Robust position, scale, and rotation invariant object recognition using higher-order neural networks

Modified high-order neural network for invariant pattern recognition

“…At this stage, all possible triangles are computed and pointers to the weights are stored (He, 1999;Spirkovska and Reid, 1992), along with the array of weights themselves. Thus, a minimum of two memory bytes is required for each pointer.…”

“…doi:10. 1016/j.patrec.2004.09.029 by structure is achieved by the structure of the neural network; good examples are the recognition (Fukushima, 2001) and high-order neural networks (HONN) (Spirkovska and Reid, 1992). This article will concentrate on the HONN and its modifications.…”

“…Weight sharing by similar triangles (Spirkovska and Reid, 1992) reduces the number of individual weights and achieves translation, rotation and partial scale invariance. Weight sharing by ''approximately similar triangles'' (Perantonis and Lisboa, 1992;He and Siyal, 1999;Takano et al, 1994) further reduces the number of individual weights and achieves translation rotation, limited scale, and limited distortional invariance.…”

“…This enables fast network learning with only one view of each pattern presented at the learning stage. However, the number of interconnections in the network grows exponentially, and this prevents the use of this method for image fields larger than 18 · 18 pixels (Spirkovska and Reid, 1992).…”

Performance Analysis of the Modified-Hybrid Optical Neural Network Object Recognition System Within Cluttered Scenes

“…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.…”