Edge Detection

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10 papers0 papers in the last four weeks, down 100% on the four weeks before. 0.0% of all new papers.

Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson's disease classification
Sep 7, 2026Sameer Bhat, Piotr SzczukoMedical Diagnosis Edge Detection
Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems Department, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland.
STEMPix: A Phase-Transition-Material-Based Pixel Sensor for Resolving Edge-Movement Direction
Sep 3, 2026Md Rahatul Islam Udoy, Sumeet Kumar Gupta, Deep Jariwala +1Edge Detection In-Sensor Computing
Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA · Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47906, USA · Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
Input-Adaptive Gating of a Dehazing Front-End for On-Device Perception in Smoke-Obscured Environments
Aug 30, 2026Seongjun Kang, Ishaan Garg, Vishnu BharadwajImage Enhancement Edge Detection
Monta Vista High School
EDGER: EDge-Guided with HEatmap Refinement for Generalizable Image Forgery Localization
May 12, 2026Minh-Khoa Le-Phan, Minh-Hoang Le, Minh-Triet Tran +1Image Forgery Detection Digital Image Forensics
University of Science - VNU-HCM, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam
Quantum Gradient-Based Approach for Edge and Corner Detection Using Sobel Kernels
May 1, 2026Mohammad Aamir Sohail, Gabriela Pinheiro, Yasemin Poyraz Kocak +4Edge Detection
Department of Electrical Engineering and Computer Science, University of Michigan, 1301 Beal Avenue, Ann Arbor, 48109-2121, Michigan, USA. · Department of Computer Science, Universidade Federal Fluminense, Campus da Praia Vermelha, Niter´oi, 24210-346, Rio de Janeiro, Brazil. · Department of Computer Technologies, ˙Istanbul University-Cerrahpa¸sa, Buyukcekmece, ˙Istanbul, 34500, Turkiye. +2
Online CS-based SAR Edge-Mapping
Apr 21, 2026Conor Flynn, Radoslav Ivanov, Birsen YaziciEdge Detection Remote Sensing
Rensselaer Polytechnic Institute, Troy, New York, USA
Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection
Mar 29, 2026Yuhan Gao, Xinqing Li, Xin He +4Edge Detection Edge Inference
VCIP, College of Computer Science, Nankai University · Academy for Advanced Interdisciplinary Studies, Nankai University · School of Computer Science and Engineering, Tianjin University of Technology +3
MEMO: Human-like Crisp Edge Detection Using Masked Edge Prediction
Mar 21, 2026Jiaxin Cheng, Yue Wu, Yicong ZhouEdge Detection Edge Inference
Department of Computer and Information Science, University of Macau, Macau, China · Capital One, USA
MatchED: Crisp Edge Detection Using End-to-End, Matching-based Supervision
Feb 24, 2026Bedrettin Cetinkaya, Sinan Kalkan, Emre AkbasEdge Detection Edge Inference
Dept. of Computer Engineering and METU ROMER Robotics Center, Ankara, Turkey
EasyControlEdge: A Foundation-Model Fine-Tuning for Edge Detection
Feb 18, 2026Hiroki Nakamura, Hiroto Iino, Masashi Okada +1Edge Detection Vision Foundation Model Adaptation
Panasonic Holdings Corporation · Kyoto University

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Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson's disease classification

Sameer Bhat, Piotr Szczuko

Faculty of Electronics, Telecommunications and Informatics, Multimedia Systems Department, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland.

Medical Diagnosis Edge Detection Medical Image Classification

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This study investigates the classification of individuals as healthy or at risk of Parkinson's disease using machine learning (ML) models, focusing on the impact of dataset size and preprocessing techniques on model performance. Four datasets are created from an original dataset: DS_0, (normal dataset), DS_1 (DS_O subjected to Canny edge detection and Hessian filtering), DS_2 (augmented DS_0), and DS_3 (augmented DS_1). We evaluate a range of ML models-Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), XGBoost (XBG), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost (AdB)-on these datasets, analyzing prediction accuracy, model size, and prediction latency. The results show that while larger datasets lead to increased model memory footprints and prediction latencies, the Canny edge detection preprocessing supplemented by Hessian filtering (used in DS_1 and DS_3) degrades the performance of most models. In our experiment, we observe that Random Forest (RF) maintains a stable memory footprint of 61 KB across all datasets, while models like KNN and SVM show significant increases in memory usage, from 5.7-7 KB on DS_0 to 102-220 KB on DS_2, and similar increases in prediction time. Logistic Regression, Decision Tree, and Naive Bayes show stable memory footprints and fast prediction times across all datasets. XGBoost's prediction time increases from 180-200 ms on DS_0 to 700-3000 ms on DS_2 (truncated)