arXivpreprint
Vu Dinh Xuan · Duc-Hai Nguyen · Minh-Dung Dao +6 authors
Qualitative comparison figures are central evidence in computer vision papers, and vision-language models (VLMs) are increasingly used to judge them. Yet existing benchmarks score only scalar quality or overall preference, so a judge can be rewarded for picking the preferred image for the wrong visual reason. We introduce VisionQ, the first benchmark built from peer-reviewed CV comparison figures that grounds every judgment in a named visual criterion: each question states the criterion, and a judge is credited only when it selects the output the authors identify as best on that criterion. We call this task criterion-conditioned visual discrimination. VisionQ comprises (1) a corpus of 1,409 CVPR and ICCV papers with 1,800+ validated comparison figures and 3,911 hand-annotated data points linking method crops to author-stated visual claims; (2) a six-axis, 51-leaf taxonomy of the visual criteria behind qualitative judgment; (3) a criterion-conditioned evaluation protocol that hides method names, captions, and paper identity and reports accuracy per criterion; and (4) VisionQ-Judge, a DPO-tuned Gemma-4-E4B judge trained on symmetric evidence pairs, which reduces last-option predictions by 7.0pp and improves accuracy by 2.5pp on a held-out test set. Evaluating 20 open- and closed-source VLM judges, we find that the strongest reach only 63.1% accuracy (chance 32.2%) and that reliability varies sharply across criteria. Code: https://github.com/ReML-AI/visionq. Data: https://huggingface.co/datasets/visionq-anon-2026/VisionQ-1k.
arXivpreprint
Stan Birchfield
This book presents a code-first introduction to computer vision, spanning classical 2D image processing, classical 3D vision, and deep learning. Organized as 44 short chapters across three parts, the book builds each topic from first principles: image arithmetic and morphology; convolution, pyramids, and frequency-domain filtering; feature detection, optical flow, and stereo; projective geometry, camera calibration, and structure from motion; and the full arc of modern deep learning, from a single neuron through convolutional networks, backpropagation, classic architectures, transfer learning, object detection, and semantic and instance segmentation, concluding with engineering considerations like mixed-precision and parallel training. Every technique is implemented directly in Python and NumPy or PyTorch and checked numerically against the corresponding OpenCV or PyTorch library function, so readers see not just the mathematics but its concrete behavior on real and synthetic data. The material was distilled with AI assistance from freely available online course notes, condensing extensive working code into concise mathematical exposition while preserving verified, reproducible results throughout. It is intended as a self-contained reference for students and practitioners who want to understand computer vision algorithms and their Python implementations.
Advanced Vision and ImagingArtificial Intelligence ApplicationsImage and Object Detection Techniques
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Dorian Tsai · Scarlett Raine · Emilio Olivastri +12 authors
Climate change is the largest threat to coral reefs, with increasing global impacts accelerating the need for scalable reef restoration technologies. Large-scale reef restoration depends on the mass production of corals, such as through coral aquaculture. Coral seeding with recruits grown in aquaculture facilities is a feasible restoration approach, but effective production requires consistent, high-frequency monitoring of tens of thousands of macroscopic (0.5-2mm diameter) recruits, making conventional manual assessment prohibitively labor-intensive. To address this monitoring bottleneck, we introduce the Coral Grow-out Robotic Assessment System (CGRAS) which combines robotic imaging and computer vision to automate data acquisition, perform multi-species detection and counting of corals, and evaluate coral health. CGRAS automatically extracts coral growth, survival and spatial distribution metrics, with the aim of providing timely feedback to operators for optimizing production, grow-out and deployment workflow processes. We demonstrate CGRAS in a large aquaculture facility on standardized coral settlement tiles, reducing the time and labor costs by a factor of 9.6 as compared to manual monitoring, whilst achieving 96.4% agreement for Acropora kenti corals relative to expert counts.
Cephalopods and Marine BiologyCoral and Marine Ecosystems StudiesSpecies Distribution and Climate Change
View paper →OpenAlexarticle
Nursiyanto Nursiyanto · Dona Yuliawati · Bayu Nugroho
Published in BULLETIN OF NETWORK ENGINEER AND INFORMATICS. Open the paper details to explore the original source.
Fire Detection and Safety SystemsSmoking Behavior and CessationVideo Surveillance and Tracking Methods
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Krismadies · Diina Maulina · Taufiq Afdi Alfaruqhi
Published in Zona Kedokteran Program Studi Pendidikan Dokter Universitas Batam. Open the paper details to explore the original source.
Ergonomics and Musculoskeletal DisordersOccupational Health and Safety ManagementTechnostress in Professional Settings
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Ehsan Mohsenikhah
Published in Intelligent Hospital. Open the paper details to explore the original source.
Medication Adherence and ComplianceMobile Health and mHealth ApplicationsPharmaceutical Quality and Counterfeiting
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Peiman Shah Nazar · Mohamad Jaber · Peter Paul Pott
Published in Current Directions in Biomedical Engineering. Open the paper details to explore the original source.
Balance, Gait, and Falls PreventionProsthetics and Rehabilitation RoboticsStroke Rehabilitation and Recovery
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Bram van Loon · Elise Verhoeven
Published in Civil Engineering Science and Technology. Open the paper details to explore the original source.
Asphalt Pavement Performance EvaluationGeophysical Methods and ApplicationsInfrastructure Maintenance and Monitoring
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Nimmi Singh
Published in Zenodo (CERN European Organization for Nuclear Research). Open the paper details to explore the original source.
Artificial Intelligence ApplicationsGaze Tracking and Assistive TechnologyIoT-based Smart Home Systems
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Nimmi Singh
Published in Zenodo (CERN European Organization for Nuclear Research). Open the paper details to explore the original source.
Artificial Intelligence ApplicationsGaze Tracking and Assistive TechnologyIoT-based Smart Home Systems
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Anantjit Publication
Published in International Journal of Emerging Technologies and Innovative Research. Open the paper details to explore the original source.
Plant Disease Management TechniquesRemote Sensing in AgricultureSmart Agriculture and AI
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Sabir Hussain,Areeba Muzaffar,Nasir Abbas,Mansoor Ali,Hafiz Qaiser Shahzad
Published in Zenodo (CERN European Organization for Nuclear Research). Open the paper details to explore the original source.
Anomaly Detection Techniques and ApplicationsFire Detection and Safety SystemsMaritime Navigation and Safety
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