Clustering, a fundamental technique in unsupervised learning, identifies similar groups within a dataset. However, clustering algorithms encounter limitations when requiring a predetermined number of clusters/centroids/labels. This paper proposes a novel approach of clustering by integrating concept...

Breast cancer, a prevalent disease among women, demands early detection for better clinical outcomes. While mammography is widely used for breast cancer screening, its limitation in e.g., dense breast tissue necessitates additional diagnostic tools. Ultrasound breast imaging provides valuable tumor&#x...

The moisture content of cocoa beans is an essential factor in their quality. Modeling it during drying is still problematic due to the wide variation in drying conditions and the wide variation in cocoa bean varieties. This article aims to investigate the possibility of modeli...

Learning-based analysis of images is commonly used in the fields of mobility and robotics for safe environmental motion and interaction. This requires not only object recognition but also the assignment of certain properties to them. With the help of this information, causally related&#...

The present work addresses the question how to design a Virtual Reality (VR) training application for a class of search and navigation tasks that dynamically adapts to users by adjusting visual aspects for visual guidance. We present a theoretical concept that dynamically adjusts v...

We inspect the ability to reproduce spectral (color) composition in random field-based texture models, test when it can neglect spectral correlation, and simplify these random models without visibly depreciating their visual quality. These probabilistic models present essential two or three-dimensiona...

In this study, we proposed a method for automatically generating high-quality CLIP(Contrastive Language Image Pre-training) training data to improve the performance of text-based image retrieval using CLIP. In general, two types of image-text pair data are used in CLIP training: correct pair...

We examine the influence of alcohol consumption on presence in Virtual Reality (VR) with both subjective and ob jective data. To measure the level of presence in VR we propose a method using four self-developed indicators, two subjective (Flow, Subjective Behaviour) and two objecti...

In this paper, we present a method for segmenting 3D discrete objects into discrete plane segments. This segmentation is the first step in obtaining a polyhedrization of a discrete object with the reversibility property. This constraint requires that the discretization result for polyhe...

The Industrial Metaverse offers new opportunities in various industrial sectors, such as product development, collaboration, sales and marketing. It relies on rendering 3D scenes of industrial plants, factories, and machines that are viewed by multiple human observers in various industrial applic...

There are many graphics algorithms that require reading textures in window 3x3, 5x5, etc. Both large window sizes and high rendering resolutions decrease performance. We propose an algorithm-independent method for reducing the number of texture samples implemented in a fragment shader for&#x...

Animal Re-Identification (ReID) is a computer vision task that aims to retrieve a query individual from a gallery of known identities across different camera perspectives. It is closely related to the well-researched topic of Person ReID, but offers a much broader spectrum of featu...

Rendering high-polygonal models from distant perspectives has certain performance issues related to high density of subpixel triangles, which can be solved by levels of detail, a classical optimization method. Since a mesh occupies a small area on the screen, an alternative representation&#x...

In this paper, we widen the naming problem studies to the rule-based graph 3D transformation modeling systems. We propose a persistent naming method taking advantage of the generalized maps’ and graph transformation rules’ formalization of simple operations. It enables a unique and homo...

Traditional image reconstruction methods often face challenges like noise, artifacts, and blurriness, requiring handcrafted algorithms for effective resolution. In contrast, deep learning techniques, notably Convolutional Neural Networks (CNNs) and Variational Autoencoders (VAEs), present more robust alternatives. T...

Lamps are difficult to market via websites. Renderings and photographs of showrooms rarely fit consumer pref erences and demands and the technical specifications for lighting are unintuitive and difficult to understand for non-experts. This poses a challenge for lighting system manufacturers ...

Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and chal lenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further&...

We developed a virtual environment (VE) for nursing students so that they can experience what a person living with schizophrenia constantly hears. In our implementation, Non Player Character (NPC) Eva interacts with the player by recognizing the facial expressions of the players wearing...

This paper introduces a new algorithm for real-time rendering that builds upon bidirectional path tracing, reservoir based spatio-temporal importance resampling, and dynamic diffuse global illumination. The combination of these algorithms produces an image with reduced noise compared to real-time run&...

This paper introduces an enhanced Denosing Autoencoder (DAE) model, incorporating a novel attention mecha nism, for the segmentation of solar coronal loops. This work is based on DAE framework to address the segmenta tion challenges posed by intricate structures of coronal loops which&#...