Addresses the problem of reliably fitting parametric and semi-parametric models to high density spot array images obtained in gene expression experiments. The goal is to measure the amount of genetic material at speci...
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Addresses the problem of reliably fitting parametric and semi-parametric models to high density spot array images obtained in gene expression experiments. The goal is to measure the amount of genetic material at specific spot locations. Many spots can be modelled accurately by a Gaussian shape. In order to deal with highly overlapping spots the authors use robust M-estimators. When the parametric method fails, they use a novel, robust semi-parametric method which can handle spots of different shapes accurately. They present the results for real data and compare the complexity of the two methods.
In this paper we present a minimum description length (MDL) framework for fuzzy clustering algorithms. This framework enables us to find an optimal number of cluster centers. We applied our approach to the fuzzy c-mea...
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ISBN:
(纸本)0769507506
In this paper we present a minimum description length (MDL) framework for fuzzy clustering algorithms. This framework enables us to find an optimal number of cluster centers. We applied our approach to the fuzzy c-means algorithm for which we designed a computationally efficient procedure. We report the results of our approach on a 2D clustering problem and on RGB color image segmentation.
Content based image retrieval is the task of searching images from a database, which are visually similar to a given example image. We present methods for content based image retrieval based on texture similarity usin...
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We propose an approach to constructing multiple eigenspaces from a set of training images based on the minimum description length (MDL) principle. The main idea is to systematically build a redundant set of eigenspace...
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ISBN:
(纸本)0769507506
We propose an approach to constructing multiple eigenspaces from a set of training images based on the minimum description length (MDL) principle. The main idea is to systematically build a redundant set of eigenspaces, which are treated as hypotheses that are then subject to a selection procedure. The selection procedure, based on the MDL principle, selects the final resulting set of eigenspaces as an optimal representation of the training set. We have tested the proposed method on a number of standard image sets, and the significance of the approach with respect to the recognition rate has been clearly demonstrated.
The concept of "nearness", which has been dealt with as soon as one started studying digital images, finds one of its rigorous forms in the notion of proximity space. It is this notion, together with "n...
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Content based image retrieval is the task of searching images from a database, which are visually similar to a given example image. We present methods for content based image retrieval based on texture similarity usin...
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ISBN:
(纸本)0769507506
Content based image retrieval is the task of searching images from a database, which are visually similar to a given example image. We present methods for content based image retrieval based on texture similarity using interest points and Gabor features. Interest point detectors are used in computer vision to detect image points with special properties, which can be geometric (corners) or non-geometric (contrast etc.). Gabor functions and Gabor filters are regarded as excellent tools for feature extraction and texture segmentation. The article combines these methods and generates a textural description of images. Special emphasis is devoted to distance measures on texture descriptions. Experimental results of a query system are given.
A framework is presented that produces the mosaic corresponding to the background object of an image sequence. It is based on the dominant motion assumption, which states that the background has a parametric motion an...
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ISBN:
(纸本)0769507506
A framework is presented that produces the mosaic corresponding to the background object of an image sequence. It is based on the dominant motion assumption, which states that the background has a parametric motion and occupies the main part of the images. The foreground objects are localised by their different motion. This localisation is computed together with the background motion in an iterative method. The regions corresponding to the background are then pasted onto the mosaic using classic methods adapted to object elimination or a new mosaicking method based on a striping that takes the foreground objects localisation into account.
The activation of ancient vessel-fragments is a time consuming but important task for archaeologists. The basis for classification and reconstruction is the profile which is the cross-section of the fragment in the di...
The activation of ancient vessel-fragments is a time consuming but important task for archaeologists. The basis for classification and reconstruction is the profile which is the cross-section of the fragment in the di...
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The activation of ancient vessel-fragments is a time consuming but important task for archaeologists. The basis for classification and reconstruction is the profile which is the cross-section of the fragment in the direction of the rotational axis of symmetry. Hence the position of a fragment (orientation) on a vessel is important. In this work the estimation of the axis of rotation out of range data by using a Hough inspired method is proposed. In order to avoid outliers a robust method for estimation of the axis is used. Classification and reconstruction are performed in a bottom-up manner using a description language, which holds all features of the fragment as primitives and all properties among features as relations. Classification of newly found fragments of unknown type is performed by comparing the description of the new fragment with the description of already classified fragments by completing graph similarity. The sub-graph with the highest similarity is then used to reconstruct the complete vessel out of the fragment.
This paper presents an application of 3D-reconstruction and graph theory in the field of archaeology. The classification and reconstruction of ancient pots and vessels out of fragments (so-called sherds) is an importa...
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This paper presents an application of 3D-reconstruction and graph theory in the field of archaeology. The classification and reconstruction of ancient pots and vessels out of fragments (so-called sherds) is an important aspect of archaeological research work. Up to now this is a time consuming, inaccurate, and subjective task which leads to tons of unclassified fragments in archives. Computer aided classification could help to get a better understanding of ancient cultures, since all data of an excavation would be accessible to the public, not only- selected parts as it is now. We propose a bottom-up strategy to classify fragments. The profile section (which is a section a the fragment in the direction of the rotational axis) is segmented into its primitives (with certain properties like length) and relations among this primitives (like position and curvature of connecting points). These primitives and the relations form a description language, different profiles have different descriptions.
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