The paper analyses the new push for stronger initiative from the EU in space-related activities, motivated by the new Regulation (EU) 2021/696, or the EU Space Regulation. The EU took its first steps ...in this area around the turn of the century, but tangible progress has in reality largely been made in the last decade. Although the EU’s Space Programme is at a comparable level with other countries leading in the global space industry, the public is still largely unfamiliar with it. The paper therefore tries to analyse the Programme through its legal and technical aspects in order to explain the EU’s activities in the main areas of today’s space-related activities – communication, the monitoring of the Earth and its surroundings, as well as different location-based services. In order to highlight the opportunities which are open to every Member State thanks to the new Regulation, a short overview of Croatia’s activities thus far in the space industry is given. Although the Regulation is a substantial document, some questions are still left open, such as Member States’ liability regarding the Space Programme, and these are discussed in the third part of the paper. The paper concludes by answering the question posed in the title – whether this new Space Policy can actually bring the EU to the forefront of one of today’s fastest growing sectors.
•We achieve state-of-the-art results for five fine art-related classification tasks.•Different convolutional neural network fine-tuning strategies are explored.•Impact of various source ...domain-dependent weight initialization is studied.•Networks pre-trained for scene and sentiment recognition perform best for art tasks.•Fine-tuned models can be used to retrieve images similar in style or content.
The increasing availability of large digitized fine art collections opens new research perspectives in the intersection of artificial intelligence and art history. Motivated by the successful performance of Convolutional Neural Networks (CNN) for a wide variety of computer vision tasks, in this paper we explore their applicability for art-related image classification tasks. We perform extensive CNN fine-tuning experiments and consolidate in one place the results for five different art-related classification tasks on three large fine art datasets. Along with addressing the previously explored tasks of artist, genre, style and time period classification, we introduce a novel task of classifying artworks based on their association with a specific national artistic context. We present state-of-the-art classification results of the addressed tasks, signifying the impact of our method on computational analysis of art, as well as other image classification related research areas. Furthermore, in order to question transferability of deep representations across various source and target domains, we systematically compare the effects of domain-specific weight initialization by evaluating networks pre-trained for different tasks, varying from object and scene recognition to sentiment and memorability labelling. We show that fine-tuning networks pre-trained for scene recognition and sentiment prediction yields better results than fine-tuning networks pre-trained for object recognition. This novel outcome of our work suggests that the semantic correlation between different domains could be inherent in the CNN weights. Additionally, we address the practical applicability of our results by analysing different aspects of image similarity. We show that features derived from fine-tuned networks can be employed to retrieve images similar in either style or content, which can be used to enhance capabilities of search systems in different online art collections.
This paper presents a research study on the subjective assessment of 3D video quality using a newly constructed 3D video database (3DVCL@FER). This database consists of 8 original 3D video sequences, ...each degraded with 22 different degradation types, including degradations specific to stereoscopic systems. The subjective assessment was done with the support of a purpose-built easily customizable grade collection platform and conducted in two research laboratories, in Croatia and Portugal. Subjective scores for quality, depth and comfort were collected and DMOS (Difference Mean Opinion Score) values were calculated. Different objective measures (for image, 3D image, 2D video and 3D video) were separately compared with DMOS values for quality, depth and comfort. The 3D video grade-annotated database described is publicly accessible and can be used in research-related activities like assessment of existing objective measures, using the entire database or parts of it, and construction of new objective measures specific to 3D video degradations. The system presented can also be used to collect and compare subjective quality grades originating from different sites to study the effect of different observation conditions and observer/graders populations on the DMOS quality values for 3D video depth and comfort.
With the emergence of large digitized fine art collections and the successful performance of deep learning techniques, new research prospects unfold in the intersection of artificial intelligence and ...art. In order to explore the applicability of deep learning techniques in understanding art images beyond object recognition and classification, we employ convolutional neural networks (CNN) to predict scores related to three subjective aspects of human perception: aesthetic evaluation of the image, sentiment evoked by the image, and memorability of the image. For each concept, we evaluate several different CNN models trained on various natural image datasets and select the best performing model based on the qualitative results and the comparison with existing subjective ratings of artworks. Furthermore, we employ different decision tree-based machine learning models to analyze the relative importance of various image features related to the content, composition, and color in determining image aesthetics, visual sentiment, and memorability scores. Our findings suggest that content and image lighting have significant influence on aesthetics, in which color vividness and harmony strongly influence sentiment prediction, while object emphasis has a high impact on memorability. In addition, we explore the predicted aesthetic, sentiment, and memorability scores in the context of art history by analyzing their distribution in regard to different artistic styles, genres, artists, and centuries. The presented approach enables new ways of exploring fine art collections based on highly subjective aspects of art, as well as represents one step forward toward bridging the gap between traditional formal analysis and the computational analysis of fine art.
Colorization is a process of converting grayscale images into visually acceptable color images. The main goal is to convince the viewer of the authenticity of the result. Grayscale images that need ...to be colorized are, in most cases, images with natural scenes. Over the last 20 years a wide range of colorization methods has been developed - from algorithmically simple, yet time- and energy-consuming because of unavoidable human intervention to more complicated, but simultaneously more automated methods. Automatic conversion has become a challenging area that combines machine learning and deep learning with art. This paper presents an overview and evaluation of grayscale image colorization methods and techniques applied to natural images. The paper provides a classification of existing colorization methods, explains the principles on which they are based, and highlights their advantages and disadvantages. Special attention is paid to deep learning methods. Relevant methods are compared in terms of image quality and processing time. Different metrics for color image quality assessment are used. Measuring the perceived quality of a color image is challenging due to the complexity of the human visual system. Multiple metrics used to evaluate colorization methods provide results by determining the difference between the predicted color value and the ground truth, which in several cases is not in coherence with image plausibility. The results show that user-guided neural networks are the most promising category for colorization because they successfully combine human intervention and neural network automation.
In this paper we present new image quality database VCL@FER (
http://www.vcl.fer.hr/quality/
) which consists of four degradation types, 6 levels of each degradation and 23 different images (552 ...degraded images). It can be used in objective image quality evaluation, as well as to develop and test new image quality measures. Results for six commonly used full reference objective quality measures are compared using newly developed image database, as well as 6 other image databases.
In this paper we describe a database of static images of human faces. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Database ...contains 4,160 static images (in visible and infrared spectrum) of 130 subjects. Images from different quality cameras should mimic real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios. In addition to database description, this paper also elaborates on possible uses of the database and proposes a testing protocol. A baseline Principal Component Analysis (PCA) face recognition algorithm was tested following the proposed protocol. Other researchers can use these test results as a control algorithm performance score when testing their own algorithms on this dataset. Database is available to research community through the procedure described at
www.scface.org
.
Understanding the historical transformation of artistic styles implies the recognition of different stylistic properties. From a computer vision perspective, stylistic properties represent complex ...image features. In our work we explore the use of convolutional neural networks for learning features that are relevant for understanding properties of artistic styles. We focus on stylistic properties described by Heinrich Wölfflin in his book Principles of Art History (1915). Wölfflin identified five key visual principles, each defined by two contrasting concepts. We refer to each principle as one high-level image feature that measures how much each of the contrasting concepts is present in an image. We introduce convolutional neural network regression models trained to predict values of the five Wölfflin’s features. We provide quantitative and qualitative evaluations of those predictions, as well as analyze how the predicted values relate to different styles and artists. The outcome of our analysis suggests that the models learn to discriminate meaningful features that correspond to the visual characteristics of concepts described by Wölfflin. This indicates that the presented approach can be used to enable new ways of exploring fine art collections based on image features relevant and well-known within art history.
•A blind image sharpness assessment model based on local contrast maps is proposed.•The higher dynamic range of contrast maps significantly improves performance.•A new and reliable image sharpness ...feature is extracted, defined and implemented.•A fast and computationally efficient algorithm was validated on seven image databases.•The model was proved to be very accurate and consistent across all tested databases.
This paper presents a fast blind image sharpness/blurriness assessment model (BISHARP) which operates in spatial and transform domain. The proposed model generates local contrast image maps by computing the root-mean-squared values for each image pixel within a defined size of local neighborhood. The resulting local contrast maps are then transformed into the wavelet domain where the reduction of high frequency content is evaluated in the presence of varying blur strengths. It was found that percentile values computed from sorted, level-shifted, high-frequency wavelet coefficients can serve as reliable image sharpness/blurriness estimators. Furthermore, it was found that higher dynamic range of contrast maps significantly improves model performance. The results of validation performed on seven image databases showed a very high correlation with perceptual scores. Due to low computational requirements the proposed model can be easily utilized in real-world image processing applications.
PDF
