LIght Detection And Ranging (LIDAR) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. However, the analysis of Digital Surface Models (DSMs) from complex LI...
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ISBN:
(纸本)0769525210
LIght Detection And Ranging (LIDAR) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. However, the analysis of Digital Surface Models (DSMs) from complex LIDAR data is still challenging. Commonly, the first task to investigate LIDAR data point clouds is to separate ground and object points as a preparatory step for further object classification. In this paper, the authors present a novel unsupervised segmentation algorithm-skewness balancing to separate object and ground points efficiently from high resolution LIDAR point clouds by exploiting statistical moments. the results presented in this paper have shown its robustness and its potential for commercial applications.
this paper presents a robust approach based on evolutionary agents for projective reconstruction in the presence of missing data and unknown depths. Agents denote possible submatrices for rank constraints, and carry o...
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ISBN:
(纸本)0769525210
this paper presents a robust approach based on evolutionary agents for projective reconstruction in the presence of missing data and unknown depths. Agents denote possible submatrices for rank constraints, and carry out some evolutionary behavior to exploit a vast solution space. Our approach combines the benefits of excellent searching ability of evolutionary agents for getting a good solution, with a proper treatment Of missing information with linear fitting. Experimental results demonstrate better performance of our approach than other typical methods in terms of accuracy and robustness to noise and missing data.
the fundamental matrix (FM) represents the perspective transform between two or more uncalibrated images of a stationary scene, and is traditionally estimated based on 2-parameter point-to-point correspondences betwee...
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ISBN:
(纸本)0769525210
the fundamental matrix (FM) represents the perspective transform between two or more uncalibrated images of a stationary scene, and is traditionally estimated based on 2-parameter point-to-point correspondences between image pairs. Recent invariant correspondence techniques however, provide robust correspondences in terms of 4 to 6-parameter invariant regions. Such correspondences contain important information regarding scene geometry, information which is lost in FM estimation techniques based solely on 2-parameter point translation. In this article, we present a method of incorporating this additional information into point-based FM estimation routines, entitled TIP (transfer of invariant parameters). the TIP method transforms invariant correspondence parameters into additional point correspondences, which can be used with FM estimation routines. Experimentation shows that the TIP methods result in more robust FM estimates in the case of sparse correspondence, and allows estimation based on as few as 3 correspondences in the case of affine-invariant features.
We are conducting research on "Embodied Proactive Human Interface". the aim of this research is to develop a new human-friendly active interface based on two key technologies, an estimation mechanism of huma...
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ISBN:
(纸本)0769525210
We are conducting research on "Embodied Proactive Human Interface". the aim of this research is to develop a new human-friendly active interface based on two key technologies, an estimation mechanism of human intention for supporting natural communication named "Proactive Interface", and a tangible device using robot technology. this paper introduces the humanoid-type Two-legged robot named "PICO-2", which was developed as a tangible telecommunication device for the proactive human interface. In order to achieve the embodied telecommunication with PICO-2, we propose new tracking technique of human gestures using a monocular video camera mounted on PICO-2, and natural gesture reproduction by PICO-2 which absorbs the difference of body structure between the user and the robot.
Disparity flow depicts the 3D motion of a scene in disparity space of a given view and can be considered as view-dependent scene flow. the disparity flow map of a given view is a 2D array of 3D vectors that depicts th...
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ISBN:
(纸本)0769525210
Disparity flow depicts the 3D motion of a scene in disparity space of a given view and can be considered as view-dependent scene flow. the disparity flow map of a given view is a 2D array of 3D vectors that depicts the 3D motion observed at different pixel locations. Estimating 3D motion in form of disparity flow map limits all computations in the 2D image space and converts the 3D motion estimation problem into a 2D labeling problem. A novel algorithm is presented in this paper for disparity flow estimation using the orthogonal reliability-based dynamic programming technique. Experimental results using captured stereo sequences show that the new algorithm can generate dense and smooth 3D motion for dynamic scenes.
the selection of the appropriate colorspace for tracking applications has not been an issue previously considered in the literature. Many color representations have been suggested, based on the invariance to illuminat...
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ISBN:
(纸本)0769525210
the selection of the appropriate colorspace for tracking applications has not been an issue previously considered in the literature. Many color representations have been suggested, based on the invariance to illumination changes. Nevertheless, none of them is invariant enough to deal with general and unconstrained environments. In tracking tasks, we might prefer to represent image pixels into a colorspace where the distance between the target and background colorpoints were maximized, simplifying the task of the tracker Based on this criterion, we propose an 'object dependent' colorspace, which is computed as a simple calibration procedure before tracking. Furthermore, this colorspace may be easily adapted at each frame. Synthetic and real experiments show how this colorspace allows for a better discrimination of the foreground and background, and permits to track in circumstances where the same tracking algorithm relying on other colorspaces wouldfail.(1)
In this paper a novel analysis of space-time volume of spherical projection image is presented. So far space-time analyses have been extensively conducted for various purposes, i.e. 3-D reconstruction, estimation of c...
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ISBN:
(纸本)0769525210
In this paper a novel analysis of space-time volume of spherical projection image is presented. So far space-time analyses have been extensively conducted for various purposes, i.e. 3-D reconstruction, estimation of camera motion and novel view synthesis and most of them consider only a planer projection and a single camera. In contrast, we conducted analysis on spherical projection for multiple cameras. Since spherical projection does not change its appearance in relation to rotation around the origin of the sphere, extrinsic camera parameters and synchronous parameters of multiple video cameras can be simultaneously estimated by registering multiple space-time volumes of spherical projection, which can be easily achieved by block-matching technique. By using the parameters, multiple video images can be successfully integrated into single omni-directional images without distortions.
In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. the LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surfa...
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ISBN:
(纸本)0769525210
In this paper, we address issues in segmentation Of remotely sensed LIDAR (LIght Detection And Ranging) data. the LIDAR data, which were captured by airborne laser scanner, contain 2.5 dimensional (2.5D) terrain surface height information, e.g. houses, vegetation, flat field, river, basin, etc. Our aim in this paper is to segment ground (flat field)from non-ground (houses and high vegetation) in hilly urban areas. By projecting the 2.5D data onto a surface, we obtain a texture map as a grey-level image. Based on the image, Gabor wavelet filters are applied to generate Gabor wavelet features. these features are then grouped into various windows. Among these windows, a combination of their first and second order of statistics is used as a measure to determine the surface properties. the test results have shown that ground areas can successfully be segmented from LIDAR data. Most buildings and high vegetation can be detected. In addition, Gabor wavelet transform can partially remove hill or slope effects in the original data by tuning Gabor parameters.
this volume contains the latest in the series of ICAPR proceedings on the state-of-the-art of different facets of patternrecognition. these conferences have already carved out a unique position among events attended ...
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ISBN:
(数字)9789812772381
ISBN:
(纸本)9789812705532
this volume contains the latest in the series of ICAPR proceedings on the state-of-the-art of different facets of patternrecognition. these conferences have already carved out a unique position among events attended by the patternrecognition community. the contributions tackle open problems in the classic fields of image and video processing, document analysis and multimedia object retrieval as well as more advanced topics in biometrics speech and signal analysis. Many of the papers focus both on theory and application driven basic research patternrecognition.
Viola and Jones (VJ) cascade classification methods have proven to be very successful in detecting objects belonging to a single class - e.g., faces. this paper addresses the more challenging "many class detectio...
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ISBN:
(纸本)0769525210
Viola and Jones (VJ) cascade classification methods have proven to be very successful in detecting objects belonging to a single class - e.g., faces. this paper addresses the more challenging "many class detection" problem: detecting and identifying objects that belong to any of a set of classes. We use a set of learned weights (corresponding to the parameters of a set of binary linear separators) to identify these objects. We show that objects within many real-world classes tend to form clusters in this induced "classifier space". As the results of a sequence of classifiers can suggest a possible label for each object, we formulate this task as a Markov Decision Process. Our system first uses a "decision tree classifier" (i.e., a policy produced using dynamic programming) to specify when to apply which classifier to produce a possible class label for each sub-image W of a test image. It then uses a cascade of classifiers, specific to each "leaf" in this tree, to confirm that W is an instance of the proposed class. We present empirical evidence to verify that our ideas work effectively: showing that our system is essentially as accurate as running a set of cascade classifiers, but is much faster than that approach.
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