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Beulah David, D.
- Image Change Detection for Differently Exposed Image Pairs
Authors
1 Sathyabama University, Chennai, IN
2 Department of CSE, Aarupadai Institute of Technology, Chennai, IN
3 Department of IT, Kumaraguru Institute of Technology, Coimbatore, IN
Source
Digital Image Processing, Vol 2, No 12 (2010), Pagination:Abstract
This paper proposes an analog Hopfield neural network (HNN) for automatic image change detection problem between the images taken at two different exposure times. This optimization relaxation approach differs from other techniques in that it provides the strength of the change rather than assigning binary labels (changed/unchanged) to each pixel. By subtracting both images pixel by pixel, a difference image is obtained. The network topology is built so that each pixel in the difference image is a node in the network. Each node is characterized by its state, which determines if a pixel has changed or unchanged. An energy function is derived, so that the network converges to stable state. The main drawback of existing binary labeling approaches is that pixels are labeled according to the information supplied by its neighbors, where its self information is ignored. The main contribution of the analog Hopfield’s model is that it allows a tradeoff between the influence of a pixel’s neighborhood and its own criterion. This is mapped under the energy function to be minimized. Also a comparison between analog and discrete HNN shows similar Percentage of Correct Classification (PCC) and Yule values. However, the analog counterpart describes the degree of change by embedding both Spatial-Contextual Information and Self-Data Information.
- Comparison of Analog and Discrete Hopfield Neural Network for Image Change Detection
Authors
1 Department of Computer Science and Engineering, Francis Xavier Engineering College, IN
2 Department of Computer Science and Engineering, Saranathan College of Engineering, IN
Source
Digital Image Processing, Vol 2, No 9 (2010), Pagination: 313-318Abstract
This paper proposes an analog Hopfield neural network (HNN) for automatic image change detection problem between two images. This optimization relaxation approach differs from other techniques in that it provides the strength of the change rather than assigning binary labels (changed/unchanged) to each pixel. A difference image is obtained by subtracting pixel by pixel both images. The network topology is built so that each pixel in the difference image is a node in the network. Each node is characterized by its state, which determines if a pixel has changed or unchanged. An energy function is derived, so that the network converges to stable state. The main drawback of existing binary labeling approaches is that pixels are labeled according to the information supplied by its neighbors, where its self information is ignored. The main contribution of the analog Hopfield’s model is that it allows a tradeoff between the influence of a pixel’s neighborhood and its own criterion. This is mapped under the energy function to be minimized. Also a comparison between analog and discrete HNN shows similar Percentage of Correct Classification and Yule values. However, the analog counterpart describes the degree of change by embedding both Spatial-Contextual Information and Self-Data Information.