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ICMV 2026: International Conference on Machine Vision

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ICMV
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投稿締切日:
2026-07-20 Extended
通知日:
2026-08-10
開催日:
2026-10-15
開催地:
Budapest, Hungary
開催回数:
19
ICORE: C   閲覧: 23297   フォロー: 11   参加: 4

会伴インデックス (CP-I)

55.8 / 100
全 5,687 件中 第 799 位 · 上位 15%

コンピュータビジョン・パターン認識 分野 246 件中 第 33 位

学術的評価 (35%)
58
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
81
コミュニティの注目度 (10%)
41
公開情報の充実度 (15%)
35

使用した入力: 収録ランク：ICORE C · 確認できる開催回数：19 · 会伴でフォローしている研究者：11 人 · 過去 24 か月にこのページを開いた研究者：9 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 80% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-30

論文募集

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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関連ジャーナル

CCF正式名称インパクトファクター出版社ISSN
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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