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- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC) - Mon Tresor, Plaine Magnien, Mauritius (2018.12.6-2018.12.7)] 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC) - Parallel Image Stitching Based on Multithreaded Processing on GPU
摘要： The paper discusses multithreaded processing of images on graphic processing units for the purposes of feature detection and matching. The problem of feature detection and feature correspondence is applied for image stitching and panorama creation. Parallel GPU implementation based on nVidia CUDA is presented and experimentally evaluated and compared by parallel multithread CPU processing for shared memory parallel computational model.
关键词： general purpose computations on GPU,feature matching,image stitching,multithreading,feature detection
更新于2025-09-23 15:22:29
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Online Stereo Camera Calibration for Automotive Vision based on HW-accelerated A-KAZE-Feature Extraction
摘要： Nowadays ongoing integration of camera based advanced driver assistance systems (ADAS) in vehicles demands increasingly complex digital image processing in order to interpret the surrounding situations. To ensure a timely reaction for obstacles lying ahead, far reaching depth information of the observed scene is necessary. Using stereo camera systems, this is achievable by enlarging the camera baseline. With rapid driving, the exact alignment of the stereo images is no longer ensured due to vibrations of the vehicle. Based on detection, extraction and matching of Accelerated-KAZE image features (A-KAZE), the geometric distortions are compensable by estimating the external camera parameters for image rectification. The indispensable frame rate for applications in vehicles and the limited power budget in combination with the SW flexibility demanded for future ADAS applications requires the usage of optimized hardware architectures. Thus, an online camera calibration based on an HW-accelerated A-KAZE extraction is introduced in this work. The suitability of A-KAZE features for an online camera calibration is proven. Furthermore, the Tensilica Vision P5-processor is evaluated regarding its suitability for real-time A-KAZE feature extraction. This processor provides a comprehensive instruction-set extension for high performance digital image processing. While preserving the initial A-KAZE accuracy, a feature descriptor length reduction of factor ×3.8 is attained compared to the initial descriptor size and an estimated frame rate of 20 fps is achieved for A-KAZE feature extraction on the Tensilica Vision P5-processor.
关键词： ASIP,Tensilica Vision P5,feature matching,advanced driver assistance systems,A-KAZE
更新于2025-09-23 15:21:01
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A Modified Local Binary Pattern Descriptor for SAR Image Matching
摘要： Image matching is an important step which is taken in most applications of synthetic aperture radar (SAR) images. In this letter, a method is proposed for SAR image matching which introduces a modified local binary pattern (LBP) as a descriptor. Multitextural feature LBP (MTF-LBP) uses the gray-level cooccurrence matrix to increase image texture information. MTF-LBP creates bit plane for each point candidate for matching. Then, using hamming distance, true matches are determined. Experiments are conducted on four spaceborne SAR image pairs including Radarsat-2, TerraSAR-X, ALOS-PALSAR, and Sentinel-1. The proposed method is compared with five common LBP approaches. The results indicate that the proposed method has a better performance in terms of the number of true matches.
关键词： Image texture analysis,LBP (MTF-LBP),synthetic aperture radar (SAR),multitextural feature,matching
更新于2025-09-23 15:21:01
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[IEEE 2018 International Symposium ELMAR - Zadar, Croatia (2018.9.16-2018.9.19)] 2018 International Symposium ELMAR - Image Feature Matching and Object Detection Using Brute-Force Matchers
摘要： The paper considers a problem of feature matching and object detection in two images using brute-force matchers. The proposed framework exploited several concurrent algorithms for feature detection and descriptor extraction, such as ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), SIFT (Scale Invariant Feature Transform) and SURF (Speeded-Up Robust Features). The feature matching is accomplished by the Brute-Force approach combined with the k-Nearest Neighbors algorithm. The obtained matches are utilized by the robust RANSAC (Random Sample Consensus) method for estimating the transformation between two consecutive images. Therefore, the RANSAC method is employed to improve the outliers removal. The proposed algorithm is designed and implemented using OpenCV library. Its effectiveness and quality are veri?ed through analyses of its execution speed and accuracy of the feature matching.
关键词： Feature matching,Brute-force algorithm,Object detection,RANSAC,Feature detection and extraction
更新于2025-09-10 09:29:36
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Image-Alignment Based Matching for Irregular Contour Defects Detection
摘要： Automatic defect inspection is attractive for high-quality workpiece manufacturing with irregular contours in order to achieve high accuracy and no contour defect. Thus, a novel image alignment-based feature matching algorithm framework is proposed in this paper. It can be used to solve the specified pixel-level defect detection and location problems for workpieces with irregular contours. A new forensic hash is firstly generated by extracting the scale, position and main orientation information of feature points. Since the forensic hash is invariant to rotation, translation and scaling, it is used for feature matching. A feature matching method based on a robust cascade estimator is secondly proposed to establish an accurate correspondence between the test image and reference image according to the obtained image hash and a parameter space voting mechanism. Thirdly, the matched feature points are used to estimate the similar transformation parameters to achieve an accurate image alignment. Finally, image difference and morphological technique are used to locate the contour defect. Experimental results demonstrate that the proposed algorithm can effectively detect and locate small contour defects in irregular stamping workpieces.
关键词： Feature matching,Irregular contour,Visual inspection,Image alignment
更新于2025-09-10 09:29:36
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Infrared and Visible Image Registration Based on Scale-Invariant PIIFD Feature and Locality Preserving Matching
摘要： Registration of multi-sensor data is a prerequisite for multimodal image analysis such as image fusion. This study focuses on the problem of infrared and visible image registration, which has played an important role for the purpose of enhancing visual perception. Existing methods based on multimodal feature descriptor such as partial intensity invariant feature descriptor (PIIFD) usually fail in correctly aligning infrared and visible image pairs, due to their signi?cant differences in resolution and appearance. In this paper, we propose a scale-invariant PIIFD (SI-PIIFD) feature and a robust feature matching method to address this problem. Speci?cally, we ?rst extract corner points as control point candidates since they are usually suf?cient and uniformly distributed across the image domain. Then, the SI-PIIFDs are calculated for all corner points and matched according to the descriptor similarity together with a locality preserving geometric constraint. Subsequently, we model the spatial transformation between an infrared and visible image pair with an af?ne function, and introduce a robust Bayesian framework to estimate it from the SI-PIIFD feature matches even if they contaminated by false matches. Finally, the backward approach is chosen for image transformation to avoid holes and overlaps in the output image. Extensive experiments on a challenging dataset with comparisons to other state-of-the-arts demonstrate the effectiveness of the proposed method, both in terms of accuracy and ef?ciency.
关键词： robust estimation,feature matching,image registration,Infrared,multimodal descriptor
更新于2025-09-10 09:29:36
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High-Precision Camera Localization in Scenes with Repetitive Patterns
摘要： This article presents a high-precision multi-modal approach for localizing moving cameras with monocular videos, which has wide potentials in many intelligent applications, including robotics, autonomous vehicles, and so on. Existing visual odometry methods often suffer from symmetric or repetitive scene patterns, e.g., windows on buildings or parking stalls. To address this issue, we introduce a robust camera localization method that contributes in two aspects. First, we formulate feature tracking, the critical step of visual odometry, as a hierarchical min-cost network flow optimization task, and we regularize the formula with flow constraints, cross-scale consistencies, and motion heuristics. The proposed regularized formula is capable of adaptively selecting distinctive features or feature combinations, which is more effective than traditional methods that detect and group repetitive patterns in a separate step. Second, we develop a joint formula for integrating dense visual odometry and sparse GPS readings in a common reference coordinate. The fusion process is guided with high-order statistics knowledge to suppress the impacts of noises, clusters, and model drifting. We evaluate the proposed camera localization method on both public video datasets and a newly created dataset that includes scenes full of repetitive patterns. Results with comparisons show that our method can achieve comparable performance to state-of-the-art methods and is particularly effective for addressing repetitive pattern issues.
关键词： Visual odometry,flow optimization,feature matching
更新于2025-09-10 09:29:36
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Spatially Coherent Matching for Robust Registration
摘要： In order to solve the registration problem, we propose a robust method called Spatially Coherent Matching (SCM), where it can get the underlying correspondences from the given putative sets of feature points for robust matching, and estimate the transformation for robust registration. Recovering correct matches and fitting transformations between image pairs are key components in the field of pattern recognition. The proposed SCM starts with a putative correspondence set which is contaminated by degradations (e.g., occlusion, deformation, rotation, and outliers), and the main goal is to identify the true correspondences and estimate the underlying transformation. Then we formulate this challenging problem by the spatially coherent matching model with a robust exponential distance loss and a spatial constraint. Based on the regularization theory, SCM preserves the topological structure of the adjacent features. Moreover, a sparse approximation strategy is used to improve the efficiency. Finally, the experimental results reveal that the proposed method outperforms current state-of-the-art methods in most test scenarios on several real image datasets and synthesized datasets.
关键词： feature matching,pattern recognition,non-rigid transformation,spatially coherent matching,registration
更新于2025-09-09 09:28:46
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[American Society of Agricultural and Biological Engineers 2018 Detroit, Michigan July 29 - August 1, 2018 - ()] 2018 Detroit, Michigan July 29 - August 1, 2018 - Classification of Tomato Impact Bruise Using Hyperspectral Imaging Based on Spatial-spectral Method
摘要： Tomatoes have various drop impacts on post-harvest process, which causes the quality deterioration. It is required to evaluate impact injuries quickly in a non-destructive method. Hyperspectral image is commonly of with multi-modal classes and ambiguous class boundary, and spatially adaptive classification of land cover with hyperspectral image is one of challenging problems in accurate classification image community. As hyperspectral image includes many interesting objects whereas each object contains variant spectral signature and the discrimination among them is less efficient. This paper presents a new spatial-spectral fusion method, which extracts patch analysis and combines spectral features to perform fruit quality classification. In spectral features, a method of tomato quality classification based on mean-square-error curve fitting and peak-feature matching is presented. It extracts peak features from known drop injury tomatoes’ spectra and unknown tomato samples spectra to compute their similarity values through multiple similarity measures, respectively. Then, the unknown sample is assigned by selecting the known quality tomato with the largest similarity value. At last, in comparison with the proposed method and the method such as partial-least square discriminate analysis (PLS-DA), support vector machine (SVM), the result shows the practicality and accuracy of the proposed method.
关键词： Tomato,Quality Classification,Peak-feature matching,Patch Analysis,Hyperspectral Image
更新于2025-09-09 09:28:46
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[ACM Press the 2018 ACM Symposium - Warsaw, Poland (2018.06.14-2018.06.17)] Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications - ETRA '18 - Automatic mapping of gaze position coordinates of eye-tracking glasses video on a common static reference image
摘要： This paper describes a method for automatic semantic gaze mapping from video obtained by eye-tracking glasses to a common reference image. Image feature detection and description algorithms are utilized to find the position of subsequent video frames and map corresponding gaze coordinates on a common reference image. This process allows aggregate experiment results for further experiment analysis and provides an alternative for manual semantic gaze mapping methods.
关键词： feature matching,computer vision,eye tracking,semantic gaze mapping
更新于2025-09-09 09:28:46