A Push-Pull CORF Model of a Simple Cell with Antiphase Inhibition Improves SNR and Contour Detection

We propose a computational model of a simple cell with push-pull inhibition, a property that is observed in many real simple cells. It is based on an existing model called Combination of Receptive Fields or CORF for brevity. A CORF model uses as afferent inputs the responses of model LGN cells with appropriately aligned center-surround receptive fields, and combines their output with a weighted geometric mean. The output of the proposed model simple cell with push-pull inhibition, which we call push-pull CORF, is computed as the response of a CORF model cell that is selective for a stimulus with preferred orientation and preferred contrast minus a fraction of the response of a CORF model cell that responds to the same stimulus but of opposite contrast. We demonstrate that the proposed push-pull CORF model improves signal-to-noise ratio (SNR) and achieves further properties that are observed in real simple cells, namely separability of spatial frequency and orientation as well as contrast-dependent changes in spatial frequency tuning. We also demonstrate the effectiveness of the proposed push-pull CORF model in contour detection, which is believed to be the primary biological role of simple cells. We use the RuG (40 images) and Berkeley (500 images) benchmark data sets of images with natural scenes and show that the proposed model outperforms, with very high statistical significance, the basic CORF model without inhibition, Gabor-based models with isotropic surround inhibition, and the Canny edge detector. The push-pull CORF model that we propose is a contribution to a better understanding of how visual information is processed in the brain as it provides the ability to reproduce a wider range of properties exhibited by real simple cells. As a result of push-pull inhibition a CORF model exhibits an improved SNR, which is the reason for a more effective contour detection.</p

Feedback and surround modulated boundary detection

Altres ajuts: CERCA Programme/Generalitat de CatalunyaEdges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The "classical approach" assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal fromV1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods

The Role of Early Recurrence in Improving Visual Representations

This dissertation proposes a computational model of early vision with recurrence, termed as early recurrence. The idea is motivated from the research of the primate vision. Specifically, the proposed model relies on the following four observations. 1) The primate visual system includes two main visual pathways: the dorsal pathway and the ventral pathway; 2) The two pathways respond to different visual features; 3) The neurons of the dorsal pathway conduct visual information faster than that of the neurons of the ventral pathway; 4) There are lower-level feedback connections from the dorsal pathway to the ventral pathway. As such, the primate visual system may implement a recurrent mechanism to improve visual representations of the ventral pathway. Our work starts from a comprehensive review of the literature, based on which a conceptualization of early recurrence is proposed. Early recurrence manifests itself as a form of surround suppression. We propose that early recurrence is capable of refining the ventral processing using results of the dorsal processing. Our work further defines a set of computational components to formalize early recurrence. Although we do not intend to model the true nature of biology, to verify that the proposed computation is biologically consistent, we have applied the model to simulate a neurophysiological experiment of a bar-and-checkerboard and a psychological experiment involving a moving contour illusion. Simulation results indicated that the proposed computation behaviourally reproduces the original observations. The ultimate goal of this work is to investigate whether the proposal is capable of improving computer vision applications. To do this, we have applied the model to a variety of applications, including visual saliency and contour detection. Based on comparisons against the state-of-the-art, we conclude that the proposed model of early recurrence sheds light on a generally applicable yet lightweight approach to boost real-life application performance

Optimality of Human Contour Integration

For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy

Peripheral vision and pattern recognition:a review

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding

Detecting shape change: Characterizing the interaction between texture-defined and contour-defined borders

The human visual system&apos;s extreme sensitivity to subtle changes in shape can often be attributed to global pooling of local information. This has been shown for shapes described by paths of contiguous elements, but it was unknown whether this global pooling translated to shapes defined by texture-segmentation borders. Also, previous research suggests that texture and luminance cues-to-shape are integrated by the visual system for shape detection but it has not been established whether they combined for shape discrimination. Controlled shapes defined either by an explicit path of Gabors, texture-segmentation borders, or both of these cues were used. Results show that all stimuli used were globally processed. Thresholds for shapes defined by both cues matched predictions based on an independent-cue vector sum of individual thresholds. Thus, while local elements are integrated around the contour and are processed by global shape-detection mechanisms, integration did not occur across different shape-cues

Perceptual learning of binocular interactions.

This dissertation focuses on the mechanisms and implications of perceptual learning of binocular interactions. Perceptual learning is an important means of adapting to the changing environment, demonstrating the possibility of neural plasticity in adults and providing a powerful approach to investigate dynamic processes in the mature perceptual system. Most studies on perceptual learning have focused on learning mechanisms that target excitatory circuits. However, we recognize that the inhibitory circuits also play a critical role in cortical plasticity, as shown by growing evidence from neurophysiological studies, and that the inhibitory connection is more dynamic than the excitatory connection in adult visual cortex. Thus, our goal is to design a psychophysical method that exploits the contribution of the inhibitory circuits to perceptual learning. This in turn helps us to implement more efficient learning paradigms for visual training. Our study capitalizes on properties of the binocular visual system, a good system for exploring both excitatory and inhibitory mechanisms. We first measured local Sensory Eye Dominance (SED) and showed that excessive SED can impede stereopsis ability. To reduce SED, a typical perceptual training paradigm (Push-only protocol) would only stimulate the weak eye to target the excitatory network. In contrast, we designed a novel Push-Pull training protocol to target both the excitatory and inhibitory networks. By presenting binocular rivalry stimuli to both eyes, the push-pull protocol can excite the visual pathway of the weak eye (push), while inhibiting the visual pathway of the strong eye (pull). We found that the push-pull training protocol, mainly affecting the early visual processes, is more effective than the push-only protocol in reducing SED and enhancing stereoacuity, even beyond the focus of top-down attention through a stimulus-driven mechanism. We further demonstrated that the perceptual learning induced by the push-pull protocol involves both feature-based and boundary-based processes, and that the learning effect can be generalized to other stimulus dimensions within early feature channels. Therefore, our psychophysical study demonstrates the important role of inhibitory synaptic circuits in neural plasticity of the adult brain, and that our push-pull training protocol can be a more effective clinical training paradigm to treat amblyopia