An important task of similarity determination in heterogeneous databases is to determine which fields refer to the same data. Neural networks have emerged as a powerful patternrecognition technique. But the most conc...
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In this paper, we propose a directional wavelet approach to remove images of interfering strokes coming from the back of a historical handwritten document due to seeping of ink during long period of storage. Our previ...
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Several features for Neural Network based document region identification are tested. Specifically, this paper examines features for headline and subheadline region identification. The Neural Network based region ident...
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Several features for Neural Network based document region identification are tested. Specifically, this paper examines features for headline and subheadline region identification. The Neural Network based region identification algorithm is a key component of a document recognition system that segments a document into regions, classifies them into text, graphic, photo, and other region types, and then uses this classification to guide the processing.and analysis of the image. The input data are unusually challenging: low quality images of newspaper documents obtained from microfilmed archives. Experiments on several newspaper documents show that the features used are capable of robust and accurate headline identification.
This paper proposes a real-time vehicle management system using a vehicle tracking and a car plate number identification technique. The system uses two cameras: one for tracking vehicles and another for capturing LP (...
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Traditional approach to automated analysis of medical data is mostly based on statistical or theoretical-decision methods of patternrecognition. Using such methods, we can obtain many valuable items, e.g. tracking of...
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This paper presents an approach to build high resolution digital elevation maps from a sequence of unregistered low altitude stereovision image pairs. The approach first uses a visual motion estimation algorithm that ...
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One of the main tasks in content-based image retrieval (CBIR) is to reduce the gap between low-level visual features and high-level human concepts. This paper presents a new semi-supervised EM algorithm (NSSEM), where...
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One of the main tasks in content-based image retrieval (CBIR) is to reduce the gap between low-level visual features and high-level human concepts. This paper presents a new semi-supervised EM algorithm (NSSEM), where the image distribution in feature space is modeled as a mixture of Gaussian densities. Due to the statistical mechanism of accumulating and processing.meta knowledge, the NSS-EM algorithm with long term learning of mixture model parameters can deal with the cases where users may mislabel images during relevance feedback. Our approach that integrates mixture model of the data, relevance feedback and long term learning helps to improve retrieval performance. The concept learning is incrementally refined with increased retrieval experiences. Experiment results on Corel database show the efficacy of our proposed concept learning approach.
We present techniques for spectral image acquisition. The spectral domain of the images is represented by a low-dimensional component image set, which is used to obtain an efficient compression of the high-dimensional...
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We present techniques for spectral image acquisition. The spectral domain of the images is represented by a low-dimensional component image set, which is used to obtain an efficient compression of the high-dimensional spectral data. First, computational techniques to design color filters with a constraint of positive spectral values is described. Then, we present two prototypes of our spectral imaging systems that can be used to acquire the low-dimensional component image set optically through rewritable color filters. The first prototype is based on a spectral synthesizer that illuminates the sample with the light corresponding to a wanted color filter. The second prototype is based on a linear variable filter (LVF) and a liquid crystal spatial light modulator (LCSLM) that implements the rewritable color filter in front of a CCD-camera. We also show how liquid crystal tunable filter (LCTF) based system can be used to implement arbitrary color filters. The optically acquired component image set can be used for computational spectral image reconstruction or it can be directly used for patternrecognition tasks.
Dynamic Partial Function (DPF), which dynamically selects a subset of features to measure pairwise image similarity, has been shown to be very effective in near-replica imagerecognition. DPF, however, suffers from th...
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Dynamic Partial Function (DPF), which dynamically selects a subset of features to measure pairwise image similarity, has been shown to be very effective in near-replica imagerecognition. DPF, however, suffers from the one-size-fits-all problem: it requires that all pairwise similarity measurements must use the same number of features. We propose methods for enhancing DPF's performance by allowing different numbers of features to be selected in a pairwise manner. Through extensive empirical studies, we show that our three schemes: thresholding, sampling and weighting, and hybrid schemes of these three basic approaches, substantially outperform DPF in near-replica imagerecognition.
We consider the problem of estimating the shape and radiance of an object from a calibrated set of views under the assumption that the reflectance of the object is non-Lambertian. Unlike traditional stereo, we do not ...
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We consider the problem of estimating the shape and radiance of an object from a calibrated set of views under the assumption that the reflectance of the object is non-Lambertian. Unlike traditional stereo, we do not solve the correspondence problem by comparing image-to-image. Instead, we exploit a rank constraint on the radiance tensor field of the surface in space, and use it to define a discrepancy measure between each image and the underlying model. Our approach automatically returns an estimate of the radiance of the scene, along with its shape, represented by a dense surface. The former can be used to generate novel views that capture the non-Lambertian appearance of the scene.
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