Психофізіологічний патерн емоції презирства: актуалізація інваріантних ознак в українській фразеології

The Psychophysiological Pattern of the Emotion of Scorn: Actualisation of Invariant Features in Ukrainian PhraseologyThis article analyses the specifics of the physiological and expressive-behavioural complex of the emotion of scorn in Ukrainian phraseology in the light of psychology of emotions, emotionology, linguoculturology, evaluative semantics and axiophraseological pragmatics. The study considers the emotion of scorn in its psychological diversity as exemplified in phraseological units expressing scorn in Ukrainian. The methods applied to analyse a phraseological unit include component analysis, contextual and interpretive analysis, and parametric analysis of semantic structure with a focus on the evaluative and emotive components. Psychofizjologiczny wzorzec uczucia pogardy. Aktualizacja cech inwariantnych we frazeologii ukraińskiejNiniejszy artykuł analizuje specyfikę fizjologicznego i ekspresyjno‑behawioralnego kompleksu uczucia pogardy we frazeologii ukraińskiej w świetle psychologii emocji, emocjonologii, lingwokulturologii, semantyki wartościującej i pragmatyki aksjofrazeologicznej. Przedmiotem badań jest uczucie pogardy w jej psychologicznej różnorodności na przykładzie jednostek frazeologicznych wyrażających pogardę w języku ukraińskim. Zastosowane metody analizy jednostki frazeologicznej obejmują analizę komponentów, analizę kontekstualną i interpretacyjną oraz analizę parametryczną struktury semantycznej ze szczególnym uwzględnieniem komponentów ewaluacyjnych i emotywnych.

Variational Bayesian Approach to Condition-Invariant Feature Extraction for Visual Place Recognition

As mobile robots perform long-term operations in large-scale environments, coping with perceptual changes becomes an important issue recently. This paper introduces a stochastic variational inference and learning architecture that can extract condition-invariant features for visual place recognition in a changing environment. Under the assumption that a latent representation of the variational autoencoder can be divided into condition-invariant and condition-sensitive features, a new structure of the variation autoencoder is proposed and a variational lower bound is derived to train the model. After training the model, condition-invariant features are extracted from test images to calculate the similarity matrix, and the places can be recognized even in severe environmental changes. Experiments were conducted to verify the proposed method, and the experimental results showed that our assumption was reasonable and effective in recognizing places in changing environments.

TIF-Reg: Point Cloud Registration with Transform-Invariant Features in SE(3)

Three-dimensional point cloud registration (PCReg) has a wide range of applications in computer vision, 3D reconstruction and medical fields. Although numerous advances have been achieved in the field of point cloud registration in recent years, large-scale rigid transformation is a problem that most algorithms still cannot effectively handle. To solve this problem, we propose a point cloud registration method based on learning and transform-invariant features (TIF-Reg). Our algorithm includes four modules, which are the transform-invariant feature extraction module, deep feature embedding module, corresponding point generation module and decoupled singular value decomposition (SVD) module. In the transform-invariant feature extraction module, we design TIF in SE(3) (which means the 3D rigid transformation space) which contains a triangular feature and local density feature for points. It fully exploits the transformation invariance of point clouds, making the algorithm highly robust to rigid transformation. The deep feature embedding module embeds TIF into a high-dimension space using a deep neural network, further improving the expression ability of features. The corresponding point cloud is generated using an attention mechanism in the corresponding point generation module, and the final transformation for registration is calculated in the decoupled SVD module. In an experiment, we first train and evaluate the TIF-Reg method on the ModelNet40 dataset. The results show that our method keeps the root mean squared error (RMSE) of rotation within 0.5∘ and the RMSE of translation error close to 0 m, even when the rotation is up to [−180∘, 180∘] or the translation is up to [−20 m, 20 m]. We also test the generalization of our method on the TUM3D dataset using the model trained on Modelnet40. The results show that our method’s errors are close to the experimental results on Modelnet40, which verifies the good generalization ability of our method. All experiments prove that the proposed method is superior to state-of-the-art PCReg algorithms in terms of accuracy and complexity.

Automatic Generation of Seamless Mosaics Using Invariant Features

The acquisition of satellite images over a wide area is often carried out across seasons because of satellite orbits and atmospheric conditions (e.g., cloud cover, dust, etc.). This results in spectral mismatch between adjacent scenes as the sun angle and the atmospheric conditions will be different for different acquisitions. In this work, we developed an approach to generate seamless mosaics using Scale-Invariant Features Transformation (SIFT). In this process, we make use of the overlapping areas between two adjacent scenes and then map spectral values of one imagery scene to another based on the filtered points detected by SIFT features to create a seamless mosaic. We make use of the Random Sample Consensus (RANSAC) method successively to filter out obtained SIFT points across adjacent tiles and to remove spectral outliers across each band of an image. Several high resolution satellite images acquired with WorldView-2 and Dubaisat-2 satellites, and medium resolution Sentinel-2 satellite imagery are used for experimentation. The experimental results show that the proposed approach can generate good seamless mosaics. Furthermore, Sentinel-2’s level 2A (L2A) product surface reflectance data is used to adjust the spectral values for color consistency.