Sep 2026· International Journal of Electrical and Electronics Engineering· Vol 13, pp. 35-46· 0 citations· 43 references
Medical Image Segmentation Techniques
TL;DR
It is found that deep learning algorithms perform better in terms of accuracy and reliability compared to other approaches, while traditional detectors are fast and suitable for simple real-time purposes.
Abstract
A joint comparison of the outcomes of edge detection techniques provides a more thorough knowledge of the capabilities of the algorithms to identify image edges. Edge detection methods are classified as conventional edge detection, soft computing edge detection and deep learning edge detection strategies. Detection efficiency is evaluated by means of a comprehensive analysis that includes F1-score and Intersection over Union (IoU). Deep learning-based models have proven to be more efficient with regard to F1-scores due to better feature extraction. High PSNR indicates good agreement between detected boundaries and actual objects. In the course of conducted experiments, Clip-MobileNetV2-Unet has shown better results concerning Recall, Precision, F1-Score/mDC, Accuracy, PSNR (dB), and mIoU. All these metrics make up a complex evaluation strategy. From this study, it is found that deep learning algorithms perform better in terms of accuracy and reliability compared to other approaches, while traditional detectors are fast and suitable for simple real-time purposes.
. The detection of pedestrians holds a very important position in application fields such as self-driving cars and intelligent monitoring systems. This research carries out a comparison between a traditional method of HOG+SVM and current deep learning models, including Faster R-CNN and YOLOv8, for the purpose of assess...
Hao-Yu Wang· Proceedings of the 4th Inter...· 0 citations
The rapid development of digital image processing and machine learning has increased the need for reliable and task-appropriate evaluation methods. However, the wide range of available metrics, each with different assumptions, strengths, and limitations, makes metric selection challenging across diverse image-processin...
Muzammil Khan, Muhammad Hamza, Sikandar Ali et al.· ICCK Journal of Image Analys...· 0 citations
The novelty of this study lies in applying a Grid-Based Histogram as a lightweight replacement for conventional anchor generation in Faster R-CNN, resulting in a preliminary speed–accuracy trade-off rather than an overall performance improvement.
Arie Satia Dharma, Herimanto, R. Siahaan et al.· Journal of Information Syste...· 0 citations
The image analysis, object localization involves several techniques for determining the position of an object, including centroids, edges, and pixel-wise segmentation. Among these, bounding box localization provides a useful compromise between simplicity in parameterization and usability for further analysis. In additi...
Sathishkumar Mani· Journal on Electronic and Au...· 0 citations
Infrared images frequently exhibit low contrast, weak target boundaries, nonuniform detector responses, and scene-dependent thermal interference, making reliable edge extraction difficult when a fixed detector and thresholding rule are used. This study presents an AI-guided infrared edge-enhancement framework with a re...
The proposed model introduces a lightweight backbone network, an optimized feature pyramid fusion structure, shallow feature enhancement for small objects, attention-based feature refinement, occlusion-aware context enhancement and Soft-NMS post-processing to reduce computational complexity while improving detection st...
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MIT News · Artificial Intelligence· news.mit.eduOct 28, 2026
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
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