In this paper, we have proposed a novel framework to achieve more effective classifier training by using unlabeled samples. By integrating concept hierarchy for semantic image concept organization, a hierarchical mixt...
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ISBN:
(纸本)0769523722
In this paper, we have proposed a novel framework to achieve more effective classifier training by using unlabeled samples. By integrating concept hierarchy for semantic image concept organization, a hierarchical mixture model is proposed to enable multi-level image concept modeling and hierarchical classifier training. To effectively learn the base-level classifiers for the atomic image concepts at the first level of the concept hierarchy, we have proposed a novel adaptive EM algorithm to achieve more effective classifier training with higher prediction accuracy. To effectively learn the classifiers for the higher-level semantic image concepts, we have also proposed a novel technique for classifier combining by using hierarchical mixture model. The experimental results on two large-scale image databases are also provided.
We demonstrate a concept of computervision as a secure, live service on the Internet. We show a platform to distribute a real lime vision algorithm using simple widely available web technologies, such as Adobe Flash....
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ISBN:
(纸本)9781424439942
We demonstrate a concept of computervision as a secure, live service on the Internet. We show a platform to distribute a real lime vision algorithm using simple widely available web technologies, such as Adobe Flash. We allow a user to access this service without downloading an executable or sharing the image stream with anyone. We support developers to publish without distribution complexity Finally the platform supports user-permitted aggregation of data for computervision research or analysis. We describe results a simple distributed motion detection algorithm. We discuss future scenarios for organically extending the horizon of computervision research.
Image-based virtual reality is emerging as a major alternative to the more traditional 3D-based VR. The main advantages of the image-based VR are its photo-quality realism and 3D illusion without any 3D information. U...
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ISBN:
(纸本)0780342364
Image-based virtual reality is emerging as a major alternative to the more traditional 3D-based VR. The main advantages of the image-based VR are its photo-quality realism and 3D illusion without any 3D information. Unfortunately, creating content for image-based VR is usually a very tedious process. This paper proposes to use a non-perspective fisheye lens to capture the spherical panorama with very few images. Unlike most of camera calibration in computervision, self-calibration of the fisheye lens poses new questions regarding the parameterization of the distortion and wrap-around effects. Because of its unique projection model and large field of view (near 180 degrees), most of the ambiguity problems in self-calibrating a traditional lens can be solved trivially. We demonstrate that with four fisheye lens images, we can seamlessly register them to create the spherical panorama, while self-calibrating its distortion and field of view.
With the aim to design a general learning framework for detecting faces of various poses or under different lighting conditions, we are motivated to formulate the task as a classification problem over data of multiple...
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We propose using the inner-distance between landmark points to build shape descriptors. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show tha...
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ISBN:
(纸本)0769523722
We propose using the inner-distance between landmark points to build shape descriptors. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that the inner-distance is articulation insensitive and more effective at capturing complex shapes with part structures than Euclidean distance. To demonstrate this idea, it is used to build a new shape descriptor based on shape contexts. After that, we design a dynamic programming based method for shape matching and comparison. We have tested our approach on a variety of shape databases including an articulated shape dataset, MPEG7 CE-Shape-1, Kimia silhouettes, a Swedish leaf database and a human motion silhouette dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.
In this paper we present an artificial vision algorithm for real-time obstacle detection in unstructured environments. The images have been taken using a stereoscopical vision system. The system uses a new approach, o...
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We describe how certain tasks in the audio domain can be effectively addressed using computervision approaches. This paper focuses on the problem of music identification, where the goal is to reliably identify a song...
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ISBN:
(纸本)0769523722
We describe how certain tasks in the audio domain can be effectively addressed using computervision approaches. This paper focuses on the problem of music identification, where the goal is to reliably identify a song given a few seconds of noisy audio. Our approach treats the spectrogram of each music clip as a 2-D image and transforms music identification into a corrupted sub-image retrieval problem. By employing pairwise boosting on a large set of Viola-Jones features, our system learns compact, discriminative, local descriptors that are amenable to efficient indexing. During the query phase, we retrieve the set of song snippets that locally match the noisy sample and employ geometric verification in conjunction with an EM-based "occlusion " model to identify the song that is most consistent with the observed signal. We have implemented our algorithm in a practical system that can quickly and accurately recognize music from short audio samples in the presence of distortions such as poor recording quality and significant ambient noise. Our experiments demonstrate that this approach significantly outperforms the current state-of-the-art in content-based music identification.
This paper presents a new model to overcome the occlusion problems coming from wide baseline multiple camera stereo. Rather than explicitly modeling occlusions in the matching cost function, it detects occlusions in t...
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In this paper we present a novel approach for personal identification which utilizes finger surface features as a biometric identifier. Using dense range data images of the hand, we calculate the curvature-based surfa...
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ISBN:
(纸本)0769523722
In this paper we present a novel approach for personal identification which utilizes finger surface features as a biometric identifier. Using dense range data images of the hand, we calculate the curvature-based surface representation, shape index, for the index, middle, and ring fingers. This representation is used for comparisons to determine subject similarity. Our experiments involve the use of a large data set of range images collected over time. We examine the performance of individual finger surfaces as a biometric identifier as well as the performance when using the three finger surfaces in conjunction. The results of our experiments are presented, which indicate that this approach performs well for a first-of-its-kind biometric technique.
We develop a method for recognizing color texture independent of rotation, scale, and illumination. Color texture is modeled using spatial correlation functions defined within and between sensor bands. Using a linear ...
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ISBN:
(纸本)0818672587
We develop a method for recognizing color texture independent of rotation, scale, and illumination. Color texture is modeled using spatial correlation functions defined within and between sensor bands. Using a linear model for surface spectral reflectance with the same number of parameters as the number of sensor classes, we show that illumination and geometry changes in the scene correspond to a linear transformation of the correlation functions and a linear transformation of their coordinates. A several step algorithm which includes scale estimation and correlation moment computation is used to achieve the invariance. The key to the method is the new result that illumination and geometry changes in the scene correspond to a specific transformation of correlation function Zernike moment matrices. These matrices can be estimated from a color image. This relationship is used to derive an efficient algorithm for recognition. The algorithm is substantiated using classification results on over 200 images of color textures obtained under various illumination conditions and geometric configurations.
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