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Sparse representation has been widely applied to multi-focus image fusion in recent years. As a key step, the construction of an informative dictionary directly decides the performance of sparsity-based image fusion. To obtain sufficient bases for dictionary learning, different geometric information of source images is extracted and analysed. The classified image bases are used to build corresponding subdictionaries by principle component analysis. All built subdictionaries are merged into one informative dictionary. Based on constructed dictionary, compressive sampling matched pursuit algorithm is used to extract corresponding sparse coefficients for the representation of source images. The obtained sparse coefficients are fused by Max-L1 fusion rule first, and then inverted to form the final fused image. Multiple comparative experiments demonstrate that the proposed method is competitive with other the state-of-the-art fusion methods.

One of the biggest challenges in the Human Action Recognition is View-point variations as the actions are captured under multiple views in real time. Furthermore, in the Skelton based action representation, for HAR, only few joints are informative and remaining joints constitutes redundancy. To sort out these problems, this paper proposes a new Action descriptor called as Scale and View Invariant Informative Joint Descriptor (SVI2JD). SVI2JD is a combination of three descriptors; they are namely Self-Similarity Joint Descriptor (SSJD), Informative Joint Descriptor (IJD) and Spherical Joint Descriptor (SJD). SSJD concentrates on the view invariance and employs a Self-Similarity Matrix (S3M) which computes pair wise distance between joints in each frame of action sequence. Next, SJD aims at describing the action through restricted movements of joints because they can’t move beyond particular angle and distance from origin of body. IJD removes the redundant joints those have less contribution towards the action. Further, a 2D Convolution Neural Network Model is proposed for feature extraction and classification. Different fusion rules are employed to fuse the individual results. The Effectiveness of proposed model is demonstrated through its simulation on two challenging datasets; NTU RGB+D dataset and Northwestern UCLA dataset.

This article focuses on the concept of European integration (the EUROINTEGRATION-concept) as an integral part of the EU philosophy, policy, and overall worldview which is ofprime interest for contemporary Ukraine that is currently undergoing a set of political, e3conomic, and sociocultural transformations on its way to becoming a full member of the European Community. In particular, the study aims at revealing the ways in which the concept is perceived in the European integration discourses of the European Union and Ukraine. Since language is a tool for structuring human thoughts, we treat language as a point of entry for analyzing the world's construal in human cognition. Thus, both conceptual structure and content are analyzed on the basis of verbalized (language) externalization of the concept in English and Ukrainian languages and political and media discourses of the European Union and Ukraine. The study rests on the theory of a three-layered conceptual model that features notional-informative, figurative-associative, and interpretative layers. The paper discusses the contents of the notional and interpretative layers of the EUROINTEGRA TION-concept. The lexicographic analysis backed by discursive interpretation helps to reveal convergences and divergences in the way European integration is conceptualized (understood and evaluated) in the European Union and Ukraine.