- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Region of Interest Detection based on Local Entropy Feature for Disaster Victim Detection System
摘要： Region of interest (ROI) detection plays an important role in object detection. It needs to be accurate and fast in some applications like real time disaster victim detection systems. ROI can reduce time and search space in detecting objects. In this paper visual saliency map is used for ROI detection. In most literature, most of ROI detection models only concentrate on reducing false positive (detecting wrong objects as intended ones) rate rather than false negative (missing intended object). In disaster victim detection, missing disaster victims is more important than detecting other objects like victim. So, the proposed method also focuses on reducing false negative error rate in object detection. In the proposed system, local entropy feature is added in Graph Based Visual Saliency (GBVS) map in addition to colour, orientation and shape feature maps.
关键词： GBVS,local entropy feature map,ROI detection,object detection,false negative
更新于2025-09-04 15:30:14
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[IEEE 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII) - Chennai (2018.3.22-2018.3.24)] 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII) - An Approach to Extract Optic-Disc from Retinal Image Using K-Means Clustering
摘要： generally, retinal picture valuation is commonly executed to appraise the diseases. In this paper, an image examination technique is implemented to extract the Retinal-Optic-Disc (ROD) to assess its condition. An approach based on the combination of Kapur’s entropy and K-means clustering is considered here to mine the optic disc region from the RGB retinal picture. During the experimental implementation, this approach is tested with the DRIVE and RIM-ONE databases. Initially, the DRIVE pictures are considered to appraise the proposed approach and is later, considered the ROD, comparative analyses with the expert’s Ground-Truths are carried out and the image similarity values are then recorded. This approach is then validated against the Otsu’s+levelset existing in the literature. All these experiments are implemented using Matlab2010. The outcome of this procedure confirms that, proposed work provides better picture similarity values compared to Otsu’s+levelset. Hence, in future, this procedure can be considered to evaluate the clinical retinal images.
关键词： Optic-disc,K-means clustering,validation.,Retinal picture,Kapur’s entropy
更新于2025-09-04 15:30:14
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[IEEE 2018 China International SAR Symposium (CISS) - Shanghai, China (2018.10.10-2018.10.12)] 2018 China International SAR Symposium (CISS) - A Frequency Nonlinearity and Fluctuation Correction Method for Terahertz ISAR Imaging
摘要： Due to the high frequency band and wide bandwidth of Terahertz imaging radar, the high frequency generally performs unstable in system. In detail, the linearly frequency modulated (LFM) signal shows nonlinearity and the frequency center fluctuates randomly with time. It will potentially lead to the degradation of Terahertz inverse synthetic aperture radar (THz-ISAR) imaging quality. To address this problem, this paper presents a frequency nonlinearity and fluctuation correction method based on minimum entropy (ME). Following the error model, both the error can be corrected by applying the ME metric to the range and azimuth direction. The feasibility and adaptability of this proposal have been validated by simulation and real data experimental result.
关键词： minimum entropy,Terahertz,error correction,inverse synthetic aperture radar
更新于2025-09-04 15:30:14
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A research on fiber-optic vibration pattern recognition based on time-frequency characteristics
摘要： To detect and recognize any type of events over the perimeter security system, this article proposes a fiber-optic vibration pattern recognition method based on the combination of time-domain features and time-frequency domain features. The performance parameters (event recognition, event location, and event classification) are very important and describe the validity of this article. The pattern recognition method is precisely based on the empirical mode decomposition of time-frequency entropy and center-of-gravity frequency. It implements the function of identifying and classifying the event (intrusions or non-intrusion) over the perimeter to secure. To achieve this method, the first-level prejudgment is performed according to the time-domain features of the vibration signal, and the second-level prediction is carried out through time-frequency analysis. The time-frequency distribution of the signal is obtained by empirical mode decomposition and Hilbert transform and then the time-frequency entropy and center-of-gravity frequency are used to form the time-frequency domain features, that is, combined with the time-domain features to form feature vectors. Multiple types of probabilistic neural networks are identified to determine whether there are intrusions and the intrusion types. The experimental results demonstrate that the proposed method is effective and reliable in identifying and classifying the type of event.
关键词： time-frequency domain features,time-frequency analysis,empirical mode decomposition,time-frequency entropy,Event recognition,pattern recognition,center-of-gravity frequency,time-domain features
更新于2025-09-04 15:30:14
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NONPARAMETRIC ROTATIONAL MOTION COMPENSATION TECHNIQUE FOR HIGH-RESOLUTION ISAR IMAGING VIA GOLDEN SECTION SEARCH
摘要： A novel rotational motion compensation algorithm for high-resolution inverse synthetic aperture radar (ISAR) imaging based on golden section search (GSS) method is presented. This paper focuses on the migration through cross-range resolution cells (MTCRRC) compensation, which requires rotation angle and center as priori information. The method performs in a nonparametric way and uses entropy criterion to estimate rotation angle and rotation center, which are used for rotational motion compensation. Experimental results show that the rotational motion in ISAR imaging can be e?ectively compensated. Moreover, the proposed method is robust and computationally more e?cient compared to the parametric methods.
关键词： entropy criterion,nonparametric method,rotational motion compensation,golden section search,ISAR imaging
更新于2025-09-04 15:30:14
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A Novel Multi-Exposure Image Fusion Method Based on Adaptive Patch Structure
摘要： Multi-exposure image fusion methods are often applied to the fusion of low-dynamic images that are taken from the same scene at different exposure levels. The fused images not only contain more color and detailed information, but also demonstrate the same real visual effects as the observation by the human eye. This paper proposes a novel multi-exposure image fusion (MEF) method based on adaptive patch structure. The proposed algorithm combines image cartoon-texture decomposition, image patch structure decomposition, and the structural similarity index to improve the local contrast of the image. Moreover, the proposed method can capture more detailed information of source images and produce more vivid high-dynamic-range (HDR) images. Speci?cally, image texture entropy values are used to evaluate image local information for adaptive selection of image patch size. The intermediate fused image is obtained by the proposed structure patch decomposition algorithm. Finally, the intermediate fused image is optimized by using the structural similarity index to obtain the ?nal fused HDR image. The results of comparative experiments show that the proposed method can obtain high-quality HDR images with better visual effects and more detailed information.
关键词： texture information entropy,adaptive selection,multi-exposure image fusion,patch structure decomposition
更新于2025-09-04 15:30:14