Gestalt Laws Research Articles

Fractal Dynamics of Visual Search as a Function of the Gestalt Law of Proximity

Fractal Dynamics of Visual Search as a Function of the Gestalt Law of Proximity

An Efficient Algorithm for Determining an Aesthetic Shape Connecting Unorganized 2D Points

AbstractWe present anefficient algorithm for determining an aesthetically pleasing shape boundary connecting all the points in a given unorganized set of 2D points, with no other information than point coordinates. By posing shape construction as a minimisation problem which follows the Gestalt laws, our desired shape is non‐intersecting, interpolates all points and minimizes a criterion related to these laws. The basis for our algorithm is an initial graph, an extension of the Euclidean minimum spanning tree but with no leaf nodes, called as the minimum boundary complex . and can be expressed similarly by parametrizing a topological constraint. A close approximation of , termed can be computed fast using a greedy algorithm. is then transformed into a closed interpolating boundary in two steps to satisfy ’s topological and minimization requirements. Computing exactly is an NP (Non‐Polynomial)‐hard problem, whereas is computed in linearithmic time. We present many examples showing considerable improvement over previous techniques, especially for shapes with sharp corners. Source code is available online.

Grouping by Proximity in Haptic Contour Detection

We investigated the applicability of the Gestalt principle of perceptual grouping by proximity in the haptic modality. To do so, we investigated the influence of element proximity on haptic contour detection. In the course of four sessions ten participants performed a haptic contour detection task in which they freely explored a haptic random dot display that contained a contour in 50% of the trials. A contour was defined by a higher density of elements (raised dots), relative to the background surface. Proximity of the contour elements as well as the average proximity of background elements was systematically varied. We hypothesized that if proximity of contour elements influences haptic contour detection, detection will be more likely when contour elements are in closer proximity. This should be irrespective of the ratio with the proximity of the background elements. Results showed indeed that the closer the contour elements were, the higher the detection rates. Moreover, this was the case independent of the contour/background ratio. We conclude that the Gestalt law of proximity applies to haptic contour detection.

외식 상차림의 게슈탈트 시지각 법칙에 따른 분류

This study analyzed restaurant table settings with Gestalt's law of visual perception to obtain basic data for future marketing strategies. The research uses methods that involve applying images of restaurant table settings to Gestalt's law of visual perception, doing content analysis, and conducting a frequency analysis as well as a Chi-square test for classification analysis by visual perception. Results show a significant difference in the laws of visual perception, especially in the laws of nearness and closure, between table settings of different countries and backgrounds, such as Korean, Japanese, Chinese, Western cultures. In terms of the law of nearness, Chinese dishes were low, while other countries' dishes and Korean dishes showed high figures. In terms of the law of closure, Japanese dishes and western dishes had low values, while other countries' dishes and Korean dishes were high in their closure. Further studies on consumer awareness by visual perception classification need to be conducted.

Field-based sub-boundary extraction from remote sensing imagery using perceptual grouping

This study presents an approach for the automatic extraction of dynamic sub-boundaries within existing agricultural fields from remote sensing imagery using perceptual grouping. We define sub-boundaries as boundaries, where a change in crop type takes a place within the fixed geometry of an agricultural field. To perform field-based processing and analysis operations, the field boundary data and satellite imagery are integrated. The edge pixels are detected using the Canny edge detector. The edge pixels are then analyzed to find the connected edge chains and from these chains the line segments are detected using the graph-based vectorization method. The spurious line segments are eliminated through a line simplification process. The perceptual grouping of the line segments is employed for detecting sub-boundaries and constructing sub-fields within the fixed geometry of agricultural fields. Our strategy for perceptual grouping involves the Gestalt laws of proximity, continuation, symmetry and closure. The processing and analysis operations are carried out on field-by-field basis. For each field, the geometries of sub-boundaries are determined through analyzing the line segments that fall within the field and the extracted sub-boundaries are integrated with the fixed geometry of the field.The experimental validation of the approach was carried out on the SPOT4 multispectral (XS) and SPOT5 XS images that cover an agricultural area located in the north-west section of Turkey. The overall matching percentages between the reference data and the automatically extracted sub-boundaries were computed to be 82.6% and 76.2% for the SPOT5 and SPOT4 images respectively. The higher matching percentage of the SPOT5 image is due to the fact that some of the boundaries present in the SPOT5 image were not detected in the coarser resolution SPOT4 image. For the SPOT5 image, of the total 292 fields processed, 177 showed a total agreement between the detected segments and the reference data. For the SPOT4 image, 154 fields showed a total agreement between the detected segments and the reference data.

Context-Based Hierarchical Unequal Merging for SAR Image Segmentation

This paper presents an image segmentation method named Context-based Hierarchical Unequal Merging for Synthetic aperture radar (SAR) Image Segmentation (CHUMSIS), which uses superpixels as the operation units instead of pixels. Based on the Gestalt laws, three rules that realize a new and natural way to manage different kinds of features extracted from SAR images are proposed to represent superpixel context. The rules are prior knowledge from cognitive science and serve as top-down constraints to globally guide the superpixel merging. The features, including brightness, texture, edges, and spatial information, locally describe the superpixels of SAR images and are bottom-up forces. While merging superpixels, a hierarchical unequal merging algorithm is designed, which includes two stages: 1) coarse merging stage and 2) fine merging stage. The merging algorithm unequally allocates computation resources so as to spend less running time in the superpixels without ambiguity and more running time in the superpixels with ambiguity. Experiments on synthetic and real SAR images indicate that this algorithm can make a balance between computation speed and segmentation accuracy. Compared with two state-of-the-art Markov random field models, CHUMSIS can obtain good segmentation results and successfully reduce running time.

Intuitive interaction with multifunctional mobile interfaces

This paper investigates intuitive interaction in the context of mobile phones. The aim is to identify what creates an intuitive interaction with an interface and therefore analyse how features may be designed to maximise the probability of an intuitive interaction. Three experimental studies were conducted. The first study recorded participants’ interaction with unmarked mobile phone button schematics with the aim of relating intuitiveness of interaction to the gestalt laws. It was demonstrated that intuitive interaction is facilitated when interfaces have layouts designed using the gestalt laws. The second study compared interactions using touchscreen and button phones to evaluate the significance of the touchscreen. It was found that participants who had never owned a touchscreen handset were still inclined to utilise the experiential knowledge of existing handsets for all of the tasks performed. The final study used the Immersion® CyberGlove® to record participants’ interactions with a prop in place of a touchscreen phone when carrying out various tasks. The aim was to determine which image schemata were prevalent in the use of a touchscreen handset. Six image schemata were identified, and it was concluded that these image schemata could improve the efficiency of inclusive design if taken into account. This research suggests that individual image schemata can be related to the continuum of knowledge sources in order to create design solutions that take advantage of the user’s basic sensorimotor experience.

A Computation Method for Symmetry

This paper puts forward a mathematical method to measure the symmetry property of two image elements—a specific instance of the Gestalt law of symmetry. The method makes an abstract of image elements by introducing vector points and experimental results show that the computation results is consistent with the human perception interpretation in most cases.

Emotions Drive Attention

In a 3 phrase research study to determine the stimuli that drives fear, Psychologists Arne Ohman, Ph.D., Anders Flykt, Ph.D. and Francisco Esteves, Ph.D., observed 3 of the control groups reaction to distinct fear driven images of reptiles, animals, faces and insects. Also, they kept data on the stimuli that had no relationship to fear such as flowers or vegetables. (Ohman et al, 2001) What the researchers discovered was that our cognitive abilities, phobias, and memory retrieved information very quickly and produced a significant reactions from the subjects. (Ohman et al, 2001) Hypervigilence, memory recall, emotional intelligence, and Gestalt Law gave inference as to how the subjects were able to interpret what they saw and give rise to reaction. The results were consistent and the researchers found that the subjects automatically and unconsciously searched for the one most feared images first; insects and snakes. However, they simultaneously looked for the distractors which were the more pleasant images of flowers and mushrooms. This affirmed the researchers’ hypothesis that phobias and the paranoia that is instilled in people regarding perceived threats. (Ohman et al, 2001; Ryan, 2011, p.61-79) It is retained in our memory and retrieved automatically to cause a reaction as a defense mechanism called “feature detection.” (Ryan, 2011, p.67)

The Effect of Gestalt Laws of Perceptual Organization on the Comprehension of Three-Variable Bar and Line Graphs

We report three experiments investigating the ability of undergraduate college students to comprehend 2 x 2 "interaction" graphs from two-way factorial research designs. Factorial research designs are an invaluable research tool widely used in all branches of the natural and social sciences, and the teaching of such designs lies at the core of many college curricula. Such data can be represented in bar or line graph form. Previous studies have shown, however, that people interpret these two graphical forms differently. In Experiment 1, participants were required to interpret interaction data in either bar or line graphs while thinking aloud. Verbal protocol analysis revealed that line graph users were significantly more likely to misinterpret the data or fail to interpret the graph altogether. The patterns of errors line graph users made were interpreted as arising from the operation of Gestalt principles of perceptual organization, and this interpretation was used to develop two modified versions of the line graph, which were then tested in two further experiments. One of the modifications resulted in a significant improvement in performance. Results of the three experiments support the proposed explanation and demonstrate the effects (both positive and negative) of Gestalt principles of perceptual organization on graph comprehension. We propose that our new design provides a more balanced representation of the data than the standard line graph for nonexpert users to comprehend the full range of relationships in two-way factorial research designs and may therefore be considered a more appropriate representation for use in educational and other nonexpert contexts.

THE INFLUENCE OF NATURAL USER EXPERIENCE ON INFORMATION LAWS / NATŪRALAUS VARTOTOJO POTYRIO ĮTAKOS PRIKLAUSOMYBĖ NUO INFORMACIJOS PASKIRSTYMO DĖSNINGUMŲ

This article reviews the main cause of user experience on development methods and laws, including Fitt’s Law, Hick-Hyman Law, Accot’s Law, Gestalt Law, proximity, similarity, closure, continuity, figure and ground, simplicity, symmetry and experience. Santrauka Darbe apžvelgiami pagrindiniai natūralaus vartotojo potyrio kūrimo metodai ir dėsniai: Fitso dėsnis, Hiko dėsnis, valdymo dėsnis, Gestalto dėsniai, artumo dėsnis, panašumo dėsnis, uždarymo dėsnis, tęsimo dėsnis, iliustracijos ir pagrindo dėsnis, paprastumo dėsnis, simetrijos dėsnis, patirties dėsnis. Parodoma, kaip dėsniai leidžia priartinti vartotoją prie kūrėjui norimo pasiekti rezultato. Norint pasiekti efektyvų rezultatą būtina atsižvelgti į skirtingose situacijose veikiančius skirtingus dėsnius. Neteisingai taikomi dėsniai gali turėti neigiamą poveikį.

Multistability, cross-modal binding and the additivity of conjoined grouping principles

We present a sceptical view of multimodal multistability--drawing most of our examples from the relation between audition and vision. We begin by summarizing some of the principal ways in which audio-visual binding takes place. We review the evidence that unambiguous stimulation in one modality may affect the perception of a multistable stimulus in another modality. Cross-modal influences of one multistable stimulus on the multistability of another are different: they have occurred only in speech perception. We then argue that the strongest relation between perceptual organization in vision and perceptual organization in audition is likely to be by way of analogous Gestalt laws. We conclude with some general observations about multimodality.

Some Varieties of Shadow Illusions: Split Shadows, Occluded Shadows, Stolen Shadows, and Shadows of Shadows

Shadows are visual objects and as such are subject to preference rules for segmenting the visual scene (such as Gestalt laws). These rules govern shadows along two dimensions: their general unity and individuation (eg they describe a certain area as a unitary shadow as opposed to a set of two distinct shadows), and their being the shadow of a given object, as opposed to being the shadow of any other object in the scene (the shadow correspondence problem Mamassian, 2004 Perception 33 1279-1290). I describe a few phenomena that indicate the interplay of preferences in determining the final visual aspect of scenes in which shadows are present.

A Mathematical Method for Computing the Contour Closure

This paper puts forward a mathematical method for computing the closure extent of object contours—a specific instance of Gestalt law of closure. The method introduces the concept of virtual line to interpret the size of contour gaps vector points and measure the closure extent. Experimental results show that the computation results is consistent with the human perception interpretation in most cases.

A Mathematical Method for Computing the Contour Closure

This paper puts forward a mathematical method for computing the closure extent of object contours—a specific instance of Gestalt law of closure. The method introduces the concept of virtual line to interpret the size of contour gaps vector points and measure the closure extent. Experimental results show that the computation results is consistent with the human perception interpretation in most cases.

A Computation Method for Continuity

This paper puts forward a mathematical method for computing the continuation extent of a specific curve-a specific instance of the Gestalt law of continuum. The method explores the curvature property of a curve to measure its continuation extent and experimental results show that the method is consistent with the human perception interpretations.

EVALUATING A SEGMENTATION-RESISTANT CAPTCHA INSPIRED BY THE HUMAN VISUAL SYSTEM MODEL

Visual CAPTCHAs are widely used these days on the Internet as a means of distinguishing between humans and computers. They help protect servers from being flooded by requests from malicious scripts. However, they are not very secure. Numerous image processing algorithms are able to discern the characters used in the CAPTCHAs. It has been suggested that CAPTCHAs can be made more secure if they are distorted in ways that makes segmentation difficult. However, out of all the reviewed distortions present in current CAPTCHAs there are none that allow for a high level of segmentation difficulty. Furthermore, CAPTCHAs also need to be used by humans who may not find certain distortions tolerable. Thus, the problem of selecting a good distortion becomes a tradeoff between user acceptability and computer solvability. It is hypothesized in this paper that rather than use low-level image distortions, optical distortions based on the Gestalt laws of perception that govern human visual system models should be applied. These distortions would ensure widespread user acceptability (as they are based on the internal workings of the HVS), and be very difficult for computers to solve (as HVS perception models have been difficult to implement in computers). This paper aims to explore the feasibility of employing Gestalt-inspired distortion in CAPTCHAs by first implementing a CAPTCHA cracker and then evaluating the performance of some manually generated Gestalt CAPTCHA’s against some existing CAPTCHAs.

IMAGE SEGMENTATION WITH COMPLEMENTARY USE OF EDGE AND REGION INFORMATION

Two familiar approaches to image segmentation are the salient contour extraction approach and the closed-contour deformation approach. The former uses Gestalt laws to link individual edge elements and construct segmentation boundaries. However, it is often difficult to have both closure and precision of the boundary addressed at the same time. The latter starts with a closed contour and deforms the contour to localize the segmentation boundary more precisely whilst maintaining the closure. The approach does not have the closure problem, but how to assign a proper initial contour for it remains an open issue. In this work, we propose a scheme that puts together the two approaches to let them work complementarily. Specifically, we design a salient contour extraction process that extracts a proper initialization of the closed contours; the process looks into edge evidence and proximity to the desired segmentation boundaries. Then, a region-based active contour in a level set formulation is adopted to refine the contour position to locate the segmentation boundaries more precisely. The scheme requires neither manual input on contour initialization nor prior knowledge about the imaged scene. Experiments on extensive benchmarking image-sets are presented to illustrate the performance of the scheme.

Spatiotemporal Features for Action Recognition and Salient Event Detection

Although the mechanisms of human visual understanding remain partially unclear, computational models inspired by existing knowledge on human vision have emerged and applied to several fields. In this paper, we propose a novel method to compute visual saliency from video sequences by counting in the actual spatiotemporal nature of the video. The visual input is represented by a volume in space--time and decomposed into a set of feature volumes in multiple resolutions. Feature competition is used to produce a saliency distribution of the input implemented by constrained minimization. The proposed constraints are inspired by and associated with the Gestalt laws. There are a number of contributions in this approach, namely extending existing visual feature models to a volumetric representation, allowing competition across features, scales and voxels, and formulating constraints in accordance with perceptual principles. The resulting saliency volume is used to detect prominent spatiotemporal regions and consequently applied to action recognition and perceptually salient event detection in video sequences. Comparisons against established methods on public datasets are given and reveal the potential of the proposed model. The experiments include three action recognition scenarios and salient temporal segment detection in a movie database annotated by humans.

An intrinsic semantic framework for recognizing image objects

The paper proposes a new approach to find semantic meanings in visual object class structure, in line with the Gestalt laws of proximity. Micro level semantic structures are formed by line segments (arcs also approximated into line segments based on pixel deviation threshold) which are in close proximity. These structures are hierarchically combined till a semantic label can be assigned. The algorithm extracts semantic groups, their inter-relations and represents these using a graph. Invariant geometrical properties of the groups and relations are used as vertex and edge labels. A graph model captures the inter class variability by analyzing the repetitiveness of structures and relations and uses it as a weighting factor for classification. The algorithm has been tested on a standard benchmark database and compared with existing approaches.