- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2018 15th European Radar Conference (EuRAD) - Madrid, Spain (2018.9.26-2018.9.28)] 2018 15th European Radar Conference (EuRAD) - A Stand Alone Millimetre Wave Imaging Scanner: System Design and Image Analysis Setup
摘要： Millimetre wave sensors are capable of measuring the structure and composition as well as detecting small variations thereof in a wide range of dielectric materials, such as plastics, dry goods and foodstuffs. To produce an image that modern image recognition algorithms can be applied on, a resolution, i.e. pixel density, comparable to those of optical cameras has to be realized. In this paper, we present a rotating scanner system that operates in a CW mode at 90 GHz and allows for a high pixel density for medium measurement object velocities using only a single measurement channel. Additionally, we present an image exploitation setup for the detection of defects in scanned goods and the fusion of amplitude and phase data as well as images acquired from an optical camera for fast and easy goods inspection.
关键词： image matching,object segmentation,microwave imaging,manufacturing industries,radar imaging,image recognition,millimeter wave radar,product safety,image fusion
更新于2025-09-09 09:28:46
-
A New Probabilistic Representation of Color Image Pixels and Its Applications
摘要： This paper proposes a novel probabilistic representation of color image pixels (PRCI) and investigates its applications to similarity construction in motion estimation and image segmentation problems. The PRCI explores the mixture representation of the input image(s) as prior information and describes a given color pixel in terms of its membership in the mixture. Such representation greatly simplifies the estimation of the probability density function from limited observations and allows us to derive a new probabilistic pixel-wise similarity measure based on the continuous domain Bhattacharyya coefficient. This yields a convenient expression of the similarity measure in terms of the pixel memberships. Furthermore, this pixel-wise similarity is extended to measure the similarity between two image regions. The usefulness of the proposed pixel/region-wise similarities is demonstrated by incorporating them respectively in a dense image descriptor-based multi-layered motion estimation problem and an unsupervised image segmentation problem. Experimental results show that i) the integration of the proposed pixel-wise similarity in dense image-descriptor construction yields improved peak signal to noise ratio performance and higher tracking accuracy in the multi-layered motion estimation problem, and ii) the proposed similarity measures give the best performance in terms of all quantitative measurements in the unsupervised superpixel-based image segmentation of the MSRC and BSD300 datasets.
关键词： Pixel-wise similarity,registration,Region-wise similarity,Image matching,and segmentation,Image descriptors,Probabilistic color representation
更新于2025-09-09 09:28:46
-
An efficient image matching algorithm based on adaptive threshold and RANSAC
摘要： The education plays a more and more important role in disseminating knowledge because of the explosive growth of knowledge. As one kind of carrier delivering knowledge, image also presents an explosive growth trend and plays an increasingly important role in education, medical, advertising, entertainment, and so on. Aiming at the long time of massive image feature extraction in the construction of smart campus, the traditional Harris corner has problems, such as low detection efficiency and many non-maximal pseudocorner points. This paper proposes a Harris image matching method that combines adaptive threshold and random sample consensus (RANSAC). First, the Harris feature points are selected based on the adaptive threshold and the Forstner algorithm in this method. On the one hand, candidate points are filtered based on the adaptive threshold. On the other hand, the Forstner algorithm is used to further select the corner points. Second, the normalized cross correlation matching and the RANSAC are applied to precisely match the detected Harris corners. The experimental results show that compared with the existing algorithms, the proposed method not only obtains a matching accuracy higher than 20% of Cui’s algorithm but also saves more than 30% detection time of corner detection and image matching. Furthermore, the proposed method obtains a matching accuracy higher than 50% of the Cui’s algorithm and saves more than 50% detection time of corner detection and image matching.
关键词： image matching,Adaptive threshold,random sample consensus,digital campus,Harris corner detection
更新于2025-09-09 09:28:46
-
Matching Multi-Sensor Remote Sensing Images via an Affinity Tensor
摘要： Matching multi-sensor remote sensing images is still a challenging task due to textural changes and non-linear intensity differences. In this paper, a novel matching method is proposed for multi-sensor remote sensing images. To establish feature correspondences, an affinity tensor is used to integrate geometric and radiometric information. The matching process consists of three steps. First, features from an accelerated segment test are extracted from both source and target images, and two complete graphs are constructed with their nodes representing these features. Then, the geometric and radiometric similarities of the feature points are represented by the three-order affinity tensor, and the initial feature correspondences are established by tensor power iteration. Finally, a tensor-based mismatch detection process is conducted to purify the initial matched points. The robustness and capability of the proposed method are tested with a variety of remote sensing images such as Ziyuan-3 backward, Ziyuan-3 nadir, Gaofen-1, Gaofen-2, unmanned aerial vehicle platform, and Jilin-1. The experiments show that the average matching recall is greater than 0.5, which outperforms state-of-the-art multi-sensor image-matching algorithms such as SIFT, SURF, NG-SIFT, OR-SIFT and GOM-SIFT.
关键词： matching blunder detection,affinity tensor,multi-sensor remote sensing image,graph theory,image matching
更新于2025-09-04 15:30:14
-
A local image descriptor based on radial and angular gradient intensity histogram for blurred image matching
摘要： Image rotation and scale change can signi?cantly degrade the ef?ciency of local descriptors in blurred image matching. Conventional local image descriptors often only employ the rectangular gradient information of detected region around each interest point. Due to unwanted errors estimated for scale and dominant orientation, the performance of these local descriptors is severely degraded when applied to blurred images. To solve this problem, we propose a novel descriptor called radial and angular gradient intensity histogram (RAGIH) which jointly utilizes gradient and intensity features. In this local descriptor, feature vectors are extracted from two concentric circular regions around each key point and using angular and radial gradients in a speci?c local coordinate system reduces the estimation errors. Extensive experiments on challenging Oxford dataset demonstrate the favorable performance of our descriptor compared to state-of-the-art approaches.
关键词： Rotation invariant,Scale invariant,Blurred image matching,Image descriptor
更新于2025-09-04 15:30:14
-
[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) - A Fast Local Analysis by Thresholding applied to image matching
摘要： Keystructures extraction and matching are key steps in computer vision. Many fields of application need large image acquisition and fast extraction of finest structures. In this study, we focus on situations where existing local feature extractors give not enough satisfying results concerning both accuracy and time processing. Among good illustrations, we can quote short-line extraction in local weakly-contrasted images. We propose a new Fast Local Analysis by threSHolding (FLASH) designed to process large images under hard time constraints. We use "micro-line" points as key feature. These are used for shape reconstruction (like lines) and local signature design. We apply FLASH on the field of concrete infrastructure monitoring where robots and UAVs are more and more used for automated defect detection (like cracks). For large concrete surfaces, there are several hard constraints such as the computational time and the reliability. Results show us that the computations are faster than several existing algorithms in image matching and FLASH has invariance to rotation, partial occlusion, and scale range from 0.7 to 1.4 without scale-space exploration.
关键词： concrete infrastructure monitoring,crack detection,computer vision,feature extraction,FLASH,image matching
更新于2025-09-04 15:30:14
-
[Studies in Computational Intelligence] Recent Advances in Computer Vision Volume 804 (Theories and Applications) || Analysis and Evaluation of Keypoint Descriptors for Image Matching
摘要： Feature keypoint descriptors have become indispensable tools and have been widely utilized in a large number of computer vision applications. Many descriptors have been proposed in the literature to describe regions of interest around each keypoint and each claims distinctiveness and robustness against certain types of image distortions. Among these are the conventional ?oating-point descriptors and their binary competitors that require less storage capacity and perform at a fraction of the matching times compared with the ?oating-point descriptors. This chapter gives a brief description to the most frequently used keypoint descriptors from each category. Also, it provides a general framework to analyze and evaluate the performance of these feature keypoint descriptors, particularly when they are used for image matching under various imaging distortions such as blur, scale and illumination changes, and image rotations. Moreover, it presents a detailed explanation and analysis of the experimental results and ?ndings where several important observations are derived from the conducted experiments.
关键词： image matching,keypoint descriptors,binary descriptors,computer vision,floating-point descriptors
更新于2025-09-04 15:30:14