Optimization of VR human-computer game interaction based on improved PIFPAF algorithm and binocular vision

Hong Zhu — 2025-07-26

To make virtual reality human-computer games more accurate and provide users with an immersive gaming experience, the study combines the method of improved part intensity field and part association field (PIFPAF) with binocular vision to optimize the interaction of VR human-computer games. The experimental results indicated that the PIFPAF algorithms performed relatively well with number of errors and target keypoint correlation of 0.22 and 0.97, respectively. In terms of processing speed, the algorithm performed faster in both 640×480 and 320×240 resolutions, with 13 fps and 19 fps, respectively. Among the five predefined gestures, the ʻʻpointingʼʼ gesture was recognized correctly the largest number of times in 30 test sessions, with 29 successful identifications. In contrast, the ʻʻclenched fistʼʼ gesture had the fewest correct recognitions, totaling 26. The success of the suggested approach is confirmed by the experimental findings, which show that the optimized human-computer interaction system has high accuracy and processing speed. This study offers a fresh approach to the advancement of human-computer interaction technology and encourages technological integration innovation in the realm of virtual reality human-computer gaming.

A review: Machine learning techniques of brain tumor classification and segmentation

Iliass Zine-dine — 2025-09-01

Classifying brain tumors in magnetic resonance images (MRI) is a critical endeavor in medical image processing, given the challenging nature of automated tumor recognition. The variability and complexity in the location, size, shape, and texture of these lesions, coupled with the intensity similarities between brain lesions and normal tissues, pose significant hurdles. This study focuses on the importance of brain tumor detection and its challenges within the context of medical image processing. Presently, researchers have devised various interventions aimed at developing models for brain tumor classification to mitigate human involvement. However, there are limitations on time and cost for this task, as well as some other challenges that can identify tumor tissues. This study reviews many publications that classify brain tumors. Mostly employed supervised machine learning algorithms like support vector machine (SVM), random forest (RF), Gaussian Naive Bayes (GNB), k-Nearest Neighbors (K-NN), and k-means and some researchers employed convolutional neural network methods, transfer learning, deep learning, and ensemble learning. Every classification algorithm aims to provide an accurate and effective system, allowing for the fastest and most precise tumor detection possible. Usually, a pre-processing approach is employed to assess the system's accuracy; other techniques, such as the Gabor discrete wavelet transform (DWT), Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Principal Component Analysis (PCA), Scale-Invariant Feature Transform (SIFT) and the descriptor histogram of oriented gradients (HOG). In this study, we examine prior research on feature extraction techniques, discussing various classification methods and highlighting their respective advantages, providing statistical analysis on their performance.

Machine Graphics & Vision

Optimization of VR human-computer game interaction based on improved PIFPAF algorithm and binocular vision

A review: Machine learning techniques of brain tumor classification and segmentation