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检索条件"任意字段=4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015"

共 180 条记录，以下是1-10 订阅

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4th international conference on pattern recognition applications and methods, icpram 2015

4th International Conference on Pattern Recognition Applicat...

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4th international conference on pattern recognition applications and methods, icpram 2015

ISBN: (纸本)9783319276762

the proceedings contain 18 papers. the special focus in this conference is on theory and methods. the topics include: Density difference detection with application to exploratory visualization;identifying and mitigating labelling errors in active learning;a holistic classification optimization framework with feature selection, preprocessing, manifold learning and classifiers;detection of abrupt changes in spatial relationships in video sequences;diffusion-based similarity for image analysis;automatic detection and recognition of symbols and text on the road surface;using BLSTM for spotting regular expressions in handwritten documents;a similarity-based color descriptor for face detection;pose estimation and movement detection for mobility assessment of elderly people in an ambient assisted living application;a non-rigid face tracking method for wide rotation using synthetic data;3-D face recognition using geodesic-map representation and statistical shape modelling;learning discriminative mid-level patches for fast scene classification;modification of polyp size and shape from two endoscope images using RBF neural network;detecting and dismantling composite visualizations in the scientific literature;tensor deep stacking networks and kernel deep convex networks for annotating natural scene images and multi-object segmentation for assisted image reconstruction.

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pattern recognition applications and methods: 4th international conference, icpram 2015 Lisbon, Portugal, January 10–12, 2015 revised selected papers 4th

Pattern recognition applications and methods: 4th internatio...

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4th international conference on pattern recognition applications and methods, icpram 2015

作者： Fred, Ana De Marsico, Maria Figueiredo, Mário Instituto de Telecomunicações Instituto Superior Técnico University of Lisbon Lisbon Portugal Sapienza Università di Roma Rome Italy

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icpram 2015 - 4th international conference on pattern recognition applications and methods, Proceedings

ICPRAM 2015 - 4th International Conference on Pattern Recogn...

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4th international conference on pattern recognition applications and methods, icpram 2015

ISBN: (纸本)9789897580772

the proceedings contain 94 papers. the topics discussed include: naive Bayes classifier with mixtures of polynomials;representation optimization with feature selection and manifold learning in a holistic classification framework;discriminative kernel feature extraction and learning for object recognition and detection;normalised diffusion cosine similarity and its use for image segmentation;on selecting useful unlabeled data using multi-view learning techniques;user-driven nearest neighbour exploration of image archives;HyperSAX: fast approximate search of multidimensional data;a hybrid BLSTM-HMM for spotting regular expressions;mobility assessment of demented people using pose estimation and movement detection - an experimental study in the field of ambient assisted living;improvement of recovering shape from endoscope images using RBF neural network;dismantling composite visualizations in the scientific literature;and automatic tooth identification in dental panoramic images with atlas-based models.

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icpram 2015 - 4th international conference on pattern recognition applications and methods, Proceedings

ICPRAM 2015 - 4th International Conference on Pattern Recogn...

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4th international conference on pattern recognition applications and methods, icpram 2015

ISBN: (纸本)9789897580765

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BEVFormer’s Plugin: Integrating Historical Detection Data, BEV-Level Information, Time Information, and Depth Information 4th

BEVFormer’s Plugin: Integrating Historical Detection Data, ...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Pan, Zeen Tong, Zongyang Dong, Yuhan Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9789819787012

this paper presents a novel approach to camera-based 3D object detection, a critical task in autonomous driving systems. We propose a method that leverages historical information and introduces a plug-and-play characteristic during training. Our approach builds upon existing methods by extending the concept of historical feature augmentation and fully exploiting historical information, significantly enhancing detection performance. We also introduce an auxiliary loss operating at the Bird’s Eye View (BEV) level, which meticulously cultivates the model’s environmental cognition. To address the temporal dimension’s inherent lack in the BEV framework, we incorporate a temporal embedding module into the BEV feature amalgamation. Our method elevates the detection paradigm by harnessing historical and temporal information and integrating a BEV-level supervisory framework. We validate our approach through extensive experiments, demonstrating its superiority in real-world scenarios and potential for applications in autonomous driving, video surveillance, and intelligent transportation systems. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Driver licensing

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Advancing Systematic Literature Reviews Methodology through Topic Modeling 4th

Advancing Systematic Literature Reviews Methodology Through ...

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

作者： Mekaoui, Salma Chaker, Ilham Zarghili, Arsalane Nikolov, Nikola S. Department of Computer Science and Information Systems University of Limerick LimerickV94 T9PX Ireland Laboratory of Intelligent Systems and Applications Faculty of Sciences and Technology University Sidi Mohamed Ben Abdellah Route Immouzer Fez Morocco

ISBN: (纸本)9783031821493

the benefits of conducting a systematic review (SR) within a research project are well recognised. Nonetheless, nowadays an SR demands significant time and effort as it typically requires manually sifting through a large corpus of papers. In this work, we propose an application that simplifies the SR process for researchers by performing topic modeling. Our approach involves more than just conducting topic modeling;it entails selecting the optimal method tailored to our system’s needs. We explore two widely recognized methods in topic modeling, namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), alongside two prominent libraries, Scikit-learn and Gensim, which provide implementations for these methods. the evaluation of these methods and libraries is done with the help of the cosine similarity metric. Our findings indicate that, for our research context, LDA implemented in Scikit-learn stands out as the most effective method for topic modeling. Additionally, our study suggests that employing 10 topics, along with stemming and lemmatization, yields better results. Our findings culminate in the development of an application to facilitate SRs for researchers. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Non-negative matrix factorization

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Accelerating Traditional Object Detection methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophis...

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, thierry Chouireb, Fatima Department of Physics Laboratory of Applied Sciences and Didactics École Normale Supérieure de Laghouat Laghouat Algeria Department of Electronics Telecommunication Signals and Systems Laboratory University Amar Telidji of Laghouat Laghouat Algeria Department of Computer Science Lab. Mathématiques Image et Applications Université de La Rochelle La Rochelle France

ISBN: (纸本)9783031821523

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. therefore, special hardware that can acquire, control, or execute in parallel is required. these specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual Processing Units VPUs, Tensor Processing Units TPUs, Neural Processing Units NPUs or Graphics Processing Units GPUs. this work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. this single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4× faster than the CPU for the HOG method and 2× for the Haar Cascade Classifier method. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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An interactive model for structural pattern recognition based on the bayes classifier 4

An interactive model for structural pattern recognition base...

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4th international conference on pattern recognition applications and methods, icpram 2015

作者： Cortés, Xavier Serratosa, Francesc Moreno-García, Carlos Francisco Universitat Rovira i Virgili Tarragona Spain

ISBN: (纸本)9789897580765

this paper presents an interactive model for structural pattern recognition based on a naïve Bayes classifier. In some applications, the automatically computed correlation between local parts of two images is not good enough. Moreover, humans are very good at locating and mapping local parts of images although any kind of global transformations had been applied to these images. In our model, the user interacts on the automatically obtained correlation (or correspondences between local parts) and helps the system to find the best correspondence while the global transformation parameters are automatically recomputed. the model is based on a Bayes classifier in which the human interaction is properly modelled and embedded in the model. We show that with little human interaction, the quality of the returned correspondences and global transformation parameters drastically increases.

关键词： Classifiers

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Shape-based object retrieval and classification with supervised optimisation 4

Shape-based object retrieval and classification with supervi...

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4th international conference on pattern recognition applications and methods, icpram 2015

作者： Yang, Cong Tiebe, Oliver Pietsch, Pit Feinen, Christian Kelter, Udo Grzegorzek, Marcin Research Group for Pattern Recognition University of Siegen Siegen Germany Software Engineering and Databases Group University of Siegen Siegen Germany

ISBN: (纸本)9789897580765

In order to enhance the performance of shape retrieval and classification, in this paper, we propose a novel shape descriptor with low computation complexity that can be easily fused with other meaningful descriptors like shape context, etc. this leads to a significant increase in descriptive power of original descriptors without adding to much computation complexity. To make the proposed shape descriptor more practical and general, a supervised optimisation strategy is introduced. the most significant scientific contributions of this paper includes the introduction of a new and simple feature descriptor with supervised optimisation strategy leading to the impressive improvement of the accuracy in object classification and retrieval scenario.

关键词： Classification (of information)

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A fast 4D facial expression recognition method for low-resolution videos 4

A fast 4D facial expression recognition method for low-resol...

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4th international conference on pattern recognition applications and methods, icpram 2015

作者： Shao, Jie Dong, Nan Department of Electronic Information Engineering Shanghai University of Electric Power Shanghai China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China

ISBN: (纸本)9789897580765

With the popularity of 3D cameras, 4D (3D+time) facial expression recognition has become one of the fresh topics in recent years. In order to meet the requirements of practical applications using non-professional 3D cameras (like Microsoft Kinect), a fast facial expression recognition method for low-resolution RGB-D videos is introduced in this paper. In the proposed solution, first faces are automatically detected and aligned from each RGB-D image sequence. then faces of each image sequence are represented by their local 4D texture features. these features are trained by Conditional Random Field (CRF) model based classifiers CRFs, HCRFs and LDCRFs respectively. Plenty of discussions are made to compare the classifiers in aspects of time and effectiveness. Our final results demonstrate the effectiveness and practicability of our approach in 4D facial expression recognition related applications.

关键词： Face recognition

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