A biologically inspired computational vision front-end based on a self-organised pseudorandomly tessellated artificial retina

Balasuriya, S.; Siebert, Paul

doi:10.1109/ijcnn.2005.1556415

Cited by 12 publications

(14 citation statements)

References 18 publications

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“…Alternatively, if the fovea is evenly spaced but the periphery is not then a discontinuity is seen at the boundary. Balasuriya and Siebert argue that no analytical approach will produce a tessellation that gives uniform density coverage in the foveal and a space varying mapping in the periphery, while maintaining a consistent local structure [3]. They have produced tessellations that are good approximations to the cellular layout of the retina by using self-organising methods with random perturbations [3].…”

Section: The Retinamentioning

confidence: 99%

A developmental algorithm for ocular–motor coordination

Chao

Lee

2010

Robotics and Autonomous Systems

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This paper presents a model of ocular-motor development, inspired by ideas and data from developmental psychology. The learning problem concerns the growth of the transform between image space and motor space necessary for the control of visual saccades. An implementation is used to produce experimental results and these are presented and discussed. The algorithm is simple, extremely fast, self calibrating, adaptive to change, and exhibits emergent stages of behaviour as learning progresses.

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Section: The Retinamentioning

confidence: 99%

A developmental algorithm for ocular–motor coordination

Chao

Lee

2010

Robotics and Autonomous Systems

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“…In the process of sampling in retina, the photoreceptor that is to transform the optical signal to the neural signal appears a nonuniform distribution [6]. In order to simulate this sampling strategy, Balasuriya designed an algorithm in which an iterative process and a distribution with different densities were experimented in the processing of image signals [7,8]. It proved that the strategy had an advantage of eliminating the redundant signals.…”

Section: Related Workmentioning

confidence: 99%

A Signal-Processing Neural Model Based on Biological Retina

Wei

Wang

Wang³

et al. 2019

Electronics

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Image signal processing has considerable value in artificial intelligence. However, due to the diverse disturbance (e.g., color, noise), the image signal processing, especially the representation of the signal, remains a big challenge. In the human visual system, it has been justified that simple cells in the primary visual cortex are obviously sensitive to vision signals with partial orientation features. In other words, the image signals are extracted and described along the pathway of visual processing. Inspired by this neural mechanism of the primary visual cortex, it is possible to build an image signal-processing model as the neural architecture. In this paper, we presented a method to process the image signal involving a multitude of disturbance. For image signals, we first extracted 4 rivalry pathways via the projection of color. Secondly, we designed an algorithm in which the computing process of the stimulus with partial orientation features can be altered into a process of analytical geometry, resulting in that the signals with orientation features can be extracted and characterized. Finally, through the integration of characterizations from the 4 different rivalry pathways, the image signals can be effectively interpreted and reconstructed. Instead of data-driven methods, the presented approach requires no prior training. With the use of geometric inferences, the method tends to be interpreted and applied in the signal processor. The extraction and integration of rivalry pathways of different colors allow the method to be effective and robust to the signals with the image noise and disturbance of colors. Experimental results showed that the approach can extract and describing the image signal with diverse disturbance. Based on the characterization of the image signal, it is possible to reconstruct signal features which can effectively represent the important information from the original image signal.

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“…Recently, researchers have constructed plausible non-uniform artificial retinae [6] based on a form of self-organisation [7] which can extract space-variant visual information from images. They demonstrated the detection of stable Laplacian of Gaussian scalespace extrema in the space-variant visual information.…”

Section: Related Workmentioning

confidence: 99%

An Architecture for Object-based Saccade Generation using a Biologically Inspired Self-organised Retina

Balasuriya

Siebert

2006

The 2006 IEEE International Joint Conference on Neural Network Proceedings

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Abstract-Our paper presents a fully automated computational mechanism for targeting a space-variant retina based on the highlevel visual content of a scene. Our retina's receptive fields are organised at a high density in the central foveal region of the retina and at a sparse resolution in the surrounding periphery in a non-uniform, locally pseudo-random tessellation similar to that found in biological vision. Multi-resolution, space-variant visual information is extracted on a scale-space continuum and interest point descriptors are extracted that represent the visual appearance of local regions. We demonstrate the vision system performing simple visual reasoning tasks with the extracted visual descriptors by combining the sparse information from its periphery (which gives it a wide field of view) and the high resolution information from the fovea (useful for accurate reasoning). High-level semantic concepts about content in the scene such as object appearances are formed using the extracted visual evidence, and the system performs saccadic explorations by serially targeting 'interesting' regions in the scene based on the location of high-level visual content and the current task it is trying to achieve.

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A biologically inspired computational vision front-end based on a self-organised pseudorandomly tessellated artificial retina

Cited by 12 publications

References 18 publications

A developmental algorithm for ocular–motor coordination

A developmental algorithm for ocular–motor coordination

A Signal-Processing Neural Model Based on Biological Retina

An Architecture for Object-based Saccade Generation using a Biologically Inspired Self-organised Retina

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