Biologically-inspired Neural Networks for Shape and Color Representation

The goal of human-level performance in artificial vision systems is yet to be achieved. With this goal, a reasonable choice is to simulate this biological system with computational models that mimic its visual processing. A complication with this approach is that the human brain, and similarly its visual system, are not fully understood. On the bright side, with remarkable findings in the field of visual neuroscience, many questions about visual processing in the primate brain have been answered in the past few decades. Nonetheless, a lag in incorporating these new discoveries into biologically-inspired systems is evident. The present work introduces novel biologically-inspired models that employ new findings of shape and color processing into analytically-defined neural networks. In contrast to most current methods that attempt to learn all aspects of behavior from data, here we propose to bootstrap such learning by building upon existing knowledge rather than learning from scratch. Put simply, the processing networks are defined analytically using current neural understanding and learned where such knowledge is not available. This is thus a hybrid strategy that hopefully combines the best of both worlds. Experiments on the artificial neurons in the proposed networks demonstrate that these neurons mimic the studied behavior of biological cells, suggesting a path forward for incorporating analytically-defined artificial neural networks into computer vision systems

初期視覚野における遮蔽・被遮蔽物体知覚に関する計算理論

Humans can distinguish the order of mutually overlapping objects in a visual scene. The border between an occluding object and the occluded object is “owned” by the occluding object. How the brain assigns these borders, or Border-ownership (BO) assignment, determines the perception of object depth order. Findings from physiological experiments reveal that some neurons in area V2 of the brain respond selectively when the object which “owns” the edge in its receptive field is located on a specific side. Several models have been proposed in existing studies to reproduce this phenomenon. However, these models are not based on a clear computational theory. This study is the first to approach BO assignment from a computational viewpoint by treating it as a well-defined problem. I propose that the direction of BO　assignment can be defined as a conservative vector field E(x,y) with arrowheads pointing towards the occluding object, and that information pertaining to depth order can be defined as its corresponding scalar field Φ(x,y). By using a theorem in electromagnetics which states that the gradient of electric potential is its electric field E(x,y) = ∇Φ(x,y), I demonstrate that the BO assignment problem can be　solved by updating an initial vector field until its rotation, or “curl”, is zero. A model developed on this computational theory can simultaneously reproduce BO assignment and perceived depth order. Results of numerical simulations agree qualitatively with the response of object-side selective neurons in V2 to stimuli containing occlusion with simple geometry. Neural networks can be deduced from the update rule curl using only one parameter for adjusting the scale of neural connections. This study also presents new interpretations of existing models in addition to insight into a possible method for calculation of depth order.外界には奥行きが異なる物体が多数存在し，これら物体の視覚情報は重なりあっている場合が多い．このとき「遮蔽物体と被遮蔽物体を隔てる境界（Border）は遮蔽物体に帰属する」．Border Ownership（BO）問題は，物体の重なり順序を計算するための基盤的問題である．本研究の具体的な成果として，境界のOwnerが存在する方向であるBO信号をベクトル場E(x,y)，重なり順序をスカラー場Φ(x,y)として見なせば，電磁気学の電位と電場に関する定理（電場は電位の勾配， E(x,y) = ∇Φ(x,y)である）を用いることで問題の定式化ができることを発見した．数値シミュレーション結果から，提案モデルは様々な遮蔽状況や形状の変化に対して頑健に，BO問題と重なり順序計算問題が解けることを見出した．電気通信大学201

Hypothesis-based image segmentation for object learning and recognition

Denecke A. Hypothesis-based image segmentation for object learning and recognition. Bielefeld: Universität Bielefeld; 2010.This thesis addresses the figure-ground segmentation problem in the context of complex systems for automatic object recognition as well as for the online and interactive acquisition of visual representations. First the problem of image segmentation in general terms and next its importance for object learning in current state-of-the-art systems is introduced. Secondly a method using artificial neural networks is presented. This approach on the basis of Generalized Learning Vector Quantization is investigated in challenging scenarios such as the real-time figure-ground segmentation of complex shaped objects under continuously changing environment conditions. The ability to fulfill these requirements characterizes the novelty of the approach compared to state-of-the-art methods. Finally our technique is extended towards online adaption of model complexity and the integration of several segmentation cues. This yields a framework for object segmentation that is applicable to improve current systems for visual object learning and recognition

Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

Combining Analytics and Simulation Methods to Assess the Impact of Shared, Autonomous Electric Vehicles on Sustainable Urban Mobility

Urban mobility is currently undergoing three fundamental transformations with the sharing economy, electrification, and autonomous vehicles changing how people and goods move across cities. In this paper, we demonstrate the valuable contribution of decision support systems that combine data-driven analytics and simulation techniques in understanding complex systems such as urban transportation. Using the city of Berlin as a case study, we show that shared, autonomous electric vehicles can substantially reduce resource investments while keeping service levels stable. Our findings inform stakeholders on the trade-off between economic and sustainability-related considerations when fostering the transition to sustainable urban mobilit

MODELING AND ANALYSIS OF AN AUTONOMOUS MOBILITY ON DEMAND SYSTEM

Ph.DDOCTOR OF PHILOSOPH

Modeling truck toll competition between two cross-border bridges under various regimes

The competition between a new publicly-owned cross-border bridge and the existing Ambassador Bridge is modeled in this thesis as a duopoly game where each bridge\u27s strategy is its toll level. We assume the Ambassador Bridge always wants to maximize its profit, while the new bridge may have various objective functions. The competition between the two bridges has a natural bi-level structure, with the upper level being the two bridges setting their respective tolls, and the lower level being the road users choosing their routes. The competition equilibrium (i.e. Nash equilibrium) emerges when each bridge cannot improve its objective function by unilaterally changing its truck toll level. The Mesh Method is employed to solve this bi-level equilibrium problem in a simulation context. The obtained results of different competition regimes provide valuable insights about the nature of the toll competition that will likely emerge in the future

Computational Models of Perceptual Organization and Bottom-up Attention in Visual and Audio-Visual Environments

Figure Ground Organization (FGO) - inferring spatial depth ordering of objects in a visual scene - involves determining which side of an occlusion boundary (OB) is figure (closer to the observer) and which is ground (further away from the observer). Attention, the process that governs how only some part of sensory information is selected for further analysis based on behavioral relevance, can be exogenous, driven by stimulus properties such as an abrupt sound or a bright flash, the processing of which is purely bottom-up; or endogenous (goal-driven or voluntary), where top-down factors such as familiarity, aesthetic quality, etc., determine attentional selection. The two main objectives of this thesis are developing computational models of: (i) FGO in visual environments; (ii) bottom-up attention in audio-visual environments. In the visual domain, we first identify Spectral Anisotropy (SA), characterized by anisotropic distribution of oriented high frequency spectral power on the figure side and lack of it on the ground side, as a novel FGO cue, that can determine Figure/Ground (FG) relations at an OB with an accuracy exceeding 60%. Next, we show a non-linear Support Vector Machine based classifier trained on the SA features achieves an accuracy close to 70% in determining FG relations, the highest for a stand-alone local cue. We then show SA can be computed in a biologically plausible manner by pooling the Complex cell responses of different scales in a specific orientation, which also achieves an accuracy greater than or equal to 60% in determining FG relations. Next, we present a biologically motivated, feed forward model of FGO incorporating convexity, surroundedness, parallelism as global cues and SA, T-junctions as local cues, where SA is computed in a biologically plausible manner. Each local cue, when added alone, gives statistically significant improvement in the model's performance. The model with both local cues achieves higher accuracy than those of models with individual cues in determining FG relations, indicating SA and T-Junctions are not mutually contradictory. Compared to the model with no local cues, the model with both local cues achieves greater than or equal to 8.78% improvement in determining FG relations at every border location of images in the BSDS dataset. In the audio-visual domain, first we build a simple computational model to explain how visual search can be aided by providing concurrent, co-spatial auditory cues. Our model shows that adding a co-spatial, concurrent auditory cue can enhance the saliency of a weakly visible target among prominent visual distractors, the behavioral effect of which could be faster reaction time and/or better search accuracy. Lastly, a bottom-up, feed-forward, proto-object based audiovisual saliency map (AVSM) for the analysis of dynamic natural scenes is presented. We demonstrate that the performance of proto-object based AVSM in detecting and localizing salient objects/events is in agreement with human judgment. In addition, we show the AVSM computed as a linear combination of visual and auditory feature conspicuity maps captures a higher number of valid salient events compared to unisensory saliency maps

Shape Representation in Primate Visual Area 4 and Inferotemporal Cortex

The representation of contour shape is an essential component of object recognition, but the cortical mechanisms underlying it are incompletely understood, leaving it a fundamental open question in neuroscience. Such an understanding would be useful theoretically as well as in developing computer vision and Brain-Computer Interface applications. We ask two fundamental questions: “How is contour shape represented in cortex and how can neural models and computer vision algorithms more closely approximate this?” We begin by analyzing the statistics of contour curvature variation and develop a measure of salience based upon the arc length over which it remains within a constrained range. We create a population of V4-like cells – responsive to a particular local contour conformation located at a specific position on an object’s boundary – and demonstrate high recognition accuracies classifying handwritten digits in the MNIST database and objects in the MPEG-7 Shape Silhouette database. We compare the performance of the cells to the “shape-context” representation (Belongie et al., 2002) and achieve roughly comparable recognition accuracies using a small test set. We analyze the relative contributions of various feature sensitivities to recognition accuracy and robustness to noise. Local curvature appears to be the most informative for shape recognition. We create a population of IT-like cells, which integrate specific information about the 2-D boundary shapes of multiple contour fragments, and evaluate its performance on a set of real images as a function of the V4 cell inputs. We determine the sub-population of cells that are most effective at identifying a particular category. We classify based upon cell population response and obtain very good results. We use the Morris-Lecar neuronal model to more realistically illustrate the previously explored shape representation pathway in V4 – IT. We demonstrate recognition using spatiotemporal patterns within a winnerless competition network with FitzHugh-Nagumo model neurons. Finally, we use the Izhikevich neuronal model to produce an enhanced response in IT, correlated with recognition, via gamma synchronization in V4. Our results support the hypothesis that the response properties of V4 and IT cells, as well as our computer models of them, function as robust shape descriptors in the object recognition process