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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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Semi-automatic tracking of markers in facial palsy

Semi-automatic tracking of markers in facial palsy

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21st International Conference on pattern recognition, ICPR 2012

作者： Limbeck, Philip Kropatsch, Walter G. Haxhimusa, Yll Vienna University of Technology Pattern Recognition and Image Processing Group Austria

ISBN: (纸本)9784990644109

We introduce a semi-automatic tracking method that can be utilized for the analysis of facial markers in the medical condition of facial palsy. Tracking of markers will help medical physicians in evaluating this medical condition quantitatively. We use particle filtering to track markers towards measuring distances needed to evaluate the degree of facial palsy. We show that by employing tracking methods, the analysis time is reduced without losing the high accuracy of the results. © 2012 ICPR Org Committee.

关键词： pattern recognition

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Reducing the Computational Complexity of the Eccentricity Transform of a Tree 13th

Reducing the Computational Complexity of the Eccentricity...

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13th IAPR-TC-15 International Workshop on Graph-Based Representations in pattern recognition, GbRPR 2023

作者： Banaeyan, Majid Kropatsch, Walter G. Pattern Recognition and Image Processing Group TU Wien Vienna Austria

ISBN: (纸本)9783031427947

This paper proposes a novel approach to reduce the computational complexity of the eccentricity transform (ECC) for graph-based representation and analysis of shapes. The ECC assigns to each point within a shape its geodesic distance to the furthest point, providing essential information about the shape’s geometry, connectivity, and topology. Although the ECC has proven valuable in numerous applications, its computation using traditional methods involves heavy computational complexity. To overcome this limitation, we present a method that computes the ECC of a tree, significantly reducing the computational complexity from O(n2log(n) ) to O(b), where n and b are the numbers of vertices and branching points in the tree, respectively. Our method begins by computing the ECC for tree structures, which are simpler representations of shapes. Subsequently, we introduce the concept of a 3D curve that corresponds to a smooth shape without holes, enabling the computation of the ECC for more complex shapes. By leveraging the 3D curve representation, our method provides an upper-bound approximation of the ECC, which can be effectively utilized in various applications. The proposed approach not only preserves the valuable properties of the ECC but also significantly reduces the computational burden, making it a more efficient and practical solution for graph-based representation and analysis of shapes in both 2D and 3D contexts. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational complexity

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Data graph formulation as the minimum-weight maximum-entropy problem 10

Data graph formulation as the minimum-weight maximum-entropy...

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10th IAPR-TC-15 International Workshop on Graph-Based Representations in pattern recognition, GbRPR 2015

作者： De Sousa, Samuel Kropatsch, Walter G. Pattern Recognition and Image Processing Group Vienna University of Technology Vienna Austria

ISBN: (纸本)9783319182230

Consider a point-set coming from an object which was sampled using a digital sensor (depth range, camera, etc). We are interested in finding a graph that would represent that point-set according to some properties. Such a representation would allow us to match two objects (graphs) by exploiting topological properties instead of solely relying on geometrical properties. The Delaunay triangulation is a common out off-the-shelf strategy to triangulate a point-set and it is used by many researchers as the standard way to create the so called data-graph and despite its positive properties, there are also some drawbacks. We are interested in generating a graph with the following properties: the graph is (i) as unique as possible, (ii) and as discriminative as possible regarding the degree distribution. We pose a combinatorial optimization problem (Min-Weight Max-Entropy Problem) to build such a graph by minimizing the total weight cost of the edges and at the same time maximizing the entropy of the degree distribution. Our optimization approach is based on Dynamic Programming (DP) and yields a polynomial time algorithm. © Springer International Publishing Switzerland 2015.

关键词： Combinatorial optimization

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Estimation of distribution algorithm for the Max-Cut problem

Estimation of distribution algorithm for the Max-Cut problem

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9th IAPR-TC-15 International Workshop on Graph-Based Representations in pattern recognition, GbRPR 2013

作者： De Sousa, Samuel Haxhimusa, Yll Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group Vienna Austria

ISBN: (纸本)9783642382208

In this paper, we investigate the Max-Cut problem and propose a probabilistic heuristic to address its classic and weighted version. Our approach is based on the Estimation of Distribution Algorithm (EDA) that creates a population of individuals capable of evolving at each generation towards the global solution. We have applied the Max-Cut problem for image segmentation and defined the edges' weights as a modified function of the L2 norm between the RGB values of nodes. The main goal of this paper is to introduce a heuristic for Max-Cut and additionally to investigate how it can be applied in the segmentation context. © 2013 Springer-Verlag.

关键词： image segmentation

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Structural cues in 2D tracking: Edge lengths vs. barycentric coordinates 1

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18th Iberoamerican Congress on pattern recognition, CIARP 2013

作者： Artner, Nicole M. Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group Vienna Austria

ISBN: (数字)9783642418273

ISBN: (纸本)9783642418266

Graph models offer high representational power and useful structural cues. Unfortunately, tracking objects by matching graphs over time is in general NP-hard. Simple appearance-based trackers are able to find temporal correspondences fast and efficient, but often fail to overcome challenging situations like occlusions, distractors and noise. This paper proposes an approach, where an attributed graph is used to represent the structure of the target object and multiple, simple trackers in combination with structural cues replace the costly graph matching. Thus, the strengths of both methodologies are combined to overcome their weaknesses. Experiments based on synthetic videos are used to evaluate two possible structural cues. Results show the superiority of the cue based on barycentric coordinates and the potential of the proposed tracking approach in challenging situations. © Springer-Verlag 2013.

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On the evaluation of graph centrality for shape matching

On the evaluation of graph centrality for shape matching

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9th IAPR-TC-15 International Workshop on Graph-Based Representations in pattern recognition, GbRPR 2013

作者： De Sousa, Samuel Artner, Nicole M. Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group Vienna Austria

ISBN: (纸本)9783642382208

Graph centrality has been extensively applied in Social Network Analysis to model the interaction of actors and the information flow inside a graph. In this paper, we investigate the usage of graph centralities in the Shape Matching task. We create a graph-based representation of a shape and describe this graph by using different centrality measures. We build a Naive Bayes classifier whose input feature vector consists of the measurements obtained by the centralities and evaluate the different performances for each centrality. © 2013 Springer-Verlag.

关键词： Graphic methods

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Removing Redundancies in Binary images 2nd

Removing Redundancies in Binary Images

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2nd International Conference on Intelligent Systems and pattern recognition, ISPR 2022

作者： Banaeyan, Majid Batavia, Darshan Kropatsch, Walter G. Pattern Recognition and Image Processing Group 193/03 TU Wien Vienna Austria

ISBN: (纸本)9783031082764

Every day a huge amount of digital data is generated. processing such big data encourages efficient data structure and parallelized operations. In this regard, this paper proposes a graph-based method reducing the memory requirement of the data storage. Graphs as a versatile representative tool in intelligent systems and pattern recognition may consist of many nonessential edges accumulating memory. This paper defines the structure of such redundant edges in the neighborhood graph of a 2D binary image. We introduce a novel approach for contracting the edges that simultaneously assists in determining the structurally redundant edges. In addition, finding a set of independent edges, the redundant edges are removed in parallel with the complexity O(1 ). Theoretically, we prove that the maximum number of redundant edges is bounded by half of all edges. Practical results show the memory requirement decreases significantly depending on the input data in different categories of binary image data sets. Using the combinatorial map as the data structure, first the topological structure of the graph is preserved. Second, the method can be extended to higher dimensions (nD). © 2022, Springer Nature Switzerland AG.

关键词： Binary images

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Parallel O(log(n) ) Computation of the Adjacency of Connected Components 3rd

Parallel O(log(n) ) Computation of the Adjacency of Conne...

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3rd International Conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Banaeyan, Majid Kropatsch, Walter G. TU Wien Pattern Recognition and Image Processing Group 193/03 Vienna Austria

ISBN: (纸本)9783031092817

Connected Component Labeling (CCL) is a fundamental task in pattern recognition and image processing algorithms. It groups the pixels into regions, such that adjacent pixels have the same label while pixels belonging to distinct regions have different labels. The common linear-time raster scan CCL techniques have a complexity of O(image- size) in a 2D binary image. To speed up the procedure of the CCL, the paper proposes a new irregular graph pyramid. To construct this pyramid, we use a new formalism [1] that introduces an order of the pixels in the base grid to detect the redundant edges through the hierarchical structure. These redundant edges, unlike the usual methods of constructing the irregular pyramid, are removed before contracting the edges. This not only simplifies the construction processes but may decrease memory consumption by approximately half. To perform the CCL task efficiently the proposed parallel algorithm reduces the complexity to O(log(n) ) where the n is the diameter of the largest connected component in the image. In addition, using an efficient combinatorial structure the topological properties of the connected components including adjacency of CCs, multi-boundaries and inclusions are preserved. Finally, the mathematical proofs provide fully parallel implementations and lead to efficient results in comparison with the state-of-the-art. © 2022, Springer Nature Switzerland AG.

关键词： Pixels

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Pyramidal Connected Component Labeling by Irregular Graph Pyramid 5

Pyramidal Connected Component Labeling by Irregular Graph Py...

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5th International Conference on pattern recognition and image Analysis, IPRIA 2021

作者： Banaeyan, Majid Kropatsch, Walter G. Vienna University of Technology Pattern Recognition and Image Processing Group 193/03 Vienna Austria

ISBN: (纸本)9781665426596

This paper presents a new logarithmic-time algorithm which simultaneously assigns labels to all connected components of a binary image in parallel. The irregular graph pyramid of an input binary image is constructed based on the optimized combinatorial structure. The novel small built pyramid has only three levels and is created at worst case with O(log(N2)) complexity for a N× N-sized (2D) binary image. To assign a label to each connected component, instead of the common linear-time raster scan techniques (with complexity O(N2)), only two important movements, namely bottom-up and top-down traversing, are needed. First, in the bottom-up traversing, the redundant connections are removed and the contraction kernels are contracted. This results in a simpler graph at top of the pyramid each of its vertices has a unique label identifying corresponding connected component. Such reduced graph preserves all connecting relations including inclusions. Second, in the top-down traversing, each unique label propagates down into each individual corresponding pixel at the base level. The complexity of the labeling propagation procedure in worst cases is O (log(image - size)). The GPU implementation of the algorithm has high performance and the bottleneck is the bandwidth of the memory or equivalently the number of available independent processing elements. Finally, the experimental results show the proposed algorithm outperforms the other state-of-the-art methods. © 2021 IEEE.

关键词： Binary images

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