ICTACT Journal on Image and Video Processing最新文献_第8页

EFFICIENT HIGH QUALITY VIDEO ASSESSMENT USING SALIENT FEATURES 使用显著特征的高效高质量视频评估

ICTACT Journal on Image and Video Processing Pub Date : 2017-08-01 DOI: 10.21917/IJIVP.2017.0222

K. Rekha, A. Kumar

{"title":"EFFICIENT HIGH QUALITY VIDEO ASSESSMENT USING SALIENT FEATURES","authors":"K. Rekha, A. Kumar","doi":"10.21917/IJIVP.2017.0222","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0222","url":null,"abstract":"High Definition (HD) devices requires HD-videos for the effective uses of HD devices. However, it consists of some issues such as high storage capacity, limited battery power of high definition devices, long encoding time, and high computational complexity when it comes to the transmission, broadcasting and internet traffic. Many existing techniques consists these above-mentioned issues. Therefore, there is a need of an efficient technique, which reduces unnecessary amount of space, provides high compression rate and requires low bandwidth spectrum. Therefore, in the paper we have introduced an efficient video compression technique as modified HEVC coding based on saliency features to counter these existing drawbacks. We highlight first, on extracting features on the raw data and then compressed it largely. This technique makes our model powerful and provides effective performance in terms of compression. Our experiment results proves that our model provide better efficiency in terms of average PSNR, MSE and bitrate. Our experimental results outperforms all the existing techniques in terms of saliency map detection, AUC, NSS, KLD and JSD. The average AUC, NSS and KLD value by our proposed method are 0.846, 1.702 and 0.532 respectively which is very high compare to other existing technique.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1575-1582"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48508646","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 IMPROVED EXEMPLAR-BASED IMAGE INPAINTING ALGORITHM FOR ERROR CONCEALMENT 一种改进的基于样本的错误隐藏图像绘制算法

ICTACT Journal on Image and Video Processing Pub Date : 2017-08-01 DOI: 10.21917/IJIVP.2017.0223

Hwang Byongsu, Jo Jonghyon, Ri Cholsu

引用次数: 2

MULTISCALE SPARSE APPEARANCE MODELING AND SIMULATION OF PATHOLOGICAL DEFORMATIONS 多尺度稀疏外观建模与病理变形仿真

ICTACT Journal on Image and Video Processing Pub Date : 2017-08-01 DOI: 10.21917/IJIVP.2017.0225

R. Zewail, Ahmed Hag-ElSafi

引用次数: 0

AUTOMATED CORPUS CALLOSUM SEGMENTATION IN MIDSAGITTAL BRAIN MR IMAGES 中矢状脑MR图像中胼胝体的自动分割

ICTACT Journal on Image and Video Processing Pub Date : 2017-08-01 DOI: 10.21917/IJIVP.2017.0220

Yue Li, Huiquan Wang, Nizam Ahmed, M. Mandal

{"title":"AUTOMATED CORPUS CALLOSUM SEGMENTATION IN MIDSAGITTAL BRAIN MR IMAGES","authors":"Yue Li, Huiquan Wang, Nizam Ahmed, M. Mandal","doi":"10.21917/IJIVP.2017.0220","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0220","url":null,"abstract":"Corpus Callosum (CC) is an important white-matter structure in the human brain. Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that provides high resolution images for the structures. Segmentation is an important step in medical image analysis. This paper proposes a fully automated technique for segmentation of CC on the midsagittal slice of T1-weighted brain MR images. The proposed technique consists of three modules. First it clusters all homogenous regions in the image with an adaptive mean shift (AMS) technique. The automatic CC contour initialization (ACI) is achieved using the region analysis, template matching and location analysis, thus identify the CC region. Finally, the boundary of recognized CC region is used as the initial contour in the Geometric Active Contour (GAC) model, and is evolved to obtain the final segmentation result of CC. Experimental results demonstrate that the proposed AMS-ACI technique is able to provide accurate initial CC contour, and the proposed AMS-ACI-GAC technique overcomes the problem of user-guided initialization in existing GAC techniques, and provides a reliable and accurate performance in CC segmentation.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"8 1","pages":"1554-1565"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41432411","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

AUTOMATIC ROI EXTRACTION IN NOISY MEDICAL IMAGES 医学图像噪声中ROI的自动提取

ICTACT Journal on Image and Video Processing Pub Date : 2017-06-01 DOI: 10.21917/IJIVP.2017.0215

S. Renukalatha, K. Suresh

{"title":"AUTOMATIC ROI EXTRACTION IN NOISY MEDICAL IMAGES","authors":"S. Renukalatha, K. Suresh","doi":"10.21917/IJIVP.2017.0215","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0215","url":null,"abstract":"Accurate segmentation of medical images is pivotal in medical image analysis as it favors the detection and quantification of abnormalities present in human anatomical structures. Since medical images are complex and sometimes noisy, effective extraction of the regions of abnormalities is a tedious process. Many semi-automatic segmentation algorithms with appreciable segmentation accuracy do exist in literature. However, these techniques are iterative, computationally expensive, involve human intervention demanding initial parameter settings and moreover, each one of them is specific to a particular modality. In addition, presence of noise further degrades the quality of the processed image. There is no general algorithm to extract the key regions from all types of noisy medical images. This paper proposes an automatic Region of Interest (ROI) extraction algorithm to detect the important regions in noisy medical images of different modalities using statistical moments. The proposed approach estimates an optimal threshold value automatically using statistical moments through histogram decomposition technique. Initially, the medical image database is preprocessed followed by ROI extraction and the performance of the proposed approach is compared with other techniques to verify its robustness.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"7 1","pages":"1505-1514"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46706957","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: 10

ELM BASED CAD SYSTEM TO CLASSIFY MAMMOGRAMS BY THE COMBINATION OF CLBP AND CONTOURLET 基于Elm的基于CLBP和contourlet相结合的乳腺x线照片分类cad系统

ICTACT Journal on Image and Video Processing Pub Date : 2017-06-01 DOI: 10.21917/IJIVP.2017.0213

S. Venkatalakshmi, J. Janet

{"title":"ELM BASED CAD SYSTEM TO CLASSIFY MAMMOGRAMS BY THE COMBINATION OF CLBP AND CONTOURLET","authors":"S. Venkatalakshmi, J. Janet","doi":"10.21917/IJIVP.2017.0213","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0213","url":null,"abstract":"Breast cancer is a serious life threat to the womanhood, worldwide. Mammography is the promising screening tool, which can show the abnormality being detected. However, the physicians find it difficult to detect the affected regions, as the size of microcalcifications is very small. Hence it would be better, if a CAD system can accompany the physician in detecting the malicious regions. Taking this as a challenge, this paper presents a CAD system for mammogram classification which is proven to be accurate and reliable. The entire work is decomposed into four different stages and the outcome of a phase is passed as the input of the following phase. Initially, the mammogram is pre-processed by adaptive median filter and the segmentation is done by GHFCM. The features are extracted by combining the texture feature descriptors Completed Local Binary Pattern (CLBP) and contourlet to frame the feature sets. In the training phase, Extreme Learning Machine (ELM) is trained with the feature sets. During the testing phase, the ELM can classify between normal, malignant and benign type of cancer. The performance of the proposed approach is analysed by varying the classifier, feature extractors and parameters of the feature extractor. From the experimental analysis, it is evident that the proposed work outperforms the analogous techniques in terms of accuracy, sensitivity and specificity.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"7 1","pages":"1489-1496"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48354703","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

SUPPORT VECTOR MACHINE BASED APPROACH FOR TRANSLATING VIDEO SCENERIES TO NATURAL LANGUAGE DESCRIPTIONS 基于支持向量机的视频场景到自然语言描述的转换方法

ICTACT Journal on Image and Video Processing Pub Date : 2017-06-01 DOI: 10.21917/IJIVP.2017.0212

V. Wankhede, R. Kagalkar

引用次数: 0

HIDING TEXT IN DIGITAL IMAGES USING PERMUTATION ORDERING AND COMPACT KEY BASED DICTIONARY 基于排列排序和紧凑密钥字典的数字图像文本隐藏

ICTACT Journal on Image and Video Processing Pub Date : 2017-06-01 DOI: 10.21917/IJIVP.2017.0214

N. Rajan, R. Sunder

引用次数: 0

IMPROVED WAVELET COMPRESSION ALGORITHM FOR COLOR IMAGE 改进的彩色图像小波压缩算法

ICTACT Journal on Image and Video Processing Pub Date : 2017-06-01 DOI: 10.21917/IJIVP.2017.0211

S. Babu, P. Eswaran, C. S. Kumar, E. Arts

{"title":"IMPROVED WAVELET COMPRESSION ALGORITHM FOR COLOR IMAGE","authors":"S. Babu, P. Eswaran, C. S. Kumar, E. Arts","doi":"10.21917/IJIVP.2017.0211","DOIUrl":"https://doi.org/10.21917/IJIVP.2017.0211","url":null,"abstract":"An image compression technique has been done in research in the recent years due to its clarity and quality compared to other techniques. Image compression based on wavelet is very important-role and occupies many applications. The objective of image compression is to help in storing the transmitted date in an efficient way by decreasing its redundancy. The wavelet compression reduces the size of the image data while retaining information and maintaining a certain wavelet compression. The proposed method of Improved Wavelet Compression (IWC) is presented in this paper. The proposed IWC gets a color image from the database. After receiving image, waveletTransformation using filter bank techniques are applied to the test image. After compression, the inverse IWC decompression algorithm receives compressed image and applied decompression technique. The image is generated and image quality is reconstructed and the original image is evaluated. The numerical measure parameters such as MSE, PSNR, are used to compare various images. From the experimental result, it is observed that the proposed method IWC gives a better compression ratio in 64.56 while compared to the existing methods.","PeriodicalId":30615,"journal":{"name":"ICTACT Journal on Image and Video Processing","volume":"7 1","pages":"1471-1481"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47020804","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

IMPROVING EFFICIENCY IN IMAGE ENCRYPTION AND COMPRESSION USING PERMUTATIONS & PREDICTIONS 使用排列和预测提高图像加密和压缩的效率

ICTACT Journal on Image and Video Processing Pub Date : 2017-06-01 DOI: 10.21917/IJIVP.2017.0210

S. Shunmugan, P. Rani

引用次数: 0