We present a novel interactive system and its user interface for removing objects in digital pictures. Our system consists of two components: (i) (partially supervised/automatic) image segmentation (2], and (ii) (guid...
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ISBN:
(纸本)0769523722
We present a novel interactive system and its user interface for removing objects in digital pictures. Our system consists of two components: (i) (partially supervised/automatic) image segmentation (2], and (ii) (guided) texture synthesis [3].
We present an automotive-grade, real-time, vision-based Driver State Monitor. Upon detecting and tracking the driver's facial features, the system analyzes eye-closures and head pose to infer his/her fatigue or di...
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ISBN:
(纸本)0769523722
We present an automotive-grade, real-time, vision-based Driver State Monitor. Upon detecting and tracking the driver's facial features, the system analyzes eye-closures and head pose to infer his/her fatigue or distraction. This information is used to warn the driver and to modulate the actions of other safety systems. The purpose of this monitor is to increase road safety by preventing drivers from falling asleep or from being overly distracted, and to improve the effectiveness of other safety systems.
The choice of a color space is of great importance for many computervision algorithms (e.g. edge detection and object recognition). It induces the equivalence classes to the actual algorithms. However the problem is ...
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ISBN:
(纸本)0769523722
The choice of a color space is of great importance for many computervision algorithms (e.g. edge detection and object recognition). It induces the equivalence classes to the actual algorithms. However the problem is how to automatically select the color space that produces the best result for a particular task. The subsequent difficulty then is how to obtain a proper weighting scheme for the algorithms so that the results are combined in an optimal setting. To achieve proper color space selection and fusion of feature detectors, in this paper we propose a method that exploits non-perfect correlation between the color models derived from the principles of diversification. As a consequence, the weighting scheme yields maximal color discrimination. The method is verified experimentally for two different feature detectors. The experimental results show that the model provides feature detection results having a discriminative power of 30 percent higher than the standard weighting scheme.
Dimensionality reduction via feature projection has been widely used in patternrecognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target va...
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ISBN:
(纸本)0769523722
Dimensionality reduction via feature projection has been widely used in patternrecognition and machine learning. It is often beneficial to derive the projections not only based on the inputs but also on the target values in the training data set. This is of particular importance in predicting multivariate or structured outputs. which is an area of growing interest. In this paper we introduce a novel projection framework which is sensitive to both input features and outputs. Based on the derived features prediction accuracy can be greatly improved. We validate our approach in two applications. The first is to model users ' preferences on a set of paintings. The second application is concerned with image categorization where each image may belong to multiple categories. The proposed algorithm produces very encouraging results in both settings.
Landuse classification is an important problem in the remote sensing field. It can be used in a wide range of applications. In this paper we propose a hybrid method fusing edges and regions information for the landuse...
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ISBN:
(纸本)0769523722
Landuse classification is an important problem in the remote sensing field. It can be used in a wide range of applications. In this paper we propose a hybrid method fusing edges and regions information for the landuse classification of multispectral images. It mainly includes the steps of image pre-processing, initial segmentation and region merging. Especially, a novel spatial mean shift procedure is proposed so that some information can be extracted and used in the successive steps. Aiming at the multispectral images processing, we also design a band weighting strategy that give a proper weight to each band adaptively according to the region to be processed. Experimental results on the Landsat TM and ETM+ images validate the performance of the proposed method.
This paper presents an axiomatic approach to corner detection. In the first part of the paper we review five currently used corner detection methods (Harris-Stephens, Forstner Shi-Tomasi, Rohr and Kenney et al.) for g...
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ISBN:
(纸本)0769523722
This paper presents an axiomatic approach to corner detection. In the first part of the paper we review five currently used corner detection methods (Harris-Stephens, Forstner Shi-Tomasi, Rohr and Kenney et al.) for graylevel images. This is followed by a discussion of extending these corner detectors to images with different pixel dimensions such as signals (pixel dimension one) and tomographic medical images (pixel dimension three) as well as different intensity dimensions such as color or LADAR images (intensity dimension three). These extensions are motivated by analyzing a particular example of optical flow in pixel and intensity space with arbitrary dimensions. Placing corner detection in a general setting enables us to state four axioms that any corner detector might reasonably be required to satisfy. Our main result is that only the Shi-Tomasi (and equivalently the Kenney et al. 2-norm detector) satisfy all four of the axioms
A novel two-stage level set evolution method for detecting man-made objects in aerial images is described The method is based on a modified Mumford-Shah model and it uses a two-stage curve evolution strategy to get a ...
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ISBN:
(纸本)0769523722
A novel two-stage level set evolution method for detecting man-made objects in aerial images is described The method is based on a modified Mumford-Shah model and it uses a two-stage curve evolution strategy to get a preferable detection. It applies fractal error metric, developed by Cooper([1]), et al. at the first curve evolution stage and adds additional constraint- texture' edge descriptor that is defined by using DCT (Discrete Cosine Transform) coefficients on the image at the next stage. Man-made objects and natural areas are optimally differentiated by evolving the partial differential equation. The method artfully avoids selecting a threshold to separate the fractal error image, while an improper threshold often results in great segmentation errors. Experiments of the segmentation show that the proposed method is efficient.
We present a class of statistical models for part-based object recognition that are explicitly parameterized according to the degree of spatial structure they can represent. These models provide a way of relating diff...
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ISBN:
(纸本)0769523722
We present a class of statistical models for part-based object recognition that are explicitly parameterized according to the degree of spatial structure they can represent. These models provide a way of relating different spatial priors that have been used for recognizing generic classes of objects, including joint Gaussian models and tree-structured models. By providing explicit control over the degree of spatial structure, our models make it possible to study the extent to which additional spatial constraints among parts are actually helpful in detection and localization, and to consider the tradeoff in representational power and computational cost. We consider these questions for object classes that have substantial geometric structure, such as airplanes, faces and motorbikes, using datasets employed by other researchers to facilitate evaluation. We find that for these classes of objects, a relatively small amount of spatial structure in the model can provide statistically indistinguishable recognition performance from more powerful models, and at a substantially lower computational cost.
In this paper we present a method for computing the localization of a mobile robot with reference to a learning video sequence. The robot is first guided on a path by a human, while the camera records a monocular lear...
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ISBN:
(纸本)0769523722
In this paper we present a method for computing the localization of a mobile robot with reference to a learning video sequence. The robot is first guided on a path by a human, while the camera records a monocular learning sequence. Then a 3D reconstruction of the path and the environment is computed off line from the learning sequence. The 3D reconstruction is then used for computing the pose of the robot in real time (30 Hz) in autonomous navigation. Results from our localization method are compared to the ground truth measured with a differential GPS.
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