5832781
Adaptive contrast gain control and information maximization
Contrast gain control is an important mechanism underlying the visual system's adaptation to contrast of luminance in varying visual environments. Our previous work showed that the threshold and saturation determine the preferred contrast sensitivity as well as the maximum information coding capacity of the neuronal model. In this report, we investigated the design principles underlying adaptation behavior in contrast gain control by an adaptive linear-nonlinear model. We found that an adaptive rescaling mechanism predicted by information transmission maximization can explain a variety of observed contrast gain control phenomena in neurophysiological experiments, including the divisive input-output relations, and the inverse power-law relation between response gain and input contrast. Our results suggest that contrast gain control in visual systems might be designed for information maximization.
17578293
Representational strategy in the visual cortex
We generalized Daubechies' (see IEEE Transactions on Information Theory, vol.36, no.5, p.961-1004, 1990) results on 1D wavelets to 2D, and elucidated the conditions in which a family of Gabor wavelets will provide a complete representation of an image. We found that the phase space (scale, orientation, x, y) sampling density of the striate cortical simple cells is sufficiently dense to form a tight wavelet frame. This tight frame approximates the continuous wavelet transform, and its redundancy allows high resolution information to be represented in the low-resolution firings of the simple cells. We presented some experimental results to demonstrate this idea.<<ETX>>
8163714
Scaling Laws in Natural Scenes and the Inference of 3D Shape
This paper explores the statistical relationship between natural images and their underlying range (depth) images. We look at how this relationship changes over scale, and how this information can be used to enhance low resolution range data using a full resolution intensity image. Based on our findings, we propose an extension to an existing technique known as shape recipes [3], and the success of the two methods are compared using images and laser scans of real scenes. Our extension is shown to provide a two-fold improvement over the current method. Furthermore, we demonstrate that ideal linear shape-from-shading filters, when learned from natural scenes, may derive even more strength from shadow cues than from the traditional linear-Lambertian shading cues.
44493552
Adaptation of the temporal receptive fields of macaque V1 neurons
Abstract Traditional view of macaque V1 neurons is that they are static spatiotemporal filters. This study shows that V1 neurons’ temporal receptive fields (kernels) are dynamic and adaptive. In order to study the adaptation of the kernels to a variety of stimuli, we developed a regression approach to recover kernels using naturalistic stimuli. Using this technique, we studied how the kernels of the V1 neurons in macaque monkeys adapt to the frequency bandwidth of naturalistic signals. We found that the temporal frequency tunings and the gains of the receptive fields of the neurons change according to the statistical context of the stimulus environment.
323922
Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels
Decoding is a strategy that allows us to assess the amount of information neurons can provide about certain aspects of the visual scene. In this study, we develop a method based on Bayesian sequential updating and the particle filtering algorithm to decode the activity of V1 neurons in awake monkeys. A distinction in our method is the use of Volterra kernels to filter the particles, which live in a high dimensional space. This parametric Bayesian decoding scheme is compared to the optimal linear decoder and is shown to work consistently better than the linear optimal decoder. Interestingly, our results suggest that for decoding in real time, spike trains of as few as 10 independent but similar neurons would be sufficient for decoding a critical scene variable in a particular class of visual stimuli. The reconstructed variable can predict the neural activity about as well as the actual signal with respect to the Volterra kernels.
42876940
An HMM-based speech recognition IC
This paper presents the design, simulation and measurement results of a Hidden Markov Model (HMM) based isolated word recognizer IC with double mixtures. Table look-up technique is employed in this design. The chip operates at 20 MHz at 3.3 V. The recognition time is 0.5 s for a 50-word speech library. The speech IC has been verified with 467 test speech data and the recognition accuracy is 93.8%. A reference software recognizer using the same algorithm and speech library has a recognition accuracy of 94.2%. The new speech IC that uses a table look up to reduce the complexity of the circuit has approximately the same recognition accuracy as an ideal software recognizer.
767826
The Spectral Independent Components of Natural Scenes
We apply independent component analysis (ICA) for learning an efficient color image representation of natural scenes. In the spectra of single pixels, the algorithm was able to find basis functions that had a broadband spectrum similar to natural daylight, as well as basis functions that coincided with the human cone sensitivity response functions. When applied to small image patches, the algorithm found homogeneous basis functions, achromatic basis functions, and basis functions with overall chromatic variation along lines in color space. Our findings suggest that ICAm ay be used to reveal the structure of color information in natural images.
21281832
Progressive mesh metamorphosis
This paper describes a new integrated scheme for metamorphosis between two closed manifold genus-0 polyhedral models. Spherical parameterizations of the source and target models are created first. To control the morphing, any number of feature vertex pairs is specified and a fold-over free warping method is used to align two spherical embeddings. Our method does not create a merged meta-mesh or execute re-meshing to construct a common connectivity for morphs. Alternatively, a scheme for the progressive connectivity transformation of two spherical parameterizations is employed to generate the intermediate meshes. A novel semi-overlay with a geomorph scheme is proposed to reduce the popping effects caused by the connectivity transformation. We demonstrate several examples of aesthetically pleasing morphing sequences using the proposed scheme. Copyright © 2005 John Wiley & Sons, Ltd.
10997807
Resource bounded symmetry of information revisited
The information contained in a string x about a string y is the difference between the Kolmogorov complexity of y and the conditional Kolmogorov complexity of y given x, i.e., I(x : y)=C(y)-C(y|x). The Kolmogorov-Levin Theorem says that I(x : y) is symmetric up to a small additive term. We investigate if this property also holds for several versions of polynomial time-bounded Kolmogorov complexity.We study symmetry of information for some variants of distinguishing complexity CD where CD(x) is the length of a shortest program which accepts x and only x. We show relativized worlds where symmetry of information does not hold in a strong way for deterministic and nondeterministic polynomial time distinguishing complexities CDpoly and CNDpoly. On the other hand, for nondeterministic polynomial time distinguishing complexity with randomness, CAMDpoly, we show that symmetry of information holds for most pairs of strings in any set in NP. Our techniques extend work of Buhrman et al. (Language compression and pseudorandom generators, in: Proc. 19th IEEE Conf. on Computational Complexity, IEEE, New York, 2004, pp. 15-28) on language compression by AM algorithms, and have the following application to the compression of samplable sources, introduced in Trevisan et al. (Compression of sample sources, in: Proc. 19th IEEE Conf. on Computational Complexity, IEEE, New York, 2004, pp. 1-15): any element x in the support of a polynomial time samplable source X can be given a description of size - log Pr[X = x] + O(log3 n), from which x can be recovered by an AM algorithm.
866852
Unsupervised Classification with Non-Gaussian Mixture Models Using ICA
We present an unsupervised classification algorithm based on an ICA mixture model. The ICA mixture model assumes that the observed data can be categorized into several mutually exclusive data classes in which the components in each class are generated by a linear mixture of independent sources. The algorithm finds the independent sources, the mixing matrix for each class and also computes the class membership probability for each data point. This approach extends the Gaussian mixture model so that the classes can have non-Gaussian structure. We demonstrate that this method can learn efficient codes to represent images of natural scenes and text. The learned classes of basis functions yield a better approximation of the underlying distributions of the data, and thus can provide greater coding efficiency. We believe that this method is well suited to modeling structure in high-dimensional data and has many potential applications.