There is increased use of medical imaging techniques that produce four dimensional (4d) datasets such as fMRI and3ddynamic echocardiograms. These datasets consume even larger amounts of resources for transmission or...
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There is increased use of medical imaging techniques that produce four dimensional (4d) datasets such as fMRI and3ddynamic echocardiograms. These datasets consume even larger amounts of resources for transmission or storage compared to the traditional 2ddata sets. In this paper, we extend the zero tree algorithms, EZW (embedded zero tree coding of wavelet coefficients) and SPIHT (set partitioning in hierarchical trees) to 4d to compress the 4ddatasets more efficiently. Integer to integer wavelet transforms scaled by appropriate subband energy weights are used to get lossy to lossless compression. We also investigate the effects of lossy compression on the end result of fMRI analysis.
In this paper, we use a model selection criterion to decide whether two cylinders should be merged as a single cylinder or they should be left separated. We compare and evaluate an extensive number of different model ...
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In this paper, we use a model selection criterion to decide whether two cylinders should be merged as a single cylinder or they should be left separated. We compare and evaluate an extensive number of different model selection criteria for this purpose and examine which factors can affect their performance. We conclude that SSC, GIC, MCAIC and CAIC have a better performance (for this particular application) compared to the other criteria.
This paper describes a 3d modeling method for wide area outdoor environments which is based on integrating omnidirectional range and color images. In the proposed method, outdoor scenes can be efficiently digitized by...
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This paper describes a 3d modeling method for wide area outdoor environments which is based on integrating omnidirectional range and color images. In the proposed method, outdoor scenes can be efficiently digitized by an omnidirectional laser rangefinder which can obtain a 3d shape with high-accuracy and by an omnidirectional multi-camera system (OMS) which can capture a high-resolution color image. Multiple range images are registered by minimizing the distances between corresponding points in the different range images. In order to register multiple range images stably, points on plane portions detected from the range data are used in registration process. The position and orientation acquired by RTK-GPS and gyroscope are used as initial values of simultaneous registration. The 3d model obtained by registration of range data is mapped by textures selected from omnidirectional images in consideration of the resolution of texture and occlusions of the model. In experiments, we have carried out 3d modeling of our campus with the proposed method.
This paper presents techniques for the merging of 3ddata coming from different sensors, such as ground and aerial laser range scans. The 3d models created are reconstructed to give a photo-realistic scene enabling in...
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This paper presents techniques for the merging of 3ddata coming from different sensors, such as ground and aerial laser range scans. The 3d models created are reconstructed to give a photo-realistic scene enabling interactive virtual walkthroughs, measurements and scene change analysis. The reconstructed model is based on a weighted integration of all available data based on sensor-specific parameters such as noise level, accuracy, inclination and reflectivity of the target, spatial distribution of points. The geometry is robustly reconstructed with a volumetric approach. Once registered and weighed, all data is re-sampled in a multi-resolution distance field using out-of-core techniques. The final mesh is extracted by contouring the iso-surface with a feature preserving dual contouring algorithm. The paper shows results of the above technique applied to Verona (Italy) city centre.
In this paper we present a flexible modeling system for obtaining the texture-mapped3d geometric model. The modeling system uses an algorithm combining shape-from-silhouette with light-sectioning. In the algorithm, a...
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In this paper we present a flexible modeling system for obtaining the texture-mapped3d geometric model. The modeling system uses an algorithm combining shape-from-silhouette with light-sectioning. In the algorithm, at first, a rough shape model is obtained by shape-from-silhouette method almost automatically. Next, concavities and complex parts on the object surface are obtained by light-sectioning method with manual scanning. For applying light-sectioning method to volume data, we propose volumetric light-sectioning algorithm. Then our modeling system can realize easy and accurate generation of 3d geometric model.
Three-dimensional ladar data are commonly used to perform scene understanding for outdoor mobile robots, specifically in natural terrain. One effective method is to classify points using features based on local point ...
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Three-dimensional ladar data are commonly used to perform scene understanding for outdoor mobile robots, specifically in natural terrain. One effective method is to classify points using features based on local point clouddistribution into surfaces, linear structures or clutter volumes. But the local features are computed using 3d points within a support-volume. Local and global point density variations and the presence of multiple manifolds make the problem of selecting the size of this support volume, or scale, challenging. In this paper, we adopt an approach inspired by recent developments in computational geometry (Mitra et al., 2005) and investigate the problem of automatic data-driven scale selection to improve point cloud classification. The approach is validated with results using data from different sensors in various environments classified into different terrain types (vegetation, solid surface and linear structure).
This work is motivated by the desire of exploiting for 3d registration purposes the photometric information current range cameras typically associate to range data. Automatic pairwise 3d registration procedures are tw...
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This work is motivated by the desire of exploiting for 3d registration purposes the photometric information current range cameras typically associate to range data. Automatic pairwise 3d registration procedures are two steps procedures with the first step performing an automatic crude estimate of the rigid motion parameters and the second step refining them by the ICP algorithm or some of its variations. Methods for efficiently implementing the first crude automatic estimate are still an open research area. Spin-images are a 3d matching technique very effective in this task. Since spin-images solely exploit geometry information it appears natural to extend their original definition to include texture information. Such an operation can clearly be made in many ways. This work introduces one particular extension of spin-images, called textured spin-images, anddemonstrates its performance for 3d registration. It will be seen that textured spin-images enjoy remarkable properties since they can give rigid motion estimates more robust, more precise, more resilient to noise than standard spin-images at a lower computational cost.
Existing techniques for view-based3d object recognition using computer vision rely on training the system on a particular object before it is introduced into an environment. This training often consists of taking ove...
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Existing techniques for view-based3d object recognition using computer vision rely on training the system on a particular object before it is introduced into an environment. This training often consists of taking over 100 images at predetermined points around the viewing sphere in an attempt to account for most angles for viewing the object. However, in many circumstances, the environment is well known and we only expect to see a small subset of all possible appearances. In this paper, we test the idea that under these conditions, it is possible to train an object recognition system on-the-fly using images of an object as it appears in its environment, with supervision from the user. Furthermore, because some views of an object are much more likely than others, the number of training images required can be greatly reduced.
We compare two transform-based indexing methods for retrieval of 3d objects. We apply 3ddiscrete Fourier transform (dFT) and3d radial cosine transform (RCT) to the voxelizeddata of 3d objects. Rotation invariant fe...
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We compare two transform-based indexing methods for retrieval of 3d objects. We apply 3ddiscrete Fourier transform (dFT) and3d radial cosine transform (RCT) to the voxelizeddata of 3d objects. Rotation invariant features are derived from the coefficients of these transforms. Furthermore we compare two different voxel representations, namely, binary denoting object and background space, and continuous after distance transformation. In the binary voxel representation the voxel values are simply set to 1 on the surface of the object and 0 elsewhere. In the continuous-valued representation the space is filled with a function of distance transform. The rotation invariance properties of the dFT and RCT schemes are analyzed. We have conducted retrieval experiments on the Princeton Shape Benchmark and investigated the retrieval performance of the methods using several quality measures.
This paper describes a 3d vision system based on a new 3d sensor technology for the detection and classification of occupants in a car. New generation of so-called "smart airbags" require the information abo...
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This paper describes a 3d vision system based on a new 3d sensor technology for the detection and classification of occupants in a car. New generation of so-called "smart airbags" require the information about the occupancy type and position of the occupant. This information allows a distinct control of the airbag inflation. In order to reduce the risk of injuries due to airbag deployment, the airbag can be suppressed completely in case of a child seat oriented in rewarddirection. In this paper, we propose a 3d vision system based on a 3d optical time-of-flight (TOF) sensor, for the detection and classification of the occupancy on the passenger seat. Geometrical shape features are extracted from the 3d image sequences. Polynomial classifier is considered for the classification task. A comparison of classifier performance with principle components (eigen-images) is presented. This paper also discusses the robustness of the features with variation of the data. The full scale tests have been conducted on a wide range of realistic situations (adults/children/child seats etc.) which may occur in a vehicle.
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