Sandip Sarkar

A possible explanation of the low-level brightness–contrast illusions in the light of an extended classical receptive field model of retinal ganglion cells

The low-level brightness–contrast illusions constitute a special class within visual illusions. Speculations exist that these illusions may be processed through the filtering action of the retinal ganglion cells without necessitating much intervention from higher order processes of visual perception. Concept of the classical receptive field of the ganglion cell, derived from early physiological studies, prompted the idea that a Difference of Gaussian (DoG) model might explain the low-level illusions. In spite of its many successes, the DoG model fails to explain some of these illusions. It has been shown in this paper that it is possible to simulate those illusions with a model that takes into cognizance the role of the extended classical receptive field

A possible mechanism of zero-crossing detection using the concept of the extended classical receptive field of retinal ganglion cells

The extended classical receptive field (ECRF) of retinal ganglion cells has been modelled as a combination of three zero-mean Gaussians at three different scales that has been shown to be equivalent to a Biharmonic or Bi-Laplacian of Gaussian filter. It has also been shown that the ECRF can be approximated by a combination of Laplacian of Gaussian (LoG) and the Dirac-delta function. Zero-crossings detected with this operator are more informative than those detected by the traditional filters like LoG or Difference of Gaussians (DoG) that had been devised using the classical receptive field of the ganglion cells. We have also explained that such an additional information processing is not in contradiction with the recent experimental findings on the physiology of retinal ganglion cells.

Understanding image structure from a new multi-scale representation of higher order derivative filters

We are proposing a biologically inspired multi-scale derivative filter in which the higher order derivatives are expressed as a linear combination of a smoothing function at various scales. One of the functions in the summation has been approximated to a Dirac-delta function to finally yield the new filter. This modification has some support from the point of view of authentic edge detection as well as from neurophysiological and psychophysical experiments at the retinal level. Besides, it improves the quality of the filter in a number of ways. The proposed filter can be optimized at any desired scale. Hence it is very effective in extracting the features from a noisy picture. The filter is rotationally symmetric. Zero-crossing map of any picture filtered with the proposed model gives a half-toning effect to the retrieved image and hence preserves the intensity information in the image even in the edge map.

A possible mechanism of stochastic resonance in the light of an extra-classical receptive field model of retinal ganglion cells

Traditionally the intensity discontinuities in an image are detected as zero-crossings of the second derivative with the help of a Laplacian of Gaussian (LOG) operator that models the receptive field of retinal Ganglion cells. Such zero-crossings supposedly form a raw primal sketch edge map of the external world in the primary visual cortex of the brain. Based on a new operator which is a linear combination of the LOG and a Dirac-delta function that models the extra-classical receptive field of the ganglion cells, we find that zero-crossing points thus generated, store in presence of noise, apart from the edge information, the shading information of the image in the form of density variation of these points. We have also shown that an optimal image contrast produces best mapping of the shading information to such zero-crossing density variation for a given amount of noise contamination. Furthermore, we have observed that an optimal amount of noise contamination reproduces the minimum optimal contrast and hence gives rise to the best representation of the original image. We show that this phenomenon is similar in nature to that of stochastic resonance phenomenon observed in psychophysical experiments.

Image enhancement by high-order gaussian derivative filters simulating non-classical receptive fields in the human visual system

The non-linearity exhibited by the non-classical receptive field in human visual system has been combined with the linear classical receptive field model. This enables us to construct higher order Gaussian Derivatives as a linear combination of lower order derivatives at different scales. Based on this, a new kernel which simulates non-classical receptive fields with extended disinhibitory surrounds, has been proposed. It is easy to implement and finds justification from an old psychophysical angle too. The proposed kernel has been shown to perform better than the well-known Laplacian kernel, which models the classical excitatory-inhibitory receptive fields.

A bio-inspired model for multi-scale representation of even order Gaussian derivatives

A linear combination of Gaussian functions at various scales is being suggested as a suitable model for the human visual system. It reduces to the DOG (difference of Gaussian) model at the most primitive level of processing. The model is actually equivalent to the experimentally observed receptive field profiles that can be fitted by various even order derivatives of Gaussians, the order being determined by the number of Gaussians in the linear combination, once again reducing to the DOG-LOG (Laplacian of Gaussian) equivalence at the most primary level of visual signal processing. The role of amacrine cells in the retina is explained in this light and the inherent multi-scale property of the model is looked upon as a suitable mechanism for enabling a unified representation for the various classes of retinal ganglion cells differing in their receptive field profiles.

Rapid and brief communication: Proposing new methods in low-level vision from the Mach band illusion in retrospect

A re-scan of the well-known Mach band illusion has led to the proposal of a Bi-Laplacian of Gaussian operation in early vision. Based on this postulate, the human visual system at low-level has been modeled from two approaches that give rise to two new tools. On one hand, it leads to the construction of a new image sharpening kernel, and on the other, to the explanation of more complex brightness-contrast illusions and the possible development of a new algorithm for robust visual capturing and display systems.

Low-level brightness-contrast illusions and non classical receptive field of mammalian retina

Among the visual illusions there is a class, which is known as low-level brightness contrast illusions. These illusions are processed probably at the retinal ganglion cell without necessitating any intervention from higher order cortical processing. The concept of classical receptive field of the ganglion cell, derived from early physiological studies, prompted the idea that a Difference of Gaussian or DoG model might reproduce the output of the ganglion cell. In spite of its many successes, the DoG model fails to explain some of these low level illusions. On the basis of recently available physiological data, we have modified the DoG model and have shown the efficacy of the modified model in understanding the low level illusions, a phenomenon that may have potential application in designing new robust visual capturing or display systems.

Retinomorphic image processing

The present work is aimed at understanding and explaining some of the aspects of visual signal processing at the retinal level while exploiting the same towards the development of some simple techniques in the domain of digital image processing. Classical studies on retinal physiology revealed the nature of contrast sensitivity of the receptive field of bipolar or ganglion cells, which lie in the outer and inner plexiform layers of the retina. To explain these observations, a difference of Gaussian (DOG) filter was suggested, which was subsequently modified to a Laplacian of Gaussian (LOG) filter for computational ease in handling two-dimensional retinal inputs. Till date almost all image processing algorithms, used in various branches of science and engineering had followed LOG or one of its variants. Recent observations in retinal physiology however, indicate that the retinal ganglion cells receive input from a larger area than the classical receptive fields. We have proposed an isotropic model for the non-classical receptive field of the retinal ganglion cells, corroborated from these recent observations, by introducing higher order derivatives of Gaussian expressed as linear combination of Gaussians only. In digital image processing, this provides a new mechanism of edge detection on one hand and image half-toning on the other. It has also been found that living systems may sometimes prefer to perceive the external scenario by adding noise to the received signals in the pre-processing level for arriving at better information on light and shade in the edge map. The proposed model also provides explanation to many brightness-contrast illusions hitherto unexplained not only by the classical isotropic model but also by some other Gestalt and Constructivist models or by non-isotropic multi-scale models. The proposed model is easy to implement both in the analog and digital domain. A scheme for implementation in the analog domain generates a new silicon retina model implemented on a hardware development platform.

Coherence resonance in a unijunction transistor relaxation oscillator

The phenomenon of coherence resonance is investigated in an unijunction transistor relaxation oscillator and quantified by estimating the normal variance (NV). Depending on the measuring points, two types of NV curves have been obtained. We have observed that the degradations in coherency at higher noise amplitudes in our system is probably the result of direct interference of coherent oscillations and the stochastic perturbation. Degradation of coherency may be minimal if this direct interference of noise and coherent oscillations is eliminated.