Novel Multiple-Image Encryption Scheme Based on Coherent Beam Combining and Equal Modulus Decomposition

In our research, we propose a novel asymmetric multiple-image encryption method using a conjugate Dammann grating (CDG), which is based on the coherent beam combining (CBC) principle. The phase generated by the Dammann grating (DG) beam splitting system is processed and added to the image to be encrypted, and then, the ciphertexts and keys are generated by equal modulus decomposition (EMD). Decryption is to combine the beams through the CDG and collect the combined images in the far field. The proposed encryption scheme is flexible and thus extendable. CDG structure parameters, such as one period length of CDG, can be used as encryption key for the increase of the complexity. The Fresnel diffraction distance can also be used as an encryption key. The power of the combined beam is stronger than that of the single beam system, which is convenient for long-distance transmission and also easy to detect. Simulation results show that the proposed method is effective and efficient for asymmetric multiple-image encryption. Sensitivity analysis of CDG alignment has also been performed showing the robustness of the system. The influence of occlusion attack and noise attack on decryption are also discussed, which proves the stability of the system.

Using hypertemporal Sentinel-1 data to predict forest growing stock volume

In this study, we assess the potential of long time series of Sentinel-1 SAR data to predict forest growing stock volume and evaluate the temporal dynamics of the predictions. The boreal coniferous forests study site is located near the Hyytiälä forest station in central Finland and covers an area of 2,500 km2 with nearly 17,000 stands. We considered several prediction approaches (linear, support vector and random forests regression) and fine-tuned them to predict growing stock volume in several evaluation scenarios. The analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SAR images and choosing feature (predictor) variables were evaluated. Our results demonstrate considerable decrease in RMSEs of growing stock volume as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3/ha RMSE (relative RMSE 50-53%), RMSE with combined images decreased to 75.6 m3/ha (relative RMSE 44%). Feature extraction and dimension reduction techniques facilitated achieving the near-optimal prediction accuracy using only 8-10 images. When using assemblages of eight consecutive images, the GSV was predicted with the greatest accuracy when initial acquisitions started between September and January.HighlightsTime series of 96 Sentinel-1 images is analysed over study area with 17,762 forest stands.Rigorous evaluation of tools for SAR feature selection and GSV prediction.Improved periodic seasonality using assemblages of consecutive Sentinel-1 images.Analysis of combining images acquired in “frozen” and “dry summer” conditions.Competitive estimates using calculation of prediction errors with stand-area weighting.

Static and Dynamic Cues to Male Attractiveness

Most studies on facial attractiveness have relied on attractiveness judged from photographs rather than video clips. Only a few studies combined images and video sequences as stimuli. In order to determine static and dynamic cues to male attractiveness, we perform behavioural and computational analyses of the Mr. World 2014 contestants. We asked 365 participants to assess the attractiveness of images or video sequences (thin slices) taken from the profile videos of the Mr. World 2014 contestants. Each participant rated the attractiveness on a 7-point scale, ranging from very unattractive to very attractive. In addition, we performed computational analyses of the landmark representations of faces in images and videos to determine which types of static and dynamic facial information predict the attractiveness ratings. The behavioural study revealed that: (1) the attractiveness assessments of images and video sequences are highly correlated, and (2) the attractiveness assessment of videos was on average 0:25 point above that of images. The computational study showed (i) that for images and video sequence, three established measures of attractiveness correlate with attractiveness, and (ii) mouth movements correlate negatively with attractiveness ratings. The conclusion of the study is that thin slices of dynamical facial expressions contribute to the attractiveness of males in two ways: (i) in a positive way and (ii) in a negative way. The positive contribution is that presenting a male face in a dynamic way leads to a slight increase in attractiveness rating. The negative contribution is that mouth movements correlate negatively with attractiveness ratings.

Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area

The monitoring of cultivated crops and the types of different land covers is a relevant environmental and economic issue for agricultural lands management and crop yield prediction. In this context, this paper aims to use and evaluate the contribution of multisensors classification based on machine learning classifiers to crop-type identification in a semiarid area of Morocco. It is a very heterogeneous zone characterized by mixed crops (tree crops with annual crops, same crop with different phenological states during the same agricultural season, crop rotation, etc.). Therefore, such heterogeneity made the crop-type discrimination more complicated. To overcome these challenges, the present work is the first study in this area which used the fusion of high spatiotemporal resolution Sentinel-1 and Sentinel-2 satellite images for land use and land cover mapping. Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. In situ observations of the year 2018, for the R3 perimeter of Haouz plain in central Morocco, were used with satellite data of the same year to perform this work. The results showed that combined images acquired in C-band and the optical range improved clearly the crop-type classification performance (overall accuracy = 89%; Kappa = 0.85) compared to the classification results of optical or SAR data alone.

New algorithms to Enhanced Fused Images from Auto-Focus Images

Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contrast value because of the added edge points from the two combined images that depend on the suggested algorithms. This enhancement in edge regions is measured and reaches to double in enhancing the contrast. Different methods are used to be compared with the suggested method.

MIRTALA PYLYPENKO’S COLLECTION IN THE MUSEUM OF HISTORY OF THE NATIONAL UNIVERSITY OF OSTROH ACADEMY

Special attention in this article is paid to the analysis of art collection of the Ukrainian artist from abroad Mirtala Pylypenko at the Museum of Ostroh Academy. In 1997 the Museum of history of the Ostroh Academy was founded. A great contribution to its development process was made by Ukrainians from abroad. They supported the museum, sent interesting exhibits, and joined in museum projects. Nowadays the museum has valuable art collections, among which sculptures of the well-known Ukrainian artist Mirtala Pylypenko. Mirtala Pylypenko was born in Ukraine. During World War II she emigrated, and since 1947 she has been living and working in the USA. She graduated from the Boston Museum’s Art School and Tufts University in Boston. Mirtala’s sculptures are not just artworks, but a profound philosophical and original vision of the world. She showed her talent not only in sculpture and art photography, but also in poetry – her poetic collections “Verses”, “Rainbow Bridge”, “Road to Oneself” have been published in various languages. Mirtala received acclaim in the US and Europe in the 1970s – 1980s. Since the early 1990s her works have been known in Ukraine, where the artist held a series of solo exhibits and presentations. Mirtala presented one collection of her works to the National University of Ostroh Academy. Now it is one of the most valuable collections in the university museum. As a sculptor with a long exhibiting career, Mirtala has combined images of her sculptures with her poems, creating a single whole, which is greater than its parts. Mirtala’s collection of sculptures is monumental, philosophic and gracious. However, at the same time, it is sunny and brings back the life-asserting symbols of eternal space and time. The artist has spent most of her life across the ocean (in the USA), but her soul remains tied to Ukraine. Mirtala Pylypenko is an extraordinary figure in the Ukrainian art. And now, many generations of university students have an opportunity to get acquainted with her unique talent. It is important that sooner or later, Ukraine reveals its artists. Therefore, the museum tries to return and represent the Ukrainian diaspora art and history in museum collections in order to create a single Ukrainian cultural space.

Crustal and lithospheric complexity beneath the North Tanzanian Divergence from seismological and geochemical analyses.

<p>We present a joint analysis of seismological images and petrophysical data in the North Tanzanian Divergence, where the lithospheric break-up is at its earliest stage. In this part of the East African Rift, the current surface deformation is related to complex interaction between tectonic (active fault, pre-rift lithospheric structure) and magmatic processes within the mantle and the crust. We present here the compilation of seismological results such as receiver function, local tomography, regional tomography on datasets collected during CRAFTI-CoLiBrEA and HaTARi projects, in a region with clearly opposite seismological and magmatic behaviours: near Natron the seismicity is well located within the upper crust and linked to present day magmatism (Lengai edifice), whereas Manyara area is characterized by a deep seismicity and no evidence of present magmatic activity at the surface. First, these different approaches deliver Vp, Vs and deduced Vp/Vs images with both different resolution and different depth investigation. The combined images of crustal and lithospheric structure provide the appropriate scale to point out the interactions between melt, gas, faults, and inherited fabrics in specific areas. We then compare those geophysical observations with magma composition, magma storage (depth of reservoir, magma volume) and ascent as well as partial melts content at depth obtained from petrophysical and geochemical analysis of lava samples. We will analyze if this combination of seismological approaches constrained with petrological and geochemical data produce accurate images of the entire current magma plumbing system.  Finally, we will discuss the results in terms of magmatic processes and how they interact with the rifting in a cratonic lithosphere.</p>

Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network

Infrared and visible image fusion can obtain combined images with salient hidden objectives and abundant visible details simultaneously. In this paper, we propose a novel method for infrared and visible image fusion with a deep learning framework based on a generative adversarial network (GAN) and a residual network (ResNet). The fusion is accomplished with an adversarial game and directed by the unique loss functions. The generator with residual blocks and skip connections can extract deep features of source image pairs and generate an elementary fused image with infrared thermal radiation information and visible texture information, and more details in visible images are added to the final images through the discriminator. It is unnecessary to design the activity level measurements and fusion rules manually, which are now implemented automatically. Also, there are no complicated multi-scale transforms in this method, so the computational cost and complexity can be reduced. Experiment results demonstrate that the proposed method eventually gets desirable images, achieving better performance in objective assessment and visual quality compared with nine representative infrared and visible image fusion methods.

Deep Learning-Based Multinational Banknote Type and Fitness Classification with the Combined Images by Visible-Light Reflection and Infrared-Light Transmission Image Sensors

Automatic sorting of banknotes in payment facilities, such as automated payment machines or vending machines, consists of many tasks such as recognition of banknote type, classification of fitness for recirculation, and counterfeit detection. Previous studies addressing these problems have mostly reported separately on each of these classification tasks and for a specific type of currency only. In other words, there has been little research conducted considering a combination of these multiple tasks, such as classification of banknote denomination and fitness of banknotes, as well as considering a multinational currency condition of the method. To overcome this issue, we propose a multinational banknote type and fitness classification method that both recognizes the denomination and input direction of banknotes and determines whether the banknote is suitable for reuse or should be replaced by a new one. We also propose a method for estimating the fitness value of banknotes and the consistency of the estimation results among input trials of a banknote. Our method is based on a combination of infrared-light transmission and visible-light reflection images of the input banknote and uses deep-learning techniques with a convolutional neural network. The experimental results on a dataset composed of Indian rupee (INR), Korean won (KRW), and United States dollar (USD) banknote images with mixture of two and three fitness levels showed that the proposed method gives good performance in the combination condition of currency types and classification tasks.