2011 International Conference on Image Information Processing最新文献

{"title":"Characterization of motion cardiac patterns in magnetic resonance cine","authors":"F. Martínez, A. Manzanera, C. Santa Marta, E. Romero","doi":"10.1109/ICIIP.2011.6108837","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108837","url":null,"abstract":"One of the most important tasks in Cardiac Magnetic resonance Cine (CMRC) consists in identifying and describing normal and abnormal dynamic heart patterns, a task usually performed by physicians. Segmentation and tracking may support decisions during a particular treatment, but their performance is dependent on the quality of the video. The acquired signal, on the other hand, is contaminated with noise coming from physiological movements and devices, resulting in cardiac blurred boundaries. This paper presents a novel method that automatically identifies flow heart patterns by establishing similarities between two consecutive frames to which a local jet feature analysis has been applied. Once a vector motion field is calculated, spatially connected regions with minimal variance are found as the sources of movement and different statistics objectively estimate movement patterns of these regions. The utility of this method is illustrated by comparing the temporal series of these regions between normal and abnormal patients.","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"01 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127159657","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}

{"title":"k-nearest neighbor based offline handwritten Gurmukhi character recognition","authors":"Munish Kumar, M. Jindal, R. Sharma","doi":"10.1109/ICIIP.2011.6108863","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108863","url":null,"abstract":"Offline handwritten character recognition has been a frontier area of research for the last few decades under pattern recognition. Recognition of handwritten characters is a difficult task owing to various writing styles of individuals. A scheme for offline handwritten Gurmukhi character recognition based on k-NN classifier is presented in this paper. The system first prepares a skeleton of the character, so that feature information about the character is extracted. There is abundant literature on the handwriting recognition on non-Indian scripts, but there are very few article available related to recognition of Indian scripts such as Gurmukhi. This paper presents an efficient offline handwritten Gurmukhi character recognition system based on diagonal features and transitions features using k-NN classifier. Diagonal and transitions features of a character have been computed based on distribution of points on the bitmap image of character. In k-NN method, the Euclidean distance between testing point and reference points is calculated in order to find the k-nearest neighbors. In this work, we have taken the samples of offline handwritten Gurmukhi characters from one hundred different writers. The partition strategy for selecting the training and testing patterns has also been experimented in this work. We have used in all 3500 images of Gurmukhi characters for the purpose of training and testing. The proposed system achieves a maximum recognition accuracy of 94.12% using diagonal features and k-NN classifier.","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126481438","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}

{"title":"Multichannel digital watermarking of color images using SVD","authors":"Rashmi Agarwal, M. Santhanam, K. Venugopalan","doi":"10.1109/ICIIP.2011.6108893","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108893","url":null,"abstract":"In this paper we propose a new invisible, non-blind digital watermarking scheme for color images based on singular vector domain (SVD). In this new approach, same or different watermarks can be embedded into the three color channels (R, G, B) of the host image in order to increase the robustness of the watermark. The proposed method demonstrates that color image watermarking is possible without affecting its visual quality and also allows for reasonable compromise between robustness and invisibility of watermarks. This research, based on our earlier work, consists of embedding a gray watermark into a gray host and a color watermark into a color host based on the singular vector domain. The main feature of our scheme is to use all channels of RGB color space to embed watermarks. Hence we are embedding maximum amount of watermark and also make it more secure. The other feature of our scheme is that it can be used for a variety of applications since we can perform single channel embedding to multichannel embedding. We then show our simulation results for one channel, two channel and three channel and also results for how it behaves under various attacks.","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127255465","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}

{"title":"Prediction of cirrhosis from liver ultrasound B-mode images based on Laws' masks analysis","authors":"J. Virmani, Vinod Kumar, N. Kalra, N. Khandelwal","doi":"10.1109/ICIIP.2011.6108894","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108894","url":null,"abstract":"In this present work, a technique for differentiation of normal and cirrhotic liver segmented regions of interest (SROIs) based on Laws' masks analysis is reported. Thirty four B-mode ultrasound images taken from 22 normal volunteers and 12 patients suffering from liver cirrhosis were collected from Department of Radiodiagnosis and Imaging, PGIMER, Chandigarh, India. The filtered texture images are obtained by convolving the SROIs with twenty five, 2D (5×5) special filters based on laws' masks. Metrics that can quantify the texture can be obtained by computing the statistics from these filtered texture images. Similar features are combined to remove the directional information as texture directionality is not important here. This results into 15 rotational invariant filtered texture images for each SROI. For each of the filtered images, five statistics namely, mean, standard deviation, skewness, kurtosis and energy are computed. Thus, a total of 75 Laws' texture features (15 filtered texture images × 5 statistical features) are computed for 82 normal SROIs and 39 cirrhotic SROIs taken from 34 B-Mode ultrasound liver images. Correlation based feature selection (CFS) method is used to find the optimal subset of Laws' texture features which can provide best discrimination between normal and cirrhotic SROIs. It has been observed that CFS method results in an optimal subset of 8 Laws' texture features {LLmean, LLsd, LEsd, SSskewness, RRenergy, LEenergy, LSenergy and LWenegy}. The classification performance of neural network (NN) classifier is compared with support vector machine (SVM) classifier. By using all 75 Laws' texture features the classification accuracy of 90.08% and 90.90% is obtained with NN and SVM classifier respectively. By using 8 Laws' features selected by CFS method the classification accuracy of 91.73% and 92.56% is obtained with NN and SVM classifier respectively. From the comparison it is can be concluded that only 8 Laws' texture features namely {LLmean, LLsd, LEsd, SSskewness, RRenergy, LEenergy, LSenergy and LWenegy} can be used to build an efficient computer aided diagnostic (CAD) system for predicting of liver cirrhosis.","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130713634","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}

{"title":"Edge based segmentation for pedestrian detection using NIR camera","authors":"Tarun Kancharla, P. Kharade, S. Gindi, K. Kutty, Vinay G. Vaidya","doi":"10.1109/ICIIP.2011.6108965","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108965","url":null,"abstract":"In this paper we present a fast and robust segmentation scheme for a nighttime pedestrian detection system. The system uses an IR source for illumination and (Near Infrared) NIR camera to capture images of a scene at night. A new vertical edge detection method is used to identify edges in images that belong to pedestrians. These edges are further combined to form potential pedestrian image blocks called as ‘candidate blocks’. Tight bound rectangular blocks are obtained from the candidate blocks using intensity profiling. The candidate blocks also undergo a process of elimination based on certain criteria, in order to reduce false positives. Further, two different types of schemes are experimented for tracking of the pedestrian blocks. One is based on template matching and the other is based on image segmentation. Performance characterization of the algorithm has been carried to evaluate its robustness against contrast variations. The experimental results show that the algorithm is robust and fast, for use in real-time applications. Invention disclosure has been filed for the method described here [9].","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126997450","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}

{"title":"Scale weight selection for feature extraction using complex wavelets: A framework","authors":"Shubha Bhat, Vindhya P. Malagi, D. R. Ramesh Babu, K. Ramakrishna, M. Ravishankar","doi":"10.1109/ICIIP.2011.6108960","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108960","url":null,"abstract":"Unmanned Air Vehicles (UAVs) have become an intelligent asset for surveillance, target tracking and reconnaissance in both urban and battlefield settings. This paper gives a framework for scale weight selection during feature extraction in aerial images from UAV. Dual-Tree Complex Waveform technique is used to extract rich feature descriptors of keypoints in images so that full phase and amplitude information can be retained at each scale. The scale weights are dependent on image characteristics such as the illumination and the contrast levels. The outcome of the framework shows promising results in terms of less redundancy of salient features from the images and hence improving the computational speed.","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129893140","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}

{"title":"Robust video denoising for better subjective evaluation","authors":"A. Acharya, S. Meher","doi":"10.1109/ICIIP.2011.6108967","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108967","url":null,"abstract":"Video denoising is a pre-processing step of many high level video processing applications since it can considerably enhance the perceived video quality, increase compression effectiveness, facilitate transmission bandwidth reduction and improve the performance of the subsequent higher level task such as feature extraction, object detection, prediction, coding and object tracking. In this paper, a new scheme for video denoising based on spatial filtering is proposed which synthesizes the blurring caused by averaging filter and sharpening due to high boost filter for effective objective and subjective video quality enhancement. The main drawback of averaging filter is that it gives a blurring effect and de-emphasizes the fine details from an image such as edges and texture. This blurring artefact is compensated through highboost filtering operation by selecting an appropriate value of the highboost weight factor intended for better video quality and superior HVS performance. Experimental results show that the proposed method achieves better performance of noise removal with much reduced blurring and fine detail preservation in terms of improved objective and subjective video quality.","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133903590","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}

{"title":"Analysis of brain MRI images of intracerebral haemorrhage using frequency domain technique","authors":"S. S. Shanbhag, G. R. Udupi, K. M. Patil, K. Ranganath","doi":"10.1109/ICIIP.2011.6108887","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108887","url":null,"abstract":"Studies were performed on a set of diffusion weighted magnetic resonance images (DW-MRI) of the human brain (in the axial plane) to find the relationship between the MRI light intensity high frequency power (HFP) parameter values and the different stages of intracerebral haemorrhage (ICH). Analysis of the results show that the differences in the light intensity HFP values in the DW-MRI for the subjects with ICH compared to their contralateral normal hemispheres, were highly significant (p < 0.05) in the areas of the brain where there was a high incidence of haemorrhage. The relative increase in the light intensity HFP values for the subjects with ICH was in the range of (101.35 – 1534.81) times compared to the corresponding HFP values in the contralateral normal hemisphere. The results demonstrate that the quantitative changes in the HFP parameter values can be assessed and possibly used in grading the different stages of the haemorrhage, to assist the neuro surgeons for rapid diagnosis and implementation of treatment to protect the patients from additional damage to their brain tissue.","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129824546","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}

{"title":"Integration of RSM model for optimization of immobilized lipase mediated solvent-free synthesis of flavour ester by genetic algorithm","authors":"Vijay Kumar Garlapati, P. Vundavilli, R. Banerjee","doi":"10.1109/ICIIP.2011.6108936","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108936","url":null,"abstract":"In the present study, optimization of immobilized lipase mediated solvent-free synthesis of octyl acetate by transesterification has been carried out based on the experimentally validated RSM model. The effect of tansesterification variables namely alcohol molarity, reaction time, temperature, Immobilized lipase amount on molar conversion (%) was investigated. A maximum molar conversion of 92.92% was obtained with the GA parameters of tournament selection of size two, uniform crossover probability (Pc) of 0.5, bit-wise mutational probability (Pm) of 0.0029, population size of 80 and maximum number of generations of 382. The optimal values of the transesterification variables were found to be 2.09 M octanol in vinyl acetate, 12.61 h, 32 °C and 26.01 U for alcohol molarity, reaction time, temperature and amount of Immobilized enzyme respectively.","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124813547","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}

{"title":"Blood microscopic image segmentation using rough sets","authors":"Subrajeet Mohapatra, D. Patra, Kundan Kumar","doi":"10.1109/ICIIP.2011.6108977","DOIUrl":"https://doi.org/10.1109/ICIIP.2011.6108977","url":null,"abstract":"Hematological disorders are mostly identified based on characterization of blood parameters i.e. erythrocytes, leukocytes and platelets. Microscopic examination of leukocytes in blood slides is the most frequent laboratory investigation performed for malignancy detection. Hematological examination of blood is an indispensable technique still today and solely depends on human visual interpretation. Such examination are subjected to inter and intra-observer variations, slowness, tiredness and operator experience. Accurate and authentic diagnosis of hematological neoplasia can help in the planning of suitable surgery and chemotherapy, and generally improve the quality of patient care. Microscopy cell image analysis is a tool which facilitates conventional blood examination for disease detection using quantitative microscopy. Thus microscopic image analysis serves as an impressive diagnostic tool for hematological disease (leukemia, malaria, psoriasis, AIDS etc) recognition. The present paper aims at leukocyte or white blood cell (WBC) segmentation which can assist in acute leukemia detection. A rough set based clustering approach is followed for color based segmentation of WBC. The segmented nucleus and cytoplasm can be used for feature extraction which can lead to classification of a leukocyte into mature lymphocyte or lymphoblast.","PeriodicalId":201779,"journal":{"name":"2011 International Conference on Image Information Processing","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121186646","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}