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IEEE Conference on Robotics, Automation and Mechatronics (RAM)

Quantum Bio-inspired Invariant Object Recognition Model on System-on-a-Chip (SoC)

Loo Chu Kiong, Teh Joo Peng

Published Sep 1, 2008• 4 citations
Publisher

Abstract

This paper presents the first attempt of designing a physical quantum-like `brain' for humanoid and experimentally proves a 2D invariant object recognition model inspired by the image processing along the human visual pathway: retina, lateral geniculate nucleus (LGN) and primary visual cortex (V1). It also describes storing the output of the model in quantum holographic memory (a hologram) and the method to reconstruct the visual representation. The work is implemented as system-on-a-chip in field-programmable gate array (FPGA).

DOI
10.1109/ramech.2008.4681389

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This paper presents the first attempt of designing a physical quantum-like `brain' for humanoid and experimentally proves a 2D invariant object recognition model inspired by the image processing along the human visual pathway: retina, lateral geniculate nucleus (LGN) and primary visual cortex (V1). It also describes storing the output of the model in quantum holographic memory (a hologram) and the method to reconstruct the visual representation. The work is implemented as system-on-a-chip in field-programmable gate array (FPGA).
It is computationally demonstrated how quantum associative networks, implemented using quantum holography, could be harnessed for object recognition. These simulated quantum nets alone execute efficient image recognition, i.e., reconstruction of an image selected from associative memory (hologram). However, optically implementable neural-net preprocessing of object-images is needed for appearance-based viewpoint-invariant recognition of objects. We present computer simulation results of two methods: Moore-Penrose orthogonalization and encoding of object-images with Gabor wavelets. A computer-supported quantum Gabor-wavelet holography is proposed. © 2005 Optical Society of America
To simulate the brain functions,a quantum associative memory combined with information preprocessing by a sparse coding model is presented. The sparse coding scheme is used to simulate the information transformation from retina up to primary visual cortex (V1) along the visual path and the quantum associative memory is used to simulate the pattern processing functions of the brain such as the pattern storing,forgetting and retrieving. Experimental results show that the model exhibits good associative ability on face recognition. Considering the huge storage capacity,mass parallel-distributed processing ability and oscillatory phenomena of the quantum system,this model might be a biological plausible implementation.
A holographic experimental procedure assuming use of quantum states of light is simulated. It uses merely interference-based image storage and nonunitary image retrieval realized by wave function collapse. Successful results of computational view-invariant recognition of object images are presented. As in neural net theory, recognition is selective reconstruction of an image from a database of many concrete images (simultaneously stored in an associative memory) after presentation of a different version of that image. That is, in the first step, we store many high-resolution images of objects into quantum memory (a hologram). In the second step, we present a “nonlearned” noisy image version. We thereby trigger memory-influenced reorganization of the state of the system so that it finally encodes those corrected object images that correspond to the newly presented version. The holographic procedure seems to be implementable with present-day quantum optics.
One of the fundamental tasks in machine learning is image classification, which serves as a key benchmark for validating algorithm performance and practical potential. However, effectively processing high-dimensional, detail-rich images, a capability that is inherent in biological vision, remains a persistent challenge. Inspired by the human brain's efficient ``Forest Before Trees'' cognition, we propose a novel Guiding Paradigm for image recognition, leveraging classical neural networks to analyze global low-frequency information and guide targeted quantum circuit towards critical high-frequency image regions. We present the Brain-Inspired Quantum Classifier (BIQC), implementing this paradigm via a complementarity architecture where a quantum pathway analyzes the localized intricate details identified by the classical pathway. Numerical simulations on diverse datasets, including high-resolution images, show the BIQC's superior accuracy and scalability compared to existing methods. This highlights the promise of brain-inspired, hybrid quantum-classical approach for developing next-generation visual systems.
In human eyes, retina cells have shown complicated, sophisticated structures, and thus capability of carrying out certain basic visual functions. In computer vision, a spherical model has been proposed to demonstrate that certain basic visual functions can be performed at the sensor/photoreceptor level for target recognition, mobile robotic control, and other applications. This paper will establish that low level computer vision in this spherical model exhibits a Hamiltonian model of quantum computation. This results will be important to the understanding of human vision as well as the development of intelligent visual sensor chips and devices in electronics and optics.
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