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Figure-ground (FG) segregation has been considered as a fundamental step towards object recognition. We explored plausible mechanisms that estimate global figure-ground segregation from local image features by investigating the human visual system. Physiological studies have reported border-ownership (BO) selective neurons in V2 which signal the local direction of figure (DOF) along a border; however, how local BO signals contribute to global FG segregation has not been clarified. The BO and FG processing could be independent, dependent on each other, or inseparable. The investigation on the differences and similarities between the BO and FG judgements is important for exploring plausible mechanisms that enable global FG estimation from local clues. We performed psychophysical experiments that included two different tasks each of which focused on the judgement of either BO or FG. The perceptual judgments showed consistency between the BO and FG determination while a longer distance in gaze movement was observed in FG segregation than BO discrimination. These results suggest the involvement of distinct neural mechanism for local BO determination and global FG segregation.

In this paper we propose a method for constructing a Gray map for a group. In an earlier paper, we suggested a new design principle of Gray maps for groups and tried to apply it to several concrete groups. Though the trial had some success, the method is not very constructive. In this paper we try to design a more constructive method based on the semidirect-product structure of the target group.

In this paper we propose a new construction method of Gray maps for groups. In a earlier paper, we succeeded the Type 1 construction for all groups of order 16 and confirmed that we can construct Type 2 maps for several groups of order 16, but failed to construct such maps for other groups. Therefore, in this paper we try to apply the new construction method to them.

Segmentation of a natural scene into objects and background is a fundamental but challenging task for recognizing objects. Investigating intermediate-level visual cortical areas with a focus on local information is a crucial step towards understanding the formation of the cortical representations of figure and ground. We examined the activity of a population of macaque V4 neurons during the presentation of natural image patches and their respective variations. The natural image patches were optimized to exclude the influence of global context but included various characteristics of local stimulus. Around one fourth of the patch-responsive V4 neurons exhibited significant modulation of firing activity that was dependent on the positional relation between the figural region of the stimulus and the classical receptive field of the neuron. However, the individual neurons showed low consistency in figure-ground modulation across a variety of image patches (55-62%), indicating that individual neurons were capable of correctly signaling figure and ground only for a limited number of stimuli. We examined whether integration of the activity of multiple neurons enabled higher consistency across a variety of natural patches by training a support vector machine to classify figure and ground of the stimuli from the population firing activity. The integration of the activity of a few tens of neurons yielded discrimination accuracy much greater than that of single neurons (up to 85%), suggesting a crucial role of population coding for figure-ground discrimination in natural images.

We investigated whether specular highlights facilitate the perception of shape from shading in a search paradigm and how highlights interact with shading to facilitate this perception. Our results indicated that stimuli containing highlights led to shorter searching time with the dependence on the light source direction (top lights make searching faster), suggesting that highlights indeed facilitate shape-from-shading processing. To examine how highlight processing interacts with shading processing, we tested unnatural stimuli for which the lighting directions for shading and highlights were inconsistent. The results indicated that unnatural highlights (bright spots) placed in a direction inconsistent with the shading either decrease or do not alter searching time. This suggests that highlights may facilitate, and not suppress, shading processing. With more physically plausible highlights generated from image-based lighting, we also observed facilitation with consistent highlights, but no change with inconsistent highlights. Finally, we examined whether highlights indeed work to facilitate depth perception in a discrimination task. The results showed that correct discrimination of depth increases when highlights are added to shading even when their lighting directions are inconsistent. These results indicate that specular highlights facilitate shading processing, and do not suppress it even when the highlights are placed in a direction inconsistent with shading. The results also elucidate the lighting constraints of the visual system.

Perception of the material quality of a surface depends on its reflectance properties. Recent physiological studies reported the neural selectivity to glossy surfaces in the Inferior Temporal cortical areas [e.g., 1]. In the present study, we examine the hypothesis that basis neurons are selective to typical materials, and that the combinations of their responses are representative of a variety of natural materials. To assess the hypotheses, we performed a psychological experiment based on adaptation. If adaptation to a specific material is observed, the presence of neurons that are selective to the specific material is predicted. We performed adaptation tests with six typical material qualities including gloss, matte, metal and wood. We observed the adaptation to certain materials but not to some other materials. This result indicates the presence of basis neurons that are selective to materials, which is fundamentally important for understanding cortical representation of surface materials.

Figure-ground (FG) segregation is a crucial function of the intermediate-level vision. Physiological studies on monkey V2 have reported border-ownership (BO) selective cells that signal the direction of figure along a local border. However, local borders in natural images are often complicated and they often do not provide a clue for FG segregation. In the present study, we hypothesize that a population of V4 cells represents FG by means of surface rather than border. We investigated this hypothesis by the computational analysis of neural signals from multiple cells in monkey V4. Specifically, we applied Support Vector Machine as an ideal integrator to the cellular responses, and examined whether the responses carry information capable of determining correct local FG. Our results showed that the responses from several tens of cells are capable of determining correct local FG in a variety of natural image patches while single-cell responses hardly determine FG, suggesting a population coding of local FG by a small number of cells in V4.

We investigated computationally the role of V1 complex cells in the simultaneous determination of depth and motion, in conjunction with spatiotemporal response characteristics of the cells. First, we examined whether a pooling of simple-cell units with monocular, spatiotemporal response profiles evokes binocular, spatiotemporal response profiles of complex cells. Second, we examined whether a population of the cascade complex-cell models yields the responses being consistent with the perception of depth and motion. We tested various stimuli including ambiguous cases that consist of interocular time delay without spatial disparity. Our simulation study showed that a pooling of simple cells leads to the generation of binocular response profiles of complex cells, and that a population of the complex cells provides a basis for the simultaneous perception of depth and motion.

We investigate which neural grouping is relevant to the perception of the global orientations of bars. We examine psychophysically whether perceptual segmentation affects the apparent tilt in X-shaped junctions constructed by solid or illusory bars. The results show that perceptual segmentation is not relevant to the global orientation, except for illusory stimuli. Early level processing, probably in the primary visual cortex (V1), seems to be involved in the determination of global orientation. We propose a network model consisting of V1 neurons and a grouping mechanism which synchronizes the cellular activities based on colinear coupling. The computed bar orientations agree quantitatively with the apparent tilt measured in comparable psychophysical experiments reported elsewhere.

We propose a network model for explaining tilt illusion based on a V1 circuit preserving retinotopy and a mechanism for grouping neuronal activities. The simulation of the model indicates that many V1 neurons show responses which correspond to the tilt effects, depending on the geometric configuration of the local visual pattern projected on to the receptive field of the neuron. Grouping V1 neurons based on the segmentation suggested by our psychophysical experiments, the model reproduces the apparent orientations of the bars in good quantitative agreement with the results of comparable psychophysical experiments reported elsewhere.

We describe a neural simulator designed for simulating very large scale models of cortical architectures. This simulator, NEXUS, uses coarse-grain parallel computing by distributing computation and data onto multiple conventional workstations connected via a local area network. Coarse-grain parallel computing offers natural advantages in simulating functionally segregated neural processes. We partition a complete model into modules with locally dense connections—a module may represent a cortical area, column, layer, or functional entily. Asynchronous data communications among workstations are established through the Network File System, which, together with the implicit modularity, decreases communications overhead, and increases overall performance. Coarse-grain parallelism also benefits from the standardization of conventional workstations and LAN, including portability between generations and vendors.

Border Ownership (BO) selective cells show complex behavior in latency, such as a shorter latency for a farther figural element. The complex behavior of latency would give crucial constraints for understanding the neuronal mechanisms underlying BO selectivity. We propose that the delays in the feed-forward mechanisms of surround modulation account for the characteristics of the latency. Specifically, combinations of delays between V1 cells, surround center and BO-selective cells underlie the latency characteristics. To examine the hypothesis, we constructed the V1-V2 feed-forward model and carried out Monte Carlo simulations with a variety of surround regions. The results indicate that physiologically realistic surround regions reproduce the puzzled behavior of latency. The results support the feed-forward mechanism of surround modulation for the neural mechanism underlying the BO selectivity.

There is little understanding on representation and reconstruction of object shape in the cortex. Physiological studies with macaque suggested that neurons in V1 respond to Medial-Axis (MA). We investigated whether (1) early visual areas could provide basis for MA representation, and (2) we could reconstruct the original shape from its MA, with a physiologically realistic computational model consisting of early- to intermediate-level visual areas. Assuming the synchronization of border-ownership selective cells at stimulus onset, our model was capable of detecting MA, indicating that early visual area could provide basis for MA representation. Furthermore, we propose a physiologically plausible reconstruction algorithm with the summation of distinct gaussians.

We propose a computational model consisting of mutually linked V1, V2, and PP modules. The model reproduces the effect of attention in the determination of border-ownership (BO) that tells which side of the contour owns the border. The V2 module determines BO based on surrounding contrast extracted by the V1 module that could be influenced by top-down spatial attention from the PP module. We carried out the simulations of the model with random-block ambiguous figures to test whether spatial attention alters BO for these meaningless stimuli. To compare quantitatively these results with human perception, we carried out psychophysical experiments corresponding to the simulations. The results of these two showed good agreement in that the perception of BO was flipped when altering the location of spatial attention. These results suggest that spatial attention is a crucial factor for the modulation of figure direction in meaningless figures, and that the effects of spatial attention in early visual area are crucial for the modulation of figure direction.

We propose a computational model consisting of mutually connected V1, V2, and PP modules, to realize the effect of attention to the determination of border-ownership (BO) that tells on which side of a contour owns the border. The V2 module determines BO from surrounding contrast extracted by the V1 module that could be affected by top-down spatial attention from the PP module. The simulation results show that the spatial attention modifies the direction of figure, and that the direction of figure is even flipped in ambiguous figures such as the Rubin's vase, although the attention is applied only to enhance local contrast in V1. These results show that the activities of BO selective cells in V2 are modified significantly when spatial attention functions in early visual area, V1.

We investigated how the visual system determines 3D depth from the integration of space and time, specifically spatial and temporal binocular disparity. We carried out psychophysical experiments to investigate whether the binocular disparity gives correct 3D depth of objects moving behind a thin slit that controls the type and amount of information available to the visual system. The results indicate that: (1) Wheatstone stereo in corresponding images gives correct depth, (2) Da Vinci stereo in stationary non-corresponding images does not give correct depth judgment, and (3) the time delay between the two images gives correct depth for a wide range of noncorrespondence. The results suggest the cortical mechanism that processes simultaneously spatial and temporal information; presumably the two are inseparable in the neural system.

Construction of surface is a crucial step toward the representation of shape through the integration of local information. Physiological studies have reported that the primary visual cortex (V1) codes the medial axis (MA) that is a skeletal structure equidistant from nearby contours, suggesting the early representation of surface in V1. Although the neural basis of surface construction has not been clarified, the onset synchronization of border ownership (BO)-selective cells is a plausible candidate for the generation of surface. We investigated computationally the representation of surface in a biophysically detailed model of primate V1-V2 networks. The simulation results showed that the simultaneous arrival of signals from BO-selective cells evoked strong responses of V1 cells located around the MA. The simulation results lead to a prediction that the perception of the direction of figure (DOF) depends on the degree of synchronous presentation of contour. We conducted a psychophysical experiment and showed that the perception of the DOF is biased toward a highly synchronized contour. These results suggest a crucial role of the onset synchronization of BO-selective cells for the construction of early representation of shape.

We investigate what computational mechanisms give rise to the nonlinearity of the complex cell receptive field in the primary visual cortex. Complex cells are characterized by their nonlinear spatial properties such as spatial phase invariance and nonlinear response to multiple bar presentations. We carry out network simulations to estimate the second-order Wiener-like kernels for several different models. Models with nonlinear spatial pooling of simple-cell-like linear subunits reproduce the second-order kernels in good agreement with physiologically estimated kernels, while models without the pooling mechanism fail to reproduce the kernel. The structure of the kernels is independent of specific nonlinear transfer functions such as half-wave rectification and squaring. The results support the cascade mechanism consisting of simple cells’ local feature extraction followed by nonlinear spatial pooling.

We propose an algorithm for the determination of three dimensional shape and perspective based on the response of the human visual system to changes in visual textures. Current computer vision algorithms are computationally intensive and show inherent difficulties in integrating additional cues for the determination of shape, such as shading, contour, or motion. In order to develop a fast and simple mechanism less constrained for integrating other cues, we incorporated aspects of the physiological properties of cortical cells in VI into a network model. We provide psychophysical evidence that the local spatial frequency spectrum is represented by the spatially averaged peak frequency (APF). After normalization, this APF measures texture compression and leads to estimates of 3D shape and depth. Simulations of the model show good agreement with human responses to a range of textured images. >

We have developed the NEXUS simulation environment as a tool for modeling large-scale neural systems. The software is written in C and runs under UNIX. A unique aspect of NEXUS is that it is particularly suited for simulating hybrid neural models (i.e. systems integrating different modeling paradigms and/or architectures.1) NEXUS is designed for large-scale simulations, and to facilitate model development, testing and analysis it incorporates several major features: network architectures based on topographic maps, programmable neural units, scalable and modular simulation, support for common learning paradigms including the generalized Hebb rule and backpropagation, and a user-friendly interface. These features make NEXUS a useful environment in which to study the “perceptual” properties of various network architectures.

NX is the Network Architectural Specification Language used for defining network structure and connectivity in NEXUS. Creating an NX file consists of defining a set of parameters which (1) describe the functional properties and spatial layout of networks of cells (or units) and (2) specify the spatial and weighting properties of the cells' connection fields. The limited syntax of the NX language makes it easy to learn-creating new architectures requires no more than editing an existing NX file and changing the relevant parameters. A particularly attractive feature of NEXUS is that it consists of an easy-to-use interactive graphics display for testing and simulating models constructed with NX. The following section will describe the menu functions available in NEXUS.

This paper presents a methodology for constructing a deductive database system consisting of an intensional processor and a relational database management system. A setting evaluation approach is introduced. The intensional processor derives a setting from the in-tensional database and a given goal and sends the setting and the relationship between setting elements to the management system. The management system performs a unit resolution with setting using relational operations for the extensional databases. An extended least fixed point operation is introduced to terminate all types of recursive queries.

This paper proposes an expanded version of the logic programming language Prolog, which is called Pure Prolog with Negation (PPN) and permits writing negative information. We present query response forms for PPN and their semantics and propose an execution algorithm. Furthermore, we discuss semantics and an execution algorithm for PPN as viewed from the standpoint of intuitionistic logic. Then, we present a knowledge assimilation system and show how PPN is suitable for such a system. These execution algorithms can be easily implemented similarly to existing Prolog systems.