5 Search Results for "Zhang, Zhen"

Document
Introduction
DOI: 10.4230/LITES.8.1.0
Introduction to the Special Issue on Embedded Systems for Computer Vision

Authors: Samarjit Chakraborty and Qing Rao

Published in: LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision. Leibniz Transactions on Embedded Systems, Volume 8, Issue 1

Abstract
We provide a broad overview of some of the current research directions at the intersection of embedded systems and computer vision, in addition to introducing the papers appearing in this special issue. Work at this intersection is steadily growing in importance, especially in the context of autonomous and cyber-physical systems design. Vision-based perception is almost a mandatory component in any autonomous system, but also adds myriad challenges like, how to efficiently implement vision processing algorithms on resource-constrained embedded architectures, and how to verify the functional and timing correctness of these algorithms. Computer vision is also crucial in implementing various smart functionality like security, e.g., using facial recognition, or monitoring events or traffic patterns. Some of these applications are reviewed in this introductory article. The remaining articles featured in this special issue dive into more depth on a few of them.
Cite as

LITES, Volume 8, Issue 1: Special Issue on Embedded Systems for Computer Vision, pp. 0:i-0:viii, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)

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@Article{chakraborty_et_al:LITES.8.1.0,
  author =	{Chakraborty, Samarjit and Rao, Qing},
  title =	{{Introduction to the Special Issue on Embedded Systems for Computer Vision}},
  booktitle =	{LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision},
  pages =	{00:1--00:8},
  journal =	{Leibniz Transactions on Embedded Systems},
  ISSN =	{2199-2002},
  year =	{2022},
  volume =	{8},
  number =	{1},
  editor =	{Chakraborty, Samarjit and Rao, Qing},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES.8.1.0},
  doi =		{10.4230/LITES.8.1.0},
  annote =	{Keywords: Embedded systems, Computer vision, Cyber-physical systems, Computer architecture}
}
Document
DOI: 10.4230/LITES.8.1.1
Susceptibility to Image Resolution in Face Recognition and Training Strategies to Enhance Robustness

Authors: Martin Knoche, Stefan Hörmann, and Gerhard Rigoll

Published in: LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision. Leibniz Transactions on Embedded Systems, Volume 8, Issue 1

Abstract
Many face recognition approaches expect the input images to have similar image resolution. However, in real-world applications, the image resolution varies due to different image capture mechanisms or sources, affecting the performance of face recognition systems. This work first analyzes the image resolution susceptibility of modern face recognition. Face verification on the very popular LFW dataset drops from 99.23% accuracy to almost 55% when image dimensions of both images are reduced to arguable very poor resolution. With cross-resolution image pairs (one HR and one LR image), face verification accuracy is even worse. This characteristic is investigated more in-depth by analyzing the feature distances utilized for face verification. To increase the robustness, we propose two training strategies applied to a state-of-the-art face recognition model: 1) Training with 50% low resolution images within each batch and 2) using the cosine distance loss between high and low resolution features in a siamese network structure. Both methods significantly boost face verification accuracy for matching training and testing image resolutions. Training a network with different resolutions simultaneously instead of adding only one specific low resolution showed improvements across all resolutions and made a single model applicable to unknown resolutions. However, models trained for one particular low resolution perform better when using the exact resolution for testing. We improve the face verification accuracy from 96.86% to 97.72% on the popular LFW database with uniformly distributed image dimensions between 112 × 112 px and 5 × 5 px. Our approaches improve face verification accuracy even more from 77.56% to 87.17% for distributions focusing on lower images resolutions. Lastly, we propose specific image dimension sets focusing on high, mid, and low resolution for five well-known datasets to benchmark face verification accuracy in cross-resolution scenarios.
Cite as

Martin Knoche, Stefan Hörmann, and Gerhard Rigoll. Susceptibility to Image Resolution in Face Recognition and Training Strategies to Enhance Robustness. In LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision. Leibniz Transactions on Embedded Systems, Volume 8, Issue 1, pp. 01:1-01:20, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)

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@Article{knoche_et_al:LITES.8.1.1,
  author =	{Knoche, Martin and H\"{o}rmann, Stefan and Rigoll, Gerhard},
  title =	{{Susceptibility to Image Resolution in Face Recognition and Training Strategies to Enhance Robustness}},
  booktitle =	{LITES, Volume 8, Issue 1 (2022): Special Issue on Embedded Systems for Computer Vision},
  pages =	{01:1--01:20},
  journal =	{Leibniz Transactions on Embedded Systems},
  ISSN =	{2199-2002},
  year =	{2022},
  volume =	{8},
  number =	{1},
  editor =	{Knoche, Martin and H\"{o}rmann, Stefan and Rigoll, Gerhard},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES.8.1.1},
  doi =		{10.4230/LITES.8.1.1},
  annote =	{Keywords: recognition, resolution, cross, face, identification}
}
Document
DOI: 10.4230/LIPIcs.ISAAC.2020.5
A Unified Framework of FPT Approximation Algorithms for Clustering Problems

Authors: Qilong Feng, Zhen Zhang, Ziyun Huang, Jinhui Xu, and Jianxin Wang

Published in: LIPIcs, Volume 181, 31st International Symposium on Algorithms and Computation (ISAAC 2020)

Abstract
In this paper, we present a framework for designing FPT approximation algorithms for many k-clustering problems. Our results are based on a new technique for reducing search spaces. A reduced search space is a small subset of the input data that has the guarantee of containing k clients close to the facilities opened in an optimal solution for any clustering problem we consider. We show, somewhat surprisingly, that greedily sampling O(k) clients yields the desired reduced search space, based on which we obtain FPT(k)-time algorithms with improved approximation guarantees for problems such as capacitated clustering, lower-bounded clustering, clustering with service installation costs, fault tolerant clustering, and priority clustering.
Cite as

Qilong Feng, Zhen Zhang, Ziyun Huang, Jinhui Xu, and Jianxin Wang. A Unified Framework of FPT Approximation Algorithms for Clustering Problems. In 31st International Symposium on Algorithms and Computation (ISAAC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 181, pp. 5:1-5:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)

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@InProceedings{feng_et_al:LIPIcs.ISAAC.2020.5,
  author =	{Feng, Qilong and Zhang, Zhen and Huang, Ziyun and Xu, Jinhui and Wang, Jianxin},
  title =	{{A Unified Framework of FPT Approximation Algorithms for Clustering Problems}},
  booktitle =	{31st International Symposium on Algorithms and Computation (ISAAC 2020)},
  pages =	{5:1--5:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-173-3},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{181},
  editor =	{Cao, Yixin and Cheng, Siu-Wing and Li, Minming},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2020.5},
  URN =		{urn:nbn:de:0030-drops-133495},
  doi =		{10.4230/LIPIcs.ISAAC.2020.5},
  annote =	{Keywords: clustering, approximation algorithms, fixed-parameter tractability}
}
Document
DOI: 10.4230/LIPIcs.ISAAC.2019.61
Improved Algorithms for Clustering with Outliers

Authors: Qilong Feng, Zhen Zhang, Ziyun Huang, Jinhui Xu, and Jianxin Wang

Published in: LIPIcs, Volume 149, 30th International Symposium on Algorithms and Computation (ISAAC 2019)

Abstract
Clustering is a fundamental problem in unsupervised learning. In many real-world applications, the to-be-clustered data often contains various types of noises and thus needs to be removed from the learning process. To address this issue, we consider in this paper two variants of such clustering problems, called k-median with m outliers and k-means with m outliers. Existing techniques for both problems either incur relatively large approximation ratios or can only efficiently deal with a small number of outliers. In this paper, we present improved solution to each of them for the case where k is a fixed number and m could be quite large. Particularly, we gave the first PTAS for the k-median problem with outliers in Euclidean space R^d for possibly high m and d. Our algorithm runs in O(nd((1/epsilon)(k+m))^(k/epsilon)^O(1)) time, which considerably improves the previous result (with running time O(nd(m+k)^O(m+k) + (1/epsilon)k log n)^O(1))) given by [Feldman and Schulman, SODA 2012]. For the k-means with outliers problem, we introduce a (6+epsilon)-approximation algorithm for general metric space with running time O(n(beta (1/epsilon)(k+m))^k) for some constant beta>1. Our algorithm first uses the k-means++ technique to sample O((1/epsilon)(k+m)) points from input and then select the k centers from them. Compared to the more involving existing techniques, our algorithms are much simpler, i.e., using only random sampling, and achieving better performance ratios.
Cite as

Qilong Feng, Zhen Zhang, Ziyun Huang, Jinhui Xu, and Jianxin Wang. Improved Algorithms for Clustering with Outliers. In 30th International Symposium on Algorithms and Computation (ISAAC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 149, pp. 61:1-61:12, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)

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@InProceedings{feng_et_al:LIPIcs.ISAAC.2019.61,
  author =	{Feng, Qilong and Zhang, Zhen and Huang, Ziyun and Xu, Jinhui and Wang, Jianxin},
  title =	{{Improved Algorithms for Clustering with Outliers}},
  booktitle =	{30th International Symposium on Algorithms and Computation (ISAAC 2019)},
  pages =	{61:1--61:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-130-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{149},
  editor =	{Lu, Pinyan and Zhang, Guochuan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2019.61},
  URN =		{urn:nbn:de:0030-drops-115573},
  doi =		{10.4230/LIPIcs.ISAAC.2019.61},
  annote =	{Keywords: Clustering with Outliers, Approximation, Random Sampling}
}
Document
DOI: 10.4230/LITES-v002-i002-a001
From Dataflow Specification to Multiprocessor Partitioned Time-triggered Real-time Implementation

Authors: Thomas Carle, Dumitru Potop-Butucaru, Yves Sorel, and David Lesens

Published in: LITES, Volume 2, Issue 2 (2015). Leibniz Transactions on Embedded Systems, Volume 2, Issue 2

Abstract
Our objective is to facilitate the development of complex time-triggered systems by automating the allocation and scheduling steps. We show that full automation is possible while taking into account the elements of complexity needed by a complex embedded control system. More precisely, we consider deterministic functional specifications provided (as often in an industrial setting) by means of synchronous data-flow models with multiple modes and multiple relative periods. We first extend this functional model with an original real-time characterization that takes advantage of our time-triggered framework to provide a simpler representation of complex end-to-end flow requirements. We also extend our specifications with additional non-functional properties specifying partitioning, allocation, and preemptability constraints. Then, we provide novel algorithms for the off-line scheduling of these extended specifications onto partitioned time-triggered architectures à la ARINC 653. The main originality of our work is that it takes into account at the same time multiple complexity elements: various types of non-functional properties (real-time, partitioning, allocation, preemptability) and functional specifications with conditional execution and multiple modes. Allocation of time slots/windows to partitions can be fully or partially provided, or synthesized by our tool. Our algorithms allow the automatic allocation and scheduling onto multi-processor (distributed) systems with a global time base, taking into account communication costs. We demonstrate our technique on a model of space flight software system with strong real-time determinism requirements.
Cite as

Thomas Carle, Dumitru Potop-Butucaru, Yves Sorel, and David Lesens. From Dataflow Specification to Multiprocessor Partitioned Time-triggered Real-time Implementation. In LITES, Volume 2, Issue 2 (2015). Leibniz Transactions on Embedded Systems, Volume 2, Issue 2, pp. 01:1-01:30, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2015)

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@Article{carle_et_al:LITES-v002-i002-a001,
  author =	{Carle, Thomas and Potop-Butucaru, Dumitru and Sorel, Yves and Lesens, David},
  title =	{{From Dataflow Specification to Multiprocessor Partitioned Time-triggered Real-time Implementation}},
  booktitle =	{LITES, Volume 2, Issue 2 (2015)},
  pages =	{01:1--01:30},
  journal =	{Leibniz Transactions on Embedded Systems},
  ISSN =	{2199-2002},
  year =	{2015},
  volume =	{2},
  number =	{2},
  editor =	{Carle, Thomas and Potop-Butucaru, Dumitru and Sorel, Yves and Lesens, David},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES-v002-i002-a001},
  doi =		{10.4230/LITES-v002-i002-a001},
  annote =	{Keywords: Time-triggered, Off-line real-time scheduling, Temporal partitioning}
}
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