Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435671
Harsha Nair, E. A. Neeba
One of the sub areas of the data mining includes sequential pattern mining. This mining algorithm is to find the repeating patterns after mining the sequence databases. These are used to find the relation between the various items in the data for different purposes. As these data keep changing according to the change in time, mining should be done on incremented or updated database to obtain the frequent sequential patterns. The proposed algorithm in this paper uses modified algorithm of sequential pattern mining including concepts of fuzzy space intervals. In this algorithm, frequently occurring sequential patterns in the sequence database are mined using apriori like method. Fuzzy theory is used for mining the space interval between the frequently occurring sequences. The sequentially occurring candidate patterns are found first. After that follows the frequently occurring sequential patterns, which are found by calculating the minimum fuzzy support along with the use of the fuzzy number. Each space cluster is found by fuzzy support computation. The final results comprises the frequently occurring fuzzy space sequentially based patterns. At last the outcome also confirms the excellence of the suggested MISPFSI algorithm.
{"title":"Improved fuzzy space-intervals based sequential pattern mining: Technical solution","authors":"Harsha Nair, E. A. Neeba","doi":"10.1109/ICCIC.2015.7435671","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435671","url":null,"abstract":"One of the sub areas of the data mining includes sequential pattern mining. This mining algorithm is to find the repeating patterns after mining the sequence databases. These are used to find the relation between the various items in the data for different purposes. As these data keep changing according to the change in time, mining should be done on incremented or updated database to obtain the frequent sequential patterns. The proposed algorithm in this paper uses modified algorithm of sequential pattern mining including concepts of fuzzy space intervals. In this algorithm, frequently occurring sequential patterns in the sequence database are mined using apriori like method. Fuzzy theory is used for mining the space interval between the frequently occurring sequences. The sequentially occurring candidate patterns are found first. After that follows the frequently occurring sequential patterns, which are found by calculating the minimum fuzzy support along with the use of the fuzzy number. Each space cluster is found by fuzzy support computation. The final results comprises the frequently occurring fuzzy space sequentially based patterns. At last the outcome also confirms the excellence of the suggested MISPFSI algorithm.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129928415","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435730
R. K. Pathak, S. Meena
This paper has been proposed for secure communication of data over wired and wireless channel with development of simple algorithm and easiness in its implementation. These features have been enabled using key rotation, inverse substitution and seed values propagation. The algorithm used in this paper can utilize both types of key that is symmetrical and asymmetrical key depending on small modification in arithmetical and logical expressions of encryption algorithm at transmitter end and also decryption algorithms at receiver end. So, algorithm is flexible with respect to key selection.
{"title":"Cryptography using inverse substitution & key rotation with seed propagation","authors":"R. K. Pathak, S. Meena","doi":"10.1109/ICCIC.2015.7435730","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435730","url":null,"abstract":"This paper has been proposed for secure communication of data over wired and wireless channel with development of simple algorithm and easiness in its implementation. These features have been enabled using key rotation, inverse substitution and seed values propagation. The algorithm used in this paper can utilize both types of key that is symmetrical and asymmetrical key depending on small modification in arithmetical and logical expressions of encryption algorithm at transmitter end and also decryption algorithms at receiver end. So, algorithm is flexible with respect to key selection.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125327526","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435721
K. L. Prasad, T. C. M. Rao, V. Kannan
In this research article the digital video watermarking technique is projected through a semi-blind pattern. The proposed method involves frame-spot matching model based on KAZE method at the initial stage, The KAZE method is deployed for matching the edge points of frame-spots with all video frames with the intention to detect the embedding and extract the respective regions. Then the frame entropy blocks are designated and converted by PCA (Principal Component Analysis) blocks. The QIM (Quantization Index Modulation) is employed to quantize the highest coefficient values on each PCA entropy chunks of every sub-band. The single shared secure key is employed to recover the watermarked content. The DWT (Discrete Wavelet Transform) is applied on every single video frame and disintegrate into group of sub-bands. During extraction this is simply reversed; however the KAZE frame-spot is harmonized through each frame edge points. The parameters like rotation, scaling and translation are assessed and the watermarked evidence can be effectively extracted. The proposed pattern is verified using a numerous of video structures and compared with other similar models such as SURF, SIFT, PCA-SIFT, KAZE and perceived high optimal results. The investigational outcomes demonstrated high imperceptibility and high strength against numerous outbreaks like JPEG encoding, addition of Gaussian noise, gamma modification, histogram equality and contrast rectification in both forms of ordinary videos and clinical videos.
本文采用半盲模式投影数字视频水印技术。该方法在初始阶段采用基于KAZE方法的帧点匹配模型，利用KAZE方法将帧点边缘点与所有视频帧进行匹配，目的是检测嵌入并提取相应区域。然后用主成分分析(PCA)块对帧熵块进行划分和转换。采用量化指数调制(quantiization Index Modulation, QIM)对各子带各主成分熵块上的最高系数值进行量化。使用单个共享安全密钥恢复带水印的内容。将离散小波变换(DWT)应用于每一帧视频，并分解成一组子带。在提取过程中，这种情况正好相反;然而，KAZE帧点通过每个帧边缘点进行协调。对旋转、缩放、平移等参数进行评估，有效提取水印证据。采用大量视频结构验证了所提出的模式，并将其与SURF、SIFT、PCA-SIFT、KAZE等其他类似模型进行了比较，并获得了较高的优化结果。研究结果表明，无论是普通视频还是临床视频，对JPEG编码、添加高斯噪声、伽玛修正、直方图相等和对比度校正等众多突发事件都具有较高的隐秘性和强度。
{"title":"A novel semi-blind video watermarking using KAZE-PCA-2D Haar DWT scheme","authors":"K. L. Prasad, T. C. M. Rao, V. Kannan","doi":"10.1109/ICCIC.2015.7435721","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435721","url":null,"abstract":"In this research article the digital video watermarking technique is projected through a semi-blind pattern. The proposed method involves frame-spot matching model based on KAZE method at the initial stage, The KAZE method is deployed for matching the edge points of frame-spots with all video frames with the intention to detect the embedding and extract the respective regions. Then the frame entropy blocks are designated and converted by PCA (Principal Component Analysis) blocks. The QIM (Quantization Index Modulation) is employed to quantize the highest coefficient values on each PCA entropy chunks of every sub-band. The single shared secure key is employed to recover the watermarked content. The DWT (Discrete Wavelet Transform) is applied on every single video frame and disintegrate into group of sub-bands. During extraction this is simply reversed; however the KAZE frame-spot is harmonized through each frame edge points. The parameters like rotation, scaling and translation are assessed and the watermarked evidence can be effectively extracted. The proposed pattern is verified using a numerous of video structures and compared with other similar models such as SURF, SIFT, PCA-SIFT, KAZE and perceived high optimal results. The investigational outcomes demonstrated high imperceptibility and high strength against numerous outbreaks like JPEG encoding, addition of Gaussian noise, gamma modification, histogram equality and contrast rectification in both forms of ordinary videos and clinical videos.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127709350","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435682
R. S. Ponmagal, N. Srinivasan
This paper is aimed at proposing a machine learning approach to analyze and make sense out of the ancient rock arts by exploring them through cloud infrastructure. The visual language of the rock art is proposed to be interpreted and transformed into the current language of human cognition. The rock arts can be captured as 3D motion pictures; ultrasonically detected images; pictures captured using laser sensors and thermography techniques. Since the countries across the Globe are rich in culture and also diverse in nature, rock arts have been explored and keeping on exploring more in quantity, the rock arts information collected through the above said methods can be represented and processed using cloud infrastructure. Further, using machine learning algorithms in the cloud is proposed, to arrive at definite, meaningful information from rock arts. Through the machine learning approach, the symbols represented by rock arts could be matched with the twenty six English alphabets. The proposed work is the interpretation of the olden rock art scripts and hence to predict the meaning that they wish to convey.
{"title":"Machine learning approach for exploring rock arts through the cloud infrastructure","authors":"R. S. Ponmagal, N. Srinivasan","doi":"10.1109/ICCIC.2015.7435682","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435682","url":null,"abstract":"This paper is aimed at proposing a machine learning approach to analyze and make sense out of the ancient rock arts by exploring them through cloud infrastructure. The visual language of the rock art is proposed to be interpreted and transformed into the current language of human cognition. The rock arts can be captured as 3D motion pictures; ultrasonically detected images; pictures captured using laser sensors and thermography techniques. Since the countries across the Globe are rich in culture and also diverse in nature, rock arts have been explored and keeping on exploring more in quantity, the rock arts information collected through the above said methods can be represented and processed using cloud infrastructure. Further, using machine learning algorithms in the cloud is proposed, to arrive at definite, meaningful information from rock arts. Through the machine learning approach, the symbols represented by rock arts could be matched with the twenty six English alphabets. The proposed work is the interpretation of the olden rock art scripts and hence to predict the meaning that they wish to convey.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127639726","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435666
N. Neelima, E. Reddy
Retrieval of user interested images based on pictorial queries is an interesting and challenging task. This paper proposes an Improved Region based image retrieval system using FCM & multiple shape, texture features. The Proposed system uses Fuzzy c-means clustering algorithm for image segmentation. Local Binary Pattern (LBP), Hu moments and Radial Chebyshev Moments are used in this work. For similarity comparison City block distance is used. The experimental results presents a comparative analysis of the proposed image retrieval system with existing system using multiple features. The Experimental results also prove that the proposed improved method provides better precision. The precision is increased from 85 to 88 percentage as per the recorded results. The precision is calculated by the ratio of number of similar images retrieved to the number of actual images retrieved from database. The Proposed method is tested by using COIL database which is freely available in web.
{"title":"An improved image retrieval system using optimized FCM & multiple shape, texture features","authors":"N. Neelima, E. Reddy","doi":"10.1109/ICCIC.2015.7435666","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435666","url":null,"abstract":"Retrieval of user interested images based on pictorial queries is an interesting and challenging task. This paper proposes an Improved Region based image retrieval system using FCM & multiple shape, texture features. The Proposed system uses Fuzzy c-means clustering algorithm for image segmentation. Local Binary Pattern (LBP), Hu moments and Radial Chebyshev Moments are used in this work. For similarity comparison City block distance is used. The experimental results presents a comparative analysis of the proposed image retrieval system with existing system using multiple features. The Experimental results also prove that the proposed improved method provides better precision. The precision is increased from 85 to 88 percentage as per the recorded results. The precision is calculated by the ratio of number of similar images retrieved to the number of actual images retrieved from database. The Proposed method is tested by using COIL database which is freely available in web.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126486704","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435784
K. Divya, K. Roshna, Shelmy Mathai
Traditional pixel level shadow detection methods cause loss of information in high resolution images. Here present an object wise methodology which can automatically detect and remove shadows from satellite images. In this method using image parameters, image segmentation is done. For seperating shadow region threshold values are used, thereby shadows are detected. Based on grayscale values Some dark objects which are mistakenly classified as shadows are ruled out and then Image featurs are taken by support vector machine for effective classification of data. Using morphological operation inner outer outline profile line (IOOPL) are created for shadow removal. Relative Radiometric Correction(RRN) is performed over each object using IOOPL sections. The application shows that the new method can effectively detect shadows from urban high-resolution remote sensing images and can accurately restore shadows.
{"title":"Shadow detection and removal by object-wise segmentation","authors":"K. Divya, K. Roshna, Shelmy Mathai","doi":"10.1109/ICCIC.2015.7435784","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435784","url":null,"abstract":"Traditional pixel level shadow detection methods cause loss of information in high resolution images. Here present an object wise methodology which can automatically detect and remove shadows from satellite images. In this method using image parameters, image segmentation is done. For seperating shadow region threshold values are used, thereby shadows are detected. Based on grayscale values Some dark objects which are mistakenly classified as shadows are ruled out and then Image featurs are taken by support vector machine for effective classification of data. Using morphological operation inner outer outline profile line (IOOPL) are created for shadow removal. Relative Radiometric Correction(RRN) is performed over each object using IOOPL sections. The application shows that the new method can effectively detect shadows from urban high-resolution remote sensing images and can accurately restore shadows.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125901549","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435632
Malepati Bala Siva Sai Akhil, P. Aashish, K. Manikantan
Face recognition is non-invasive due to various challenges like illumination variation, pose variation and limitation of 2D images from most of the image capturing technologies. In this paper three novel techniques are proposed namely Binary Artificial Bee Colony (BABC), horizontal feature extraction and feature gallery expansion. BABC is a binary version of Artificial Bee Colony (ABC) which is employed as feature selection technique for efficient reduction in selected features. It optimally selects the features from the feature vector space. Horizontal feature extraction is used for extracting unique features for face images. Feature gallery expansion is employed to increase the feature galley size for better recognition. Experimental results on two standard face databases namely LFW and CAS-PEAL indicates the consistency of the proposed techniques and prominent enhancement in face recognition.
{"title":"Feature selection using Binary-ABC algorithm for DWT-based face recognition","authors":"Malepati Bala Siva Sai Akhil, P. Aashish, K. Manikantan","doi":"10.1109/ICCIC.2015.7435632","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435632","url":null,"abstract":"Face recognition is non-invasive due to various challenges like illumination variation, pose variation and limitation of 2D images from most of the image capturing technologies. In this paper three novel techniques are proposed namely Binary Artificial Bee Colony (BABC), horizontal feature extraction and feature gallery expansion. BABC is a binary version of Artificial Bee Colony (ABC) which is employed as feature selection technique for efficient reduction in selected features. It optimally selects the features from the feature vector space. Horizontal feature extraction is used for extracting unique features for face images. Feature gallery expansion is employed to increase the feature galley size for better recognition. Experimental results on two standard face databases namely LFW and CAS-PEAL indicates the consistency of the proposed techniques and prominent enhancement in face recognition.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125147059","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435806
Santosh Kumar, R. Udaykumar
The concept of connecting group of electric vehicles (EVs) to the grid for power transaction is known as Vehicle-to-Grid (V2G). The EVs can be connected to the grid through V2G integrators and charging slots. The arrival of EVs in parking lot is time varying and random in nature and facilitating them to participate in power transaction is challenging and aggregator's responsibility. In this paper, development of Markov chain based stochastic model of Electric Vehicle Parking Lot (EVPL) occupancy is proposed. A developed model is simulated using MATLAB and the results are presented.
{"title":"Markov chain based stochastic model of electric vehicle parking lot occupancy in vehicle-to-grid","authors":"Santosh Kumar, R. Udaykumar","doi":"10.1109/ICCIC.2015.7435806","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435806","url":null,"abstract":"The concept of connecting group of electric vehicles (EVs) to the grid for power transaction is known as Vehicle-to-Grid (V2G). The EVs can be connected to the grid through V2G integrators and charging slots. The arrival of EVs in parking lot is time varying and random in nature and facilitating them to participate in power transaction is challenging and aggregator's responsibility. In this paper, development of Markov chain based stochastic model of Electric Vehicle Parking Lot (EVPL) occupancy is proposed. A developed model is simulated using MATLAB and the results are presented.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128418249","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435781
Shelmy Mathai, Paul P. Mathai, K. Divya
3d technology brings a new era of entertainment to the human race. It offered a wide array of possibilities in near future in almost every walk of life and in entertainment segment. 3D content generation is the important step in 3D systems. Special cameras such as stereoscopic dual camera, depth range camera etc. are designed to generate the 3D model of a scene directly. There are different techniques to generate the 3D content. But the problem is our current and past media data are in 2D which needs to convert into 3D. This is where the importance of 2D to 3D transformation arises. In this paper proposed real time 3D image and video creation by depth map estimation. Depth map estimation can be done in two methods. One is based on depth fusion method and other is based on saliency map of an image. In dataset image estimate the depth map from depth fusion method and then depth is refined by color spatial variance. In non dataset images we find depth map by global saliency method. Experimental result demonstrates that the proposed technique convey better performance compared to the state-of-the-art of methods.
{"title":"Automatic 2D to 3D video and image conversion based on global depth map","authors":"Shelmy Mathai, Paul P. Mathai, K. Divya","doi":"10.1109/ICCIC.2015.7435781","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435781","url":null,"abstract":"3d technology brings a new era of entertainment to the human race. It offered a wide array of possibilities in near future in almost every walk of life and in entertainment segment. 3D content generation is the important step in 3D systems. Special cameras such as stereoscopic dual camera, depth range camera etc. are designed to generate the 3D model of a scene directly. There are different techniques to generate the 3D content. But the problem is our current and past media data are in 2D which needs to convert into 3D. This is where the importance of 2D to 3D transformation arises. In this paper proposed real time 3D image and video creation by depth map estimation. Depth map estimation can be done in two methods. One is based on depth fusion method and other is based on saliency map of an image. In dataset image estimate the depth map from depth fusion method and then depth is refined by color spatial variance. In non dataset images we find depth map by global saliency method. Experimental result demonstrates that the proposed technique convey better performance compared to the state-of-the-art of methods.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130389906","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435798
Sajal Pahariya, S. Tiwari
In this paper, we are using maximum similarity region merging(MSRM), anisotropic diffusion (AD), noise adaptive fuzzy switching median filter, active countour /snake model. In the proposed approach, work on both gray or color images. With the use of MSRM, merge the maximum similarity area/region. AD is used to smooth the image. NAFSM is used for removing noise from an image. In the last step, we used a Snake model for removing blur effect from an image. The results on peak signal noise ratio (PSNR), Mean square error (MSE), accuracy and time method give better performance in terms of brightness and contrast of the enhanced image remove noise and increase brightness.
{"title":"Image segmentation using snake model with nosie adaptive fuzzy switching median filter and MSRM method","authors":"Sajal Pahariya, S. Tiwari","doi":"10.1109/ICCIC.2015.7435798","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435798","url":null,"abstract":"In this paper, we are using maximum similarity region merging(MSRM), anisotropic diffusion (AD), noise adaptive fuzzy switching median filter, active countour /snake model. In the proposed approach, work on both gray or color images. With the use of MSRM, merge the maximum similarity area/region. AD is used to smooth the image. NAFSM is used for removing noise from an image. In the last step, we used a Snake model for removing blur effect from an image. The results on peak signal noise ratio (PSNR), Mean square error (MSE), accuracy and time method give better performance in terms of brightness and contrast of the enhanced image remove noise and increase brightness.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131967233","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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