Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure

The Gestalt laws of perceptual organization, which describe how visual elements in an image are grouped and interpreted, have traditionally been thought of as innate. Given past research showing that these laws have ecological validity, we investigate whether deep learning methods infer Gestalt laws from the statistics of natural scenes. We examine the law of closure, which asserts that human visual perception tends to “close the gap” by assembling elements that can jointly be interpreted as a complete figure or object. We demonstrate that a state-of-the-art convolutional neural network, trained to classify natural images, exhibits closure on synthetic displays of edge fragments, as assessed by similarity of internal representations. This finding provides further support for the hypothesis that the human perceptual system is even more elegant than the Gestaltists imagined: a single law—adaptation to the statistical structure of the environment—might suffice as fundamental.

Treefrogs exploit temporal coherence to form perceptual objects of communication signals

For many animals, navigating their environment requires an ability to organize continuous streams of sensory input into discrete ‘perceptual objects’ that correspond to physical entities in visual and auditory scenes. The human visual and auditory systems follow several Gestalt laws of perceptual organization to bind constituent features into coherent perceptual objects. A largely unexplored question is whether nonhuman animals follow similar Gestalt laws in perceiving behaviourally relevant stimuli, such as communication signals. We used females of Cope's grey treefrog ( Hyla chrysoscelis ) to test the hypothesis that temporal coherence—a powerful Gestalt principle in human auditory scene analysis—promotes perceptual binding in forming auditory objects of species-typical vocalizations. According to the principle of temporal coherence, sound elements that start and stop at the same time or that modulate coherently over time are likely to become bound together into the same auditory object. We found that the natural temporal coherence between two spectral components of advertisement calls promotes their perceptual binding into auditory objects of advertisement calls. Our findings confirm the broad ecological validity of temporal coherence as a Gestalt law of auditory perceptual organization guiding the formation of biologically relevant perceptual objects in animal behaviour.

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.

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.

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.