- 标题
- 摘要
- 关键词
- 实验方案
- 产品
过滤筛选
- 2015
- Bessel Function
- Coupling Coefficient
- Fusion temperature and Elongation speed
- Physics
- UIN Suska Riau
- University of Riau
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Multifocus image fusion scheme based on discrete cosine transform and spatial frequency
摘要： Multifocus images are different images of the same scene captured with different focus in the cameras. These images when considered individually may not give good quality. Hence to obtain a good quality image, this work proposes an algorithm for fusing multifocus images using Discrete Cosine Transform and spatial frequency. The proposed algorithm works for fusing any number of images. The second step calculates the average and maximum of all the source images and reduces the source images to be processed as two. Then Discrete Cosine Transform (DCT) is applied over the two input images. Min-Max normalization is done on the DCT coefficients and fusion is done using spatial frequency. Inclusion of the second step of the proposed algorithm in some existing algorithms such as Stationary Wavelet Transform, Principal Component Analysis and spatial fusion improves the performance. The metrics used for evaluation proves that the proposed algorithm gives better results than the other algorithms using DCT and state of the art techniques.
关键词： DCT,Min-Max normalization,Image fusion,Spatial frequency
更新于2025-09-23 15:23:52
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Hyperspectral Image Classification Based on Belief Propagation with Multi-features and Small Sample Learning
摘要： In order to solve the "massive information but low accuracy" problem of hyperspectral image (HSI) classification, a novel HSI classification method MFSSL-BPMRF based on belief propagation (BP) Markov random field (MRF) using multi-features and small sample learning (MFSSL) is proposed in this paper. Firstly, an extended morphological multi-attributes profiles algorithm is used to extract spatial information of HSI, and a spatial–spectral multi-features fusion model is established to improve classification results. Then, BPMRF is used for image segmentation and classification because of its superiority in the spatial–spectral combination classification. MRF can describe the spatial distribution features of ground objects based on neighborhood model, and the spectral information of pixels can be integrated into the calculation of conditional probability. BP is used to learn the marginal probability distributions from the multi-features fusion information. Finally, the small sample training set is selected to enhance the computational efficiency. In the experiments of several hyperspectral images, the proposed method provides higher classification accuracy than other methods, and it is efficient for the classification with limited labeled training samples.
关键词： Features fusion,Belief propagation,Hyperspectral image,Classification
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Chengdu (2018.3.26-2018.3.28)] 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - High Resolution 2d-Imaging Based on Data Fusion Technique
摘要： The range resolution of the traditional single radar imaging system is limited by the bandwidth of the transmitted signal, while the cross resolution is limited by its observation angle range. In this paper, a high resolution 2d-imaging method using data fusion technique is proposed. First, we introduce the theoretical basis of multi-radar data fusion imaging based on the 2d-radar echo sparse representation model. Then, sparse parameters of multi-radar echo are obtained by ExCoV algorithm. Finally, we get lost echo data by interpolation and extrapolation and realize the fusion process. The simulation results show that the image quality is improved after radar data fusion, which is better than that of the single radar echo, verifying the effectiveness of our method.
关键词： ExCoV,high resolution,2d-imaging,data fusion
更新于2025-09-23 15:23:52
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[IEEE 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Singapore, Singapore (2018.11.18-2018.11.21)] 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) - Downside Hemisphere Object Detection and Localization of MAV by Fisheye Camera
摘要： For a multirotor micro aerial vehicle (MAV) flying in the outdoor environment, its downside hemisphere has richest visual information. All that information can be obtained by a single fisheye camera with larger than 180 degrees field of view (FOV). Traditionally, the unrestored fisheye image is restored to a flat image before subsequent processing, which is both resource and time consuming. In this paper, to save resource and time, a method of fisheye object detection and localization on the unrestored fisheye image is proposed. A single-stage neural network is built for object detection. To improve the performance of detector, its submodules are designed specifically by combining the central rotational property and severe distortion of the fisheye image. To meet the real-time requirements of onboard computation, the detector is also tuned to be light-weight. After that, the detected objects are localized with assistance of by a data fusion on the fisheye model and MAV sensory data (altitude, attitude, etc.). The experimental results have validated the effectiveness of the proposed methods in this paper.
关键词： deep learning,object detection,data fusion
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) - Qingdao, China (2018.9.14-2018.9.16)] 2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) - A Real-time Detection Algorithm for Unmanned Aerial Vehicle Target in Infrared Search System
摘要： Aiming at the difficulty of infrared target detection of 'low and slow small' unmanned aerial vehicles (UAV) in complex low-altitude background, this paper proposes a new target detection algorithm based on multiscale fusion filtering. Combined with spatial multiscale decomposition filtering and temporal multiscale difference processing, the algorithm can effectively overcome many difficulties such as complex low-altitude background interference, unknown target scale, unknown angular velocity and low target signal-to-noise ratio (SNR). The test result shows that the algorithm can effectively detect the UAV targets with different distances in complex low-altitude background, and the false alarm rate is low. The algorithm is realized in TI 6657 DSP and realizes 100Hz real-time processing of mid-wave infrared images with 640*512 resolution, which has been effectively applied to the large-field circumferential scanning infrared search system developed by ATR Lab.
关键词： real-time algorithm,multiscale fusion filtering,UAV target detection,low-altitude background,low and slow small targets
更新于2025-09-23 15:23:52
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Polarimetric Interferometric SAR Change Detection Discrimination
摘要： A coherent change detection (CCD) image, computed from a geometrically matched, temporally separated pair of complex-valued synthetic aperture radar (SAR) image sets, conveys the pixel-level equivalence between the two observations. Low-coherence values in a CCD image are typically due to either some physical change in the corresponding pixels or a low signal-to-noise observation. A CCD image does not directly convey the nature of the change that occurred to cause low coherence. In this paper, we introduce a mathematical framework for discriminating between different types of change within a CCD image. We utilize the extra degrees of freedom and information from polarimetric interferometric SAR (PolInSAR) data and PolInSAR processing techniques to define a 29-dimensional feature vector that contains information capable of discriminating between different types of change in a scene. We also propose two change-type discrimination functions that can be trained with feature vector training data and demonstrate change-type discrimination on an example image set for three different types of change. Furthermore, we also describe and characterize the performance of the two proposed change-type discrimination functions by way of receiver operating characteristic curves, confusion matrices, and pass matrices.
关键词： polarimetric interferometric synthetic aperture radar (PolInSAR),H/A/α filter,probabilistic feature fusion (PFF) model,feature vector,Coherent change detection (CCD),optimum coherence (OC),H/A/α decomposition
更新于2025-09-23 15:23:52
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - HSVCNN: CNN-Based Hyperspectral Reconstruction from RGB Videos
摘要： Hyperspectral video acquisition usually requires high complexity hardware and reconstruction algorithms. In this paper, we propose a low complexity CNN-based method for hyperspectral reconstruction from ubiquitous RGB videos, which effectively exploits the temporal redundancies within RGB videos and generates high-quality hyperspectral output. Specifically, given an RGB video, we first design an efficient motion compensation network to align the RGB frames and reduce the large motion. Then, we design a temporal-adaptive fusion network to exploit the inter-frame correlation. The fusion network has the ability to determine the optimum temporal dependency within successive frames, which further promotes the hyperspectral reconstruction fidelity. Preliminary experimental results validate the superior performance of the proposed method over previous learning-based methods. To the best of our knowledge, this is the first time that RGB videos are utilized for hyperspectral reconstruction through deep learning.
关键词： Hyperspectral reconstruction,temporal-adaptive fusion,RGB videos,motion compensation
更新于2025-09-23 15:23:52
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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) - Accurate 3-D Reconstruction with RGB-D Cameras using Depth Map Fusion and Pose Refinement
摘要： Depth map fusion is an essential part in both stereo and RGB-D based 3-D reconstruction pipelines. Whether produced with a passive stereo reconstruction or using an active depth sensor, such as Microsoft Kinect, the depth maps have noise and may have poor initial registration. In this paper, we introduce a method which is capable of handling outliers, and especially, even significant registration errors. The proposed method first fuses a sequence of depth maps into a single non-redundant point cloud so that the redundant points are merged together by giving more weight to more certain measurements. Then, the original depth maps are re-registered to the fused point cloud to refine the original camera extrinsic parameters. The fusion is then performed again with the refined extrinsic parameters. This procedure is repeated until the result is satisfying or no significant changes happen between iterations. The method is robust to outliers and erroneous depth measurements as well as even significant depth map registration errors due to inaccurate initial camera poses.
关键词： point cloud,3-D reconstruction,RGB-D cameras,pose refinement,depth map fusion,registration errors
更新于2025-09-23 15:23:52
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Sensor Fusion and Registration of Lidar and Stereo Camera without Calibration Objects
摘要： Environment perception is an important task for intelligent vehicles applications. Typically, multiple sensors with different characteristics are employed to perceive the environment. To robustly perceive the environment, the information from the different sensors are often integrated or fused. In this article, we propose to perform the sensor fusion and registration of the LIDAR and stereo camera using the particle swarm optimization algorithm, without the aid of any external calibration objects. The proposed algorithm automatically calibrates the sensors and registers the LIDAR range image with the stereo depth image. The registered LIDAR range image functions as the disparity map for the stereo disparity estimation and results in an effective sensor fusion mechanism. Additionally, we perform the image denoising using the modified non-local means filter on the input image during the stereo disparity estimation to improve the robustness, especially at night time. To evaluate our proposed algorithm, the calibration and registration algorithm is compared with baseline algorithms on multiple datasets acquired with varying illuminations. Compared to the baseline algorithms, we show that our proposed algorithm demonstrates better accuracy. We also demonstrate that integrating the LIDAR range image within the stereo’s disparity estimation results in an improved disparity map with significant reduction in the computational complexity.
关键词： stereo camera,LIDAR,sensor fusion
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Hybrid Change Detection Based on ISFA for High-Resolution Imagery
摘要： Hybrid change detection (HCD) for high-resolution imagery usually adopt decision-level method and rely on artificial design. To address this issue, we propose a novel feature-level fusion strategy for HCD based on iterative slow feature analysis (ISFA). First, objects are obtained by multi-resolution segmentation of bi-temporal images respectively, and corresponding feature sets are constructed through stacking pixel- and object-level spectral features. Then, slow feature analysis (SFA) is used for transforming the feature sets into a new feature space at the first time. And iteration method with variable weights is introduced to get the last slow feature fusion map, where the changed pixels and unchanged pixels can be separated more easily. At last, K-means cluster is adopted to separate changed area and unchanged area automatically and generate final change result. Experiments were conducted on bi-temporal multi-spectral images, demonstrating the good performance of the proposed approach.
关键词： hybrid change detection,multi-scale fusion,feature-level fusion,iterative slow feature analysis
更新于2025-09-23 15:22:29