Enhancing the Potential of the Conventional Gaussian Mixture Model for Segmentation: from Images to Videos
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Summary
This dissertation proposes an efficient way to include multiresolution features in Gaussian Mixture Model (GMM) and proposes a more accurate foreground segmentation method by enhancing GMM with the use of Adaptive Support Weights and Histogram of Gradients.
- Type
- article
- Published
- 2014-01-01
- Cited by
- 0
- References
- 199
- Access
- Open access
- OpenAlex
- https://openalex.org/W39242377
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:59794269
Keywords
Artificial intelligence, Computer vision, Segmentation, Mixture model, Computer science
References
- Video-based detection of street-parking violation
- Moving Object Detecting Using Gradient Information, Three-Frame-Differencing and Connectivity Testing
- Exact Maximum A Posteriori Estimation for Binary Images
- Vision-based detection, tracking and classification of vehicles using stable features with automatic camera calibration
- Digital Image Processing
- Advances in image and video segmentation
- Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges
- Using the Discrete Hadamard Transform to Detect Moving Objects in Surveillance Video
- Bayesian network based computer vision algorithm for traffic monitoring using video
- Pattern Recognition and Machine Learning
- Fast automatic unsupervised image segmentation and curve detection in spatial point patterns
- An empirical approach to grouping and segmentation
- Moving target classification and tracking from real-time video
- Local mode filtering
- A real-time system for monitoring of cyclists and pedestrians
- A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
- Motion foreground detection based on wavelet transformation and color ratio difference
- Background segmentation with feedback: The Pixel-Based Adaptive Segmenter
- Algorithms for Clustering Data
- A multi-resolution framework for multi-object tracking in Daubechies complex wavelet domain
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