Accurate object contour tracking based on boundary edge selection

Roh, Myung-Cheol; Kim, Tae-Yong; Park, Jihun; Lee, Seong–Whan

doi:10.1016/j.patcog.2006.06.014

Cited by 67 publications

(29 citation statements)

References 12 publications

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“…Each boundary is uniquely determined by specifying the location of its initial point and a sequence of points having the same labels as the initial point. Contour tracking have been done using Left Most Looking (LML) algorithm [15]. By taking the Discrete Fourier Transform (DFT) of the object contours, we obtained 16 Fourier descriptors feature vector, which have been handled to become rotation, scale and translation invariant, the same way as described in [16].…”

Section: Related Workmentioning

confidence: 99%

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A Feature Fusion Approach for Hand Tools Classification

Ibrahim¹,

Ahmed²

2017

ijacsa

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Abstract-The most important functions in objects classification and recognition system are to segment the objects from the input image, extract common features from the objects, and classify these objects as a member of one of the considered object classes. In this paper, we present a new approach for feature-based objects classification. The main idea of the new approach is the fusion of two different feature vectors that are calculated using Fourier descriptors and moment invariants. The fused moment-Fourier feature vector is invariant to image scaling, rotation, and translation. The fused feature vector for a reference object is used for training feed-forward neural network classifier. Classification of some hand tools is used to evaluate the performance of the proposed classification approach. The results show an appreciable increase in the classification accuracy rate with a considerable decrease in the classifier learning time.

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Section: Related Workmentioning

confidence: 99%

“…Hu's seven moment invariants that are invariant under translation, rotation, and scaling have been calculated from the objects [15], [18]- [20]. …”

Section: Related Workmentioning

confidence: 99%

A Feature Fusion Approach for Hand Tools Classification

Ibrahim¹,

Ahmed²

2017

ijacsa

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“…We can mainly classify it into four categories: region-based approaches, which are based on the variation of image regions in motion (Khan et al, 2001); feature-based (Wren et al, 1997;Fieguth and Terzopoulos, 1997;Li et al, 2003), that usually utilize information about color, texture, etc. ; contour-based (Isard and Blake, 1996;Nguyen et al, 2002;Roh et al, 2007), that make use of the bounding contours to represent the target outline; and model-based methods (Gavrila and Davis, 1996;Andriluka et al, 2010) that explicitly require a 2D or 3D model of a person for tracking. However, a too detailed review of all the approaches is beyond the scope of our paper, therefore, in the following we will focus on people tracking-by-detection methodologies, more related to our work.…”

Section: Related Workmentioning

confidence: 99%

Multi-Camera People Tracking With Hierarchical Likelihood Grids

Chen

Panin

Knoll

2011

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:In this paper, we present a grid-based tracking by detection methodology, applied to 3D people tracking for multi-camera video surveillance. In particular, frame-by-frame detection is performed by means of hierarchical likelihood grids, using edge matching through the oriented distance transform on each camera view and a simple person model, followed by likelihood grids clustering in state-space. Subsequently, the tracking module performs a global nearest neighbor data association, in order to initiate, maintain and terminate tracks automatically. The proposed system can easily include additional features, such as color or background subtraction, it can be scaled to more camera views, and it can be used to track other items as well. We demonstrate it through experiments in indoor sequences, using a calibrated multi-camera setup.

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“…Alternatively, boundary based optical flow may also be considered, e.g. in [21] a Canny edge detector was used to identify suitable locations for optical flow estimation. Here we also track the object by estimating the optical flow along the boundary points.…”

Section: Boundary Based Optical Flowmentioning

confidence: 99%

Tracking with Active Contours Using Dynamically Updated Shape Information

Chiverton

Xie

Mirmehdi

2008

Procedings of the British Machine Vision Conference 2008

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An active contour based tracking framework is described that generates and integrates dynamic shape information without having to learn a priori shape constraints. This dynamic shape information is combined with fixative photometric foreground model matching and background mismatching. Boundary based optical flow is also used to estimate the location of the object in each new video frame, incorporating Procrustes based shape alignment. Promising results under complex deformations of shape, varied levels of noise, and close-to-complete occlusion in the presence of complex textured backgrounds are presented.

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Accurate object contour tracking based on boundary edge selection

Cited by 67 publications

References 12 publications

A Feature Fusion Approach for Hand Tools Classification

A Feature Fusion Approach for Hand Tools Classification

Multi-Camera People Tracking With Hierarchical Likelihood Grids

Tracking with Active Contours Using Dynamically Updated Shape Information

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