International Symposium on Multispectral Image Processing and Pattern Recognition最新文献

{"title":"Research on compressed light-field and infrared reflection characteristics of patterned metallic micro-nano-structure arrays","authors":"J. Yi, Chai Hu, D. Wei, Wenda He, Xinyu Zhang","doi":"10.1117/12.2539277","DOIUrl":"https://doi.org/10.1117/12.2539277","url":null,"abstract":"Metallic micro-nano-structure arrays can be used to induce a collective oscillation of free electrons on the surface of metal films, so as to generate relatively strong surface plasmons (SPs) at the metal and medium interface and further localized light field under the excitation of incident lightwaves. As the oscillating light field propagating along the interface, the field strength can be increased reasonably at the functioned metal surface such as the incident light energy being localized in the sub-wavelength region defined by the functioned micro-nano-structures. The common beam diffraction limit formed during lightwave transmission or process can be broken effectively. Through constructing SPs over the special micro-nano-structures, the infrared reflection characteristics can be changed and then the local light field originated from incident infrared radiation also be enhanced significantly so as to efficiently perform infrared detection. Generally, the reflectivity and light field distribution behaviors of the functioned metal surface can be modulated by changing featured parameters of the metallic micro-nano-structural arrays. In this paper, a metal micro-nano-patterned structures with an arrayed tip is established for compressing the incident light field and then reducing the reflectivity of the metal surface and thus sensing incident light energy. A finite integral method for simulating and analyzing the structural characters such as the distance between tips, the tip sharpness, the thickness of the metal film, is utilized to acquire the reflectivity and field enhancement characteristics. The infrared reflection spectrum and the near-field intensity distribution of the metallic micro-nano-structure are compared and analyzed. The results show that the response frequency and excitation intensity of SPs over the nano-tip array, the intensity and distribution region of the strong light field, can be controlled by matching the structural parameters and layout. The optimization of the metallic micro-nanostructure arrays is conducted so as to lay a solid foundation for further development of the similar technologies.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115385051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel spatial-frequency filtering scheme for x-ray angiograms with simultaneous denoising and enhancement","authors":"Zhenghua Huang, Liang Chen, Hao Fang, Tianxu Zhang","doi":"10.1117/12.2538950","DOIUrl":"https://doi.org/10.1117/12.2538950","url":null,"abstract":"X-ray angiograms, which suffer from low-contrast and noise, need to be improved by both the image enhancement and denoising techniques. However, the goals of these two tasks usually conflict, which makes it difficult to efficiently combine the enhancement and denoising in one scheme. To solve this problem, we propose a novel spatial-frequency filtering (SFF) scheme to simultaneously enhance and denoise low-quality X-ray cardiovascular angiogram images. The proposed scheme includes three key components: Firstly, a relative total variation method is employed as a guide filter to separate an input image into two parts, including the base layer with strong structures and the detail layer with weak structures and noise. Then the base layer is enhanced by a proposed improved histogram equalization (IHE) method while the detail layer is extracted by a short-time Fourier transform and is further enhanced by using a proposed adaptive correction parameter. Finally, the improved image is the combination of results obtained by the two components. Both quantitative and qualitative results of experiments on real-world low-quality X-ray angiogram images demonstrate that the proposed method outperforms the state-of-the-arts in terms of contrast enhancement, structure preservation, and noise reduction.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132535189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Object-based loss function in segmented neural networks","authors":"Jin Liu, Qun Li","doi":"10.1117/12.2541908","DOIUrl":"https://doi.org/10.1117/12.2541908","url":null,"abstract":"This paper proposes Object-based Loss Function in Segmented Neural Networks. Traditional Segmented Neural Network(SNN) are based on Pixel-based Back Propagation(PBP). Since the pixel ratios of the images occupied by different sizes of objects are not the same, the weight of the small objects in the segmentation is small, which means using PBP may greatly affects the accuracy of the detection when there are a large number of small objects. Considering this defect of PBP, we propose a Object-based Back Propagation(OBP) loss function weight design, that is, the back propagation weights of different objects are not equal, which is inversely proportional to the area occupied by the object. Segmented Neural Networks data set test.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131602055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of bare soil extraction methods in black soil zone for AHSI/GF-5 remote sensing data","authors":"K. Shang, C. Xiao, Shuneng Liang","doi":"10.1117/12.2539360","DOIUrl":"https://doi.org/10.1117/12.2539360","url":null,"abstract":"The black soil zone in the northeast of China is one of the three largest black soil zones in the world, and the most important cultivated area for growing food crops in China. Remote sensing can obtain regional soil information of large area more rapidly with less labor and money. One of the key issues of soil investigation is the extraction of bare soil. Hyperspectral remote sensing data have more spectral bands and nearly continuous spectral curve, indicating more detailed information of the soil properties than traditional multispectral images. By using hyperspectral data, we can obtain reliable bare soil information. This study aims to compare different bare soil extraction methods for black soil zone and analyze their feasibilities to be applied to AHSI/GF-5 data. Baoqing County in Heilongjiang Province is chosen as our study area. To perform a comprehensive and complete comparison of bare soil extraction methods, we compare 8 classical target detection algorithms and analyze the impacts of spectral dimension reduction and the spatial filter on the extraction results. The results show that it is feasible to extract bare soil information in the black soil zone based on AHSI/GF-5 hyperspectral data. MF and CEM can get the best extraction results with NPSAD and MNF through human-computer interaction parameter adjustment, while MTMF can also obtain a good extraction result without human interference.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127507524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increment adaptive correlation filter for visual tracking","authors":"Gangbiao Chen, Zhiwen Fang, Zhou Yue, Bo Liu, Yang Xiao, Yanan Li","doi":"10.1117/12.2541744","DOIUrl":"https://doi.org/10.1117/12.2541744","url":null,"abstract":"Currently, the correlation filter is widely used in visual tracking because of its effectiveness and efficiency. To adapt the representation to changing target appearances, a linear interpolation is used to update tracking models according to a manually designed learning rate. However, The limitation of manually tricks make methods only apply to some special scenes because the threshold parameters are sensitive to different response maps in complex scenes. In this paper, to overcome this problem, an adaptive increment correlation filter based tracker is proposed. Different from traditional linear interpolation depending on a manual learning rate, the increment is learned by linear regression based on the history tracking model and the current training samples. Experimentally, we show that our algorithm can outperform state-of-the-art key point-based trackers.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125369817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image fusion for multimodality image via domain transfer and nonrigid transformation","authors":"Yicheng Yang, Huabing Zhou","doi":"10.1117/12.2541803","DOIUrl":"https://doi.org/10.1117/12.2541803","url":null,"abstract":"Infrared images can distinguish targets from their backgrounds on the basis of difference in thermal radiation, which works well at all day/night time and under all weather conditions. By contrast, visible images can provide texture details with high spatial resolution and definition in a manner consistent with the human visual system. We addressed the multimodality image fusion problem through three steps. Firstly, Domain transfer technique is introduced to transfer an image from one domain to another. For example, from visible image to infrared image. It can capture content characteristics of one image collection and figure out how these characteristics could be translated into the other image collection, all in the absence of any paired training examples. Secondly, we employ the nonrigid transformation method to match the domain transferred image and the target image, let the images pairs align in pixel level. Then we focus on fusion the domain transferred and spatial transformed image with the target image. Through translation and transformation, we simplify the fusion problem into a simple combination.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"51 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113937063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dimensionality reduction of hyperspectral images based on subspace combination clustering and adaptive band selection","authors":"Chunsen Zhang, Hengheng Liu","doi":"10.1117/12.2539312","DOIUrl":"https://doi.org/10.1117/12.2539312","url":null,"abstract":"This paper proposes a method of hyperspectral image dimensionality reduction based on automatic subspace partition, k-means clustering based on mutual information and adaptive band selection. This method first automatic subspace division method is used to determine the initial subspace, in various initial subspace through the mutual information between image variance and band and K - means to determine the clustering center and clustering center from two adjacent band selection and their mutual information between the difference between the absolute minimum band as a boundary to delimit the molecular space, and then in the subspace of division of each band is obtained by applying the method of adaptive band selection index, get the biggest index of each subspace of band and from big to small order according to the index, at last in the first three band is the selection of bands. OMIS hyperspectral data were used to conduct experiments, and this method has a higher classification accuracy than the previous band selection methods.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114120713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determination and elimination of fiber cable fault points","authors":"Jianping Yu, Jie-Li Xiao","doi":"10.1117/12.2535529","DOIUrl":"https://doi.org/10.1117/12.2535529","url":null,"abstract":"Optical fiber communication is one of the main modes of modern communication, has developed rapidly and been widely used. Fiber optic cable fault is the primary factor of communication interruption, and about two-thirds of the statistical faults are fiber optic cable faults. In order to ensure the uninterrupted communication and repair the faults of optical cable line in time, it is necessary to locate the faults accurately and repair the faulty optical cable quickly. Converting the ground length of the faulty optical cable according to the test curve of Optical Time Domain Reflectometer and the original data can quickly find the fault point, greatly shorten the search time of the difficult fault point, and then eliminate the fault.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116890439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"View prediction using manifold learning in non-linear feature subspace","authors":"Maliha Arif, Abhijit Mahalanobis","doi":"10.1117/12.2539521","DOIUrl":"https://doi.org/10.1117/12.2539521","url":null,"abstract":"In this paper, we make use of a convolutional autoencoder to predict multiple unseen views of an object in the infrared domain. The dataset we use for this purpose is called ‘DSIAC-ATR Image database’ which has never been used before for view prediction in the non-linear feature subspace. Our method involves exploiting the underlying feature subspace – the manifold of the object - to predict an unseen view. We address a more challenging task of view prediction by working with greyscale images- the infrared images collected both during the day and night. We propose multiple architectures that not only predict how an object (a military vehicle in this case) will look like at a certain orientation but also learn to predict day or night infrared image and produce either as asked. We train our networks and show via experiments that the weights do not learn the geometry of transformation in the Euclidean space but rather in the Riemannian space. We explore the underlying feature subspace and observe that the networks learn the manifolds and thereby produce sharp, distinct and natural-looking images.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127544568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved dynamic double threshold Canny edge detection algorithm","authors":"Zhikang Xiao, Yang Zou, Zhen Wang","doi":"10.1117/12.2539300","DOIUrl":"https://doi.org/10.1117/12.2539300","url":null,"abstract":"With the upgrade of the industry, robots urgently need to track moving targets at high speed. Therefore, the detection algorithms in machine vision technology need to be improved. Aiming at the problem that high and low thresholds need to be fixed in traditional Canny edge detection algorithm, an improved dynamic double threshold Canny algorithm is proposed. Constantly increasing the size of the threshold, Using the size of the area where the image edge is closed as a standard, finally to determine the best threshold, In order to achieve the best detection effect. Experimental results show that, Improved dynamic double threshold Canny algorithm not only improves the edge detection effect by 9% on average compared with the traditional algorithm, but also detects more complete image information and has stronger adaptability.","PeriodicalId":384253,"journal":{"name":"International Symposium on Multispectral Image Processing and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121201556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}