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2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)最新文献

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A Robotic System for Doing Six-sided Puzzle 六面拼图的机器人系统
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9651128
Chen-Yu Lin, Chun-Ting Tsai, Hsiang-Chieh Chen
{"title":"A Robotic System for Doing Six-sided Puzzle","authors":"Chen-Yu Lin, Chun-Ting Tsai, Hsiang-Chieh Chen","doi":"10.1109/ISPACS51563.2021.9651128","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9651128","url":null,"abstract":"This study presents a robotic system that is demonstrated to complete a six-sided 3D puzzle. The main components of our system consists of a SCARA, a stereo camera, a conveyor belt, motors controlled by Arduino boards, and a computing station. In addition to the hardware composition, a set of specified markers, namely ArUco, is used to determine the target position where the puzzle cubes should be placed. The main procedure of our proposed algorithm for realizing a 3D puzzle game includes the following stages: initializing robotic system, localizing the target positions through ArUco markers, detecting any puzzle cube in an image capture, extracting the upper surface of the detected cube, defining the drawing point and pose, and picking and placing the puzzle. After all the cubes of puzzle are placed on their target positions, two single-axis slider are used to put the cubes closely. The main achievements of this study is making the robotic have vision and then combining deep learning and image processing.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130235411","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}
引用次数: 0
The Algorithm and VLSI Architecture of an Efficient Image Sharpening Scheme Based on the Frequency Domain Analysis 一种基于频域分析的高效图像锐化方案的算法和VLSI结构
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9651010
Hui Luo, Chien-Ju Hsueh, Chung-An Shen
{"title":"The Algorithm and VLSI Architecture of an Efficient Image Sharpening Scheme Based on the Frequency Domain Analysis","authors":"Hui Luo, Chien-Ju Hsueh, Chung-An Shen","doi":"10.1109/ISPACS51563.2021.9651010","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9651010","url":null,"abstract":"Feature augmentation of the images plays a big role in the image recognition system and using a sharper image can enhance the accuracy of the image recognition. This paper presents a non-iterative image sharpening algorithm based on the frequency-domain analysis. This algorithm greatly improves the noise sensitive issue in classical unsharp masking technique. Furthermore, the non-iterative property is conducive to the hardware acceleration design. Considering the design of hardware acceleration, we focus on the three parts with the highest computation load of the algorithm, including FFT/IFFT unit, Wiener deconvolution process, and the least square method. Through the hardware acceleration, this algorithm is more suitable for applying to the real-time image recognition system. The experiment results show that this algorithm enhances the feature and contrast of image with effective noise reduction and achieves a throughput 30fps.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129206967","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}
引用次数: 0
AI-based Holl-filling with Temporal Consistency 基于时间一致性的人工智能霍尔填充
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9651045
Li-Jyun Chen, Jie Yang, Li Hong
{"title":"AI-based Holl-filling with Temporal Consistency","authors":"Li-Jyun Chen, Jie Yang, Li Hong","doi":"10.1109/ISPACS51563.2021.9651045","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9651045","url":null,"abstract":"Depth image-based rendering (DIBR) has been recently considered as a significant technology for generating 3D virtual views. However, the hole-filling method is still the main challenge in the DIBR engine because of the occlusions of foreground objects. While looking into the video sequences, the occluded parts in a frame may be revealed in the past or future frames. The temporal information could be the useful cues to recover the occlusions of the current frame. In this paper, we design an encoder-decoder neural model with a bounded region attention module to effectively fill the holes. This attention module is aim to extract the useful hints from neighbor frames.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125573527","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}
引用次数: 0
Optimum Distributed Highpass Filter Design Using Spur-line Pairs 利用激励线对优化分布式高通滤波器设计
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9651054
Hao-Hui Chen, Yi-Rong Chen, Kuan-Chi Chen, Yao-Wen Hsu
{"title":"Optimum Distributed Highpass Filter Design Using Spur-line Pairs","authors":"Hao-Hui Chen, Yi-Rong Chen, Kuan-Chi Chen, Yao-Wen Hsu","doi":"10.1109/ISPACS51563.2021.9651054","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9651054","url":null,"abstract":"This study presents a modified design of the optimum distributed highpass filter (HPF). First, a novel equivalent circuit of the π-network is proposed. The equivalent circuit is composed of two spur lines connected back-to-back, and the impedances of the spur lines are half of those of the line elements in the π-network. The proposed equivalent circuit is then applied to replace the high impedance stubs in the conventional optimum distributed HPF design. An improved design of line elements with reasonable impedances can thus be developed for the practical implementation of optimum distributed highpass filters. A test example with the pole of 11 and the primary passband being 1 to 5 GHz is designed and implemented. Comparisons between the measured and theoretical simulated results are performed to verify the proposed design.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121307801","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}
引用次数: 0
Finger-Vein Recognition Using a NASNet with a Cutout 使用带有切口的NASNet进行手指静脉识别
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9650980
I. S. Wang, Hung-Tse Chan, Chih-Hsien Hsia
{"title":"Finger-Vein Recognition Using a NASNet with a Cutout","authors":"I. S. Wang, Hung-Tse Chan, Chih-Hsien Hsia","doi":"10.1109/ISPACS51563.2021.9650980","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9650980","url":null,"abstract":"Traditional information security systems use passwords or identification cards that might be deciphered or stolen. Many methods have been developed to improve the security of personal information, such as the finger-vein recognition to replace traditional recognition. This study proposes a cutout for data augmentation (DA) and a neural architecture search network (NASNet). Experiments show that the proposed method is 98.89% accurate for the FV-USM public dataset.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122521835","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}
引用次数: 7
Minimizing Response Time for MapReduce Applications on In-Storage Processing Architecture 最小化存储处理架构下MapReduce应用的响应时间
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9651112
Zhengwu Lu, Chih-Chi Chang, Ya-Shu Chen
{"title":"Minimizing Response Time for MapReduce Applications on In-Storage Processing Architecture","authors":"Zhengwu Lu, Chih-Chi Chang, Ya-Shu Chen","doi":"10.1109/ISPACS51563.2021.9651112","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9651112","url":null,"abstract":"Integrating In-Storage Processing architecture with the Hadoop model offers a potential solution for accelerating applications with large-scale data requirements. However, the data movement overhead among nodes and the task interference delay among multiple applications are getting serious with the increased data size of applications. To minimize the response time of applications while maintaining the throughput, we propose workload-aware scheduling and waiting-time-aware dispatcher to manage multiple MapReduce applications under the In-Storage Processing architecture.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131172576","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}
引用次数: 0
Convolutional Dictionary Learning with Huber Error and l1 Regularization Terms 基于Huber误差和l1正则化项的卷积字典学习
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9651025
Satoshi Yoda, Hironori Kawazoe, Y. Kuroki
{"title":"Convolutional Dictionary Learning with Huber Error and l1 Regularization Terms","authors":"Satoshi Yoda, Hironori Kawazoe, Y. Kuroki","doi":"10.1109/ISPACS51563.2021.9651025","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9651025","url":null,"abstract":"This paper addresses a robust convolutional dictionary learning method against outliers. Convolutional dictionary learning approximates a signal with the sum of dictionary filters and corresponding coefficients, and its cost function consists of the weighted sum of the two terms: error and regularization terms. Many studies employ the l2 and the l1 norms for the former and the latter respectively, and to increase the robustness, the l1 norm is substituted for the error term. For such optimization problems with the sum of the two convex terms, the proximal gradient method is a powerful solver; however, it is not applicable for the two l1 terms, of which gradient is not continuous at any point. This paper tries to apply the Moreau envelope for the l1 error term, and the l1 error is expressed as Huber error function, which is differentiable and Lipschitz continuous. Experimental results show that dictionaries generated with the proposed method are robuster than those with the l2 error term.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132689129","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}
引用次数: 0
FoD Enroll Image Quality Classification Method for Fingerprint Authentication System 指纹认证系统的FoD注册图像质量分类方法
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9651102
Xiu-Zhi Chen, Jhe-Li Lin, Yen-Lin Chen
{"title":"FoD Enroll Image Quality Classification Method for Fingerprint Authentication System","authors":"Xiu-Zhi Chen, Jhe-Li Lin, Yen-Lin Chen","doi":"10.1109/ISPACS51563.2021.9651102","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9651102","url":null,"abstract":"Typical fingerprint authentication system flow including preprocessing, feature extraction, and feature matching. To improve the user experience of it, more intelligent process for such system is needed. Fingerprint on display (FoD) is a popular kind of sensing technique in recent years, in this research, we proposed an enroll image quality classification method for the preprocessing step of the system, which is able to reject the invalid input, in order to shorten the response time, especially for FoD applications. We had evaluated our proposed method through a self-collected FoD sensing image dataset, including 50,130 fingerprint images, and proved that our method is able to reach 95.83% accuracy, which is really helpful for the improvement of the system’s user experience.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125037558","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}
引用次数: 2
Mapping 3D road model to 2D street-view video using Image and Semantic Feature Matching 使用图像和语义特征匹配将3D道路模型映射到2D街景视频
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9651097
Kuan-Ting Chen, Jheng-Wei Su, K. Hsiao, Kuo-Wei Chen, Chih-Yuan Yao, Ruen-Rone Lee, Hung-Kuo Chu
{"title":"Mapping 3D road model to 2D street-view video using Image and Semantic Feature Matching","authors":"Kuan-Ting Chen, Jheng-Wei Su, K. Hsiao, Kuo-Wei Chen, Chih-Yuan Yao, Ruen-Rone Lee, Hung-Kuo Chu","doi":"10.1109/ISPACS51563.2021.9651097","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9651097","url":null,"abstract":"We propose a novel method to estimate the mapping between the 3D road model and the 2D street-view video. Our results show that our method can perform a high- quality 2D-to-3D mapping on various street-view videos.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134457919","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}
引用次数: 0
Learn from one image: Dynamic One-shot learning based on parameter generation 从一张图像中学习:基于参数生成的动态一次性学习
2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) Pub Date : 2021-11-16 DOI: 10.1109/ISPACS51563.2021.9651100
N. S. Kumar, M. Phirke, Anupriya Jayapal
{"title":"Learn from one image: Dynamic One-shot learning based on parameter generation","authors":"N. S. Kumar, M. Phirke, Anupriya Jayapal","doi":"10.1109/ISPACS51563.2021.9651100","DOIUrl":"https://doi.org/10.1109/ISPACS51563.2021.9651100","url":null,"abstract":"State-of-the-art deep learning algorithms are usually pre-trained on datasets containing millions of images. Adding new classes to these pre-trained networks, require large number of images for each of the new classes. Formulation of such large scale datasets usually require a lot of effort and time. The aim of this paper is to develop novel deep learning based one-shot learning framework which can achieve state-of-the-art results on new classes (one-shot classes) which have only one image each during the training phase. Adding these new one-shot classes, should not degrade the performance of the model on pre-trained classes. Multi-layer transformation function has been proposed in this paper for one-shot learning, where activations of a class are converted to their corresponding parameters. The model is pre-trained on large-scale base classes and the model adapts to new classes with zero training. Experiments were conducted on opensource datasets like MiniImageNet and Pascal-VOC using Nvidia K80 GPU. The model achieves an accuracy of 93.14% for large scale base classes and 64.69% for one-shot classes which is more than 3% better than the current state-of-the-art models.","PeriodicalId":359822,"journal":{"name":"2021 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132055145","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}
引用次数: 0
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