A vision based method to recover human faces from video sequences is presented. Although video sequences acquired from multiple static synchronized CCD cameras have been used as a tool for 3-D reconstruction of the fa...
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The comparison of different approaches to classification of multichannel remotesensingimages obtained by spaceborne imaging systems is presented. It is demonstrated that it is reasonable to compress original noisy i...
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image panoramas are of importance for virtual navigation in remote or synthetic environments. To process these panoramas, different representations have been proposed;this paper presents a study of cubic panoramas. St...
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In this keynote address, we introduce three-dimensional (3D) sensing, visualization and recognition of microorganisms using microscopy-based single-exposure on-line (SEOL) digital holography. A coherent Mach-Zehnder i...
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ISBN:
(纸本)081946290X
In this keynote address, we introduce three-dimensional (3D) sensing, visualization and recognition of microorganisms using microscopy-based single-exposure on-line (SEOL) digital holography. A coherent Mach-Zehnder interferometer records Fresnel diffraction field by a single on-line exposure to generate a microscopic digital hologram. Complex amplitude distribution is numerically reconstructed by the inverse Fresnel transform at arbitrary depth planes. After the reconstruction of volumetric complex images, 3D biological micro-objects are segmented and features are extracted by Gabor-based wavelets. The graph matching technique searches predefined 3D morphological shapes of reference biological microorganisms. Preliminary experimental results using sphacelaria alga and tribonema aequale alga are presented.
The paper presents a new automated pattern classification method. At first original data points are partitioned by unsupervised self-organizing map network (SOM). Then from the above clustering results, some labelled ...
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ISBN:
(纸本)3540464816
The paper presents a new automated pattern classification method. At first original data points are partitioned by unsupervised self-organizing map network (SOM). Then from the above clustering results, some labelled points nearer to each clustering center are chosen to train supervised generalization regression neural network model (GRNN). Then utilizing the decided GRNN model, we reclassify these original data points and gain new clustering results. At last from new clustering results, we choose some labelled points nearer to new clustering center to train and classify again, and so repeat until clustering center no longer changes. Experimental results for Iris data, Wine data and remotesensing data verify the validity of our method.
Current surveillance and reconnaissance systems require improved capability to enable the co-registration of larger images, combining enhanced temporal, spatial, and spectral resolutions. However, such proficient remo...
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ISBN:
(纸本)0819462918
Current surveillance and reconnaissance systems require improved capability to enable the co-registration of larger images, combining enhanced temporal, spatial, and spectral resolutions. However, such proficient remotesensing systems cannot employ traditional manual exploitation techniques to cope successfully with the avalanche of data to be processed and analyzed. Automated image exploitation tools may be employed if the images are already co-registered together. Therefore, there is a need to develop fully automated co-registration algorithms able to deal with different scenarios, and helpful to be used successively for numerous applications such as image data fusion, change detection, and target detection. This paper describes the Automated Multi-sensor image Registration (AMIR) system and embedded algorithms under development at DRDC-Valcartier. The AMIR system provides a framework for the automated multi-date registration of electro-optic images, acquired from different sensors and from dissimilar oblique view angles. The system is characterized by its fully automated nature, where no user intervention prevailed. Advanced image algorithms are used in order to supply the capability to register multi-date electro-optic images acquired from different viewpoints, under singular operational conditions, multiple scenarios (e.g. airport, harbor, vegetation, urban, etc.), different spatial resolutions (e.g. IKONOS/QuickBird, Airborne/Spaceborne), while providing sub-pixel accuracy registration level.
The following topics are discussed: computer vision and image analysis; face and human analysis; face recognition; character recognition and document analysis; clustering algorithms; signal, speech and image processin...
The following topics are discussed: computer vision and image analysis; face and human analysis; face recognition; character recognition and document analysis; clustering algorithms; signal, speech and imageprocessing; signal coding and compression; document image enhancement; visualization and restoration; systems, robotics and applications; biometrics; biomedical imaging; fingerprints; range imaging and remotesensing applications; cognitive approaches and soft computing; gesture and emotion recognition; human computer interaction; semantic analysis for content retrieval; pattern and shape analysis; stereo and motion; learning algorithms; visual patternrecognition; image and data representation; image registration; object detection and recognition; pattern matching; pattern classification; gait, body pose and writer recognition; medical imageprocessing; super-resolution; multimodal recognition; texture analysis; image segmentation; illumination and feature analysis; finger, palm and iris recognition; information retrieval; kernel methods; smart sensors; and surveillance
Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remotesensing. However, the increasing volume of image data is exceeding the cur...
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ISBN:
(纸本)0819462918
Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remotesensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for feature-based registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.
A non-rigid feature registration method using the Hatisdorff distance is presented in this paper. Based on B-splines, the proposed method is able to handle elastic deformations between the images to be registered. Sin...
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ISBN:
(纸本)0780395840
A non-rigid feature registration method using the Hatisdorff distance is presented in this paper. Based on B-splines, the proposed method is able to handle elastic deformations between the images to be registered. Since no correspondence needs to be established between two point sets, the method is robust to outliers. The performance of the proposed method is demonstrated and validated in two image registration experiments. While the results are somewhat preliminary, they clearly demonstrate the applicability of our method to real world tasks involving feature-based elastic registration.
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