The position-orientation masking approach to parametric search for template matching

Paglieroni, David W.; Ford, Gary E.; Tsujimoto, Eiji

doi:10.1109/34.297956

Cited by 31 publications

(17 citation statements)

References 10 publications

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“…In chamfer matching, similarity between Q and p ∈ P is not binary, but is instead regulated by distance between associated pixels [19][20][21]. For = 0, chamfer matching is equivalent to numerical matching.…”

Section: Boundary or Edge Pixel Association By Proximitymentioning

confidence: 99%

Design considerations for image segmentation quality assessment measures

Paglieroni¹

2004

Pattern Recognition

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Section: Boundary or Edge Pixel Association By Proximitymentioning

confidence: 99%

Design considerations for image segmentation quality assessment measures

Paglieroni¹

2004

Pattern Recognition

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“…Note that with a few exceptions e.g. 12 m uch of the previous work on Looking at People" 7 has involved a static camera; initial segmentation was possible by (12) C (8) C (11) C (9) Td(15) [13][14][15][16][17][18] Td(9) [7][8][9][10][11][12] Tu(9) [7][8][9][10][11][12] Tu(15) [13][14][15][16][17][18] Td (R) : Tu (R) : C (R) : background subtraction. Furthermore, it is di cult for a user to hard-code the shape hierarchy; it needs to be constructed automatically from examples.…”

Section: Pedestriansmentioning

confidence: 99%

Real-time object detection for "smart" vehicles

Gavrila

Philomin

1999

Proceedings of the Seventh IEEE International Conference on Computer Vision

520

313

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This paper presents an e cient shape-based object detection method based on Distance Transforms and describes its use for real-time vision on-board vehicles. The method uses a template hierarchy to capture the variety of object shapes; e cient hierarchies can be generated o ine for given shape distributions using stochastic optimization techniques i.e. simulated annealing. Online, matching involves a simultaneous coarse-to-ne approach o ver the shape hierarchy and over the transformation parameters. Very large speedup factors are typically obtained when comparing this approach with the equivalent brute-force formulation; we h a ve measured gains of several orders of magnitudes.We present experimental results on the real-time detection of tra c signs and pedestrians from a moving vehicle. Because of the highly time sensitive nature of these vision tasks, we also discuss some hardwarespeci c implementations of the proposed method as far as SIMD parallelism is concerned.

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“…Most of the works on curve matching deal with identifying some known curve from an image, and then initiating various algorithms to follow it [28][29][30][31][32][33][34], while [35] deals only with a parameter-free approach to register images using elasticity theory. Further work deals with how best to find out contours from an image to identify and classify a target [36][37][38][39][40][41].…”

Section: Curve Matchingmentioning

confidence: 99%

Human tracking with an infrared camera using a curve matching framework

Lee

Shah

Bhattacharya

et al. 2012

EURASIP J. Adv. Signal Process.

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The Kalman filter (KF) has been improved for a mobile robot to human tracking. The proposed algorithm combines a curve matching framework and KF to enhance prediction accuracy of target tracking. Compared to other methods using normal KF, the Curve Matched Kalman Filter (CMKF) method predicts the next movement of the human by taking into account not only his present motion characteristics, but also the previous history of target behavior patterns-the CMKF provides an algorithm that acquires the motion characteristics of a particular human and provides a computationally inexpensive framework of human-tracking system. The proposed method demonstrates an improved target tracking using a heuristic weighted mean of two methods, i.e., the curve matching framework and KF prediction. We have conducted the experimental test in an indoor environment using an infrared camera mounted on a mobile robot. Experimental results validate that the proposed CMKF increases prediction accuracy by more than 30% compared to normal KF when the characteristic patterns of target motion are repeated in the target trajectory.

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The position-orientation masking approach to parametric search for template matching

Cited by 31 publications

References 10 publications

Design considerations for image segmentation quality assessment measures

Design considerations for image segmentation quality assessment measures

Real-time object detection for "smart" vehicles

Human tracking with an infrared camera using a curve matching framework

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