analysis of their performances when video are transmitted over IEEE 802.11e WLANs. In order to validate our works, we have used an NS2 simulator environment based on SVEF (Scalable Video-Streaming Evaluation Framework). We have suggested evaluating H.264/SVC video coding over wireless network architecture, specifically when DCF or EDCA access mechanisms are used. In our analysis we have focused on Spatial, Quality, and Temporal video scalability approaches. The obtained results show that temporal scalability clearly outperforms other scalability category.
{"title":"H.264/SVC scalability performance analysis","authors":"Olfa Ben Rhaiem, L. Chaari","doi":"10.1109/ICSIPA.2013.6708004","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708004","url":null,"abstract":"The H.264/SVC was developed as an extension of H.264/AVC; the latest scalable H.264 codec (SVC) provides combined temporal, quality and spatial scalability. This paper provides theoretical concepts of these different scalability approaches and analysis of their performances when video are transmitted over IEEE 802.11e WLANs. In order to validate our works, we have used an NS2 simulator environment based on SVEF (Scalable Video-Streaming Evaluation Framework). We have suggested evaluating H.264/SVC video coding over wireless network architecture, specifically when DCF or EDCA access mechanisms are used. In our analysis we have focused on Spatial, Quality, and Temporal video scalability approaches. The obtained results show that temporal scalability clearly outperforms other scalability category.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"90 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114027404","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}
Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6708039
A. Ali, V. Asirvadam, A. Malik
This work proposed a geometrical model based on multiple triangular features for the purpose of handling the challenge of scale variations that affect the process of face recognition especially in real time applications where the test images are usually taken in random scales that may not be of the same scale as the probe image. Geometrical approaches have proved to be robust to lighting and illumination variation. Furthermore geometrical methods in general do not hold computational complexity and have the benefit of faster processing time, which make them appropriate for real time applications. Fifteen triangle similarity measurement equations were derived and used to build a class of feature vectors for each subject. Ten images in ten different scales were taken for each subject for a total of fifty samples. Classification results show that the proposed model is promising in handling the challenge of scale variations.
{"title":"Scale- invariant face recognition using triangular geometrical model","authors":"A. Ali, V. Asirvadam, A. Malik","doi":"10.1109/ICSIPA.2013.6708039","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708039","url":null,"abstract":"This work proposed a geometrical model based on multiple triangular features for the purpose of handling the challenge of scale variations that affect the process of face recognition especially in real time applications where the test images are usually taken in random scales that may not be of the same scale as the probe image. Geometrical approaches have proved to be robust to lighting and illumination variation. Furthermore geometrical methods in general do not hold computational complexity and have the benefit of faster processing time, which make them appropriate for real time applications. Fifteen triangle similarity measurement equations were derived and used to build a class of feature vectors for each subject. Ten images in ten different scales were taken for each subject for a total of fifty samples. Classification results show that the proposed model is promising in handling the challenge of scale variations.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"506 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115337171","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}
Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6707972
S. Hussain, Sami M. Gorashi
Image denoising is an active area of research and probably one of the most studied problems in the image processing fields. In this paper we describe a new hybrid image denoising algorithm which combines Gaussian based neighborhood spatial filter with wavelet transform that based on neighborhood thresholding function which takes the correlation of the magnitude of the wavelet coefficient with its neighbors into consideration to decide whether the coefficient is noisy or noise free. Accordingly, noises are detected with the help of the surrounding information and are removed. Experimental results show that the proposed algorithm can effectively remove the image noises with less processing time as compared with the state-of-the-art denoising algorithm.
{"title":"Image denoising algorithm based on hybrid neighborhood filter","authors":"S. Hussain, Sami M. Gorashi","doi":"10.1109/ICSIPA.2013.6707972","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6707972","url":null,"abstract":"Image denoising is an active area of research and probably one of the most studied problems in the image processing fields. In this paper we describe a new hybrid image denoising algorithm which combines Gaussian based neighborhood spatial filter with wavelet transform that based on neighborhood thresholding function which takes the correlation of the magnitude of the wavelet coefficient with its neighbors into consideration to decide whether the coefficient is noisy or noise free. Accordingly, noises are detected with the help of the surrounding information and are removed. Experimental results show that the proposed algorithm can effectively remove the image noises with less processing time as compared with the state-of-the-art denoising algorithm.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121781526","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}
Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6708008
E. A. Awalludin, M. S. Hitam, Z. Bachok, W. Yussof, Aidy Mohamed Shawal M. Muslim
This paper presents a new edge detection method to efficiently detect coral reefs edges. The new edge detection method is based on an anisotropic diffusion where it is used to produce minimum image noise disturbances without reducing the significant edge information and at the same time preserving salient edges. The proposed method has been compared with other established edge detection methods such as Sobel, Prewitt, Roberts, LoG and Canny edge detectors under various noisy environments. The coral reefs images obtained at 3 meters depth were used in this study. The performance of the proposed method is compared with other established methods using visual inspection as well as parametric measure, i.e. Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The experimental results show that the proposed method outperformed other established edge detection methods in both aspects.
{"title":"Anisotropic diffusion based edge detector for detecting coral reefs edges","authors":"E. A. Awalludin, M. S. Hitam, Z. Bachok, W. Yussof, Aidy Mohamed Shawal M. Muslim","doi":"10.1109/ICSIPA.2013.6708008","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708008","url":null,"abstract":"This paper presents a new edge detection method to efficiently detect coral reefs edges. The new edge detection method is based on an anisotropic diffusion where it is used to produce minimum image noise disturbances without reducing the significant edge information and at the same time preserving salient edges. The proposed method has been compared with other established edge detection methods such as Sobel, Prewitt, Roberts, LoG and Canny edge detectors under various noisy environments. The coral reefs images obtained at 3 meters depth were used in this study. The performance of the proposed method is compared with other established methods using visual inspection as well as parametric measure, i.e. Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). The experimental results show that the proposed method outperformed other established edge detection methods in both aspects.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"30 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123990316","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}
Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6707976
M. Shimizu
This paper presents a proposal of a method to estimate a light environment using a single image of cylinder as a calibration object with a Lambertian surface. The light environment to be estimated is modeled as a single light source with size and an ambient light. The proposed method estimates the model parameter directly from pixel values of a single cylinder image. Experimental results demonstrate the effectiveness of the proposed method, which can estimate the model parameter with sufficient resolution for rendering a computer graphics object superimposed to a real image.
{"title":"Light environment estimation from a single cylinder image","authors":"M. Shimizu","doi":"10.1109/ICSIPA.2013.6707976","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6707976","url":null,"abstract":"This paper presents a proposal of a method to estimate a light environment using a single image of cylinder as a calibration object with a Lambertian surface. The light environment to be estimated is modeled as a single light source with size and an ambient light. The proposed method estimates the model parameter directly from pixel values of a single cylinder image. Experimental results demonstrate the effectiveness of the proposed method, which can estimate the model parameter with sufficient resolution for rendering a computer graphics object superimposed to a real image.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125503181","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}
Pub Date : 2013-10-01DOI: 10.1109/ICSIPA.2013.6708018
Ismail M. El-Badawy, A. Aziz, S. Gasser, M. Khedr
Prediction of exons locations in deoxyribonucleic acid (DNA) sequences is a significant issue for biologists. This paper proposes a new method to solve this problem. Unlike the published studies, in which the prediction of exons locations depends on hard decisions from a single classifier, the proposed prediction approach depends on fusion of soft decisions from two classifiers. In the proposed approach we utilize the sliding window discrete Fourier transform (DFT), which is normally used to detect exons 3-base periodicity feature, in a different manner. The novelty here depends on obtaining soft decisions, rather than hard decisions, from two classifiers using different numerical mapping schemes, and fuses them in a decision fusion center to obtain a final global decision about the prediction of exons locations. Simulation results based on real data performed on the HMR195 dataset showed that the proposed soft decisions fusion method achieves better prediction performance compared to the traditional hard decision single classifier method. Moreover the proposed method can easily be extended to more than two classifiers.
{"title":"A new multiple classifiers soft decisions fusion approach for exons prediction in DNA sequences","authors":"Ismail M. El-Badawy, A. Aziz, S. Gasser, M. Khedr","doi":"10.1109/ICSIPA.2013.6708018","DOIUrl":"https://doi.org/10.1109/ICSIPA.2013.6708018","url":null,"abstract":"Prediction of exons locations in deoxyribonucleic acid (DNA) sequences is a significant issue for biologists. This paper proposes a new method to solve this problem. Unlike the published studies, in which the prediction of exons locations depends on hard decisions from a single classifier, the proposed prediction approach depends on fusion of soft decisions from two classifiers. In the proposed approach we utilize the sliding window discrete Fourier transform (DFT), which is normally used to detect exons 3-base periodicity feature, in a different manner. The novelty here depends on obtaining soft decisions, rather than hard decisions, from two classifiers using different numerical mapping schemes, and fuses them in a decision fusion center to obtain a final global decision about the prediction of exons locations. Simulation results based on real data performed on the HMR195 dataset showed that the proposed soft decisions fusion method achieves better prediction performance compared to the traditional hard decision single classifier method. Moreover the proposed method can easily be extended to more than two classifiers.","PeriodicalId":440373,"journal":{"name":"2013 IEEE International Conference on Signal and Image Processing Applications","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116003672","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}
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