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Praneesh, M.
- Blurred Image Restoration Using Canny Edge Detection and Blind Deconvolution Algorithm
Abstract Views :135 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, Sankara College of Science and Commerce, Coimbatore, IN
1 Department of Computer Science, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, Sankara College of Science and Commerce, Coimbatore, IN
Source
Digital Image Processing, Vol 4, No 8 (2012), Pagination: 444-447Abstract
Restoring the original image from blurred or degraded image due to motion blur, noise or camera misfocus has long been a challenging problem in digital imaging. Image restoration is thus a process of recovering the actual image from the degraded image. The purpose of the paper is to restore the blurred/degraded images using blind deconvolution algorithm with canny edge detector. The task of image deblurring is to deconvolute the degraded image with the point spread function (PSF) that describes the distortion. Firstly the original image is degraded using degradation model. It can be done by low pass filters like Gaussian filter or others to blur an image. The ringing effect at the edges of blurred image can be detected using Canny Edge Detection method. Blind deconvolution algorithm is applied to the blurred image where image recovery is performed with little or no prior knowledge of the degrading PSF. Also the penalized maximum likelihood Estimation Technique is used with blind deconvolution algorithm.Keywords
Edge Detection, Deconvolution Algorithm, Image, Pixels.- A Comparative Analysis of Clustering Algorithms for Content Based Image Retrieval
Abstract Views :318 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, Sankara College of Science and Commerce, Coimbatore, IN
1 Department of Computer Science, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, Sankara College of Science and Commerce, Coimbatore, IN
Source
Digital Image Processing, Vol 4, No 8 (2012), Pagination: 448-450Abstract
Content based image retrieval is a set of techniques for retrieving semantically relevant images from an image data based on automatically derived image features. In CBIR, Image are indexed by their visual content, such as color, texture and shapes. Further research has suggested that the usage of clustering technique of image retrieval. For this paper we compare Fuzzy Possiblistic C-Means clustering algorithm for retrieving the most similar images. In our experimental results shows that the modify Fuzzy Possiblistic Clustering Algorithm is better retrieval.Keywords
Content-Based Image Retrieval, Query, Modify Fuzzy Possiblistic C-Means.- A Comparative Analysis of Clustering Algorithms for Content Based Image Retrieval
Abstract Views :329 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, School of Computer Science and Engineering, Bharathiar University, Coimbatore, IN
1 Department of Computer Science, School of Computer Science and Engineering, Bharathiar University, Coimbatore, IN
Source
Fuzzy Systems, Vol 4, No 3 (2012), Pagination: 112-114Abstract
Content based image retrieval is a set of techniques for retrieving semantically relevant images from an image data based on automatically derived image features. In CBIR, Image are indexed by their visual content, such as color, texture and shapes. Further research has suggested that the usage of clustering technique of image retrieval. For this paper we compare Fuzzy Possiblistic C-Means clustering algorithm for retrieving the most similar images. Inour experimental results shows that the modify Fuzzy Possiblistic Clustering Algorithm is better retrieval.Keywords
Query, Modify Fuzzy Possiblistic C-Means, Content-Based Image Retrieval.- Classification of Image Using Fuzzy Lattice Neural Model
Abstract Views :166 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
3 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
1 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
3 Department of Computer Science, School of Computer Science Engineering, Bharathiar University, Coimbatore, IN
Source
Fuzzy Systems, Vol 3, No 9 (2011), Pagination: 365-368Abstract
Computer hallucination, unlike humans, still has not fully acquired the facility to categories a person’s age group from an image of the person’s face. Successful gender and age classification could be used to boot the performance of face recognition system. Fuzzy models have been used and analyzed in this work to achieve the desired results. The concept of fuzzy lattice neural model is introduced and is applied to classify the age group of a person from the gray scale facial image. Next the fuzzy lattice relation model is constructed and is used to classify the age group of a person. Then the fuzzy lattice neural model is applied to segment an aerial gray scale image.
Keywords
Fuzzy Lattice Neural Model, Image Classification, Clustering, Image Processing.- Content based Image Retrieval using Texture and Color Extraction based Binary Tree Structure
Abstract Views :345 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, Bharathiar University, IN
2 Department of Computer Science, Sankara College of Science and Commerce, IN
1 Department of Computer Science, Bharathiar University, IN
2 Department of Computer Science, Sankara College of Science and Commerce, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 5 (2012), Pagination: 345-348Abstract
There are different methods of image retrieval where the meta-data is associated with the image, commonly called as keywords. Content based image retrieval is important research field in many applications. In this paper the CBIR system is proposed which introduces a new binary tree approach along with color and texture common in most of the CBIR system for finding similar images from the database to a given query image. There are different features of an image such as color, texture, shape, orientation, etc. In the proposed system color and texture are used as basic features to describe all the images. In addition, a binary tree structure is used to describe higher level features of an image. To extract color information, two histograms i.e. Hue and saturation of the image are used. And to extract texture information image quantization and wavelet decomposition is applied to each image blocks. The Hue is quantized into 360 levels and the saturation into 100 levels The binary tree structure is implemented based on steps provided .In this system, the feature extraction and wavelet decomposition for texture extraction is used to compute the feature vectors of any image which helps in retrieval process. This approach combines the color and texture features and binary partitioning tree method in order to find the images similar to a specific query image. The Minkowski difference equation is used to measure the distance. The image processing toolbox is available in the Matlab which consists of various inbuilt function to perform various operation on the image easily, which are difficult if they are implement using user defined function. The proposed system is implemented using the functions of Matlab software.Keywords
Binary Tree Structure, Color Information, Image Reterival, Texture.- IoT Architecture Reference Model for Analysis of Applications Based on Security, Privacy and Threads
Abstract Views :181 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, Sankara College of Science and Commerce, Bharathiar University, IN
2 Department of Master of Computer Application, Sankara College of Science and Commerce, Bharathiar University, IN
1 Department of Computer Science, Sankara College of Science and Commerce, Bharathiar University, IN
2 Department of Master of Computer Application, Sankara College of Science and Commerce, Bharathiar University, IN