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ICMV 2026: International Conference on Machine Vision

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ICMV
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투고 마감일:
2026-07-20 Extended
통보일:
2026-08-10
개최일:
2026-10-15
개최지:
Budapest, Hungary
개최 횟수:
19
ICORE: C   조회: 23296   팔로우: 11   참가: 4

회반 지수 (CP-I)

55.8 / 100
전체 5,687개 중 799위 · 상위 15%

컴퓨터 비전·패턴 인식 분야 246개 중 33위

학술적 인정 (35%)
58
투고 선별성 (20%) 데이터 없음 — 중립 기준값 50점으로 계산 —
개최 횟수 (20%)
81
커뮤니티 관심도 (10%)
41
공개 자료 충실도 (15%)
35

사용한 입력: 수록 등급: ICORE C · 확인되는 개최 횟수: 19 · 회반에서 팔로우 중인 연구자: 11명 · 지난 24개월 동안 이 페이지를 연 연구자: 9명

공개 자료에서 빠진 항목: 역대 게재율 (+4.5) · 역대 회차 (+3.0) · 최우수 논문 기록 (+2.3)
주최자는 학회를 인증 신청한 뒤 이 페이지에서 바로 추가할 수 있습니다. 점수는 매일 밤 다시 계산됩니다. 이 점수를 올리는 방법

신뢰도 80% — 점수 중 중립 기준값이 아니라 실제 관측된 데이터에 근거한 비율. 이 점수는 어떻게 계산되나 · 전체 순위 보기 · 알고리즘 버전 1.1 · 산출일 2026-09-30

논문 모집

ICMV 2026 (International Conference on Machine Vision) is a ICORE C conference held in Budapest, Hungary on 2026-10-15. The paper submission deadline is 2026-07-20 (extended). Acceptance notifications are sent on 2026-08-10.

The theoretical bases of Machine Vision provide the groundwork for developing algorithms and systems that can analyze and interpret visual data effectively. The theoretical bases of machine vision include several key concepts and principles like: Image Formation: Understanding how images are captured through sensors, including concepts like perspective projection, lens optics, and lighting conditions. Image Processing: Techniques for enhancing and manipulating images, such as filtering, noise reduction, histogram equalization, and edge detection. Feature Extraction: Methods to identify and quantify relevant features in images, including geometric shapes, textures, and colors, using techniques like contour detection and SIFT (Scale-Invariant Feature Transform). Pattern Recognition: Algorithms that classify and identify objects or patterns within images, often using machine learning methods like neural networks, support vector machines, or decision trees. Deep learning/Neural networks: Neural networks are computational models inspired by the human brain, designed to recognize patterns and learn from data. Neural networks learn through a process called training, where they adjust their weights based on the error of their predictions using algorithms like backpropagation. Computer Vision Algorithms: Fundamental algorithms for tasks such as object detection, segmentation, and tracking, including deep learning approaches like convolutional neural networks (CNNs). 3D Reconstruction: Techniques to infer three-dimensional structures from two-dimensional images, involving concepts like stereo vision and depth estimation. Geometric Transformations: Theoretical foundations for manipulating image coordinates, including translation, rotation, scaling, and perspective transformations. Machine/Deep Learning: Theoretical underpinnings of supervised, unsupervised, and reinforcement learning, which are used to train models for various vision tasks. Human Vision Models: Insights from biology and psychology that inform how machines can mimic human visual perception, including color theory and visual attention mechanisms. Machine vision fields in their essence leverage advanced technologies that enhance efficiency, accuracy, and decision-making across various sectors. Machine vision encompasses several fields and applications, including: Industrial Automation: Used for quality control, inspection, and monitoring in manufacturing processes. Medical Imaging: Assists in diagnostics and surgical procedures through image analysis and interpretation. Robotics: Enables robots to perceive and interact with their environment, facilitating tasks like object recognition and navigation. Autonomous Vehicles: Critical for obstacle detection, navigation, and traffic sign recognition in self-driving cars. Agricultural Technology: Used in precision farming for crop monitoring, pest detection, and yield estimation. Surveillance and Security: Enhances security systems through facial recognition, anomaly detection, and real-time monitoring. Augmented and Virtual Reality: Supports real-time image processing for immersive experiences and object recognition. Retail and E-commerce: Facilitates automated checkout processes, inventory management, and customer behavior analysis. Sports Analytics: Used for performance analysis, tracking player movements, and improving coaching strategies. Environmental Monitoring: Assists in tracking wildlife, monitoring ecosystems, and assessing environmental changes.
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관련 학회

CCFICORECP-I약칭정식 명칭투고 마감개최일
AA*92.5SIGIRInternational Conference on Research and Development in Information Retrieval2026-01-152026-07-20
AA*97.7AAAIAAAI Conference on Artificial Intelligence2026-07-212027-02-16
AA*94.3CVPRIEEE Conference on Computer Vision and Pattern Recognition2026-11-102027-06-20
BA*89.7ICRAInternational Conference on Robotics and Automation2026-09-152027-05-24
BA*94.1IJCAIInternational Joint Conference on Artificial Intelligence2026-01-312026-08-15
AA*92.5STOCACM Symposium on Theory of Computing2026-11-022027-06-06
C87.4ICCInternational Conference on Communications2026-10-022027-05-30
CB62.7IJCNNInternational Joint Conference on Neural Networks2027-01-312027-06-14
B91.2ICASSPInternational Conference on Acoustics, Speech and Signal Processing2026-09-162027-05-16
BA*79.6PODSACM SIGMOD Conference on Principles of DB Systems2026-12-032027-06-13

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
CMachine Vision and Applications2.3Springer0932-8092
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536

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