Pub Date : 2008-11-01DOI: 10.1109/IPTA.2008.4743765
Junyan Tan, Linlin Zhang, V. Fresse, A. Legrand, D. Houzet
The presented parameterised and predictive architecture is dedicated for image analysis algorithms implementations on FPGAs. Image analysis algorithms have shared characteristics. These characteristics serve as a basis for the presented parameterised architecture. The architecture design is based on the linear effort property and reusable IP. For a new algorithm implementation, adaptations only concern a small part of the entire architecture. New IPs are developed in handel-C using the DK design suite tool provided by Celoxica. The design space exploration (DSE) is made off-line with the use of prediction models which results in a shorter design time and the generated architecture will satisfy the given constraints. An example of the design process is presented with the multispectral imaging implementation instead of the particle image velocimetry (PIV) algorithm.
{"title":"A predictive and parametrized architecture for image analysis algorithm implementations on FPGA adapted to multispectral imaging","authors":"Junyan Tan, Linlin Zhang, V. Fresse, A. Legrand, D. Houzet","doi":"10.1109/IPTA.2008.4743765","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743765","url":null,"abstract":"The presented parameterised and predictive architecture is dedicated for image analysis algorithms implementations on FPGAs. Image analysis algorithms have shared characteristics. These characteristics serve as a basis for the presented parameterised architecture. The architecture design is based on the linear effort property and reusable IP. For a new algorithm implementation, adaptations only concern a small part of the entire architecture. New IPs are developed in handel-C using the DK design suite tool provided by Celoxica. The design space exploration (DSE) is made off-line with the use of prediction models which results in a shorter design time and the generated architecture will satisfy the given constraints. An example of the design process is presented with the multispectral imaging implementation instead of the particle image velocimetry (PIV) algorithm.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128695172","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 : 2008-11-01DOI: 10.1109/IPTA.2008.4743757
A. Zidouri, Mohamed Deriche
License plate recognition (LPR) systems are a key to many traffic related applications such as road traffic monitoring or parking lots access control. We propose an automatic license plate recognition system for GCC license plates. The system presents an algorithm for the extraction of license plate and recognition of Arabic characters and numerals. Preliminary work on the system has been investigated on real images of vehicles captured under various illumination conditions. Real time LPR plays a major role in automatic monitoring of traffic rules and maintaining law enforcement on public roads. The automatic identification of vehicles by the contents of their license plates is important in private transport applications.
{"title":"Recognition of Arabic License Plates using NN","authors":"A. Zidouri, Mohamed Deriche","doi":"10.1109/IPTA.2008.4743757","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743757","url":null,"abstract":"License plate recognition (LPR) systems are a key to many traffic related applications such as road traffic monitoring or parking lots access control. We propose an automatic license plate recognition system for GCC license plates. The system presents an algorithm for the extraction of license plate and recognition of Arabic characters and numerals. Preliminary work on the system has been investigated on real images of vehicles captured under various illumination conditions. Real time LPR plays a major role in automatic monitoring of traffic rules and maintaining law enforcement on public roads. The automatic identification of vehicles by the contents of their license plates is important in private transport applications.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126359872","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 : 2008-11-01DOI: 10.1109/IPTA.2008.4743754
M. Maher, B. Ismail, H. Frigui, Joshua Caudill
The performance of content-based image retrieval systems has proved to be inherently constrained by the used low-level features, and cannot give satisfactory results when the user's high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. To overcome this limitation, several approaches that can annotate images in a semi-supervised or unsupervised way have emerged. In this paper, we outline and compare four different algorithms. The first one is simple and assumes that image annotation can be viewed as the task of translating from a vocabulary of fixed image regions to a vocabulary of words. The second approach uses a set of annotated images as a training set and learns the joint distribution of regions and words. The third and fourth approaches are based on segmenting the images into homogeneous regions. Both of these approaches rely on a clustering algorithm to learn the association between visual features and keywords. The clustering task is not trivial as it involves clustering a very high-dimensional and sparse feature spaces. To address this, the third approach uses semi-supervised constrained clustering while the fourth approach relies on an algorithm that performs simultaneous clustering and feature discrimination. These four algorithms were implemented and tested on a data set that includes 6000 images using four-fold cross validation.
{"title":"Empirical Comparison of Automatic Image Annotation Systems","authors":"M. Maher, B. Ismail, H. Frigui, Joshua Caudill","doi":"10.1109/IPTA.2008.4743754","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743754","url":null,"abstract":"The performance of content-based image retrieval systems has proved to be inherently constrained by the used low-level features, and cannot give satisfactory results when the user's high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. To overcome this limitation, several approaches that can annotate images in a semi-supervised or unsupervised way have emerged. In this paper, we outline and compare four different algorithms. The first one is simple and assumes that image annotation can be viewed as the task of translating from a vocabulary of fixed image regions to a vocabulary of words. The second approach uses a set of annotated images as a training set and learns the joint distribution of regions and words. The third and fourth approaches are based on segmenting the images into homogeneous regions. Both of these approaches rely on a clustering algorithm to learn the association between visual features and keywords. The clustering task is not trivial as it involves clustering a very high-dimensional and sparse feature spaces. To address this, the third approach uses semi-supervised constrained clustering while the fourth approach relies on an algorithm that performs simultaneous clustering and feature discrimination. These four algorithms were implemented and tested on a data set that includes 6000 images using four-fold cross validation.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121895033","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 : 2008-11-01DOI: 10.1109/IPTA.2008.4743759
T. Pham, U. Eisenblatter
Study in restoring images from their degraded states has been an important research topic in image processing and has potential applications in complex pattern recognition. We propose in this paper a new adaptive image restoration method using the concept of random-function realizations in geostatistics. This conceptual framework allows us to derive the model means and variances in the context of spatial statistics. Experimental results demonstrate the superior performance of the proposed approach to other image restoration algorithms.
{"title":"A New Spatial Approach to Image Restoration","authors":"T. Pham, U. Eisenblatter","doi":"10.1109/IPTA.2008.4743759","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743759","url":null,"abstract":"Study in restoring images from their degraded states has been an important research topic in image processing and has potential applications in complex pattern recognition. We propose in this paper a new adaptive image restoration method using the concept of random-function realizations in geostatistics. This conceptual framework allows us to derive the model means and variances in the context of spatial statistics. Experimental results demonstrate the superior performance of the proposed approach to other image restoration algorithms.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114210540","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 : 2008-11-01DOI: 10.1109/IPTA.2008.4743795
A. Hadid
The local binary pattern (LBP) operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Due to its discriminative power and computational simplicity, the LBP texture operator has become a popular approach in various applications, including visual inspection, image retrieval, remote sensing, biomedical image analysis, motion analysis, environment modelling, and outdoor scene analysis. Recent developments showed that the local binary pattern texture method also provides outstanding results in representing and analyzing faces in both still images and video sequences. This paper describes the tutorial that will be lectured at The International Workshops on Image Processing Theory, Tools and Applications (IPTA'08) and presents an overview of applying LBP approach to various face analysis related tasks, including eye detection, face recognition, face detection, facial expression recognition, visual-speech recognition and gender classification.
{"title":"The Local Binary Pattern Approach and its Applications to Face Analysis","authors":"A. Hadid","doi":"10.1109/IPTA.2008.4743795","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743795","url":null,"abstract":"The local binary pattern (LBP) operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Due to its discriminative power and computational simplicity, the LBP texture operator has become a popular approach in various applications, including visual inspection, image retrieval, remote sensing, biomedical image analysis, motion analysis, environment modelling, and outdoor scene analysis. Recent developments showed that the local binary pattern texture method also provides outstanding results in representing and analyzing faces in both still images and video sequences. This paper describes the tutorial that will be lectured at The International Workshops on Image Processing Theory, Tools and Applications (IPTA'08) and presents an overview of applying LBP approach to various face analysis related tasks, including eye detection, face recognition, face detection, facial expression recognition, visual-speech recognition and gender classification.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130013218","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 : 2008-11-01DOI: 10.1109/IPTA.2008.4743799
Mourad Oussalah
In this paper we discuss the state of the art of the content based image retrieval highlighting the main components and reviewing various approaches employed at each stage, while enhancing the main challenges and key contributions. Along these lines, some analogy with text retrieval systems will be discussed.
{"title":"Content Based Image Retrieval: Review of State of Art and Future Directions","authors":"Mourad Oussalah","doi":"10.1109/IPTA.2008.4743799","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743799","url":null,"abstract":"In this paper we discuss the state of the art of the content based image retrieval highlighting the main components and reviewing various approaches employed at each stage, while enhancing the main challenges and key contributions. Along these lines, some analogy with text retrieval systems will be discussed.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"7 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132090744","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 : 2008-11-01DOI: 10.1109/IPTA.2008.4743798
J. Kludas, E. Bruno, S. Marchand-Maillet
Information fusion, especially for high dimensional multimedia data, is still an open research problem. In this article, we present a new approach to target this problem. Feature information interaction is an information-theoretic dependence measure that can determine synergy and redundancy between attributes, which then can be exploited with feature selection and construction towards more efficient information fusion. This also leads to improved performances for algorithms that rely on information fusion like multimedia document classification. We show that synergetic and redundant feature pairs require different fusion strategies for optimal exploitation. The approach is compared to classical feature selection strategies based on correlation and mutual information.
{"title":"Exploiting document feature interactions for efficient information fusion in high dimensional spaces","authors":"J. Kludas, E. Bruno, S. Marchand-Maillet","doi":"10.1109/IPTA.2008.4743798","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743798","url":null,"abstract":"Information fusion, especially for high dimensional multimedia data, is still an open research problem. In this article, we present a new approach to target this problem. Feature information interaction is an information-theoretic dependence measure that can determine synergy and redundancy between attributes, which then can be exploited with feature selection and construction towards more efficient information fusion. This also leads to improved performances for algorithms that rely on information fusion like multimedia document classification. We show that synergetic and redundant feature pairs require different fusion strategies for optimal exploitation. The approach is compared to classical feature selection strategies based on correlation and mutual information.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115473711","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 : 2008-11-01DOI: 10.1109/IPTA.2008.4743746
Huaizhong Zhang, P. Morrow, Sally I. McClean, K. Saetzler
In this paper we discuss how the classical geodesic active contours (GAC) model is enhanced by incorporating `prior' information into the scheme. The modified model is applied to biomedical imagery, specifically serial ultrathin electron microscopy sections. The approach used is to apply prior analysis on a training set of data and provide geometric information about the target object during the process of curve evolution. The experimental results and analysis for both synthetic and real images show that the approach performs better than our previous method. It can be implemented semi-automated fashion giving significant improvements compared to a manual approach.
{"title":"Contour Detection of Labelled Cellular Structures from Serial Ultrathin Electron Microscopy Sections using GAC and Prior Analysis","authors":"Huaizhong Zhang, P. Morrow, Sally I. McClean, K. Saetzler","doi":"10.1109/IPTA.2008.4743746","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743746","url":null,"abstract":"In this paper we discuss how the classical geodesic active contours (GAC) model is enhanced by incorporating `prior' information into the scheme. The modified model is applied to biomedical imagery, specifically serial ultrathin electron microscopy sections. The approach used is to apply prior analysis on a training set of data and provide geometric information about the target object during the process of curve evolution. The experimental results and analysis for both synthetic and real images show that the approach performs better than our previous method. It can be implemented semi-automated fashion giving significant improvements compared to a manual approach.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125382211","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 : 2008-11-01DOI: 10.1109/IPTA.2008.4743768
Mohammed Saaidia, S. Lelandais, M. Ramdani
Quality measurement of face localization using neural networks is presented in this communication. First, neural network was trained with Zernike moments feature parameters vectors. Coordinate vectors of pixels surrounding faces in images were used as target vectors on the supervised training procedure. Thus, trained neural network provides on its output layer a coordinate's vector (p, Theta) representing pixels surrounding the face contained in treated image. In second stage, another neural network, trained using TSL color space of images, is used to give a measure quantifying the quality of the localization obtained in the first stage. Experiments of the proposed method were carried out on the XM2VTS database.
{"title":"Online Quality measurement of face localization obtained by neural networks trained with Zernike moments feature vectors","authors":"Mohammed Saaidia, S. Lelandais, M. Ramdani","doi":"10.1109/IPTA.2008.4743768","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743768","url":null,"abstract":"Quality measurement of face localization using neural networks is presented in this communication. First, neural network was trained with Zernike moments feature parameters vectors. Coordinate vectors of pixels surrounding faces in images were used as target vectors on the supervised training procedure. Thus, trained neural network provides on its output layer a coordinate's vector (p, Theta) representing pixels surrounding the face contained in treated image. In second stage, another neural network, trained using TSL color space of images, is used to give a measure quantifying the quality of the localization obtained in the first stage. Experiments of the proposed method were carried out on the XM2VTS database.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134105484","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 : 2008-11-01DOI: 10.1109/IPTA.2008.4743763
G. Badrinath, Phalguni Gupta
This paper describes the design and development of a prototype of robust biometric system for personnel verification. The system uses features extracted using scale invariant feature transform (SIFT) operator of human hand. The hand image for features is acquired using a low cost scanner. The palmprint region extracted is robust to hand translation and rotation on the scanner. The use of SIFT operator for feature extractions makes the system robust to scale or spatial resolution of the hand images acquired. The system is tested on IITK database of 200 images and PolyU database of 7751 images. The design of the system with low cost scanner as input device, robustness to translation, rotation and spatial resolution, and testing performance, FAR 0.02%, FRR 0.62%, and accuracy 99.67% suggests that the system can be used for civilian applications and high-security environments.
{"title":"Palmprint Verification using SIFT features","authors":"G. Badrinath, Phalguni Gupta","doi":"10.1109/IPTA.2008.4743763","DOIUrl":"https://doi.org/10.1109/IPTA.2008.4743763","url":null,"abstract":"This paper describes the design and development of a prototype of robust biometric system for personnel verification. The system uses features extracted using scale invariant feature transform (SIFT) operator of human hand. The hand image for features is acquired using a low cost scanner. The palmprint region extracted is robust to hand translation and rotation on the scanner. The use of SIFT operator for feature extractions makes the system robust to scale or spatial resolution of the hand images acquired. The system is tested on IITK database of 200 images and PolyU database of 7751 images. The design of the system with low cost scanner as input device, robustness to translation, rotation and spatial resolution, and testing performance, FAR 0.02%, FRR 0.62%, and accuracy 99.67% suggests that the system can be used for civilian applications and high-security environments.","PeriodicalId":384072,"journal":{"name":"2008 First Workshops on Image Processing Theory, Tools and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129290822","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：