Pub Date : 2007-10-29DOI: 10.1109/ICIAP.2007.4362788
O. Schreer, S. Ngongang
Gesture recognition becomes feasible even for realtime applications and offers therefore a wide range of new capabilities for novel approaches of human computer interaction, but also for many multi-media services. In this paper, we present a real-time solution for gesture recognition applied in a novel avatar animation videocommunication service. We focus on user friendliness, robustness and easy usage. Hence, the algorithm does not require any training or adaptation to a specific user and can be applied in arbitrary unconstrained environment.
{"title":"Real-time Gesture Recognition in Advanced Videocommunication Services","authors":"O. Schreer, S. Ngongang","doi":"10.1109/ICIAP.2007.4362788","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.4362788","url":null,"abstract":"Gesture recognition becomes feasible even for realtime applications and offers therefore a wide range of new capabilities for novel approaches of human computer interaction, but also for many multi-media services. In this paper, we present a real-time solution for gesture recognition applied in a novel avatar animation videocommunication service. We focus on user friendliness, robustness and easy usage. Hence, the algorithm does not require any training or adaptation to a specific user and can be applied in arbitrary unconstrained environment.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116304430","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}
Tracking is usually performed at a single level of data resolution. This paper describes a multi-resolution tracking framework developed with efficiency and robustness in mind. Efficiency is achieved by processing low resolution data whenever possible. Robustness results from multiple level coarse-to-fine searching in the tracking state space. We combine sequential filtering both in time and resolution levels into a probabilistic framework. A color blob tracker is implemented and the tracking results are evaluated in a number of experiments.
{"title":"Object Tracking at Multiple Levels of Spatial Resolutions","authors":"S. D. Tran, L. Davis","doi":"10.1109/ICIAP.2007.95","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.95","url":null,"abstract":"Tracking is usually performed at a single level of data resolution. This paper describes a multi-resolution tracking framework developed with efficiency and robustness in mind. Efficiency is achieved by processing low resolution data whenever possible. Robustness results from multiple level coarse-to-fine searching in the tracking state space. We combine sequential filtering both in time and resolution levels into a probabilistic framework. A color blob tracker is implemented and the tracking results are evaluated in a number of experiments.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116764354","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}
This paper proposes the exploitation of a dynamic programming technique for efficiently comparing people trajectories adopting an encoding scheme that jointly takes into account both the direction and the velocity of movement. With this approach, each pair of trajectories in the training set is compared and the corresponding distance computed. Clustering is achieved by using the k-medoids algorithm and each cluster is modeled with a 1-D Gaussian over the distance from the medoid. A MAP framework is adopted for the testing phase. The reported results are encouraging.
{"title":"A Dynamic Programming Technique for Classifying Trajectories","authors":"S. Calderara, R. Cucchiara, A. Prati","doi":"10.1109/ICIAP.2007.6","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.6","url":null,"abstract":"This paper proposes the exploitation of a dynamic programming technique for efficiently comparing people trajectories adopting an encoding scheme that jointly takes into account both the direction and the velocity of movement. With this approach, each pair of trajectories in the training set is compared and the corresponding distance computed. Clustering is achieved by using the k-medoids algorithm and each cluster is modeled with a 1-D Gaussian over the distance from the medoid. A MAP framework is adopted for the testing phase. The reported results are encouraging.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127231850","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}
Digital images in general, and radiographies in particular, are mainly affected by photon counting noise. In this paper, we provide a method to estimate the gain of the imaging sensor and the variance of the photon counting noise associated to each image grey level. This is useful to multiple scopes, like reverse engineering and denoising.
{"title":"A new and reliable Poisson noise estimator for radiographic images","authors":"I. Frosio, M. Lucchese, N. A. Borghese","doi":"10.1109/ICIAP.2007.12","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.12","url":null,"abstract":"Digital images in general, and radiographies in particular, are mainly affected by photon counting noise. In this paper, we provide a method to estimate the gain of the imaging sensor and the variance of the photon counting noise associated to each image grey level. This is useful to multiple scopes, like reverse engineering and denoising.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127025515","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}
C. Stefano, C. D'Elia, A. Marcelli, A. S. D. Freca
In the framework of multiple classifier systems, we suggest to reformulate the classifier combination problem as a pattern recognition one. Following this approach, each input pattern is associated to a feature vector composed by the output of the classifiers to be combined. A Bayesian Network is used to automatically infer the probability distribution for each class and eventually to perform the final classification. We propose to use Bayesian Networks because they not only provide a basis for efficient probabilistic inference, but also a natural and compact way to encode exponentially sized joint probability distributions. Two systems adopting an ensemble of Back-Propagation neural network and an ensemble of Learning Vector Quantization neural network, respectively, have been tested on the Image database from the UCI repository. The performance of the proposed systems have been compared with those exhibited by multi-expert systems adopting the same ensembles, but the Majority Vote, the Weighted Majority vote and the Borda Count for combining them.
{"title":"Using Bayesian Network for combining classifiers","authors":"C. Stefano, C. D'Elia, A. Marcelli, A. S. D. Freca","doi":"10.1109/ICIAP.2007.129","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.129","url":null,"abstract":"In the framework of multiple classifier systems, we suggest to reformulate the classifier combination problem as a pattern recognition one. Following this approach, each input pattern is associated to a feature vector composed by the output of the classifiers to be combined. A Bayesian Network is used to automatically infer the probability distribution for each class and eventually to perform the final classification. We propose to use Bayesian Networks because they not only provide a basis for efficient probabilistic inference, but also a natural and compact way to encode exponentially sized joint probability distributions. Two systems adopting an ensemble of Back-Propagation neural network and an ensemble of Learning Vector Quantization neural network, respectively, have been tested on the Image database from the UCI repository. The performance of the proposed systems have been compared with those exhibited by multi-expert systems adopting the same ensembles, but the Majority Vote, the Weighted Majority vote and the Borda Count for combining them.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116444246","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}
A robust iris localization and tracking algorithm based on computer vision is presented. The iris localization algorithm acts as a bootstrap for the tracking algorithm, providing it with a set of multiple hypotheses to restart from in the case of a tracking failure. Tracking is performed with a RANSAC-like robust method for ellipse fitting that incorporates search constraints so as to increase the overall accuracy with respect to the standard RANSAC approach. Experimental results show that the algorithm is fast, accurate and robust enough for applications in the field of human-machine interaction, being particularly suitable for users with severe motor disabilities.
{"title":"Robust Iris Localization and Tracking based on Constrained Visual Fitting","authors":"C. Colombo, Dario Comanducci, A. Bimbo","doi":"10.1109/ICIAP.2007.113","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.113","url":null,"abstract":"A robust iris localization and tracking algorithm based on computer vision is presented. The iris localization algorithm acts as a bootstrap for the tracking algorithm, providing it with a set of multiple hypotheses to restart from in the case of a tracking failure. Tracking is performed with a RANSAC-like robust method for ellipse fitting that incorporates search constraints so as to increase the overall accuracy with respect to the standard RANSAC approach. Experimental results show that the algorithm is fast, accurate and robust enough for applications in the field of human-machine interaction, being particularly suitable for users with severe motor disabilities.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114546591","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}
The paper presents the design and implementation of a system of autonomous navigation of a mobile robot with stereo vision.
介绍了一种具有立体视觉的移动机器人自主导航系统的设计与实现。
{"title":"Efficient Stereo Vision for Obstacle Detection and AGV Navigation","authors":"R. Cucchiara, E. Perini, Giuliano Pistoni","doi":"10.1109/ICIAP.2007.59","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.59","url":null,"abstract":"The paper presents the design and implementation of a system of autonomous navigation of a mobile robot with stereo vision.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117043986","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}
L. Payá, Ó. Reinoso, M. A. Vicente, A. Gil, J. Pedrero
The appearance-based approach in visual robot navigation supposes several advantages, such as its application to non-structured environments and the relatively simple extraction of control laws that it offers. However, the main drawback is the requirement of extensive memories and the high computational cost. This way, the nature and the quantity of information to store about the environment is very important. This work presents how to reduce the dimension of the database, by means of calculating just the most significant information of each image. We show how it can be done working in the PCA subspace. This method allows lowering the computational cost without necessity of reducing the resolution of the images, what implies that it could be used in very non-structured environments, in the presence of partial occlusions and with considerably high translational speed of the robot.
{"title":"Subspace Reduction for Appearance-Based Navigation of a Mobile Robot","authors":"L. Payá, Ó. Reinoso, M. A. Vicente, A. Gil, J. Pedrero","doi":"10.1109/ICIAP.2007.121","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.121","url":null,"abstract":"The appearance-based approach in visual robot navigation supposes several advantages, such as its application to non-structured environments and the relatively simple extraction of control laws that it offers. However, the main drawback is the requirement of extensive memories and the high computational cost. This way, the nature and the quantity of information to store about the environment is very important. This work presents how to reduce the dimension of the database, by means of calculating just the most significant information of each image. We show how it can be done working in the PCA subspace. This method allows lowering the computational cost without necessity of reducing the resolution of the images, what implies that it could be used in very non-structured environments, in the presence of partial occlusions and with considerably high translational speed of the robot.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129058813","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}
We present Face3, a system for the automatic recognition of people using 2D and 3D images. The system implements several innovative algorithms. A full 3D face detector allows the detection effaces regardless their pose and orientation in the scene. A face restoration module allows the recognition even in presence of occlusions. The recognition is performed using a fusion of 2D and 3D data. Finally, in order to relax the hardware constraints, a simplification algorithm is used to reduce the data complexity of the 3D face models.
{"title":"Face^3 a 2D+3D Robust Face Recognition System","authors":"A. Colombo, C. Cusano, R. Schettini","doi":"10.1109/ICIAP.2007.64","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.64","url":null,"abstract":"We present Face3, a system for the automatic recognition of people using 2D and 3D images. The system implements several innovative algorithms. A full 3D face detector allows the detection effaces regardless their pose and orientation in the scene. A face restoration module allows the recognition even in presence of occlusions. The recognition is performed using a fusion of 2D and 3D data. Finally, in order to relax the hardware constraints, a simplification algorithm is used to reduce the data complexity of the 3D face models.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129639618","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}
Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We present some extensions to the method for moving object detection presented in H. Fujiyoshi and T. Kanade, (2004). Our main contributions are related to the pre-processing of intermediate results (transience maps), aimed at enhancing the accuracy of detection results, and to the parallelization of some of the most computationally intensive steps using SSE2 instructions, in order to enhance efficiency and allow for real-time applications.
{"title":"Moving Object Detection for Real-Time Applications","authors":"L. Maddalena, A. Petrosino","doi":"10.1109/ICIAP.2007.89","DOIUrl":"https://doi.org/10.1109/ICIAP.2007.89","url":null,"abstract":"Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We present some extensions to the method for moving object detection presented in H. Fujiyoshi and T. Kanade, (2004). Our main contributions are related to the pre-processing of intermediate results (transience maps), aimed at enhancing the accuracy of detection results, and to the parallelization of some of the most computationally intensive steps using SSE2 instructions, in order to enhance efficiency and allow for real-time applications.","PeriodicalId":118466,"journal":{"name":"14th International Conference on Image Analysis and Processing (ICIAP 2007)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127895538","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}
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