Abstract-This paper considers the construction of a biologically inspired front-end for computer vision based on an artificial retina 'pyramid' with a self-organised pseudo-randomly tessellated receptive field tessellation. The organisation of photoreceptors and receptive fields in biological retinae locally resembles a hexagonal mosaic, whereas globally these are organised with a very densely tessellated central foveal region which seamlessly merges into an increasingly sparsely tessellated periphery. In contrast, conventional computer vision approaches use a rectilinear sampling tessellation which samples the whole field of view with uniform density.Scale-space interest points which are suitable for higher level attention and reasoning tasks are efficiently extracted by our vision front-end by performing hierarchical feature extraction on the pseudo-randomly spaced visual information. All operations were conducted on a geometrically irregular foveated representation (data structure for visual information) which is radically different to the uniform rectilinear arrays used in conventional computer vision.
We present a novel method of image expansion using vector quantisation. The algorithm is inspired by fractal coding and uses a statistical model of the relationship between details at different scales of the image to interpolate detail at one octave above the highest spatial frequency in the original image. Our method aims at overcoming the drawbacks associated with traditional approaches such as pixel interpolation, which smoothes the scaled-up images, or fractal coding, which bears high computational cost and has limited use due to patent restrictions. The proposed method is able to regenerate plausible image detail that was irretrievable when traditional approaches are used. The vector quantisation-based method outperforms conventional approaches in terms of both objective and subjective evaluations.
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.
Abstract:We present a new algorithm for rescaling images inspired by fractal coding. It uses a statistical model of the relationship between detail at different scales of the image to interpolate detail at one octave above the highest spatial frequency in the original image. We compare it with Bspline and bilinear interpolation techniques and show that it yields a sharper looking rescaled image.
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