Kamales Bhaumik

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.

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.

A Weighted Sum of Multi-scale Gaussians Generates New Near-ideal Interpolation Functions

Interpolation is a very important technique in medical image processing. Of the different generations of interpolation kernels, the one using combinations of Gaussians and its partial derivatives, is locally compact, has excellent Fourier properties and is easy to handle analytically. But the de-constancy behaviour i.e. the sum of the samples of these Gaussian kernels is not necessarily one and also the zero-crossings do not fit exactly. These deviations from the ideal behaviour contribute to artifacts during interpolation. We propose in this article a novel approach for the generation of kernels from the combinations of Gaussians at different scales. We will show that these kernels are locally compact, have excellent Fourier properties and the zero-crossings fit exactly. The DC-constancy behaviour is better than those reported. It has been shown that the proposed kernels are likely to be very useful in medical images

The Theory of Edge Detection and Low-level Vision in Retrospect

:“To suppose that the eye with all its inimitable contrivances for adjusting the focus to different distances, for admitting different amounts of light, and for the correction of spherical and chromatic aberration, could have been formed by natural selection, seems, I freely confess, absurd in the highest degree. When it was first said that the sun stood still and the world turned round, the common sense of mankind declared the doctrine false; but the old saying of Vox populi, vox Dei, as every philosopher knows, cannot be trusted in science. Reason tells me, that if numerous gradations from a simple and imperfect eye to one complex and perfect can be shown to exist, each grade being useful to its possessor, as is certainly the case; if further, the eye ever varies and the variations be inherited, as is likewise certainly the case and if such variations should be useful to any animal under changing conditions of life, then the difficulty of believing that a perfect and complex eye could be formed by natural selection, though insuperable by our imagination, should not be considered as subversive of the theory. ” The purpose of the present chapter would be to understand and explain some of the aspects of this highly complicated organ and how it is likely to coordinate with the brain at the stage of early vision. Pioneering contributions in this domain came from renowned philosophers and vision scientists like Wilhelm Wundt, Hermann von Helmholtz (Helmholtz, 1867) and Ernst Mach (Mach, 1865). The British empiricist school of Locke, Hume and Berkeley led to the structuralist viewpoint of Wundt and the empirio-critical view of Mach, that defined visual perception as a

A New Silicon Retina Model and Its Advantages

A new model of silicon retina based on the receptive field structure of retinal ganglion cells has been proposed. Unlike previous neuromorphic models, the proposed model directly incorporates into the receptive field model, contribution from both the inner and outer plexiform layer of the retina, as a linear combination of the two. It has been shown that such a system is capable of aiding in the computation of zero-crossing maps, in higher regions of the brain, using a fourth or higher order derivative. This model is likely to have a neuromorphic implication in generating and implementing a simplistic derivative analyzer mimetic of the Human Visual system (HVS) and is also endowed with additional advantages from the perspective of image retrieval

A Bio-inspired Interpolation Kernel for Medical Image Processing Implemented on DSP Processor

Post processing of medical images often needs interpolation. Taking cues from human visual system, we propose here an interpolation kernel consisting of linear combination of Gaussians at different scales. We compare the efficacy of the proposed kernel with other interpolation kernels, particularly in the processing of medical images. The basic algorithm has been implemented on a TI DM642 based hardware platform for realtime filtering and programmed for post-processing of ultrasound video frames (20 fames/s) from the commercially available Siemens medical ultrasound scanner.

Theoretical simulation of the calcium action potential in squid giant synapse : the plateau termination

A biophysical model is proposed for the simulation of the experimentally observed calcium action potential at the squid giant synapse. It is observed that while Ca activation at the synapse is responsible for the generation of the upstroke of the action potential, a repolarizing process needs to be invoked to simulate the plateau termination and other long-time effects. Out of the likely candidates, the Ca-activated K current has been chosen as the most plausible repolarizing process. The model can reproduce all the observed features of calcium action potential excepting its behaviour after repetitive stimulation.

An adaptive scale Gaussian filter to explain White’s illusion from the viewpoint of lightness assimilation for a large range of variation in spatial frequency of the grating and aspect ratio of the targets

The variation between the actual and perceived lightness of a stimulus has strong dependency on its background, a phenomena commonly known as lightness induction in the literature of visual neuroscience and psychology. For instance, a gray patch may perceptually appear to be darker in a background while it looks brighter when the background is reversed. In the literature it is further reported that such variation can take place in two possible ways. In case of stimulus like the Simultaneous Brightness Contrast (SBC), the apparent lightness changes in the direction opposite to that of the background lightness, a phenomenon often referred to as lightness contrast, while in the others like neon colour spreading or checkerboard illusion it occurs opposite to that, and known as lightness assimilation. The White’s illusion is a typical one which according to many, does not completely conform to any of these two processes. This paper presents the result of quantification of the perceptual strength of the White’s illusion as a function of the width of the background square grating as well as the length of the gray patch. A linear filter model is further proposed to simulate the possible neurophysiological phenomena responsible for this particular visual experience. The model assumes that for the White’s illusion, where the edges are strong and quite a few, i.e., the spectrum is rich in high frequency components, the inhibitory surround in the classical Difference-of-Gaussians (DoG) filter gets suppressed, and the filter essentially reduces to an adaptive scale Gaussian kernel that brings about lightness assimilation. The linear filter model with a Gaussian kernel is used to simulate the White’s illusion phenomena with wide variation of spatial frequency of the background grating as well as the length of the gray patch. The appropriateness of the model is presented through simulation results, which are highly tuned to the present as well as earlier psychometric results.

New vision tools from the comparative study of an old psychophysical and a modern computational model

A comparative study has been made between a one and half century old psychophysical model of vision and a modern computational model. The Mach band illusion has been studied from a new angle, that led to concluding that a Bi-Laplacian of Gaussian operation is a likely possibility in the visual system along with the traditional Laplacian operation. As a follow-up to this, exploring the human visual system through a two-pronged approach, based on the two models mentioned above, has helped in the construction of a new image sharpening kernel, on one hand and possibilities of new algorithms for robust visual capturing and image halftoning and compression on the other.