Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic segmentation network combined with edge detection for building extraction in remote sensing images

Extracting buildings from remote sensing images is a significant task with many applications such as map drawing, city planning, population estimation, etc. However, traditional methods that rely on artificially designed features struggle to perform well due to the diverse appearance and complicated background. In this paper, we design an end-to-end convolutional neural network that combines semantic segmentation and edge detection for building extraction. In addition, we propose a residual unit combined with spatial pyramid pooling (SPP-RU) to yield representations of different size receptive fields by multi-branch network. We conduct experiments on WHU building dataset, and the experimental results demonstrate the effectiveness of our method in terms of quantitative and qualitative performance compared with state-of-the-art methods.
Machine vision based 2D measurement method for industrial glass

In order to achieve high efficiency, automatic and accurate measurement, the paper takes the two-dimensional measurement of industrial glass under the experimental conditions. The main contents of this paper includes: Analyzing the structure and hardware performance parameters of the system, building a measuring platform including computer, Charge-coupled Device image sensor, lens, etc., using high-precision camera to take the image of glass, preprocessing of glass image data and acquiring edge information of glass. The system use second filtering method to filter the image and Canny operator to acquire the edge of the industry glass, transforming computer coordinate system into world coordinate system through coordinate transformation method, and finally calculate the two-dimensional size information of industrial glass. The system measures the two-dimensional length and width of polygonal glass, the experimental results show that the measurement method in this paper meet the accuracy requirements of general industrial measurement, and the detection system is feasible.
DPCAE: denoising point cloud auto-encoder for 6D object detection

6D pose estimation for robotic gripping is greatly affected by cluttering, rendering and occlusion. Unlike the mainstream method with RGB images which is troubled by rendering, our approach for 3D orientation estimation is based on a Denoising Point Cloud Auto-encoder (DPCAE) which can avoid the rendering problem and eliminate cluttering and occlusion. Independent of the real pose-annotated training data, the Auto-encoder uses the point cloud data generated by the random object coverage of each object surface in the simulated environment, with the ability to obtain an implicit representation of object orientation and remove outliers to restore the surface of the objects. Experiments on the LineMod dataset show that our proposed approach is superior to those that require similar model-based approaches and competes with state-of-the-art approaches with real pose-annotated images.
Spectral group attention networks for hyperspectral image classification

Attention mechanism in deep learning is similar to information selection mechanism, and the goal of attention is to select critical information for the current task. In hyperspectral classification, the distinction of some categories depends on the subtle differences, however, most of the classification methods have the problem of insufficient expression ability to discriminate the fine differences of categories. In this paper, a classification method based on group attention is proposed to enhance the difference of hyperspectral data between categories. Firstly, we slice the hyperspectral sample into several groups on spectral channels, and extract the group CNN features. Then we use the attention module to obtain the attention weights for each spectral group. Finally, the "feature recalibration" strategy is used to recalibrate the spectral group CNN features. The experiment show that the proposed approach can improve the classification accuracy of categories with subtle differences.
A new robust image feature point detector

A scale space-variant filter (SVF) is proposed on the basis of Harris arithmetic operators, which can smoothly isolate noise efficiently at the situation of keeping edge information of the image. Comparing SVF with Gaussian filter under step jump signal and initial image input, the result indicates that SVF is better than Gaussian filter. Using SVF to detect feature points of an image, the experiment shows that feature points detected from SVF output contain more edge information. Using 2D space limitations, Euclidian distance limitation and angle limitation, we can eliminate redundant feature points so that all the useful feature points are distributed in all regions of the image evenly. From the result of the examination for noise-contained image, we can draw the conclusions that the new robust feature point detector can get more accurate position of feature points and the distribution of the points is more rational than that of the points without those limitations.
Infrared ship target detection based on the combination of Bayesian theory and SVM

Detecting ship targets distributed in infrared images of clouds, waves and other complex disturbances in an unknown complex background, and determining the true ship target in the case where the target and the false alarm are very similar are widely used and challenging tasks. This paper proposes an infrared ship targets detection algorithm based on Bayesian theory and SVM combination. Bayesian theory can be used to estimate the probability of partial unknowns with incomplete information. Bayesian formula can be used to correct the probability of occurrence, and finally we can use the expected value and the modified probability to make the optimal decision. At the same time, support vector machine (SVM) is a novel small sample learning method with solid theoretical foundation. It does not involve probability measures and laws of large numbers, so it is different from existing statistical methods. Firstly, the paper introduces the infrared ship target detection method based on Bayesian theory. Then, the image is post-processed to remove redundant false alarm targets. Finally, the paper introduces the experimental data and the performance evaluation indicators of ship detection results, and compares with other ship detection methods to obtain experimental results.
ATSGPN: adaptive threshold instance segmentation network in 3D point cloud

We introduce an adaptive threshold instance segmentation network in point cloud based on similarity group proposal network(SGPN), named adaptive threshold similarity group proposal network(ATSGPN). SGPN learns the feature of point cloud to process similarity matrix and clusters. In our experiments, we find that we cannot always get the proper threshold by heuristic method to divide the points although the similarity matrix is good enough. Based on this idea, we introduce the Threshold Map to learn segmentation threshold. We also improve the feature extraction using edge convolution(EdgeConv). The point cloud first passes EdgeConv to extract features and learns the similarity matrix in feature space. The semantic label of each point and the segmentation threshold can help to generate groups and then calculates confidence to evaluate the group quality and backpropagation. ATSGPN has higher accuracy on Stanford Large- Scale 3D Indoor Spaces Dataset (S3SID) and fewer steps than SGPN, and there are some experiments can be shown in the paper for its good performance.
Similarity evaluation technology of spectral imaging simulation based on analytic hierarchy process model

This paper is mainly aimed at the development requirement of infrared seeker precision guided weapon system. The infrared sensor of seeker is modeled and simulated, and the image sequence is output. The imaging simulation quality of infrared seeker sensor and the adaptability of terminal guidance algorithm based on simulation real-time graph are evaluated from the perspectives of multi-granularity similarity parameter index system construction, multi-saliency fusion feature consistency and bio-visual feature similarity evaluation. At the same time, the analytic hierarchy process (AHP) model is applied to the process of similarity evaluation to solve the comprehensive decision-making problem in multi-index similarity evaluation, and to evaluate the performance of the simulation system from the perspective of the combination of human-computer intelligence, so as to guide the improvement of the simulation system. Based on the above research results, a set of mature and reliable simulation similarity evaluation software is formed. The research results are of great significance to the development and standardization of the whole infrared simulation technology.
Input limited Wasserstein GAN

Generative adversarial networks (GANs) has proven hugely successful, but suffer from train instability. The recently proposed Wasserstein GAN (WGAN) has largely overcome the problem, but can still fail to converge in some case or be to complex. It has been found that the use of weight clipping in WGAN to enforce a Lipschitz constraint on the critic, is the cause of the failure. We modify network architecture: use domain constraint layer instead of the use of weight clipping in WGAN. Experimental results show that our proposed method generates higher quality images than WGAN with weight clipping. And architecture is sample. Beside the network is more stable and easier to train.
Perception of gender-stereotype in films: a case study on "Captain Marvel" superhero movie

This paper aims to examine the perception of Malaysians on the issues related to gender-stereotypes in superhero genre movies in general, and Captain Marvel movie in specific. The paper aims to call into insight as to whether or not Malaysians accept the female superhero and relevant message delivered. Do they perceive the movie as something positive, agree with the manner and behaviour portrayed, or biased against female superheroes? This research recruited 98 respondents and quantitative analysis reveals that majority of the respondents accepted the Captain Marvel, and responded positively to the feminine power depicted in the movie.
Theory and methodology of multifractal interpretation of aerospace images

This paper introduces a specific type of aerospace image interpretation (AII), which is called as multifractal interpretation (MI) and provides the identification and description of natural objects on aerospace images (AIs) by their multifractal analysis (MA). The paper also presents a generalization of standard (moment-based) multifractal formalism (SMF), which can be considered as a theoretical basis of MI. This generalized multifractal formalism (GMF) is based on the use of kernels constructed using discrete orthonormal polynomials (OPs). It is shown that proposed GMF, in contrast to SMF, can be used to obtain one-dimensional (1D) spectra of global scaling exponents, spectra of local scaling exponents and firstly introduced two-dimensional (2D) spectra of global scaling exponents. The last part of the paper is devoted to the proposed MI methodology that includes MA based on GMF as the main block of the methodology.
Chebyshev multifractal signatures and their use in multifractal interpretation of SAR images of ice-covered sea areas

Chebyshev multifractal signatures for characterization of a multifractal nature of image textures for natural objects are proposed. These signatures can be obtained by generalized multifractal formalism (GMF) using Chebyshev polynomial (CP) kernels and can be considered as alternatives to traditional multifractal spectra. The paper also presents properties of introduced multifractal signatures: in particular, it is shown that Chebyshev multifractal signatures, similar to traditional multifractal spectra, are invariant to image scaling. To illustrate recognition possibilities of multifractal signatures, the application of the signatures for multifractal interpretation of synthetic-aperture radar (SAR) images of ice-covered sea areas are shown. It is established that using parameters of multifractal signature approximations calculated by Sentinel-1 SAR image regions, we can separate sea areas with very close ice, close ice and very open ice. The obtained results allow to say that it is possible to use the introduced multifractal signatures at the preliminary stage of object-oriented classification of SAR or other images to assess a textural separability of image objects.
Electron scattering on acoustic phonons in plane semiconductor GaN/AlN nanostructures

Using the effective mass model for an electron within the layers of a nanosystem and rectangular potential wells and barriers for the effective potential of a GaN/AlN resonant tunneling structure, analytical solutions of the Schrödinger equation are obtained. The theory of a stationary electronic spectrum and wave functions of an electron is developed. Using the elastic continuum model, exact solutions of the equations describing the elastic displacement of the semiconductor medium of the studied nanostructure are obtained. Using these solutions, the theory of acoustic modes of the nanosystem has been developed. Using the Hamiltonian of acoustic phonons and electrons in the representation of secondary quantization, the quantummechanical theory of electron scattering on acoustic phonons is developed. Using the parameters of the nanosystem under study, based on the analysis of the poles of the Green's function, the displacements of the electronic spectrum associated with acoustic phonons at T = 0 К are obtained.
Probing Cortical Activity During Head-Fixed Behavior

The cortex is crucial for many behaviors, ranging from sensory-based behaviors to working memory and social behaviors. To gain an in-depth understanding of the contribution to these behaviors, cellular and sub-cellular recordings from both individual and populations of cortical neurons are vital. However, techniques allowing such recordings, such as two-photon imaging and whole-cell electrophysiology, require absolute stability of the head, a requirement not often fulfilled in freely moving animals. Here, we review and compare behavioral paradigms that have been developed and adapted for the head-fixed preparation, which together offer the needed stability for live recordings of neural activity in behaving animals. We also review how the head-fixed preparation has been used to explore the function of primary sensory cortices, posterior parietal cortex (PPC) and anterior lateral motor (ALM) cortex in sensory-based behavioral tasks, while also discussing the considerations of performing such recordings. Overall, this review highlights the head-fixed preparation as allowing in-depth investigation into the neural activity underlying behaviors by providing highly controllable settings for precise stimuli presentation which can be combined with behavioral paradigms ranging from simple sensory detection tasks to complex, cross-modal, memory-guided decision-making tasks.
Study on sophisticated vegetation classification for AHSI/GF-5 remote sensing data

A detailed distribution map of different vegetation classes is of great importance for us to analyze the global ecosystem. Compared with traditional remote sensing data, hyperspectral remote sensing (HRS) data have hundreds of spectral bands and continuous spectral curves, showing great potential in sophisticated vegetation classification. And the AHSI (Advance Hyper-Spectral Imager) on-board GF-5 satellite has addressed the problem of lacking in satellite HRS data. According to the characteristics of AHSI data, we propose a modified sophisticated vegetation classification method by constructing and optimizing a vegetation feature set (FBS). This method takes the band quality, vegetation biochemical parameters, and neighborhood pixels’ spectral angle distance into consideration. The results show that our method can obtain better classification results than traditional methods with higher overall accuracy and less salt and pepper noise, indicating that it is feasible to distinguish different kinds of vegetation using the AHSI/GF-5 data.
PALEOENVIRONMENTAL IMPLICATIONS OF TIME-AVERAGING AND TAPHONOMIC VARIATION OF SHELL BEDS IN LAKE TANGANYIKA, AFRICA

Abstract: The lake bottom along structural platforms in Lake Tanganyika, Africa, is carpeted with numerous large shell beds, known to be of late Holocene age, but of uncertain assemblage process. The shell beds may be the result of sedimentological (physical) assembly processes, or biological processes, or both. Previous work focused on the distribution of shell-rich facies, and showed time averaging of the surficial shell bioclasts over the last ∼ 1600 calendar years BP. We focus on an extensive shell deposit along a deltaic platform in Kungwe Bay, Tanzania and examine time-averaging and taphonomy of Neothauma tanganyicense shells to constrain sedimentological and biological processes forming concentrations of shells. New radiocarbon dating indicates that Neothauma shells are time-averaged over the last ∼ 3000 calendar years. Younger shells predominate shallow-water and exhibit unimodal age distributions, while shells from deeper-water exhibit a broader age distribution. Taphonomic results indicate that water depth and distance from the delta river mouth influence shell abrasion and encrustation with more encrustation developing away from sediment input points. Shells with black coatings and reddish-orange oxidation patinas suggest local burial and exposure. The age-frequency distributions of the shells suggest production rates of the shells vary over time and with water depth, tracking climatically driven lake-level changes (e.g., Little Ice Age, ∼ 100–650 BP). In addition, age-distributions suggest that (1) mixing of different populations are more prevalent along the steeper deltaic slopes, and (2) recent decreasing production rates may reflect anthropogenic land-use change and attendant sedimentation, which has implications for Neothauma itself, and for organisms that are obligate occupants of the shell beds. These results suggest both climatic and depositional processes play unique roles in the distribution and accumulation of shell beds in Lake Tanganyika, which informs interpretation of similar paleoenvironments in the geologic record.
Adaptive locality preserving projection for hyperspectral image classification

In hyperspectral image classification, small number of labeled samples versus high dimensional data is one of major challenges. Semi-supervised learning has shown potential to relief the dilemma. Compared with its supervised learning counterpart, semi-supervised learning exploits both intrinsic structure of labeled and unlabeled samples. In this work, we proposed a graph fusion based semi-supervised learning method for hyperspectral image classification. More specially, two graphs are constructed from spectral-spatial Gabor features and original spectral signatures, respectively, and then are integrated using an affine combination. Experimental results from an AVIRIS hyperspectral dataset verify the excellent classification performance of our method.
An Introductory Review of Deep Learning for Prediction Models With Big Data

Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in many other domains providing big data seem possible. On a downside, the mathematical and computational methodology underlying deep learning models is very challenging, especially for interdisciplinary scientists. For this reason, we present in this paper an introductory review of deep learning approaches including Deep Feedforward Neural Networks (D-FFNN), Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Autoencoders (AEs), and Long Short-Term Memory (LSTM) networks. These models form the major core architectures of deep learning models currently used and should belong in any data scientist's toolbox. Importantly, those core architectural building blocks can be composed flexibly—in an almost Lego-like manner—to build new application-specific network architectures. Hence, a basic understanding of these network architectures is important to be prepared for future developments in AI.
Object detection based on hierarchical visual perception mechanism

The available high-resolution remote sensing images are growing exponentially in recent years due to the rapid development of remote sensing imaging. However, several problems still exist: 1) How to solve the difficulty caused by the scale and shape of object. 2) How to detect the object quickly and accurately. Inspired by the hierarchical visual perception mechanism, we propose a fusion method combining the low-level feature and high-level feature obtained by convolution neural networks to detect ship target. At the same time, we introduce deformable CNN layer into convolution neural networks to solve the diverse scale and shape of object. Finally, based on the visual attention mechanism, the object contextual information is integrated into the network. The experiment results show that our model can achieve good detection performance and the framework has good expansibility.
Monte Carlo simulation of magnetic properties of AA and AB stacked nano-graphene bilayer within Ising-like model

Magnetic properties of AA and AB stacked bilayers were investigated by means of Monte Carlo technique based on Metropolis algorithm within Ising-like model. A higher values of blocking temperature for AB stacked bilayer was obtained, which indicates that AB nano-graphene bilayer is more energetically beneficial compared to AA stacked. Transition between ferromagnetic and paramagnetic phases of the considered types of bilayers, was established. This makes such nanosystems promising in sensor applications (magnetic field sensors) and spintronics. Hysteresis near absolute zero temperature for ferromagnetic case was obtained, the existence of which strongly depends on the values of exchange interaction and temperature. Obtained results of computer simulation agree with theoretical and experimental ones of other authors.
A real-time noise suppression method for target image on unmanned underwater vehicle platform

This paper analyzes the causes of image noise in seawater and the influence of noise on the target image of UUV(unmanned underwater vehicle), and points out the shortcomings of existing methods of noise suppression. In view of the above problems, we propose a real-time noise suppression method for the target image of the UUV platform. The algorithm is divided into three steps: (1) Firstly, the image is binarized by finding an appropriate threshold based on the dispersion between classes. (2) Then, the binary image is subjected to rapid morphological processing to separate the sticky noise. (3) Finally, the target connected domain is calibrated by the four-neighbor method and the pixel values outside the target are gradually reduced based on the principle of human vision to achieve the purpose of noise suppression. Experiments and results show that the method is able to preserve the edge and details of the target well, suppress the noise, and the speed is fast, which satisfies the accuracy and timeliness required for underwater video processing.