Physiologically motivated image fusion for object detection using a pulse coupled neural network

Abstract

This paper presents the first physiologically motivated pulse coupled neural network (PCNN)-based image fusion network for object detection. Primate vision processing principles, such as expectation driven filtering, state dependent modulation, temporal synchronization, and multiple processing paths are applied to create a physiologically motivated image fusion network. PCNN's are used to fuse the results of several object detection techniques to improve object detection accuracy. Image processing techniques (wavelets, morphological, etc.) are used to extract target features and PCNN's are used to focus attention by segmenting and fusing the information. The object detection property of the resulting image fusion network is demonstrated on mammograms and Forward Looking Infrared Radar (FLIR) images. The network removed 94% of the false detections without removing any true detections in the FLIR images and removed 46% of the false detections while removing only 7% of the true detections in the mammograms. The model exceeded the accuracy obtained by any individual filtering methods or by logical ANDing the individual object detection technique results.

Remote sensing image fusion based on orientation information in nonsubsampled contourlet transform domain

“…In image processing, PCNN is a single layer pulse coupled neural cells with a two-dimensional connection [8]. The PCNN method has a good effect on image fusion fields.…”

Review of multiscale geometric decompositions in a remote sensing context

“…In fact, in Ref. 83, a fusion method of SAR images based on pulse couple neural 84 is proposed. The highlight of this algorithm is to use the global feature of source images and calculate the gradient information of them after being decomposed into several directions.…”

Image fusion algorithm based on orientation information motivated Pulse Coupled Neural Networks

“…PCNN is a feedback network and each PCNN neuron consists of three parts: the receptive field, the modulation field, and the pulse generator [3].In image processing, PCNN is a single layer pulse coupled neural cells with a twodimensional connection [4] as shown in Fig.3. In the existed PCNN-based fusion algorithms [4]- [8], pixels in spatial domain or MSD domain are input to PCNN, there exists a one-to-one correspondence between the pixels and the neurons.…”

“…In the existed PCNN-based fusion algorithms [4]- [8], pixels in spatial domain or MSD domain are input to PCNN, there exists a one-to-one correspondence between the pixels and the neurons. Each neuron is connected with neighbouring neurons in linking range.…”

“…Recently, PCNN has been employed in image fusion in [4]- [8]. Owing to the coupling links in neurons, PCNN successfully utilize the local image information of images.…”