Gestalt Laws Research Articles

Automatic Extraction of Built-Up Areas from Very High-Resolution Satellite Imagery Using Patch-Level Spatial Features and Gestalt Laws of Perceptual Grouping

Automatic extraction of built-up areas from very high-resolution (VHR) satellite images has received increasing attention in recent years. However, due to the complexity of spectral and spatial characteristics of built-up areas, it is still a challenging task to obtain their precise location and extent. In this study, a patch-based framework was proposed for unsupervised extraction of built-up areas from VHR imagery. First, a group of corner-constrained overlapping patches were defined to locate the candidate built-up areas. Second, for each patch, its salient textures and structural characteristics were represented as a feature vector using integrated high-frequency wavelet coefficients. Then, inspired by visual perception, a patch-level saliency model of built-up areas was constructed by incorporating Gestalt laws of proximity and similarity, which can effectively describe the spatial relationships between patches. Finally, built-up areas were extracted through thresholding and their boundaries were refined by morphological operations. The performance of the proposed method was evaluated on two VHR image datasets. The resulting average F-measure values were 0.8613 for the Google Earth dataset and 0.88 for the WorldView-2 dataset, respectively. Compared with existing models, the proposed method obtains better extraction results, which show more precise boundaries and preserve better shape integrity.

Spatial Reasoning Based on the Tendency of Gestalt Law to Solve Geometry Problems

Spatial reasoning is the processing of non-verbal information in the form of images or symbols to solve problems. In this regard, this research aims to describe students' spatial reasoning based on Gestalt law trend to solve geometry problems. The approach of this research is qualitative research. Qualitative research is used to describe students' reasoning based on Gestalt law tendency to solve geometry problems. The subjects in this research were students who had studied geometry lessons. This research describes one categories of students' spatial reasoning based on Gestalt law, namely students' reasoning with the closeness law tendency.

A Pure Visual Approach for Automatically Extracting and Aligning Structured Web Data

Database-driven websites and the amount of data stored in their databases are growing enormously. Web databases retrieve relevant information in response to users’ queries; the retrieved information is encoded in dynamically generated web pages as structured data records. Identifying and extracting retrieved data records is a fundamental task for many applications, such as competitive intelligence and comparison shopping. This task is challenging due to the complex underlying structure of such web pages and the existence of irrelevant information. Numerous approaches have been introduced to address this problem, but most of them are HTML-dependent solutions that may no longer be functional with the continuous development of HTML. Although a few vision-based techniques have been introduced, various issues exist that inhibit their performance. To overcome this, we propose a novel visual approach, i.e., programming-language-independent, for automatically extracting structured web data. The proposed approach makes full use of the natural human tendency of visual object perception and the Gestalt laws of grouping. The extraction system consists of two tasks: (1) data record extraction, where we apply three of the Gestalt laws (i.e., laws of continuity, proximity, and similarity), which are used to group the adjacently aligned visually similar data records on a web page; and (2) data item extraction and alignment, where we employ the Gestalt law of similarity, which is utilized to group the visually identical data items. Our experiments upon large-scale test sets show that the proposed system is highly effective and outperforms the two state-of-art vision-based approaches, ViDE and rExtractor. The experiments produce an average F1 score of 86.02%, which is approximately 55% and 36% better than that of ViDE and rExtractor for data record extraction, respectively; and an average F1 score of 86.19%, which is approximately 39% better than that of ViDE for data item extraction.

Choreographing Time: Developing a System of Screen-Less Animation

The Diasynchronoscope is an experimental medium that draws on tropes from animation, film and Gestalt grouping principles to create the perception of screen-less apparent motion. Each developing artwork reveals more formalisms and boundaries inherent to the medium. Through documentation and discussion of selected artistic case studies this research interrogates the Diasynchronoscope as an emergent new medium. Formalisms are developed through studio practice and tested in exhibited artworks. The results of these evolving interactions are observed and contextualised using existing frameworks from Film Studies and Psychology. Inside the Diasynchronoscope static objects are grouped according to Gestalt laws and animation principles to create sequential visual cues that, when lit with projected light, demand selective attention; thus creating the illusion of animated movement. The animation is immersive, sharing the same space as the observer so that they perceive a continuous flow of movement that resembles, at a physiological level, interaction with a real-life moving object. The technique has been named the ‘diasynchronic’ technique and the system; the ‘Diasynchronoscope’. The name combines diachronic, (the study of a phenomenon as it changes through time) with synchronous and scope (view). In being so named, it evokes the early animation simulators such as the phenakistoscope and the zoetrope, regarded as direct ancestors of the project as they too function both as art objects and experimental media. This research intends to show that the Diasynchronoscope is a new medium for experiencing apparent movement, and as such, it offers an opportunity for updating some theorisations in Visual Studies. It is a further conjecture of this thesis that as a system capable of producing replicable experiments of enactive perception of apparent motion the Diasynchronoscope could provide a useful new tool for perceptual psychologists and that some discoveries made in this hybrid study may provide potential useful cross-over areas of study.

DESIGN OF ORIENTATION ASSESSMENT FUNCTIONS FOR GESTALT-GROUPING UTILIZING LABELED SAMPLE-DATA

Abstract. Psychological evidence is given that perceptual grouping is an important help for various visual tasks. Object recognition and land use classification from remotely sensed imagery is an example. In machine vision, such a grouping process can be implemented by coding Gestalt laws such as proximity, symmetry, or good continuation. Since geometric relations are rarely fulfilled exactly, soft membership functions are utilized called Gestalt assessments. Hierarchical grouping is possible on increasing scales. Such an approach to hierarchical Gestalt grouping is modified in this paper. In its original form, the approach uses rather heuristic default assessment functions, which are a possible choice as long as no labeled example data are given. The assessment functions can be parameterized so as to improve the perceptual grouping, guiding it by the Gestalten salient to human perception. To this end, we use orientation statistics from the publicly available data set given for the ICCV symmetry recognition competition 2017. Also, with a particular recognition task at hand, labeled example data can serve as the desired foreground. Here we use the ground-truth layer for buildings of the Vaihingen benchmark of the ISPRS. A mixture distribution containing two von Mises-distributions and the uniform component for the clutter in the background is fitted using expectation maximization.

Limits of the Application of Bayesian Modeling to Perception.

The general lines of Bayesian modeling (BM) in the study of perception are outlined here. The main thesis argued here is that BM works well only in the so-called secondary processes of perception, and in particular in cases of imperfect discriminability between stimuli, or when a judgment is required, or in cases of multistability. In cases of “primary processes,” on the other hand, it is often arbitrary and anyway superfluous, as with the laws of Gestalt. However, it is pointed out that in these latter cases, simpler and more well-established methodologies already exist, such as signal detection theory and individual choice theory. The frequent recourse to arbitrary values of a priori probabilities is also open to question.

The influence of edge bundling on visual information search

Millions of nodes and edges in a node-link diagram representing the network structure of big data may clutter the space, reducing the aesthetic quality and hindering the process of information search. Edge bundling offers an approach to solve this problem. However, to what extent that edge bundling can solve the problem remains unclear. Addressing the literature in visual complexity and Gestalt law of prägnanz, we empirically examine the influence of edge bundling on users’ information search performance, i.e., the accuracy, responding time and perceptual fluency under different levels of bundling strength. The paper reports our findings with detailed discussions.

Toward Deep Universal Sketch Perceptual Grouper.

Human free-hand sketches provide the useful data for studying human perceptual grouping, where the grouping principles such as the Gestalt laws of grouping are naturally in play during both the perception and sketching stages. In this paper, we make the first attempt to develop a universal sketch perceptual grouper. That is, a grouper that can be applied to sketches of any category created with any drawing style and ability, to group constituent strokes/segments into semantically meaningful object parts. The first obstacle to achieving this goal is the lack of large-scale datasets with grouping annotation. To overcome this, we contribute the largest sketch perceptual grouping dataset to date, consisting of 20 000 unique sketches evenly distributed over 25 object categories. Furthermore, we propose a novel deep perceptual grouping model learned with both generative and discriminative losses. The generative loss improves the generalization ability of the model, while the discriminative loss guarantees both local and global grouping consistency. Extensive experiments demonstrate that the proposed grouper significantly outperforms the state-of-the-art competitors. In addition, we show that our grouper is useful for a number of sketch analysis tasks, including sketch semantic segmentation, synthesis, and fine-grained sketch-based image retrieval.

The grouping effect of common fate in multiple object tracking

<p id=C2>The automatic grouping effect is the finding that inter-target grouping improves performance, while target-distractor grouping impairs performance during multiple object tracking (MOT). Many features can induce the automatic grouping effect, such as color, shape, interpolation, and symmetry. However, none of these features are spatial-temporal. Motion information is the principal spatial-temporal feature, and it plays an important role in multiple object tracking (MOT). Using motion information stored in visual spatial working memory (VSWM), people can not only keep track of multiple moving targets among identical distractors, but also periodically extrapolate the motion trajectory of targets. We investigated whether or not participants can use motion information as the basis of the automatic grouping effect just as they can with other feature (e.g. color, shape) during attention tracking. <p id=C60>To test this question, we used classic random motion as the baseline and added two new types of chasing motion in MOT tasks. One chasing motion condition was target-chase-target; the other chasing motion condition was distractor-chase-target. Given that surface features can influence the grouping effect based on motion information, we next tested the motion-based grouping effect in a condition where targets and distractors are assigned different color combinations (Experiment 2) and different shape combinations (Experiment 3). The different color combinations included identical color between targets and distractors, distinct colors between targets and distractors, and mixed colors between targets and distractors. The shape combinations were analogous to the color combinations. <p id=C61>Results of Experiment 1 showed that, compared to the random motion condition, tracking performance was better when targets chased targets and worse when distractors chased targets. In other words, motion information can be used as an automatic grouping cue. Results of Experiment 2 indicated that the tracking performance in the target-chase-target condition was always better than the distractor-chase-target condition when colors were identical, distinct, or mixed. However, compared with the identical color condition, the difference between chasing conditions (target-chase-target condition minus distractor-chase-target condition) was smaller in both the distinct color condition and the mixed color condition. When Experiment 3 varied the surface feature from color to shape, the results were the same as Experiment 2. The surface feature (i.e., color and shape) did not eliminate the motion-based grouping effect, but it did reduce the difference between the target-chase-target condition and distractor-chase-target condition. <p id=C62>From these results, we can conclude that motion information can be used in multiple object tracking and may be an automatic grouping feature. This suggests that spatial-temporal features can also induce automatic grouping, regardless of the effect of surface feature in MOT tasks. It may also imply that, although motion information is encoded automatically and in parallel during tracking, it may share visual spatial working memory resources with surface feature. Finally, motion-based grouping effect in MOT showed that the gestalt law was embodied in attentive tracking.

Erratum: Hu, P.B., et al. toward Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws. Remote Sens. 2018, 10, 1127

The authors wish to make a correction to their paper [...]

Towards Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws

3D building models are an essential data infrastructure for various applications in a smart city system, since they facilitate spatial queries, spatial analysis, and interactive visualization. Due to the highly complex nature of building structures, automatically reconstructing 3D buildings from point clouds remains a challenging task. In this paper, a Roof Attribute Graph (RAG) method is proposed to describe the decomposition and topological relations within a complicated roof structure. Furthermore, top-down decomposition and bottom-up refinement processes are proposed to reconstruct roof parts according to the Gestalt laws, generating a complete structural model with a hierarchical topological tree. Two LiDAR datasets from Guangdong (China) and Vaihingen (Germany) with different point densities were used in our study. Experimental results, including the assessment on Vaihingen standardized by the International Society for Photogrammetry and Remote Sensing (ISPRS), show that the proposed method can be used to model 3D building roofs with high quality results as demonstrated by the completeness and correctness metrics presented in this paper.

Cross-Browser Differences Detection Based on an Empirical Metric for Web Page Visual Similarity

This article aims to develop a method to detect visual differences introduced into web pages when they are rendered in different browsers. To achieve this goal, we propose an empirical visual similarity metric by mimicking human mechanisms of perception. The Gestalt laws of grouping are translated into a computer compatible rule set. A block tree is then parsed by the rules for similarity calculation. During the translation of the Gestalt laws, experiments are performed to obtain metrics for proximity, color similarity, and image similarity. After a validation experiment, the empirical metric is employed to detect cross-browser differences. Experiments and case studies on the world’s most popular web pages provide positive results for this methodology.

Measures of Prägnanz?

Summary Prägnanz was suggested by Max Wertheimer in the 1920s as subsuming all “Laws of Gestalt” as they apply to visual awareness. Thus, it assumes a prominent position in any account of Gestalt phenomena. From a phenomenological perspective, some visual stimuli evidently “have more Prägnanz” than others, so Prägnanz seems to be an intensive quality. Here, we investigate the intricacies that need to be faced on the way to a definition of formal scales. Such measures naturally depend both upon the stimulus and upon the observer. Structural complexity bottlenecks of visual systems play a role, as well as the relevance to biological fitness, that is the affinity to the optical user interface. This positions the notion of Prägnanz squarely within the realm of biology. Indeed, the familiar “releasers” of ethology are singular cases of extremely high Prägnanz.

Connection-based and object-based grouping in multiple-object tracking: A developmental study.

Developmental research on Gestalt laws has previously revealed that, even as young as infancy, we are bound to group visual elements into unitary structures in accordance with a variety of organizational principles. Here, we focus on the developmental trajectory of both connection‐based and object‐based grouping, and investigate their impact on object formation in participants, aged 9–21 years old (N = 113), using a multiple‐object tracking paradigm. Results reveal a main effect of both age and grouping type, indicating that 9‐ to 21‐year‐olds are sensitive to both connection‐based and object‐based grouping interference, and tracking ability increases with age. In addition to its importance for typical development, these results provide an informative baseline to understand clinical aberrations in this regard. Statement of contribution What is already known on this subject? The origin of the Gestalt principles is still an ongoing debate: Are they innate, learned over time, or both?Developmental research has revealed how each Gestalt principle has its own trajectory and unique relationship to visual experience.Both connectedness and object‐based grouping play an important role in object formation during childhood. What does this study add? The study identifies how sensitivity to connectedness and object‐based grouping evolves in individuals, aged 9–21 years old.Using multiple‐object tracking, results reveal that the ability to track multiple objects increases with age.These results provide an informative baseline to understand clinical aberrations in different types of grouping.

The effect of the personalization principle on multimedia learning: the role of student individual interests as a predictor

By focusing on the personalization principle, the present study investigates whether two different language presentation formats of a multimedia presentation influence students’ learning outcome and how individual interest towards the learning material moderates this impact. German students (N = 104) were randomly assigned to either a conversational or a formal language format of the multimedia presentation that taught Gestalt Laws. The results of this study showed that there were more positive effects on the learning performance in the conversational presentation format than the formal language format. This was, therefore, an indication of the effects of personalization. Furthermore, individual interest is a significant predictor for learning in this study. By testing different percentiles of individual interest, the findings showed that students with intermediate interest were affected by the format of language style. In contrast, learning results of students with the very lowest and very highest individual interest were not affected by different language styles. These results underline the necessity to take into account individual motivational prerequisites when designing multimedia learning environments.

Boundary Assignment: A Cognitive Motivation for Lexicalization

Lexicalization is a process of structural innovation in language change, which is motivated by a human cognitive ability of structural boundary assignment in the construction of linguistic structures. In this process, the first innovative form may be a result of a language user’s intentional violation of a structural organization rule for a certain communicative purpose. The receiver, trying to correctly understand what the producer means, will have to make cognitive efforts to establish a structural gestalt by way of assigning structural boundaries. If the receiver’s boundary assignment comes to an agreement with the producer’s and accords with Gestalt Laws in human cognition, the innovative form will be accepted and spread in the language community and then finally entrench itself as new grammatical convention. That is the general process of lexicalization. The boundary assigning tendency manifested in the process of linguistic structure construal is a cognitive ability dominated by Gestalt Laws in human cognition of the world and world relations, which is actually a driving force for linguistic structural organization, and therefore an important motivation for lexicalization.

Gestalt laws based tracklets analysis for human crowd understanding

Crowded scene analysis is a popular research topic due to its great application potentials, such as intelligent video surveillance and crowd density estimation. In this paper, we propose a novel approach to detecting crowd groups and learning semantic regions with a unified hierarchical clustering framework. According to the Gestalt laws of grouping, we propose three priors to define a unified similarity metric to measure the similarities of pairs of original tracklets and pairs of representative tracklets from different crowd groups, so that the short-term crowd groups and the long-term semantic paths commonly composed of several short-term crowd groups can be detected by a bottom-up hierarchical clustering algorithm simultaneously. In order to verify our method at the longer time duration video sequences in the crowded scene, we construct a new crowd database (CASIA crowd database 1) with various crowd densities in real scenes. Extensive experiments on our CASIA crowd database, Collective Motion Database and CUHK database are performed, and the results demonstrate that our approach is effective and reliable for crowd detection and semantic scene understanding in various crowd densities, especially for the crowd analysis in long temporal video clips.

Recognizing building groups for generalization: a comparative study

ABSTRACTRecognition of building groups is a critical step in building generalization. To find building groups, various approaches have been developed based on the principles of grouping (or the Gestalt laws of grouping), and the effectiveness of these approaches needs to be evaluated. This study presents a comparative analysis of nine typical such approaches, including three approaches that only consider proximity principle and six approaches that consider multiple grouping principles. Real-life dataset at 1:5000, 1:10,000, and 1:50,000 scales provided by National Geomatics Center of China is used to evaluate the performance of these approaches. Buildings at smaller scales are used to construct the benchmarks to test the grouping results at larger scales, and the adjusted Rand index is adopted to indicate the accuracy of the detected groups. Significant tests (Friedman test and Wilcoxon signed-rank test) are also performed to provide both the overall and pairwise comparisons of these approaches. The results show that (1) the average accuracy of most existing approaches is between 0.3 and 0.5, and the performances of these approaches are significantly different; (2) when only proximity is considered, the buffer analysis approach performs significantly better than other approaches; (3) when multiple grouping principles are considered, the local constraint-based approach usually performs better than other approaches; (4) existing approaches that consider similarity and/or continuity seldom improve the performance of building grouping.

Local and Global Gestalt Laws: A Neurally Based Spectral Approach.

This letter presents a mathematical model of figure-ground articulation that takes into account both local and global gestalt laws and is compatible with the functional architecture of the primary visual cortex (V1). The local gestalt law of good continuation is described by means of suitable connectivity kernels that are derived from Lie group theory and quantitatively compared with long-range connectivity in V1. Global gestalt constraints are then introduced in terms of spectral analysis of a connectivity matrix derived from these kernels. This analysis performs grouping of local features and individuates perceptual units with the highest salience. Numerical simulations are performed, and results are obtained by applying the technique to a number of stimuli.

Application of Proximity in Translation of Alice's Adventures in Wonderland

Childhood serves as the crucial period of human life. Children’s literature translation has drawn more and more attention of researchers and scholars. In the light of Gestalt law of proximity, this paper analyzes the language feature—explicitation of word meaning in Zhao Yuanren’s version of Alice’s Adventures in Wonderland. The result shows that proximity law is helpful in determining the meaning of words in contexts, thus making the translation more accurate and easily accessible for child readers.