A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote Sensing

From coastal landscapes to biodiversity remote sensing can on the one hand capture all the natural heritage elements and on the other hand can help in maintaining protected species. In a typical remote sensing application, a few thousands of super high-resolution images are captured and need to be processed. The next step of the processing involves converting those images to an appropriate format for visual display of the data. Then, the image analyst needs to define the regions of interests (ROIs) in each captured image. Next, ROIs need to be defined for identifying specific objects or extracting the required information. First drawback of this processing cycle is the use of image analysis tools which provide them only with scaling or zooming features. Second, there is no conceptual connection between the image analysis tools and the actual processing cycle. Third, such existing tools do not usually automate any steps in the processing cycle. We combine an optical correlator with a supervised or an unsupervised classifier learning algorithm and show how our proposed novel cognitive architecture is conceptually connected with the image analysis processing cycle. We test the architecture with captured images and describe how it can automate the processing cycle

Distortion tolerant non-linear filters designed using artificial neural networks

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Object Recognition within Cluttered Scenes Employing a Hybrid Optical Neural Network Filter

We propose a hybrid filter, which we call the hybrid optical neural network (HONN) filter. This filter combines the optical implementation and shift invariance of correlator-type filters with the nonlinear superposition capabilities of artificial neural network methods. The filter demonstrates good performance in maintaining high-quality correlation responses and resistance to clutter to nontraining in-class images at orientations intermediate to the training set poses. We present the design and implementation of the HONN filter architecture and assess its object recognition performance in clutter

Logarithmic r-? map for hybrid optical neural network filter for object recognition within cluttered scenes

Space-variant imaging sensors can be designed to exhibit in-plane rotation and scale invariance to image data. We combine the complex logarithmic r-? mapping of a space-variant imaging sensor with the hybrid optical neural network filter to achieve, with a single pass over the input data, simultaneous invariance to: out-of-plane rotation; in-plane rotation; scale; projection and shift invariance. The resulting filter we call a complex logarithmic r-? mapping for the hybrid optical neural network filter. We include in the L-HONN filter's design a window based unit for registering the translation invariance of the input objects, initially lost by applying the logarithmic mapping. We test and record the results of the L-HONN filter for single and multiple input objects of the same class within cluttered still images and video frame sequence

Implementation of the Maximum Average Correlation Height (MACH) filter in the spatial domain for object recognition from clutter backgrounds

A moving space domain window is used to implement a Maximum Average Correlation Height (MACH) filter which can be locally modified depending upon its position in the input frame. This enables adaptation of the filter dependant on locally variant background clutter conditions and also enables the normalization of the filter energy levels at each step. Thus the spatial domain implementation of the MACH filter offers an advantage over its frequency domain implementation as shift invariance is not imposed upon it. The only drawback of the spatial domain implementation of the MACH filter is the amount of computational resource required for a fast implementation. Recently an optical correlator using a scanning holographic memory has been proposed by Birch et al [1] for the real-time implementation of space variant filters of this type. In this paper we describe the discrimination abilities against background clutter and tolerance to in-plane rotation, out of plane rotation and changes in scale of a MACH correlation filter implemented in the spatial domain. © 2010 SPIE