Object shape before boundary shape: Scale-space medial axes

Pizer, Stephen M.; Burbeck, Christina A.; Coggins, James M.; Fritsch, Daniel S.; Morse, Bryan S.

doi:10.1007/bf01254105

Cited by 66 publications

(29 citation statements)

References 19 publications

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“…For line extraction at coarse scales, we use the method of Steger [32], which builds upon previous works on multi-scale ridge detection [8,15,27,16,20], and is specifically adapted to road extraction, including an analysis of the scale-space behavior of roads. Baumgartner et.…”

Section: Model and Strategymentioning

confidence: 99%

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Automatic extraction of roads from aerial images based on scale space and snakes

Laptev¹,

Mayer

Lindeberg³

et al. 2000

Machine Vision and Applications

194

123

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We propose a new approach for automatic road extraction from aerial imagery with a model and a strategy mainly based on the multi-scale detection of roads in combination with geometry-constrained edge extraction using snakes. A main advantage of our approach is, that it allows for the rst time a bridging of shadows and partially occluded areas using the heavily disturbed evidence in the image. Additionally, it has only few parameters to be adjusted. The road network is constructed after extracting crossings with varying shape and topology. We show the feasibility of the approach not only by presenting reasonable results but also by e v aluating them quantitatively based on ground truth.A revised version of this paper will appear in Machine Vision and Applications, 2000.

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Section: Model and Strategymentioning

confidence: 99%

“…al. [27] and Lindeberg [20,21]. Sing and Sowmya illustrate in [31] how grouping parameters might belearned.…”

Section: Introductionmentioning

confidence: 99%

Automatic extraction of roads from aerial images based on scale space and snakes

Laptev¹,

Mayer

Lindeberg³

et al. 2000

Machine Vision and Applications

194

123

View full text Add to dashboard Cite

show abstract

“…points in scale-space at which normalized differential measures of feature strength assume local maxima with respect to scale. (Pizer et al 1994) and his co-workers (Gauch & Pizer 1993) have proposed closely related descriptors, focusing on multi-scale ridge representations for medical image analysis. Psychophysical results by (Burbeck & Pizer 1995) support the belief that such hierarchical multi-scale representations are relevant for object representation.…”

Section: Choice Of Image Representation For Feature Trackingmentioning

confidence: 99%

Qualitative Multi-scale Feature Hierarchies for Object Tracking

Bretzner¹,

Lindeberg²

1999

Scale-Space Theories in Computer Vision

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This paper shows how the performance of feature trackers can be improved by building a hierarchical view-based object representation consisting of qualitative relations between image structures at different scales. The idea is to track all image features individually, and to use the qualitative feature relations for avoiding mismatches, resolving ambiguous matches and for introducing feature hypotheses whenever image features are lost. Compared to more traditional work on view-based object tracking, this methodology has the ability to handle semi-rigid objects and partial occlusions. Compared to trackers based on threedimensional object models, this approach is much simpler and of a more generic nature. A hands-on example is presented showing how an integrated application system can be constructed from conceptually very simple operations.

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“…Specifically, we will be concerned with graph-like and qualitative image representations in terms of multi-scale image features (Koenderink, 1984;Crowley and Sanderson, 1987;Lindeberg, 1993;Pizer et al, 1994;Triesch and von der Malsburg, 1996;Shokoufandeh et al, 1999;Bretzner and Lindeberg, 1999a), which are expressed within a context of feature detection with automatic scale selection (Lindeberg, 1998a(Lindeberg, , 1998b.…”

Section: Introductionmentioning

confidence: 99%

Untitled

Laptev

Lindeberg

2003

International Journal of Computer Vision

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Abstract. This paper presents two approaches for evaluating multi-scale feature-based object models. Within the first approach, a scale-invariant distance measure is proposed for comparing two image representations in terms of multi-scale features. Based on this measure, the maximisation of the likelihood of parameterised feature models allows for simultaneous model selection and parameter estimation.The idea of the second approach is to avoid an explicit feature extraction step and to evaluate models using a function defined directly from the image data. For this purpose, we propose the concept of a feature likelihood map, which is a function normalised to the interval [0, 1], and that approximates the likelihood of image features at all points in scale-space.To illustrate the applicability of both methods, we consider the area of hand gesture analysis and show how the proposed evaluation schemes can be integrated within a particle filtering approach for performing simultaneous tracking and recognition of hand models under variations in the position, orientation, size and posture of the hand. The experiments demonstrate the feasibility of the approach, and that real time performance can be obtained by pyramid implementations of the proposed concepts.

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Object shape before boundary shape: Scale-space medial axes

Cited by 66 publications

References 19 publications

Automatic extraction of roads from aerial images based on scale space and snakes

Automatic extraction of roads from aerial images based on scale space and snakes

Qualitative Multi-scale Feature Hierarchies for Object Tracking

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