Representation Of Spatial Relations Research Articles

Fractal-Based Analysis of Histological Features of Brain Tumors.

The structural complexity of brain tumor tissue represents a major challenge for effective histopathological diagnosis. Tumor vasculature is known to be heterogeneous, and mixtures of patterns are usually present. Therefore, extracting key descriptive features for accurate quantification is not a straightforward task. Several steps are involved in the texture analysis process where tissue heterogeneity contributes to the variability of the results. One of the interesting aspects of the brain lies in its fractal nature. Many regions within the brain tissue yield similar statistical properties at different scales of magnification. Fractal-based analysis of the histological features of brain tumors can reveal the underlying complexity of tissue structure and angiostructure, also providing an indication of tissue abnormality development. It can further be used to quantify the chaotic signature of disease to distinguish between different temporal tumor stages and histopathological grades.Brain meningioma subtype classifications' improvement from histopathological images is the main focus of this chapter. Meningioma tissue texture exhibits a wide range of histological patterns whereby a single slide may show a combination of multiple patterns. Distinctive fractal patterns quantified in a multiresolution manner would be for better spatial relationship representation. Fractal features extracted from textural tissue patterns can be useful in characterizing meningioma tumors in terms of subtype classification, a challenging problem compared to histological grading, and furthermore can provide an objective measure for quantifying subtle features within subtypes that are hard to discriminate.

Towards a Framework for Functional Representation of Spatial Relations

Towards a Framework for Functional Representation of Spatial Relations

Interactive and incremental learning of spatial object relations from human demonstrations.

Humans use semantic concepts such as spatial relations between objects to describe scenes and communicate tasks such as "Put the tea to the right of the cup" or "Move the plate between the fork and the spoon." Just as children, assistive robots must be able to learn the sub-symbolic meaning of such concepts from human demonstrations and instructions. We address the problem of incrementally learning geometric models of spatial relations from few demonstrations collected online during interaction with a human. Such models enable a robot to manipulate objects in order to fulfill desired spatial relations specified by verbal instructions. At the start, we assume the robot has no geometric model of spatial relations. Given a task as above, the robot requests the user to demonstrate the task once in order to create a model from a single demonstration, leveraging cylindrical probability distribution as generative representation of spatial relations. We show how this model can be updated incrementally with each new demonstration without access to past examples in a sample-efficient way using incremental maximum likelihood estimation, and demonstrate the approach on a real humanoid robot.

Dissociation of categorical and coordinate spatial relations on dynamic network organization states.

Humans can flexibly represent both categorical and coordinate spatial relations. Previous research has mainly focused on hemisphere lateralization in representing these two types of spatial relations, but little is known about how distinct network organization states support representations of the two. Here we used dynamic resting-state functional connectivity (FC) to explore this question. To do this, we separated a meta-identified navigation network into a ventral and two other subnetworks. We revealed a Weak State and a Strong State within the ventral subnetwork and a Negative State and a Positive State between the ventral and other subnetworks. Further, we found the Weak State (i.e., weak but positive FC) within the ventral subnetwork was related to the ability of categorical relation recognition, suggesting that the representation of categorical spatial relations was related to weak integration among focal regions in the navigation network. In contrast, the Negative State (i.e., negative FC) between the ventral and other subnetworks was associated with the ability of coordinate relation processing, suggesting that the representation of coordinate spatial relations may require competitive interactions among widely distributed regions. In sum, our study provides the first empirical evidence revealing different focal and distributed organizations of the navigation network in representing different types of spatial information.

Sleep facilitates spatial memory but not navigation using the Minecraft Memory and Navigation task

Sleep facilitates hippocampal-dependent memories, supporting the acquisition and maintenance of internal representation of spatial relations within an environment. In humans, however, findings have been mixed regarding sleep's contribution to spatial memory and navigation, which may be due to task designs or outcome measurements. We developed the Minecraft Memory and Navigation (MMN) task for the purpose of disentangling how spatial memory accuracy and navigation change over time, and to study sleep's independent contributions to each. In the MMN task, participants learned the locations of objects through free exploration of an open field computerized environment. At test, they were teleported to random positions around the environment and required to navigate to the remembered location of each object. In study 1, we developed and validated four unique MMN environments with the goal of equating baseline learning and immediate test performance. A total of 86 participants were administered the training phases and immediate test. Participants' baseline performance was equivalent across all four environments, supporting the use of the MMN task. In study 2, 29 participants were trained, tested immediately, and again 12 h later after a period of sleep or wake. We found that the metric accuracy of object locations, i.e., spatial memory, was maintained over a night of sleep, while after wake, metric accuracy declined. In contrast, spatial navigation improved over both sleep and wake delays. Our findings support the role of sleep in retaining the precise spatial relationships within a cognitive map; however, they do not support a specific role of sleep in navigation.

Representation of spatial relations between multiple faces, bodies or people in the visual cortex

Representation of spatial relations between multiple faces, bodies or people in the visual cortex

Using Tangram as a Manipulative Tool for Transition between 2D and 3D Perception in Geometry

Creating a mental image of our spatial environment is a key process for further abstract geometric thinking. Building a mental representation can be understood as a part of the process of visualisation. From the wide concept of visualisation, in this article, we will focus on the part where the mental representation of spatial relations, mental objects and mental constructions are created, and their manifestations as a 3D physical object and its plane representations arise. Our main goal is to follow the transition between 2D and 3D representations of physical objects and also to observe how and when such a transition happens in students’ thinking. For that purpose, we also use Tangram, because manipulation with the Tangram pieces in space and filling out planar figures by them indicates the transition between 3D and 2D. Our research, using an action research methodology, was conducted on the students of three 5th grade primary school classes as a part of a larger long-term project. We pointed out a relationship between spatial abilities and the perception of 2D–3D relationships in students’ mind.

The impact of age and familiarity with the environment on categorical and coordinate spatial relation representations.

Retrieving spatial information is a crucial everyday ability that is affected by age-related changes. Previous research has shown that this change is mediated by familiarity with an environment. The present research uses a series of landmark location tasks to extend and deepen our understanding of the role of aging in spatial mental representations of more or less familiar environments, also disentangling the contribution of coordinate and categorical spatial relations. The study tested the following hypotheses: (1) younger adults only have an advantage over the elderly in less familiar environments; (2) the advantage for categorical over coordinate spatial relations is mainly found for less familiar environments; and finally; (3) interactions between age, familiarity, and spatial relations might reveal that the effects of age and familiarity take different trajectories for coordinate and categorical spatial relations. Results confirmed that: (1) young people outperform the elderly only in less familiar environments; (2) there is a reduction in the difference between coordinate and categorical accuracy with increasing familiarity with the environment; while (3) the interaction between age and level of familiarity did not significantly differentiate coordinate from categorical spatial relations. In conclusion, the present study provides new evidence for the role of familiarity with geographical areas and its impact on the representation of categorical and coordinate relations, with practical implications for the assessment of topographical disorientation in aging.

The Automated Taxonomic Concept Reasoner

We present a visual and interactive taxonomic Artificial Intelligence (AI) tool, the Automated Taxonomic Concept Reasoner (ATCR), whose graphical web interface is under development and will also become available via an Application Programming Interface (API). The tool employs automated reasoning (Beeson 2014) to align multiple taxonomies visually, in a web browser, using user or expert-provided taxonomic articulations, i.e. "Region Connection Calculus (RCC-5) relationships between taxonomic concepts, provided in a specific logical language (Fig. 1). It does this by representing the problem of taxonomic alignment under these constraints in terms of logical inference, while performing these inferences computationally and leveraging the powerful Microsoft Z3 Satisfiability Modulo Theory (SMT) solver (de Moura and Bjørner 2008). This tool represents further development of utilities for the taxonomic concept approach, which fundamentally addresses the challenge of robust biodiversity data aggregation in light of multiple conflicting sources (and source classifications) from which primary biodiversity data almost invariably originate. The approach has proven superior to aggregation, based just on the syntax and semantics provided by the Darwin Core standard Franz and Sterner 2018). Fig. 1 provides an artificial example of such an alignment. Two taxonomies, A and B, are shown. There are five taxonomic concepts, A.One, A.Two, A.Three, B.One and B.Two. A.Two and A.Three are sub-concepts (children) of A.One, and B.Two is a sub-concept (child) of B.One. These are represented by the direction of the grey arrows. The undirected mustard-coloured lines represent relationships, i.e., the articulations referred to in the previous paragraph. These may be of five kinds: congruent (==), includes (<) and included in (>), overlap (><), and disjointness. These five relationships are known in the AI literature as the Region Connection Calculus-5 (RCC-5) (Randell et al. 1992, Bennett 1994, Bennett 1994), and taken exclusively and in conjunction with each other, have certain desirable properties with respect to the representation of spatial relationships. The provided relationship (i.e. the articulation) may also be an arbitrary disjunction of these five fundamental kinds, thus allowing for representation of some degree of logical uncertainty. Then, and under three assumptions that: "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. "sibling" concepts are disjoint in their instances, all instances of a parent concept are instances of at least one of its child concepts, and every concept has at least one instance - the SMT-based automated reasoner is able to deduce the relationships represented by the undirected green lines. It is also able to deduce disjunctive relationships where these are logically implied. ATCR is related to Euler/X (Franz et al. 2015), an existing tool for the same kinds of taxonomic alignment problems, which was used, for example, to obtain an alignment of two influential primate classifications (Franz et al. 2016). It differs from Euler/X in that it employs a different logical encoding that enables more efficient and more informative computational reasoning, and also in that it provides a graphical web interface, which Euler/X does not.

Research on GIS and CAE cooperative coupled dynamic geographical situational response

The relationship between geographic entities and geographical environment is a vital research topic of geography, and the response characteristics of geographic entities to geographical environment are the basis of Geo-design and geographical decision. Building entities and geography modeling is the key to intelligent cities and disaster response. In the traditional GIS domain, the modeling of building entities was limited to the representation of geometric features and spatial relations, which was unable to simulate the "action-response" relationship between geographic entities and geographical environments for the lack of the expression of the structural characteristics of architectural entities. In this paper, the structural model of the building, which is taken as a link between the organic coupling of GIS and CAE two heterogeneous systems, is built to achieve geographical entities on the dynamic response of the geography of the environment simulation, thus providing the support for the intelligent geographic decision and disaster emergency. This paper focuses on the definition, composition and expression of the building structure model, which is geared to the coupling of GIS and CAE. It also discusses the methods of GIS-based dynamic geographical simulation and CAE-based response analysis. Moreover, the "role-response" model taken the building structure model as the core is built between "Model-simulation-evaluation-decision" geographic entities and geographical environment. The results of structural analysis and the evaluation of diagnosis results are visualized by means of "3D thematic map", and the components are modified and optimized based on the interactive modification method. The structure analysis and design efficiency are improved and the iteration cycle of structure design is accelerated. Design of building structure and modeling quality are been improved. This research extends the static spatial characteristics expression of GIS to dynamic geography scene simulation, further expending the study category of relationship between geography entities and geography environment.

Explaining \u201cspatial purport of perception\u201d: a predictive processing approach

Despite the large interest in the human ability to perceive space present in neuroscience, cognitive science and psychology, as well as philosophy of mind, the issues regarding egocentric space representation received relatively less attention. In this paper I take up a unique phenomenon related to this faculty: the “spatial purport” of perceptual experiences. The notion was proposed by Rick Grush to describe the subjective, qualitative aspects of egocentric representations of spatial properties and relations. Although Grush offered an explanation of the mechanism giving rise to appearance of spatial purport, his model had considerable shortcomings. In the paper I thoroughly analyze both the notion of spatial purport and Grush’s explanation of the mechanism at its core in order to develop his theory using the insights provided by the predictive processing theory of mind, and more particularly by the active inference framework. The extended account I offer, named Predictive and Hierarchical Skill Theory, explains phenomena that escaped Grush’s model and furthers the research on egocentric space representation from the perspective of both neuroscience and philosophy of mind.

Dissociable dynamic network organization states for representations of relative and absolute spatial relations

Dissociable dynamic network organization states for representations of relative and absolute spatial relations

Knowledge graph construction with structure and parameter learning for indoor scene design

We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor scene design, and further develop a weaklysupervised algorithm for extracting the knowledge graph representation from a small dataset using both structure and parameter learning. The proposed framework is flexible, transferable, and readable. We present a variety of computer-aided indoor scene design applications using this representation, to show the usefulness and robustness of the proposed framework.

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

AbstractDelineating the distribution of oil and natural gas resources is a prerequisite of exploitation. The delineation methods usually include conventional techniques of reservoir evaluation and mathematical models. The conventional reservoir evaluation results generally depend on the experts' knowledge and experience in the field. The mathematical methods mostly require accurate models to be proposed. Considering spatial relationships and characteristics of geological reservoir problems, including nonlinearity, complexity and uncertainty, a novel model called geospatial case‐based reasoning for oil–gas reservoir evaluation was proposed in this article. The key components of the new model, including: (1) the joint representation of spatial relationship and attribute features; (2) the model of spatial relationship and attribute similarity joint reasoning; and (3) the methods of establishing weights for the spatial relationship and attribute features, are completely constructed. A case study of the proposed model for gas reservoir evaluation was carried out. Compared with the backpropagation artificial neural network (BP‐ANN) and the geological empirical evaluation (GEE) methods, the model presented in this article performs 6.38% and 46.81% better than BP‐ANN and GEE, respectively. Furthermore, its execution is simpler, more convenient, and importantly, its utilization hardly requires any professional knowledge of the field.

Robust Student's-t mixture modelling via Markov random field and its application in image segmentation

Finite mixture model has been widely applied to image segmentation. However, the technique does not consider the spatial information in images that leads to unsatisfactory results for image segmentation. To address this problem, in this paper, a Student's-t mixture model is proposed for image segmentation based on Markov random field (MRF). There are three advantages in the proposed model. Firstly, a representation of spatial relationships among pixels is given. Secondly, Student's t-distribution is chosen to be the component function of the proposed model instead of the Gaussian distribution because of its heavy tail. Thirdly, to deduce the parameters of the proposed model, a gradient descent method is applied during the inference process. Comprehensive experiments are carried out on greyscale noisy images and real-world colour images. The experimental results have shown the effectiveness and robustness of the proposed model.

Robust Student's-t mixture modelling via Markov random field and its application in image segmentation

Finite mixture model has been widely applied to image segmentation. However, the technique does not consider the spatial information in images that leads to unsatisfactory results for image segmentation. To address this problem, in this paper, a Student's-t mixture model is proposed for image segmentation based on Markov random field (MRF). There are three advantages in the proposed model. Firstly, a representation of spatial relationships among pixels is given. Secondly, Student's t-distribution is chosen to be the component function of the proposed model instead of the Gaussian distribution because of its heavy tail. Thirdly, to deduce the parameters of the proposed model, a gradient descent method is applied during the inference process. Comprehensive experiments are carried out on greyscale noisy images and real-world colour images. The experimental results have shown the effectiveness and robustness of the proposed model.

What Develops in Infants' Spatial Categorization? Korean Infants' Categorization of Containment and Tight-Fit Relations.

Korean-learning infants' categorization of two spatial categories, one consistent and one inconsistent with the Korean semantic category of "kkita," was examined. Infants of 10months (n=32) and 18months (n=49) were tested on their categorization of containment or tight fit spatial relations. At 10months, infants only formed a category of containment, but at 18months, their categorization of tight fit was significantly stronger than containment. The results suggest that Korean infants benefit from their language environment in forming a category of tight fit when the exemplars are perceptually diverse. In particular, infants' language environment may bolster their ability to generalize across diverse exemplars to form abstract categorical representations of spatial relations.

The bounds of distortion: truth, meaning and efficacy in digital geographic representation

ABSTRACTEven maps that strive for a precise representation of spatial relationships use techniques of distortion to embed a geographic extent within a two-dimensional plane – be it a page or a screen. The graphical design of geospatial information does however conform to a consensus around the conceptual limits of that distortion, with an overall design framework that constrains the design to ensure comprehension and the effective recognition of geographic entities and relations. Although constraints are necessary, we argue that the advent of digital technology, particularly in mobile mapping, warrants re-examination of the parameters of these distortions. Here we introduce the concept of ‘the bounds of distortion’ as a device for considering the conceptual boundaries of map design, and as a foundation for further work to investigate how these bounds may be redefined to better support map users with more effective graphical information. The focus here is navigational information, and in particular urban navigation and interaction with the graphical representation of urban geography and public transport networks.

Are Categorical Spatial Relations Encoded by Shifting Visual Attention between Objects?

Perceiving not just values, but relations between values, is critical to human cognition. We tested the predictions of a proposed mechanism for processing categorical spatial relations between two objects—the shift account of relation processing—which states that relations such as ‘above’ or ‘below’ are extracted by shifting visual attention upward or downward in space. If so, then shifts of attention should improve the representation of spatial relations, compared to a control condition of identity memory. Participants viewed a pair of briefly flashed objects and were then tested on either the relative spatial relation or identity of one of those objects. Using eye tracking to reveal participants’ voluntary shifts of attention over time, we found that when initial fixation was on neither object, relational memory showed an absolute advantage for the object following an attention shift, while identity memory showed no advantage for either object. This result is consistent with the shift account of relation processing. When initial fixation began on one of the objects, identity memory strongly benefited this fixated object, while relational memory only showed a relative benefit for objects following an attention shift. This result is also consistent, although not as uniquely, with the shift account of relation processing. Taken together, we suggest that the attention shift account provides a mechanistic explanation for the overall results. This account can potentially serve as the common mechanism underlying both linguistic and perceptual representations of spatial relations.

Two modes for seeing relations between objects

Past work has revealed two distinct types of spatial relation representations: categorical (e.g., 'X left of Y') and coordinate (e.g., 'X is 1 cm away from Y'; Kosslyn et al., 1989). Does a similar dissociation exist for relations based on features other than spatial location, such as size? By measuring eye movements, we show that people adopt distinct attentional modes when representing different types of magnitude relations in the same display (two bars). In Experiment 1, when asked to judge a directional relation ('Are the bars arranged short-tall or tall-short?'), participants systematically isolated one bar with their attention. In contrast, when asked to judge a nondirectional relation ('Are the bars the same or different heights?'), participants systematically shifted their gaze toward the right. A rightward shift would place the stimuli in the left visual field, allowing the bars to be processed more holistically by the right hemisphere. In a second experiment, we encouraged one group of participants (holistic group) to extract relations by imagining a line connecting the tops of the two bars (directional: 'Is the line sloped positively or negatively?'; nondirectional: 'Is the line flat or sloped?'). As a control, we included an individuated group of participants who used the same perceptual framing as Experiment 1. The holistic framing changed participants' eye movements such that directional relations were extracted more similarly to nondirectional relations than directional relations in the individuated group. Together, these results reveal two distinct modes for extracting magnitude relations (locally versus holistically), and that these modes map onto how categorical and coordinate spatial relations are processed, respectively. They also show that people can be induced to extract relations locally or holistically for the same display, either by manipulating the type of judgment (directional/nondirectional) or the perceptual framing (individuated/holistic). Meeting abstract presented at VSS 2016