**10. References**


34 Advances in Object Recognition Systems

If we are willing to accept that the what/where/which model of episodic memory is a *model* of human episodic memory, and therefore, concede that it does not encompass the human characteristics of consciousness and mental time travel, then we can use this model to investigate the tripartite what/where/which code. One of the main strengths of this model is that it allows for episodic-like memory to be studied in a wide range of species. Comparative work should focus on the ability of other animals, including pigs, to recall information without the stimuli being re-presented on test (similar to that of Eacott et al.,

This chapter reflects research supported by individual NSERC Discovery Grants to G. Martin, D. Skinner, C. Thorpe, and C. Walsh, and by a NSERC Canadian Graduate

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**3**

Ioannis Kypraios

*London, UK* 

**Performance Analysis of the**

*Department of Engineering & Computing, ICTM,* 

**Modified-Hybrid Optical Neural Network Object** 

In literature, we could categorise two broad main approaches for pattern recognition systems. The first category consists of linear combinatorial-type filters (LCFs) (Stamos, 2001) where commonly image analysis is done in the frequency domain with the help of Fourier Transformation (FT) (Lynn & Fuerst, 1998; Proakis & Manolakis, 1998). The second category consists of pure neural modelling methods. (Wood, 1996) has given a brief but clear review of invariant pattern recognition methods. His survey has divided the methods into two further sub-categories of solving the invariant pattern recognition problem. The first subcategory has two distinct stages of separately calculating the features of the training set pattern to be invariant to certain distortions and then classifying the extracted features. The second sub-category, instead of having two separate stages, has a single stage which parameterises the desired invariances and then adapts them. (Wood, 1996) has also described the integral transforms, which fall under the first sub-category of feature extractors. They are based on Fourier analysis, such as the multidimensional Fourier transform, Fourier-Mellin transform, triple correlation (Delopoulos et al., 1994) and others. Part of the first sub-category is also the group of algebraic invariants, such as Zernike moments (Khotanzad & Hong, 1990; Perantonis & Lisboa, 1992), generalised moments (Shvedov et al., 1979) and others. Wood has given examples of the second sub-category, the main representative being based on artificial neural network (NNET) architectures. He has presented the weight-sharing neural networks (LeCun, 1989; LeCun et al. 1990), the highorder neural networks (Giles & Maxwell, 1987; Kanaoka et al. 1992; Perantonis & Lisboa, 1992; Spirkovska & Reid, 1992), the time-delay neural networks (TDNN) (Bottou et al., 1990; Simard & LeCun, 1992; Waibel et al., 1989) and others. Finally, he has included an additional third sub-category with all the methods which cannot be placed under either the featureextraction feature-classification approach or the parameterised approach. Such methods are image normalisation pre-processing (Yuceer & Oflazer, 1993) methods for achieving invariance to certain distortions. (Dobnikar et al., 1992) have compared the invariant pattern classification (IPC) neural network architecture versus the Fourier Transform method. They used for their comparison black-and-white images. They have proven the generalisation

**1. Introduction** 

**Recognition System Within Cluttered Scenes** 

