Local Spatial Relations Research Articles

A Novel Fuzzy-Based Remote Sensing Image Segmentation Method.

Image segmentation is a well-known image processing task that consists of partitioning an image into homogeneous areas. It is applied to remotely sensed imagery for many problems such as land use classification and landscape changes. Recently, several hybrid remote sensing image segmentation techniques have been proposed that include metaheuristic approaches in order to increase the segmentation accuracy; however, the critical point of these approaches is the high computational complexity, which affects time and memory consumption. In order to overcome this criticality, we propose a fuzzy-based image segmentation framework implemented in a GIS-based platform for remotely sensed images; furthermore, the proposed model allows us to evaluate the reliability of the segmentation. The Fast Generalized Fuzzy c-means algorithm is implemented to segment images in order to detect local spatial relations between pixels and the Triple Center Relation validity index is used to find the optimal number of clusters. The framework elaborates the composite index to be analyzed starting by multiband remotely sensed images. For each cluster, a segmented image is obtained in which the pixel value represents, transformed into gray levels, the graph belonging to the cluster. A final thematic map is built in which the pixels are classified based on the assignment to the cluster to which they belong with the highest membership degree. In addition, the reliability of the classification is estimated by associating each class with the average of the membership degrees of the pixels assigned to it. The method was tested in the study area consisting of the south-western districts of the city of Naples (Italy) for the segmentation of composite indices maps determined by multiband remote sensing images. The segmentation results are consistent with the segmentations of the study area by morphological and urban characteristics, carried out by domain experts. The high computational speed of the proposed image segmentation method allows it to be applied to massive high-resolution remote sensing images.

Predicting peak stresses in microstructured materials using convolutional encoder–decoder learning

This work presents a machine-learning approach to predict peak-stress clusters in heterogeneous polycrystalline materials. Prior work on using machine learning in the context of mechanics has largely focused on predicting the effective response and overall structure of stress fields. However, their ability to predict peak – which are of critical importance to failure – is unexplored, because the peak-stress clusters occupy a small spatial volume relative to the entire domain, and hence require computationally expensive training. This work develops a deep-learning-based convolutional encoder–decoder method that focuses on predicting peak-stress clusters, specifically on the size and other characteristics of the clusters in the framework of heterogeneous linear elasticity. This method is based on convolutional filters that model local spatial relations between microstructures and stress fields using spatially weighted averaging operations. The model is first trained against linear elastic calculations of stress under applied macroscopic strain in synthetically generated microstructures, which serves as the ground truth. The trained model is then applied to predict the stress field given a (synthetically generated) microstructure and then to detect peak-stress clusters within the predicted stress field. The accuracy of the peak-stress predictions is analyzed using the cosine similarity metric and by comparing the geometric characteristics of the peak-stress clusters against the ground-truth calculations. It is observed that the model is able to learn and predict the geometric details of the peak-stress clusters and, in particular, performed better for higher (normalized) values of the peak stress as compared to lower values of the peak stress. These comparisons showed that the proposed method is well-suited to predict the characteristics of peak-stress clusters.

BoVSG: bag of visual SubGraphs for remote sensing scene classification

ABSTRACT Remote sensing scene classification is gaining much more interest in the recent few years for many strategic fields such as security, land cover and land use monitoring. Several methods have been proposed in the literature and they can be divided into three main classes based on the features used: handcrafted features, features obtained by unsupervised learning and those obtained from deep learning. Handcrafted features are generally time consuming and suboptimal. Unsupervised learning based features which have been proposed later gave better results but their performances are still limited because they mainly rely on shallow networks and are not able to extract powerful features. Deep learning based features are recently investigated and gave interesting results. But, they cannot be usually used because of the scarcity of labelled remote sensing images and are also computationally expensive. Most importantly, whatever kind of feature is used, the neighbourhood information of them is ignored. In this paper, we propose a novel remote sensing scene representation and classification approach called Bag of Visual SubGraphs (BoVSG). First, each image is segmented into superpixels in order to summarize the image content while retaining relevant information. Then, the superpixels from all images are clustered according to their colour and texture features and a random label is assigned to each cluster that probably corresponds to some material or land cover type. Thus superpixels belonging to the same cluster have the same label. Afterwards, each image is modelled with a graph where nodes correspond to labelled superpixels and edges model spatial neighbourhoods. Finally, each image is represented by a histogram of the most frequent subgraphs corresponding to land cover adjacency patterns. This way, local spatial relations between the nodes are also taken into account. Resultant feature vectors are classified using standard classification algorithms. The proposed approach is tested on three popular datasets and its performance outperforms state-of-the-art methods, including deep learning methods. Besides its accuracy, the proposed approach is computationally much less expensive than deep learning methods.

LBP based Watermarking Scheme for Image Tamper Detection and Recovery

In recent years, digital images are in use in a wide range of Applications and for multiple purposes. They also play an important role in the storage and transfer of visual information, especially the secret ones. With this widespread usage of digital images, in addition to the increasing number of tools and software of digital images editing, it has become easy to manipulate and change the actual information of the image. Therefore, it has become necessary to check the authenticity and the integrity of the image by using modern and digital techniques, which contribute to analysis and understanding of the images’ content, and then make sure of their integrity Digital image fragile watermarking is an information hiding technique which adds the watermark into the host image for authentication .while achieving the high integrity one should not compromise with quality distortion of images. numbers of watermarking schemes exist today for balancing between the tamper detection rate and quality of reconstructed images in propose scheme we aim at maintain high tamper detection rate as well as high Peak to Signal Noise Ratio (PSNR) of reconstructed images for their quality. For that we utilize Local Binary Pattern (LBP) for this purpose to obtain the optimum solution. In the proposes scheme we used a fragile image watermarking scheme with recover ability based on local binary pattern (LBP). The local binary pattern operator used to extract localized spatial features. A local binary pattern is used to represent the localized relations of a pixel with its neighborhood pixels. Every pixel measured by the LBP operator and obtained its own local binary pattern as representation of local spatial relations. We utilizes the LBP operator to generate authentication data which are embedded into each image block with 3 × 3 pixels size for tamper detection and recovery. The recovery information is obtained by calculating the mean value of each image block, and then the mean value is converted into a binary string which is embedded into eight neighboring pixels’ LSBs of each image block for image recovering In the propose scheme we can take the input as 256 × 256 as well as 512×512 dimension image, one of the advantage compare to other existing system is that it can also processed the color image. The quality is calculated by the PSNR but in the proposed scheme PSNR at peak point is also calculated to get better result. General Terms Digital image watermarking, fragile image watermarking, local binary pattern (LBP), PSNR (peak signal to noise ratio)

Semantic image classification using statistical local spatial relations model

In this paper, a statistical model called statistical local spatial relations (SLSR) is presented as a novel technique of a learning model with spatial and statistical information for semantic image classification. The model is inspired by probabilistic Latent Semantic Analysis (PLSA) for text mining. In text analysis, PLSA is used to discover topics in a corpus using the bag-of-word document representation. In SLSR, we treat image categories as topics, therefore an image containing instances of multiple categories can be modeled as a mixture of topics. More significantly, SLSR introduces spatial relation information as a factor which is not present in PLSA. SLSR has rotation, scale, translation and affine invariant properties and can solve partial occlusion problems. Using the Dirichlet process and variational Expectation-Maximization learning algorithm, SLSR is developed as an implementation of an image classification algorithm. SLSR uses an unsupervised process which can capture both spatial relations and statistical information simultaneously. The experiments are demonstrated on some standard data sets and show that the SLSR model is a promising model for semantic image classification problems.

The effects of face inversion on the perception of long-range and local spatial relations in eye and mouth configuration.

A recent study hypothesized a configurational anisotropy in the face inversion effect, with vertical relations more difficult to process. However, another difference in the stimuli of that report was that the vertical but not horizontal shifts lacked local spatial references. Difficulty processing long-range spatial relations might also be predicted from a relevance-interaction explanation, which proposes that in inverted faces, spatial relations are processed efficiently only within high-relevance local regions. The authors performed 2 experiments to distinguish between these hypotheses. Experiment 1 showed that the inversion effect for vertical shifts of the eyes alone was more similar to that for horizontal eye shifts than for vertical shifts of the eyes and eyebrows. In Experiment 2, focused attention reduced the inversion effect for vertical mouth position more than that for vertical shifts of the eyes and brows. The authors concluded that face inversion impairs the perception of both local spatial relations in low-relevance regions and long-range spatial relations extending across multiple facial regions, consistent with a loss of efficient whole-face processing of the spatial relations between features.

Distance perception across spatial discontinuities

We investigated the use of nested contact relations in perceiving the relative distance of locations on discontinuous surfaces. Observers viewed computer-generated displays under monocular static conditions and adjusted a marker to match the perceived distance of a cube. The marker and cube were raised above the ground by two different platforms separated by a gap. The relative heights and distances of the platforms were varied. We found the following: (1) When spatially discontinuous surfaces are coplanar, locations of objects resting on these surfaces appear to be compared directly, bypassing relations with the underlying ground plane. (2) Spatial displacement between the platforms produces a bias, in the direction of the displacement, in the perceived relative locations of objects resting on the platforms. This suggests that local spatial relations between objects and their platforms are only partially integrated with more global spatial relations between the discontinuous surfaces of the platforms.

Quantification of local symmetry: application to texture discrimination

Symmetry is one of the most prominent cues in visual perception as well as in computer vision. We have recently presented a Generalized Symmetry Transform that receives as input an edge map, and outputs a symmetry map, where every point marks the intensity and orientation of the local generalized symmetry. In the context of computer vision, this map emphasizes points of high symmetry, which, in turn, are used to detect regions of interest for active vision systems. Many psychophysical experiments in texture discrimination use images that consist of various micro-patterns. Since the Generalized Symmetry Transform captures local spatial relations between image edges, it has been used here to predict human performance in discrimination tasks. Applying the transform to micro-patterns in some well-studied quantitative experiments of human texture discrimination, it is shown that symmetry, as characterized by the present computational scheme, can account for most of them.