Готові списки джерел за темами / Detection feature

Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Добірка наукової літератури з теми "Detection feature"

Автор: Grafiati
Опубліковано: 3 червня 2025
Оновлено: 31 липня 2025

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Статті в журналах з теми "Detection feature"

1

Ling, Xiao, Chuan Zhang, Zhijun Yan, Bo Wang, Qinghong Sheng, and Jun Li. "Infrared Dim and Small Target Detection Based on Local–Global Feature Fusion." Applied Sciences 14, no. 17 (2024): 7878. http://dx.doi.org/10.3390/app14177878.

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Анотація:
Infrared detection, known for its robust anti-interference capabilities, performs well in all weather conditions and various environments. Its applications include precision guidance, surveillance, and early warning systems. However, detecting infrared dim and small targets presents challenges, such as weak target features, blurred targets with small area percentages, missed detections, and false alarms. To address the issue of insufficient target feature information, this paper proposes a high-precision method for detecting dim and small infrared targets based on the YOLOv7 network model, whi
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2

Mo, Fuchuan, Shen Liu, Sitong Wu, Ruiyuan Chen, and Tiecheng Song. "YOLO-MFD: Object Detection for Multi-Scenario Fires." Information 16, no. 7 (2025): 620. https://doi.org/10.3390/info16070620.

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Fire refers to a disaster caused by combustion that is uncontrolled in the temporal and spatial dimensions, occurring in diverse complex scenarios where timely and effective detection is crucial. However, existing fire detection methods are often challenged by the deformation of smoke and flames, resulting in missed detections. It is difficult to accurately extract fire features in complex backgrounds, and there are also significant difficulties in detecting small targets, such as small flames. To address this, this paper proposes a YOLO-Multi-scenario Fire Detector (YOLO-MFD) for multi-scenar
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3

Zhang, Shijie, Xu Yang, Chao Geng, and Xinyang Li. "A Reparameterization Feature Redundancy Extract Network for Unmanned Aerial Vehicles Detection." Remote Sensing 16, no. 22 (2024): 4226. http://dx.doi.org/10.3390/rs16224226.

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In unmanned aerial vehicles (UAVs) detection, challenges such as occlusion, complex backgrounds, motion blur, and inference time often lead to false detections and missed detections. General object detection frameworks encounter difficulties in adequately tackling these challenges, leading to substantial information loss during network downsampling, inadequate feature fusion, and being unable to meet real-time requirements. In this paper, we propose a Real-Time Small Object Detection YOLO (RTSOD-YOLO) model to tackle the various challenges faced in UAVs detection. We further enhance the adapti
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4

Deng, Xiangyu, and Shanshan Li. "An Improved SSD Object Detection Algorithm Based on Attention Mechanism and Feature Fusion." Journal of Physics: Conference Series 2450, no. 1 (2023): 012088. http://dx.doi.org/10.1088/1742-6596/2450/1/012088.

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Abstract The Single Shot MultiBox Detector (SSD) is a well-known object detection method, but its detection of small objects is not effective. This paper makes modifications to the SSD object detection method to address its insufficient semantic information in low-level feature maps, thus enhancing the detectability for small objects. First, the Feature Pyramid Network (FPN) is incorporated into the SSD so that the shallow feature map, which is primarily utilized for detecting small objects, contains more semantic information in addition to rich location information. Second, the Convolutional
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Liu, Gang, Jiangtao Xi, Jun Tong, and Hongpeng Xu. "An Infrared Aircraft Detection Algorithm Based on Context Perception Feature Enhancement." Electronics 13, no. 14 (2024): 2695. http://dx.doi.org/10.3390/electronics13142695.

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Анотація:
To address the issue of insufficient extraction of target features and the resulting impact on detection performance in long-range infrared aircraft target detection caused by small imaging area and weak radiation intensity starting from the idea of perceiving target context to enhance the features extracted by convolutional neural network, this paper proposes a detecting algorithm based on AWFGLC (adaptive weighted fusion of global–local context). Based on the mechanism of AWFGLC, the input feature map is randomly reorganized and partitioned along the channel dimension, resulting in two featu
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6

Jin, Ruixia, Yihao Wang, Yuanyuan Ma, Tao Li, and Xintao Duan. "Gain-Loss Evaluation-Based Generic Selection for Steganalysis Feature." Symmetry 13, no. 10 (2021): 1775. http://dx.doi.org/10.3390/sym13101775.

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Fewer contribution feature components in the image high-dimensional steganalysis feature are able to increase the spatio-temporal complexity of detecting the stego images, and even reduce the detection accuracy. In order to maintain or even improve the detection accuracy while effectively reducing the dimension of the DCTR steganalysis feature, this paper proposes a new selection approach for DCTR feature. First, the asymmetric distortion factor and information gain ratio of each feature component are improved to measure the difference between the symmetric cover and stego features, which prov
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7

Khalid, Noor Aldeen A., Muhammad Imran Ahmad, Thulfiqar H. Mandeel, and Mohd Nazrin Md Isa. "Palmprint features matching based on KAZE feature detection." Journal of Physics: Conference Series 1878, no. 1 (2021): 012055. http://dx.doi.org/10.1088/1742-6596/1878/1/012055.

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Fu, Xinchuan, Rui Yu, Weinan Zhang, Li Feng, and Shihai Shao. "Pedestrian Detection by Feature Selected Self-Similarity Features." IEEE Access 6 (2018): 14223–37. http://dx.doi.org/10.1109/access.2018.2803160.

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9

Zhang, Yifan, Haiyan Kang, and Qiang Wang. "MMFDetect: Webshell Evasion Detect Method Based on Multimodal Feature Fusion." Electronics 14, no. 3 (2025): 416. https://doi.org/10.3390/electronics14030416.

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Анотація:
In the context of escalating network adversarial challenges, effectively identifying a Webshell processed using evasion techniques such as encoding, obfuscation, and nesting remains a critical challenge in the field of cybersecurity. To address the poor detection performance of the existing Webshell detection methods for evasion samples, this study proposes a multimodal feature fusion-based evasion Webshell detection method (MMF-Detect). This method extracts RGB image features and textual vector features from two modalities: the visual and semantic modalities of Webshell file content. A multim
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Zhang, Zhiqiang, Xin Qiu, and Yongzhou Li. "Learning Balance Feature for Object Detection." Electronics 11, no. 17 (2022): 2765. http://dx.doi.org/10.3390/electronics11172765.

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Анотація:
In the field of studying scale variation, the Feature Pyramid Network (FPN) replaces the image pyramid and has become one of the most popular object detection methods for detecting multi-scale objects. State-of-the-art methods have FPN inserted into a pipeline between the backbone and the detection head to enable shallow features with more semantic information. However, FPN is insufficient for object detection on various scales, especially for small-scale object detection. One of the reasons is that the features are extracted at different network depths, which introduces gaps between features.
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Дисертації з теми "Detection feature"

1

Sharpe, Lauren. "Feature sets for screenshot detection." Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/34741.

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Approved for public release; distribution is unlimited<br>As digital media capacity continues to increase and the cost continues to decrease, digital forensic examiners need progressively more efficient, effective, and tailored tools in order to perform useful media triage. This thesis documents the development of feature sets for classifying images as either screenshots or non-screenshots. Using linear- and intensity-based image information we developed the first (to our knowledge) screenshot detection algorithm. Four feature sets were developed and combinations of these feature sets were tes
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2

Wang, Aijing. "SELECTIVE AUTOMATIC IMAGE FEATURE DETECTION." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1316218638.

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3

Nilsson, Niklas. "Feature detection for geospatial referencing." Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-159809.

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With the drone industry's recent explosive advancement, aerial photography is becoming increasingly important for an array of applications ranging from construction to agriculture. A drone flyover can give a better overview of regions that are difficult to navigate, and is often significantly faster, cheaper and more accurate than man-made sketches and other alternatives. With this increased use comes a growing need for image processing methods to help in analyzing captured photographs. This thesis presents a method for automatic location detection in aerial photographs using databases of aeri
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4

Hajro, Neira 1978. "Automated nasal feature detection for the lexical access from features project." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28401.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (leaves 150-151).<br>The focus of this thesis was the design, implementation, and evaluation of a set of automated algorithms to detect nasal consonants from the speech waveform in a distinctive feature-based speech recognition system. The study used a VCV database of over 450 utterances recorded from three speakers, two male and one female. The first stage of processing for each speech waveform included automated 'pivot' estimation using t
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5

Sefidcon, Azimeh. "Feature interactions detection in intelligent networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0020/MQ47832.pdf.

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Heiberg, Einar Brandt. "Automated feature detection in multidimensional images /." Linköping : Univ, 2004. http://www.bibl.liu.se/liupubl/disp/disp2005/tek917s.pdf.

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7

Gollapudi, Venkata Lakshmi Sirisha. "Services for biological network feature detection." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/13022/.

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Анотація:
The complex environment of a living cell contains many molecules interacting in a variety of ways. Examples include the physical interaction between two proteins, or the biochemical interaction between an enzyme and its substrate. A challenge of systems biology is to understand the network of interactions between biological molecules, derived experimentally or computationally. Sophisticated dynamic modelling approaches provide detailed knowledge about single processes or individual pathways. However such methods are far less tractable for holistic cellular models, which are instead represented
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8

Song, Jingping. "Feature selection for intrusion detection system." Thesis, Aberystwyth University, 2016. http://hdl.handle.net/2160/3143de58-208f-405e-ab18-abcecfc8f33b.

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Анотація:
Intrusion detection is an important task for network operators in today?s Internet. Traditional network intrusion detection systems rely on either specialized signatures of previously seen attacks, or on labeled traffic datasets that are expensive and difficult to reproduce for user-profiling to hunt out network attacks. Machine learning methods could be used in this area since they could get knowledge from signatures or as normal-operation profiles. However, there is usually a large volume of data in intrusion detection systems, for both features and instances. Feature selection can be used t
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Treash, Katherine (Katherine Diane) 1975. "Feature detection in grayscale aerial images." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/79989.

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Linguraru, Marius George. "Feature detection in mammographic image analysis." Thesis, University of Oxford, 2004. http://ora.ox.ac.uk/objects/uuid:b92185f0-c7bf-40e1-bc17-bf71065f001f.

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In modern society, cancer has become one of the most terrifying diseases because of its high and increasing death rate. The disease's deep impact demands extensive research to detect and eradicate it in all its forms. Breast cancer is one of the most common forms of cancer, and approximately one in nine women in the Western world will develop it over the course of their lives. Screening programmes have been shown to reduce the mortality rate, but they introduce an enormous amount of information that must be processed by radiologists on a daily basis. Computer Aided Diagnosis (CAD) systems aim
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Книги з теми "Detection feature"

1

Barbieri, Gillian Sylvia Anna-Stasia. The role of spatial derivatives in feature detection. University of Birmingham, 2000.

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2

Zhang, Zhifen, Guangrui Wen, and Wenjing Ren. Multiple Sensing-Driven Feature Extraction and Defect Detection. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4808-5.

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3

Li, Jian. Synthetic aperture radar target detection, feature extraction, and image formation techniques. National Aeronautics and Space Administration, 1994.

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4

United States. National Aeronautics and Space Administration., ed. Synthetic aperture radar target detection, feature extraction, and image formation techniques. National Aeronautics and Space Administration, 1994.

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5

Neelanarayanan, ed. Feature Analysis for Abnormality Detection in Breast Thermogram Sequences Subject to Cold Stress. Association of Scientists, Developers and Faculties, 2014.

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6

Levin, Boris. Seĭsmichnostʹ Tikhookeanskogo regiona: Vyi︠a︡vlenie globalʹnykh zakonomernosteĭ = Seismicity of the Pacific Region : global feature detection. "I︠A︡nus-K", 2012.

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7

L, Jankovsky Amy, and Lewis Research Center, eds. Real-time sensor validation, signal reconstruction, and feature detection for an RLV propulsion testbed. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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8

L, Jankovsky Amy, and Lewis Research Center, eds. Real-time sensor validation, signal reconstruction, and feature detection for an RLV propulsion testbed. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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9

L, Jankovsky Amy, and Lewis Research Center, eds. Real-time sensor validation, signal reconstruction, and feature detection for an RLV propulsion testbed. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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10

L, Jankovsky Amy, and Lewis Research Center, eds. Real-time sensor validation, signal reconstruction, and feature detection for an RLV propulsion testbed. National Aeronautics and Space Administration, Lewis Research Center, 1998.

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Частини книг з теми "Detection feature"

1

Scherer, Rafał. "Feature Detection." In Studies in Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12195-2_2.

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2

Toennies, Klaus D. "Feature Detection." In Guide to Medical Image Analysis. Springer London, 2017. http://dx.doi.org/10.1007/978-1-4471-7320-5_5.

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Toennies, Klaus D. "Feature Detection." In Guide to Medical Image Analysis. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2751-2_5.

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Purves, Dale. "Feature Detection." In Why Brains Don't Compute. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71064-4_23.

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Smeulders, With contributions by Arnold W. M., and Andrew D. Bagdanov. "Color Feature Detection." In Color in Computer Vision. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118350089.ch13.

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6

Klette, Reinhard. "Feature Detection and Tracking." In Undergraduate Topics in Computer Science. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6320-6_9.

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7

Sowell, Eric. "Device and Feature Detection." In Mobile ASP.NET MVC 5. Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5057-9_7.

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Szeliski, Richard. "Feature detection and matching." In Texts in Computer Science. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-935-0_4.

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Ramacher, Ulrich, and Christoph von der Malsburg. "Nets for Feature Detection." In On the Construction of Artificial Brains. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-00189-5_7.

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Chung, Bryan WC. "Feature Detection and Matching." In Pro Processing for Images and Computer Vision with OpenCV. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2775-6_7.

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Тези доповідей конференцій з теми "Detection feature"

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Yuan, Run, and Haonan Long. "Driver fatigue detection based on multi-feature fusion facial features." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762609.

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Judy, Philip F., and Richard G. Swensson. "Detection of features at specified and random locations on noisy images." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.fv3.

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Observers detected cylindrical features superimposed at known (prespecified) or unknown (random) locations within uniform circular areas (pedestals) on noisy computed-tomographic images. The experimental conditions varied the feature's contrast and size (3.0, 7.0, and 16.1 mm in diameter) for large (63 mm) pedestals, and varied the pedestal’s size from 9.8 to 63 mm for a fixed (7.0 mm) feature. As the pedestal increased in size, the feature’s detectability (measured by ROC analysis) increased to a constant level for all six observers, whether the feature’s location was known or random. There w
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Noble, J. A. "Morphological Feature Detection." In Alvey Vision Conference 1988. Alvey Vision Club, 1988. http://dx.doi.org/10.5244/c.2.32.

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Nayar, S. K., S. Baker, and H. Murase. "Parametric feature detection." In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/cvpr.1996.517114.

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Chungsu Lee, Jonghee Kim, Eunsoo Park, et al. "Multi-feature Vehicle Detection Using Feature Selection." In 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). IEEE, 2013. http://dx.doi.org/10.1109/smc.2013.46.

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Bulla, Christopher, and Peter Hosten. "Detection of false feature correspondences in feature based object detection systems." In 2013 28th International Conference of Image and Vision Computing New Zealand (IVCNZ). IEEE, 2013. http://dx.doi.org/10.1109/ivcnz.2013.6726987.

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Song, Caijiao, Qianqian Wang, Bin Shan, et al. "Feature importance evaluation on LiDAR system atmospheric backscatter impact." In Optical Sensing and Detection VII, edited by Francis Berghmans and Ioanna Zergioti. SPIE, 2022. http://dx.doi.org/10.1117/12.2624463.

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Carlotto, Mark J. "Feature-based anomaly detection." In Defense and Security Symposium, edited by Ivan Kadar. SPIE, 2007. http://dx.doi.org/10.1117/12.721149.

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Sun, Shuyang, Xiaoyu Yue, Xiaojuan Qi, Wanli Ouyang, Victor Prisacariu, and Philip Torr. "Aggregation with Feature Detection." In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021. http://dx.doi.org/10.1109/iccv48922.2021.00057.

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Zweng, A., and M. Kampel. "A Spatio-Temporal Feature Descriptor for Action Recognition using Feature Relations." In 5th International Conference on Imaging for Crime Detection and Prevention (ICDP 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/ic.2013.0278.

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Звіти організацій з теми "Detection feature"

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Guan, Yawen, Deborah Sulsky, J. Tucker, and Christian Sampson. Feature Detection. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769711.

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Robinson, K. Feature detection for spatial templates. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/179294.

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Lukow, Steven, Ross Lee, Jonathan Gigax, and David Grow. Improving Non-Destructive Detection Technology Through SAVY Feature Detection. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1884728.

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Stevens, Mark R., and J. R. Beveridge. Optical Linear Feature Detection Based on Model Pose. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada308546.

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Karasaki, Kenzi, John Apps, Christine Doughty, et al. Feature Detection, Characterization and Confirmation Methodology: Final Report. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/945944.

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West, Brendan. Linear- and repetitive-feature detection within remotely sensed imagery. Cold Regions Research and Engineering Laboratory (U.S.), 2017. http://dx.doi.org/10.21079/11681/22151.

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Downs, Christine, Jason Heath, and Teeratorn Kadeethum. Persistent homology-based feature detection from remote-sensing data. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1887492.

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Sherlock, Barry G. Wavelet Based Feature Extraction for Target Recognition and Minefield Detection. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada401966.

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Sherlock, Barry G. Wavelet Based Feature Extraction for Target Recognition and Minefield Detection. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada371103.

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Rudin, Leonid, and Stanley Osher. Accurate Feature Detection and Estimation Using Nonlinear and Multiresolution Analysis. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada290267.

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