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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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Open-close by reconstruction on CNNUM

Open-close by reconstruction on CNNUM

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9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA

作者： Zhang, Qingli Zhang, Zhaoyang Image Processing and Pattern Recognition Laboratory Shanghai University 200072 Shanghai China Image Processing Laboratory Group of Image Processing and Transmission Shanghai University 200072 Shanghai China

Open-dose by reconstruction is one of the most important algorithms in mathematical morphology, it was used widely in image and video processing, but it requires the huge computational power, what is the bottleneck for application. So in this paper, Cellular Neural Network (CNN) is used to solve the problem, new +1 template and "and" template are designed here, along with the already developed templates and image preprocessing technique, the gray-scale open-close by reconstruction is realized. Experimental results based on CNN simulator are shown, proved that the speed on CNN is about 300 times faster than in traditional PC, the real time processing can be realized.

关键词： Cellular neural networks

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The influence of hubness on nearest-neighbor methods in object recognition

The influence of hubness on nearest-neighbor methods in obje...

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2011 IEEE 7th International Conference on Intelligent Computer Communication and processing, ICCP 2011

作者： Tomašev, Nenad Brehar, Raluca Mladenić, Dunja Nedevschi, Sergiu Artificial Intelligence Laboratory Jožef Stefan Institute Ljubljana Slovenia Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781457714788

Object recognition from images is one of the essential problems in automatic image processing. In this paper we focus specifically on nearest neighbor methods, which are widely used in many practical applications, not necessarily related to image data. It has recently come to attention that high dimensional data also exhibit high hubness, which essentially means that some very influential data points appear and these points are referred to as hubs. Unsurprisingly, hubs play a very important role in the nearest neighbor classification. We examine the hubness of various image data sets, under several different feature representations. We also show that it is possible to exploit the observed hubness and improve the recognition accuracy. © 2011 IEEE.

关键词： Object recognition

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Minimizing the topological structure of line images 7th

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7th Joint IAPR International Workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Kropatsch, Walter G. Burge, Mark Vienna University of Technology Institute of Automation 183/2 Pattern Recognition and Image Processing Group Treitlstr.3 WienA-1040 Austria Johannes Kepler University Institute of Systems Science Computer Vision Laboratory LinzA-4040 Austria

ISBN: (纸本)3540648585

We present a new algorithm based on Dual Graph Contraction (DGC) to transform the Run Graph into its Minimum Line Property Preserving (MLPP) form which, when implemented in parallel, requires O(log(longestcurve)) steps. A MLPP graph of a line image compliments the structural information in geometric graph representations like the run graph. Using such a graph and its dual, line image analysis systems can efficiently detect topological features like loops and holes and make use of relations like containment. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Topology

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Vehicle Routing Problem with Time Windows using Hybrid Metaheuristic Dragonfly Algorithm and Variable Neighborhood Search: Work on Progress 11

Vehicle Routing Problem with Time Windows using Hybrid Metah...

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11th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Yunita, Yunita Stiawan, Deris Rini, Dian Palupi Sriwijaya University Faculty of Computer Science Indonesia Sriwijaya University Intelligent System Research Group Faculty of Computer Science South Sumatera Palembang Indonesia Sriwijaya University Communication Network and Information Security Research Group Faculty of Computer Science South Sumatera Palembang Indonesia Sriwijaya University Image Processing Dan Pattern Recognition Laboratory Group Faculty of Computer Science South Sumatera Palembang Indonesia

ISBN: (纸本)9798350355314

One of the problems with Smart Transportation is the problem of cost and travel time. This problem is known as the Variable Routing Problem (VRP). In some real cases, in addition to considering route selection, there are limitations on the capacity and time period for the vehicle to serve each customer known as Time Windows, so VRP has developed into Variable Routing Problem with Time Windows (VRPTW). One way to solve VRPTW is to use metaheuristic methods. However, the metaheuristic method has a weakness, which are it can get stuck in local optimums and fail to find better global solutions. To overcome this, a combination with other methods or known as hybrid is needed. The formulation of this research problem is how to develop a Smart transportation model using a metaheuristic hybrid algorithm. The purpose of this study is to develop a Smart transportation model using a metaheuristic hybrid algorithm. The method used is to combine two metaheuristic algorithms, which are the Dragonfly Algorithm (DA) and the Variable Neighborhood Search (VNS). The result of this research is a new algorithm model which is a hybrid between DA and VNS. Through a combination of global exploration using DA and local exploitation using VNS, this algorithm is expected to be able to find a better solution and faster convergence in solving VRPTW problems © 2024 IEEE.

关键词： Travel time

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A new approach to finite wordlength coefficient fir digital filter design using the branch and bound technique 10

A new approach to finite wordlength coefficient fir digital ...

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2000 10th European Signal processing Conference, EUSIPCO 2000

作者： Belbachir, A.N. Boulerial, B. Belbachir, M.F. Signal and System Laboratory Electronic Institute U.S.T.O. B.P. 1505 El Mnouer Oran Algeria Vienna University of Technology Pattern Recognition and Image Processing Group Favritenstr. 9/1832 ViennaA-1040 Austria

ISBN: (纸本)9789521504433

It has been shown that the branch and bound technique is effective for the design of finite wordlength optimal digital filters. This technique is however expensive in computing time. In this paper, we present a robust branch and bound branching strategy named Sequential and Progressive Search, improving the design of filters on a large wordlength processor in a reasonable computing cost. The details of the algorithm and many examples are given and compared to the other methods. © 2000 EUSIPCO.

关键词： Digital filters

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Ancient coins & ceramics - 3d and 2d documentation for preservation and retrieval of lost heritage 2

Ancient coins & ceramics - 3d and 2d documentation for prese...

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2nd ISPRS International Workshop on Virtual Reconstruction and Visualization of Complex Architectures, 3D-ARCH 2007

作者： Mara, Hubert Kampel, Martin Niccolucci, Franco Sablatnig, Robert PIN - Servizi Didattici e Scientifici Universit di Firenze VAST-Laboratory Piazza Ciardi 25 Prato59100 Italy Vienna University of Technology Institute for Computer Aided Automation Pattern Recognition Image Processing Group Favoritenstrasse 9/183-2 Vienna1040 Austria

Motivated by the requirements of archaeologists we are developing an automated system for acquisition, documentation and management of daily finds of excavations. These daily finds can be separated into large objects like remainders of architecture and small objects of ancient daily life - like ceramics and coins. Ceramics especially are found in numbers of tens of thousands on virtually every excavation, because ceramics have been in use for thousands of years. Until the present day these finds are documented by manual drawings. There is a similar situation in the case of coins, where manual drawings are often used to abstract them from photographs. Therefore we propose an automated acquisition and documentation system based on digital cameras and structured light for small findings. For ceramics we provide further processing to estimate horizontal cross-sections (profile-lines) for printed documentation, as it is done by manual drawing. For this a proper orientation of the acquired 3D-model is required and automatically estimated based on the assumption that ceramics were made on rotational plates (wheels). We are aware that ceramics might not always have been manufactured on rotational plates, because the wheel was not invented everywhere as for the example in the Americas. Even though ceramics from such areas appear to be rotational symmetric, we developed a method based on shape and symmetry analysis to determine the manufacturing techniques of ceramics. This helps to answer another archaeological question regarding the technological advance of an ancient culture. Results for accuracy and performance are shown on real data from recent interdisciplinary projects together with archaeologists from Austria, Germany, Israel and Peru. Furthermore we present preliminary results of the integration of coin classification in our documentation system. Additionally we are currently adapting the London Charter to ensure the intellectual integrity, reliability, transparency,

关键词： Excavation

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Online LiDAR-Camera Extrinsic Parameters Self-checking

arXiv

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arXiv 2022年

作者： Wei, Pengjin Yan, Guohang Li, Yikang Fang, Kun Yang, Jie Liu, Wei The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China The Autonomous Driving Group Shanghai AI Laboratory China

With the development of neural networks and the increasing popularity of automatic driving, the calibration of the LiDAR and the camera has attracted more and more attention. This calibration task is multi-modal, where the rich color and texture information captured by the camera and the accurate three-dimensional spatial information from the LiDAR is incredibly significant for downstream tasks. Current research interests mainly focus on obtaining accurate calibration results through information fusion. However, they seldom analyze whether the calibrated results are correct or not, which could be of significant importance in real-world applications. For example, in large-scale production, the LiDARs and the cameras of each smart car have to get well-calibrated as the car leaves the production line, while in the rest of the car life period, the poses of the LiDARs and cameras should also get continually supervised to ensure the security. To this end, this paper proposes a self-checking algorithm to judge whether the extrinsic parameters are well-calibrated by introducing a binary classification network based on the fused information from the camera and the LiDAR. Moreover, since there is no such dataset for the task in this work, we further generate a new dataset branch from the KITTI dataset tailored for the task. Our experiments on the proposed dataset branch demonstrate the performance of our method. To the best of our knowledge, this is the first work to address the significance of continually checking the calibrated extrinsic parameters for autonomous driving. The code is open-sourced on the Github website at https://***/OpenCalib/LiDAR2camera_self-check. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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The influence of hubness on nearest-neighbor methods in object recognition

The influence of hubness on nearest-neighbor methods in obje...

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IEEE International Conference on Intelligent Computer Communication and processing (ICCP)

作者： Nenad Tomašev Raluca Brehar Dunja Mladenić Sergiu Nedevschi Jožef Stefan Institute Artificial Intelligence Laboratory Ljubljana Slovenia Image Processing and Pattern Recognition Group Technical University of Cluj-Napoca Romania

关键词： Feature extraction Object recognition Entropy Histograms Visualization image color analysis Shape

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CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene

arXiv

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arXiv 2022年

作者： Wei, Pengjin Yan, Guohang Li, Yikang Fang, Kun Cai, Xinyu Yang, Jie Liu, Wei The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China The Autonomous Driving Group Shanghai AI Laboratory China

Sensor-based environmental perception is a crucial part of the autonomous driving system. In order to get an excellent perception of the surrounding environment, an intelligent system would configure multiple LiDARs (3D Light Detection and Ranging) to cover the distant and near space of the car. The precision of perception relies on the quality of sensor calibration. This research aims at developing an accurate, automatic, and robust calibration strategy for multiple LiDAR systems in the general road scene. We thus propose CROON (automatic multi-LiDAR Calibration and Refinement methOd in rOad sceNe), a two-stage method including rough and refinement calibration. The first stage can calibrate the sensor from an arbitrary initial pose, and the second stage is able to precisely calibrate the sensor iteratively. Specifically, CROON utilize the nature characteristics of road scene so that it is independent and easy to apply in large-scale conditions. Experimental results on real-world and simulated data sets demonstrate the reliability and accuracy of our method. All the related data sets and codes are open-sourced on the Github website https://***/OpenCalib/LiDAR2LiDAR. Copyright © 2022, The Authors. All rights reserved.

关键词： Intelligent systems

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