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Search Results: 1 - 10 of 9402 matches for " Local Feature "
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Matching DSIFT Descriptors Extracted from CSLM Images  [PDF]
Stefan G. Stanciu, Dinu Coltuc, Denis E. Tranca, George A. Stanciu
Engineering (ENG) , 2013, DOI: 10.4236/eng.2013.510B042
Abstract:

The matching of local descriptors represents at this moment a key tool in computer vision, with a wide variety of methods designed for tasks such as image classification, object recognition and tracking, image stitching, or data mining relying on it. Local feature description techniques are usually developed so as to provide invariance to photometric variations specific to the acquisition of natural images, but are nonetheless used in association with biomedical imaging as well. It has been previously shown that the matching of gradient based descriptors is affected by image modifications specific to Confocal Scanning Laser Microscopy (CSLM). In this paper we extend our previous work in this direction and show how specific acquisition or post-processing methods alleviate or accentuate this problem.

Medical image fusion based on pulse coupled neural networks and multi-feature fuzzy clustering  [PDF]
Xiaoqing Luo, Xiaojun Wu
Journal of Biomedical Science and Engineering (JBiSE) , 2012, DOI: 10.4236/jbise.2012.512A111
Abstract:

Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and get more reliable results, a novel medical image fusion algorithm based on pulse coupled neural networks (PCNN) and multi-feature fuzzy clustering is proposed, which makes use of the multi-feature of image and combines the advantages of the local entropy and variance of local entropy based PCNN. The results of experiments indicate that the proposed image fusion method can better preserve the image details and robustness and significantly improve the image visual effect than the other fusion methods with less information distortion.

The Study of Multi-Expression Classification Algorithm Based on Adaboost and Mutual Independent Feature  [PDF]
Liying Lang, Zuntao Hu
Journal of Signal and Information Processing (JSIP) , 2011, DOI: 10.4236/jsip.2011.24038
Abstract: In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are separated as mutual independent elements for facial feature extraction and classification. The multi-expression classification algorithm which is based on Adaboost and mutual independent feature is proposed. In order to effectively and quickly train threshold values of weak classifiers of features, Sample of training is carried out simple improvement. We obtain a good classification results through experiments.
Preserving Global and Local Features for Robust Face Recognition under Various Noisy Environments
Ruba Soundar Kathavarayan,Murugesan Karuppasamy
International Journal of Image Processing , 2010,
Abstract: Much research on face recognition considering the variations in visual stimulusdue to illumination conditions, viewing directions or poses, and facial expressionshas been done earlier. However, in reality the noises that may embed into animage document will affect the performance of face recognition algorithms.Though different filtering algorithms are available for noise reduction, applying afiltering algorithm that is sensitive to one type of noise to an image which hasbeen degraded by another type of noise lead to unfavorable results. Theseconditions stress the importance of designing a robust face recognition algorithmthat retains recognition rates even under noisy conditions. In this work, numerousexperiments have been conducted to analyze the robustness of our proposedCombined Global and Local Preserving Features (CGLPF) algorithm along withother existing conventional algorithms under different types of noises such asGaussian noise, speckle noise, salt and pepper noise and quantization noise.
Identification of Untrained Facial Image in Combined Global and Local Preserving Feature Space
Murugesan Karuppasamy,Ruba Soundar Kathavarayan
International Journal of Biometric and Bioinformatics , 2010,
Abstract: In real time applications, biometric authentication has been widely regarded as the most foolproof - or at least the hardest to forge or spoof. Several research works on face recognition based on appearance, features like intensity, color, textures or shape have been done over the last decade. In those works, mostly the classification is achieved by using the similarity measurement techniques that find the minimum distance among the training and testing feature set. When presenting This leads to the wrong classification when presenting the untrained image or unknown image, since the classification process locates at least one wining cluster that having minimum distance or maximum variance among the existing clusters. But for the real time security related applications, these new facial image should be reported and the necessary action has to be taken accordingly. In this paper we propose the following two techniques for this purpose: i. Uses a threshold value calculated by finding the average of the minimum matching distances of the wrong classifications encountered during the training phase. ii. Uses the fact that the wrong classification increases the ratio of within-class distance and between-class distance. Experiments have been conducted using the ORL facial database and a fair comparison is made with these two techniques to show the efficiency of these techniques.
Lossless Location Coding for Image Feature Points
Gyeongmin Choi,Hyunil Jung,Haekwang Kim
International Journal of Machine Learning and Computing , 2013, DOI: 10.7763/ijmlc.2013.v3.290
Abstract: Image retrieval research activity has moved its focus from global descriptors to local descriptors of feature point such as SIFT. Currently MPEG is working on standardization of effective coding of location and local descriptors of feature point in the context mobile based image search driven application in the name of MPEG-7 CDVS (Compact Descriptor for Visual Search). While CDVS is dealing with lossy compression of location information, this paper presents various lossless compression methods of locations and provides comparative experimental results for applications requiring fine matching precision. Among 4 methods presented in this paper, Experimental results show that Block-based lossless location coding using circular scan order method shows the best compression results with compression ratio achieving 2.5 to 1 with reference to Fixed-Length lossless location coding method.
A Novel Image Correlation Matching Approach
Baoming Shan
Journal of Multimedia , 2010, DOI: 10.4304/jmm.5.3.268-275
Abstract: In this paper we present a novel approach which is combined local invariant feature descriptor named ARPIH (Angular Radial Partitioning Intensity Histogram) with histogram-based similar distance (HSD). The approach succeeds the ARPIH descriptor’s distinctive advantage and provides higher robustness in deformation image matching, such as rotation image, illumination changing image and perspective image, etc. Based on the MCD algorithm, we present the HSD algorithm. This algorithm transforms the image matching into the histogram matching by calculating the number of the similar points between template histogram and target histogram in order to decrease the calculation complicacy and improve the matching efficiency. A large amount groups of images are used in testing the approach presented in this paper. The matching results presented here indicate that the presented algorithm is efficient to figure out both the geometric deformation image matching and the illumination changing image matching. Contrast with the traditional matching algorithm, the approach presented in this paper has the obvious advantage of high matching precision, robustness and performance efficiency.
Supervised Fuzzy Mixture of Local Feature Models  [PDF]
Mingyang Xu, Michael Golay
Intelligent Information Management (IIM) , 2011, DOI: 10.4236/iim.2011.33011
Abstract: This paper addresses an important issue in model combination, that is, model locality. Since usually a global linear model is unable to reflect nonlinearity and to characterize local features, especially in a complex sys-tem, we propose a mixture of local feature models to overcome these weaknesses. The basic idea is to split the entire input space into operating domains, and a recently developed feature-based model combination method is applied to build local models for each region. To realize this idea, three steps are required, which include clustering, local modeling and model combination, governed by a single objective function. An adaptive fuzzy parametric clustering algorithm is proposed to divide the whole input space into operating regimes, local feature models are created in each individual region by applying a recently developed fea-ture-based model combination method, and finally they are combined into a single mixture model. Corre-spondingly, a three-stage procedure is designed to optimize the complete objective function, which is actu-ally a hybrid Genetic Algorithm (GA). Our simulation results show that the adaptive fuzzy mixture of local feature models turns out to be superior to global models.
Appearance Global and Local Structure Fusion for Face Image Recognition
Arif Muntasa,Indah Agustien Sirajudin,Mauridhi Hery Purnomo
TELKOMNIKA , 2011,
Abstract: Principal component analysis (PCA) and linear descriminant analysis (LDA) are an extraction method based on appearance with the global structure features. The global structure features have a weakness; that is the local structure features can not be characterized. Whereas locality preserving projection (LPP) and orthogonal laplacianfaces (OLF) methods are an appearance extraction with the local structure features, but the global structure features are ignored. For both the global and the local structure features are very important. Feature extraction by using the global or the local structures is not enough. In this research, it is proposed to fuse the global and the local structure features based on appearance. The extraction results of PCA and LDA methods are fused to the extraction results of LPP. Modelling results were tested on the Olivetty Research Laboratory database face images. The experimental results show that our proposed method has achieved higher recognation rate than PCA, LDA, LPP and OLF Methods.
A Robust Algorithm for Subspace Clustering of High-Dimensional Data*
Hongfang Zhou,Boqin Feng,Lintao Lv,Yue Hui
Information Technology Journal , 2007,
Abstract: Subspace clustering has been studied extensively and widely since traditional algorithms are ineffective in high-dimensional data spaces. Firstly, they were sensitive to noises, which are inevitable in high-dimensional data spaces; secondly, they were too severely dependent on some distance metrics, which cannot act as virtual indicators as in high-dimensional data spaces; thirdly, they often use a global threshold, but different groups of features behave differently in various dimensional subspaces. Accordingly, traditional clustering algorithms are not suitable in high-dimensional spaces. On the analysis of the advantages and disadvantages inherent to the traditional clustering algorithm, we propose a robust algorithm JPA (Joining-Pruning Algorithm). Our algorithm is based on an efficient two-phase architecture. The experiments show that our algorithm achieves a significant gain of runtime and quality in comparison to nowadays subspace clustering algorithms.
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