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Journal articles on the topic 'Feature detection and description'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

Feng, Lu, Zhuangzhi Wu, and Xiang Long. "Fast Image Diffusion for Feature Detection and Description." International Journal of Computer Theory and Engineering 8, no. 1 (2016): 58–62. http://dx.doi.org/10.7763/ijcte.2016.v8.1020.

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Yuan, Chaofeng, Yuelei Xu, Jingjing Yang, Zhaoxiang Zhang, and Qing Zhou. "A Pseudoinverse Siamese Convolutional Neural Network of Transformation Invariance Feature Detection and Description for a SLAM System." Machines 10, no. 11 (2022): 1070. http://dx.doi.org/10.3390/machines10111070.

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Simultaneous localization and mapping (SLAM) systems play an important role in the field of automated robotics and artificial intelligence. Feature detection and matching are crucial aspects affecting the overall accuracy of the SLAM system. However, the accuracy of the position and matching cannot be guaranteed when confronted with a cross-view angle, illumination, texture, etc. Moreover, deep learning methods are very sensitive to perspective change and do not have the invariance of geometric transformation. Therefore, a novel pseudo-Siamese convolutional network of a transformation invarian

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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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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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Hoffmann, Annika. "On the Benefits of Color Information for Feature Matching in Outdoor Environments." Robotics 9, no. 4 (2020): 85. http://dx.doi.org/10.3390/robotics9040085.

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The detection and description of features is one basic technique for many visual robot navigation systems in both indoor and outdoor environments. Matched features from two or more images are used to solve navigation problems, e.g., by establishing spatial relationships between different poses in which the robot captured the images. Feature detection and description is particularly challenging in outdoor environments, and widely used grayscale methods lead to high numbers of outliers. In this paper, we analyze the use of color information for keypoint detection and description. We consider gra

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Oliveira, António José, Bruno Miguel Ferreira, and Nuno Alexandre Cruz. "A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation." Journal of Marine Science and Engineering 9, no. 4 (2021): 361. http://dx.doi.org/10.3390/jmse9040361.

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In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environment

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Wang Fangbin, 汪方斌, 储朱涛 Chu Zhutao, 朱达荣 Zhu Darong, 刘涛 Liu Tao, 徐德军 Xu Dejun, and 许露 Xu Lu. "An Improved KAZE Feature Detection and Description Algorithm." Laser & Optoelectronics Progress 55, no. 9 (2018): 091007. http://dx.doi.org/10.3788/lop55.091007.

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Pang, Yanwei, Xianbin Cao, Lei Zhang, and Amir Hussein. "Special issue on image feature detection and description." Neurocomputing 120 (November 2013): 1–3. http://dx.doi.org/10.1016/j.neucom.2013.02.036.

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Huang, Wei, Yongjie Li, Zhaonan Xu, Xinwei Yao, and Rongchun Wan. "Improved Deep Support Vector Data Description Model Using Feature Patching for Industrial Anomaly Detection." Sensors 25, no. 1 (2024): 67. https://doi.org/10.3390/s25010067.

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In industrial contexts, anomaly detection is crucial for ensuring quality control and maintaining operational efficiency in manufacturing processes. Leveraging high-level features extracted from ImageNet-trained networks and the robust capabilities of the Deep Support Vector Data Description (SVDD) model for anomaly detection, this paper proposes an improved Deep SVDD model, termed Feature-Patching SVDD (FPSVDD), designed for unsupervised anomaly detection in industrial applications. This model integrates a feature-patching technique with the Deep SVDD framework. Features are extracted from a

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9

Zhang, Wei, and Guoying Zhang. "Image Feature Matching Based on Semantic Fusion Description and Spatial Consistency." Symmetry 10, no. 12 (2018): 725. http://dx.doi.org/10.3390/sym10120725.

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Image feature description and matching is widely used in computer vision, such as camera pose estimation. Traditional feature descriptions lack the semantic and spatial information, and give rise to a large number of feature mismatches. In order to improve the accuracy of image feature matching, a feature description and matching method, based on local semantic information fusion and feature spatial consistency, is proposed in this paper. Once object detection is used on images, feature points are then extracted, and image patches with various sizes surrounding these points are clipped. These

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Stanciu, Stefan G., Radu Hristu, Radu Boriga, and George A. Stanciu. "On the Suitability of SIFT Technique to Deal with Image Modifications Specific to Confocal Scanning Laser Microscopy." Microscopy and Microanalysis 16, no. 5 (2010): 515–30. http://dx.doi.org/10.1017/s1431927610000371.

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AbstractComputer vision tasks such as recognition and classification of objects and structures or image registration and retrieval can provide significant information when applied to microscopy images. Recently developed techniques for the detection and description of local features make the extraction and description of local image features that are invariant to various changes possible. The invariance and robustness of feature detection and description techniques play a key role in the design and implementation of object recognition, image registration, or image mosaicing applications. The s

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Petrakis, Georgios, and Panagiotis Partsinevelos. "Keypoint Detection and Description through Deep Learning in Unstructured Environments." Robotics 12, no. 5 (2023): 137. http://dx.doi.org/10.3390/robotics12050137.

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Feature extraction plays a crucial role in computer vision and autonomous navigation, offering valuable information for real-time localization and scene understanding. However, although multiple studies investigate keypoint detection and description algorithms in urban and indoor environments, far fewer studies concentrate in unstructured environments. In this study, a multi-task deep learning architecture is developed for keypoint detection and description, focused on poor-featured unstructured and planetary scenes with low or changing illumination. The proposed architecture was trained and e

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Jiang, Zhaoyin, Shucheng Huang, and Mingxing Li. "A Pedestrian Detection Network Based on an Attention Mechanism and Pose Information." Applied Sciences 14, no. 18 (2024): 8214. http://dx.doi.org/10.3390/app14188214.

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Pedestrian detection has recently attracted widespread attention as a challenging problem in computer vision. The accuracy of pedestrian detection is affected by differences in gestures, background clutter, local occlusion, differences in scales, pixel blur, and other factors occurring in real scenes. These problems lead to false and missed detections. In view of these visual description deficiencies, we leveraged pedestrian pose information as a supplementary resource to address the occlusion challenges that arise in pedestrian detection. An attention mechanism was integrated into the visual

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Prajakta, H. Umale Chanchal H. Sahani Aboli S. Patil Anisha A. Gedam Kajal V. Kawale Prof. Aditya Turankar. "Planer Object Detection Using Sift and Surf in Image Processing." International Journal of Research in Computer & Information Technology 7, no. 2 (2022): 31–34. https://doi.org/10.5281/zenodo.6676111.

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Object Detection refers to the capability of computers and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique that works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) and propose various geometric transformations. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points

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YANG, Heng, and Qing WANG. "A Novel Local Invariant Feature Detection and Description Algorithm." Chinese Journal of Computers 33, no. 5 (2010): 935–44. http://dx.doi.org/10.3724/sp.j.1016.2010.00935.

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Zhao, Yu Dan, Jing Wen Xu, Jun Fang Zhao, Xin Li, and Shuang Liu. "Study on Insect Pests Detection Based on Digital Image." Applied Mechanics and Materials 701-702 (December 2014): 357–60. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.357.

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This paper mainly performs Cascade AdaBoost algorithm based on multi-feature to detect the images of Eurydema dominulus, which will cause harm to crucifer. Firstly, the mixing of HAAR features and LBP features is adopted instead of the single-feature of traditional model, which makes description of images more comprehensively from the angle of the gradient and texture. And then use the best features selected by Gentle AdaBoost algorithm to compose the weak classifier and the strong classifier. And the cascade detector is composed of the trained classifiers of each layer according to a certain

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Qin, Qin, and Josef Vychodil. "Pedestrian Detection Algorithm Based on Improved Convolutional Neural Network." Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no. 5 (2017): 834–39. http://dx.doi.org/10.20965/jaciii.2017.p0834.

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This paper proposes a new multi-feature detection method of local pedestrian based on a convolutional neural network (CNN), which provides a reliable basis for multi-feature fusion in pedestrian detection. According to the standard of pedestrian detection ratio, the pedestrian under the detection window would be segmented, using the sample labels to guide the local characteristics of CNN learning, the supervised learning after the network can obtain the local feature fusion more pedestrian description ability. Finally, a large number of experiments have been performed. The experimental results

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17

Zhang, Ruixing, Tao Yao, and Lianshan Yan. "Feature Point Detection and Description Networks Based on Asymmetric Convolution and the Cross-ResolutionImage-Matching Method." International Journal of Intelligent Systems 2023 (February 20, 2023): 1–15. http://dx.doi.org/10.1155/2023/5131440.

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Image matching can be transformed into the problem of feature point detection and matching of images. The current neural network methods have a weak detection effect on feature points and cannot extract enough sparse and uniform feature points. In order to improve the detection and description ability of feature points, this paper proposes a self-supervised feature point detection and description network based on asymmetric convolution: ACPoint. Specifically, first, feature point pseudolabels are learned from an unlabeled dataset, and pseudolabels are used for supervised learning; then, the le

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Kumawat, Anchal, and Sucheta Panda. "Feature Detection and Description in Remote Sensing Images using a Hybrid Feature Detector." Procedia Computer Science 132 (2018): 277–87. http://dx.doi.org/10.1016/j.procs.2018.05.176.

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Xu, Chuan, Qi Zhang, Liye Mei, et al. "Dense Multiscale Feature Learning Transformer Embedding Cross-Shaped Attention for Road Damage Detection." Electronics 12, no. 4 (2023): 898. http://dx.doi.org/10.3390/electronics12040898.

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Road damage detection is essential to the maintenance and management of roads. The morphological road damage contains a large number of multi-scale features, which means that existing road damage detection algorithms are unable to effectively distinguish and fuse multiple features. In this paper, we propose a dense multiscale feature learning Transformer embedding cross-shaped attention for road damage detection (DMTC) network, which can segment the damage information in road images and improve the effectiveness of road damage detection. Our DMTC makes three contributions. Firstly, we adopt a

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NAGAO, MAKOTO. "SHAPE RECOGNITION BY HUMAN-LIKE TRIAL AND ERROR RANDOM PROCESSES." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 05 (1996): 473–90. http://dx.doi.org/10.1142/s021800149600030x.

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Pattern recognition and object detection systems so far developed required the algorithmic description of every detail of the objects to be recognized by bottom-up process from pixel-to-pixel relation to line, corner, and structural description. Because this low-level process does not see global information, feature detection is highly sensitive to noise. To overcome this problem and to give human-like flexibility to machine recognition process, we developed a new system which had non-algorithmic feature detection functions by seeing a comparatively large area at once. It uses a variable size

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21

Yang, Chengzhi. "An Image Multi-scale Feature Recognition Method Based on Image Saliency." International Journal of Circuits, Systems and Signal Processing 15 (April 8, 2021): 280–87. http://dx.doi.org/10.46300/9106.2021.15.32.

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Image recognition refers to the technology which processes, analyzes and understands images with computer so as to recognize various targets and objects of different patterns. To effectively combine image recognition and intelligent algorithm can enhance the efficiency of image feature analysis, improve the detection accuracy and guarantee real-time detection. In image feature recognition, the following problems exist: the description of accurate object features, object blockage, complex and changeable scenes. Whether these problems can be effectively solved has great significance in improving

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22

Lin, Tsun-Kuo. "A Novel Edge Feature Description Method for Blur Detection in Manufacturing Processes." Journal of Sensors 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6506249.

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A novel inspection sensor by using an edge feature description (EFD) algorithm based on a support vector machine (SVM) is proposed for industrial inspection of images. This method detects and adaptively segments blurred images by using the proposed algorithm, which uses EFD to effectively classify blurred samples and improve the conventional methods of inspecting blurred objects; the algorithm selects and optimally tunes suitable features. The proposed sensor applies a suitable feature-extraction strategy on the basis of the sensing results. Experimental results demonstrate that the proposed m

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Wang, Han, Xiang Ji, Lei Jin, Yujiao Ji, and Guangcheng Wang. "Image-Acceleration Multimodal Danger Detection Model on Mobile Phone for Phone Addicts." Sensors 24, no. 14 (2024): 4654. http://dx.doi.org/10.3390/s24144654.

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With the popularity of smartphones, a large number of “phubbers” have emerged who are engrossed in their phones regardless of the situation. In response to the potential dangers that phubbers face while traveling, this paper proposes a multimodal danger perception network model and early warning system for phubbers, designed for mobile devices. This proposed model consists of surrounding environment feature extraction, user behavior feature extraction, and multimodal feature fusion and recognition modules. The environmental feature module utilizes MobileNet as the backbone network to extract e

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Kiran, Chavan, Kadam Gayatri, Kankaria Ruchika, Kate Raksha, and Ladekar Ashvini. "Object Detection for Image Captioning." Journal of Image Processing and Artificial Intelligence 5, no. 1 (2019): 17–24. https://doi.org/10.5281/zenodo.2551870.

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Generation of description of pictures victimization tongue sentences is gaining a lot of quality of late. It's a difficult task, because it needs not solely understanding a picture, however to translate that visual data into sentence description. So as to caption a picture, we tend to 1st have to be compelled to discover the objects within the image. Object detection has become one amongst the international widespread analysis fields. 1st the paper introduced the distinction between deep learning and machine learning for object detection. Second the techniques for object detection are surv

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Li, Jun, Xiang Li, Yifei Wei, Mei Song, and Xiaojun Wang. "Multi-Level Feature Aggregation-Based Joint Keypoint Detection and Description." Computers, Materials & Continua 73, no. 2 (2022): 2529–40. http://dx.doi.org/10.32604/cmc.2022.029542.

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Bektešević, Dino, and Dejan Vinković. "Linear feature detection algorithm for astronomical surveys – I. Algorithm description." Monthly Notices of the Royal Astronomical Society 471, no. 3 (2017): 2626–41. http://dx.doi.org/10.1093/mnras/stx1565.

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Zhang, Jiaming, Xuejuan Hu, Tan Zhang, et al. "Binary Neighborhood Coordinate Descriptor for Circuit Board Defect Detection." Electronics 12, no. 6 (2023): 1435. http://dx.doi.org/10.3390/electronics12061435.

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Due to the periodicity of circuit boards, the registration algorithm based on keypoints is less robust in circuit board detection and is prone to misregistration problems. In this paper, the binary neighborhood coordinate descriptor (BNCD) is proposed and applied to circuit board image registration. The BNCD consists of three parts: neighborhood description, coordinate description, and brightness description. The neighborhood description contains the grayscale information of the neighborhood, which is the main part of BNCD. The coordinate description introduces the actual position of the keypo

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Umale, Prajakta, Aboli Patil, Chanchal Sahani, Anisha Gedam, and Kajal Kawale. "PLANER OBJECT DETECTION USING SURF AND SIFT METHOD." International Journal of Engineering Applied Sciences and Technology 6, no. 11 (2022): 36–39. http://dx.doi.org/10.33564/ijeast.2022.v06i11.008.

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Object Detection refers to the capability of computer and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded up robust features (SURF) and propose a various geometric transformation. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points are

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Liu, Qian, Feng Yang, and XiaoFen Tang. "A New Pedestrian Feature Description Method Named Neighborhood Descriptor of Oriented Gradients." International Journal of Information Technology and Web Engineering 16, no. 1 (2021): 23–55. http://dx.doi.org/10.4018/ijitwe.2021010102.

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In view of the issue of the mechanism for enhancing the neighbourhood relationship of blocks of HOG, this paper proposes neighborhood descriptor of oriented gradients (NDOG), an improved feature descriptor based on HOG, for pedestrian detection. To obtain the NDOG feature vector, the algorithm calculates the local weight vector of the HOG feature descriptor, while integrating spatial correlation among blocks, concatenates this weight vector to the tail of the HOG feature descriptor, and uses the gradient norm to normalize this new feature vector. With the proposed NDOG feature vector along wit

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Hau Sim Choo, Chia Yee Ooi, Nordinah Ismail, Michiko Inoue, and Chee Hoo Kok. "Improving Hardware Trojan Detection Coverage by Utilizing Features at Different Abstraction Levels." Journal of Advanced Research in Applied Sciences and Engineering Technology 32, no. 1 (2023): 73–86. http://dx.doi.org/10.37934/araset.32.1.7386.

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In this paper, we introduced a solution to improve hardware Trojan (HT) detection coverage by analyzing features at different abstraction levels. We demonstrated our solution with a supervised classification of HT branching statement (BS) in register-transfer-level (RTL) description. The proposed classifier was trained with a double-abstraction-level feature vector consisting of features extracted at RTL and gate level (GL). In the experiment, we evaluated the HT detection coverage of the trained classifier by applying them on 24 self-designed HT circuits. The proposed classifier achieved the

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Rahmanimanesh, Mohammad, Jalal al-Din Nasiri, Saeid Jalili, and Charkari Nasrolah Moghaddam. "Adaptive three‑phase support vector data description." Pattern Analysis and Applications 22, no. 2 (2019): 491–504. https://doi.org/10.1007/s10044-017-0646-3.

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We add a new phase, called reforming phase, to support vector data description (SVDD) between the training and testing phases.&lrm; The reforming phase enables us to reconsider the SVDD’s assumption of the uniformity of features in calculating the distance of an object to the center of hypersphere.&lrm; In the reforming phase, the features are assumed as a group of experts who have different impacts in overall outlier detection.&lrm; In doing so, the proportion of each feature in the distance of an object to the center of hypersphere is specified.&lrm; Subsequently, the opinions of the e

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Qian, Chun Hua, He Qun Qiang, and Sheng Rong Gong. "Orange Feature Extraction and Description Based on Image Processing." Applied Mechanics and Materials 713-715 (January 2015): 1804–7. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1804.

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Automatic orange quality classification based on computer image processing is accurate and efficient. In this paper, we discuss the orange feature extraction and description method based on image processing. Design an orange image edge detection method based on Canny operator, color characteristics description methods based on HIS model and shape characteristics description methods based on Fourier descriptor operator. The experiment result proof that Canny operator is high SNR,high accuracy and low computation;HIS model is more accord with human vision and low computation also; shape characte

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Huang, Xinyao, Tao Xu, Xiaomin Zhang, et al. "ALIKE-APPLE: A Lightweight Method for the Detection and Description of Minute and Similar Feature Points in Apples." Agriculture 14, no. 3 (2024): 339. http://dx.doi.org/10.3390/agriculture14030339.

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Current image feature extraction methods fail to adapt to the fine features of apple image texture, resulting in image matching errors and degraded image processing accuracy. A multi-view orthogonal image acquisition system was constructed with apples as the research object. The system consists of four industrial cameras placed around the apple at different angles and one camera placed on top. Following the image acquisition through the system, synthetic image pairs—both before and after transformation—were generated as the input dataset. This generation process involved each image being subje

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Liu, Chenyu, and Konstantinos Gryllias. "A Deep Support Vector Data Description Method for Anomaly Detection in Helicopters." PHM Society European Conference 6, no. 1 (2021): 9. http://dx.doi.org/10.36001/phme.2021.v6i1.2957.

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Helicopters are high-value mechanical assets which has gained much attention from condition monitoring practitioners. Modern helicopter health management system leverages various sensors to collect in-flight signals. In order to trigger the alarm when an anomaly happens, signal processing methods are used to construct health indicators that require expert knowledge. On the other hand, classic features are always case-specific and may fail to discriminate anomalous in practical applications. Support Vector Data Description (SVDD) is a machine learning method used as a one-class classifier to se

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Tian, Yang, Meng Yu, and Yingying Zhang. "A novel 3D feature detection and matching approach for autonomous planetary landing mission." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 6 (2018): 2241–49. http://dx.doi.org/10.1177/0954410018774696.

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In an autonomous planetary/asteroid landing mission, landmark recognition is crucial to the success of the navigation system. The failure of feature detection or matching could lead to evident increase of bias in lander pose estimation. To this end, we propose a novel 3D feature detection and matching algorithm in this paper. The spherical harmonic coefficients are adopted to describe a 3D natural feature, and a relative distance set feature description approach is proposed as a supplement feature descriptor to enhance the distinctiveness of 3D feature. Simulation results demonstrate the effec

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Cui, Zhoujuan, Yuqi Dai, Yiping Duan, and Xiaoming Tao. "Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching." Applied Sciences 14, no. 23 (2024): 11098. http://dx.doi.org/10.3390/app142311098.

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Addressing the challenges in online multi-object tracking algorithms under complex scenarios, where the independence among feature extraction, object detection, and data association modules leads to both error accumulation and the difficulty of maintaining visual consistency for occluded objects, we have proposed an end-to-end multi-object tracking method based on hypergraph matching (JDTHM). Initially, a feature extraction and object detection module is introduced to achieve preliminary localization and description of the objects. Subsequently, a deep feature aggregation module is designed to

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Zhang, Zhi Qin, and Wei Zhu. "Robust Hand Gesture Detection Based on Feature Classifier." Advanced Materials Research 823 (October 2013): 626–30. http://dx.doi.org/10.4028/www.scientific.net/amr.823.626.

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In this article, a novel automatic hand gesture detection approach based on the boosted classifiers is proposed, which analyze the hand gesture features. At first, hand gesture images are processed with local binary pattern (LBP) operator which has the powerful capability of texture feature description. And then these features are presented with LBP descriptor of hand gesture image which is divided into several blocks, because of too much dimension of feature vector, using Principal Component Analysis (PCA) to reduce dimension and compression. Finally, Kalman predictor is adopted to detect han

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Hussnain, Zille, Sander Oude Elberink, and George Vosselman. "AUTOMATIC FEATURE DETECTION, DESCRIPTION AND MATCHING FROM MOBILE LASER SCANNING DATA AND AERIAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 609–16. http://dx.doi.org/10.5194/isprs-archives-xli-b1-609-2016.

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In mobile laser scanning systems, the platform’s position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of

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Hussnain, Zille, Sander Oude Elberink, and George Vosselman. "AUTOMATIC FEATURE DETECTION, DESCRIPTION AND MATCHING FROM MOBILE LASER SCANNING DATA AND AERIAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 609–16. http://dx.doi.org/10.5194/isprsarchives-xli-b1-609-2016.

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In mobile laser scanning systems, the platform’s position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of

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Liu, Cuiyin, Jishang Xu, and Feng Wang. "A Review of Keypoints’ Detection and Feature Description in Image Registration." Scientific Programming 2021 (December 1, 2021): 1–25. http://dx.doi.org/10.1155/2021/8509164.

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For image registration, feature detection and description are critical steps that identify the keypoints and describe them for the subsequent matching to estimate the geometric transformation parameters between two images. Recently, there has been a large increase in the research methods of detection operators and description operators, from traditional methods to deep learning methods. To solve the problem, that is, which operator is suitable for specific application problems under different imaging conditions, the paper systematically reviewed commonly used descriptors and detectors from art

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Kechagias-Stamatis, Odysseas, Nabil Aouf, and Mark A. Richardson. "Performance evaluation of single and cross-dimensional feature detection and description." IET Image Processing 14, no. 10 (2020): 2035–51. http://dx.doi.org/10.1049/iet-ipr.2019.1523.

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Li, Yuze, Wushour Silamu, Zhenchao Wang, and Miaomiao Xu. "Attention-Based Scene Text Detection on Dual Feature Fusion." Sensors 22, no. 23 (2022): 9072. http://dx.doi.org/10.3390/s22239072.

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The segmentation-based scene text detection algorithm has advantages in scene text detection scenarios with arbitrary shape and extreme aspect ratio, depending on its pixel-level description and fine post-processing. However, the insufficient use of semantic and spatial information in the network limits the classification and positioning capabilities of the network. Existing scene text detection methods have the problem of losing important feature information in the process of extracting features from each network layer. To solve this problem, the Attention-based Dual Feature Fusion Model (ADF

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Zhang, Li Hong. "Human Detection Based on SVM and Improved Histogram of Oriented Gradients." Applied Mechanics and Materials 380-384 (August 2013): 3862–65. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3862.

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Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experim

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Zhou, Zhongwen, Siwei Xia, Mengying Shu, and Hong Zhou. "Fine-grained Abnormality Detection and Natural Language Description of Medical CT Images Using Large Language Models." International Journal of Innovative Research in Computer Science and Technology 12, no. 6 (2024): 52–62. http://dx.doi.org/10.55524/ijircst.2024.12.6.8.

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Medical report generation demands accurate abnormality detection and precise description generation from CT images. While large language models have shown promising results in natural language processing tasks, their application in medical imaging analysis faces challenges due to the complexity of fine-grained feature detection and the requirement for domain-specific knowledge. This paper presents a novel framework integrating large language models with specialized medical image processing techniques for fine-grained abnormality detection and natural language description generation. Our approa

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Wang, Qi, Xiang Gao, Fan Wang, Zhihang Ji, and Xiaopeng Hu. "Feature Point Matching Method Based on Consistent Edge Structures for Infrared and Visible Images." Applied Sciences 10, no. 7 (2020): 2302. http://dx.doi.org/10.3390/app10072302.

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Infrared and visible image match is an important research topic in the field of multi-modality image processing. Due to the difference of image contents like pixel intensities and gradients caused by disparate spectrums, it is a great challenge for infrared and visible image match in terms of the detection repeatability and the matching accuracy. To improve the matching performance, a feature detection and description method based on consistent edge structures of images (DDCE) is proposed in this paper. First, consistent edge structures are detected to obtain similar contents of infrared and v

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Li, Feng, Lina Yuan, Kun Zhang, and Wenqing Li. "A defect detection method for unpatterned fabric based on multidirectional binary patterns and the gray-level co-occurrence matrix." Textile Research Journal 90, no. 7-8 (2019): 776–96. http://dx.doi.org/10.1177/0040517519879904.

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A new texture-feature description operator, called the multidirectional binary patterns (MDBP) operator, is proposed in this paper. The operator can extract the detailed distribution of textures in local regions by comparing the differences in the gray levels between neighboring pixels. Moreover, the texture expression ability is enhanced by focusing on the texture features in the linear neighborhood of the image in multiple directions. The MDBP operator was modified by introducing a “uniform” pattern to reduce the grayscale values in the image. Combining the “uniform” MDBP operator and the gr

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Weimert, Achim, Xueting Tan, and Xubo Yang. "Natural Feature Detection on Mobile Phones with 3D FAST." International Journal of Virtual Reality 9, no. 4 (2010): 29–34. http://dx.doi.org/10.20870/ijvr.2010.9.4.2788.

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In this paper, we present a novel feature detection approach designed for mobile devices, showing optimized solutions for both detection and description. It is based on FAST (Features from Accelerated Segment Test) and named 3D FAST. Being robust, scale-invariant and easy to compute, it is a candidate for augmented reality (AR) applications running on low performance platforms. Using simple calculations and machine learning, FAST is a feature detection algorithm known to be efficient but not very robust in addition to its lack of scale information. Our approach relies on gradient images calcul

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Song, Sensen, Yue Li, Zhenhong Jia, and Fei Shi. "Salient Object Detection Based on Optimization of Feature Computation by Neutrosophic Set Theory." Sensors 23, no. 20 (2023): 8348. http://dx.doi.org/10.3390/s23208348.

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In recent saliency detection research, too many or too few image features are used in the algorithm, and the processing of saliency map details is not satisfactory, resulting in significant degradation of the salient object detection result. To overcome the above deficiencies and achieve better object detection results, we propose a salient object detection method based on feature optimization by neutrosophic set (NS) theory in this paper. First, prior object knowledge is built using foreground and background models, which include pixel-wise and super-pixel cues. Simultaneously, the feature ma

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Qiu, Kepeng, Weihong Song, and Peng Wang. "Abnormal data detection for industrial processes using adversarial autoencoders support vector data description." Measurement Science and Technology 33, no. 5 (2022): 055110. http://dx.doi.org/10.1088/1361-6501/ac4f02.

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Abstract Abnormal data detection for industrial processes is essential in industrial process monitoring and is an important technology to ensure production safety. However, for most industrial processes, it is a challenge to establish an effective abnormal data detection model due to the following issues: (a) weak model performance due to the small amount of process data; (b) trade-offs between model sparsity and accuracy; and (c) weak generalization ability of abnormal data detection model. To address these issues, a method based on adversarial autoencoders support vector data description (AA

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Lee, Dah-Jye, Samuel G. Fuller, and Alexander S. McCown. "Optimization and Implementation of Synthetic Basis Feature Descriptor on FPGA." Electronics 9, no. 3 (2020): 391. http://dx.doi.org/10.3390/electronics9030391.

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Feature detection, description, and matching are crucial steps for many computer vision algorithms. These steps rely on feature descriptors to match image features across sets of images. Previous work has shown that our SYnthetic BAsis (SYBA) feature descriptor can offer superior performance to other binary descriptors. This paper focused on various optimizations and hardware implementation of the newer and optimized version. The hardware implementation on a field-programmable gate array (FPGA) is a high-throughput low-latency solution which is critical for applications such as high-speed obje

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