Video facial landmark detection and tracking are important computer vision tasks with many applications such as face anti-spoofing, animation and recognition. Most of existing facial landmark detection and tracking me...
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A discussion on involvement of knowledge based methods in implementation of user friendly computer programs for disabled people is the goal of this paper. the paper presents a concept of a computer program that is aim...
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In feature embedding, the recovery of associated discriminative information in the reduced subspace is critical for downstream classifiers. In this study, a supervised feature embedding method is proposed inspired by ...
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ISBN:
(纸本)9781728188089
In feature embedding, the recovery of associated discriminative information in the reduced subspace is critical for downstream classifiers. In this study, a supervised feature embedding method is proposed inspired by the well-known word embedding technique, word2vec. Proposed embedding method is implemented as representative learning of rank-based neighbor-hoods. the notion of context words in word2vec is extended into neighboring instances within a given window. Neighborship is defined using ranks of instances rather than their values so that regions with different densities are captured properly. Each sample is represented by a unique one-hot vector whereas its neighbors are encoded by several two-hot vectors. the two-hot vectors are identical for neighboring samples of the same class. A feed-forward neural network with a continuous projection layer, then learns the mapping from one-hot vectors to multiple two-hot vectors. the hidden layer determines the reduced subspace for the train samples. the obtained transformation is then applied on test data to find a lower-dimensional representation. Proposed method is tested in classification problems on 10 UCI data sets. Experimental results confirm that the proposed method is effective in finding a discriminative representation of the features and outperforms several supervised embedding approaches in terms of classification performance.
Recently, the horizon of dynamic time warping (DTW) for matching two sequential patterns has been extended to deal with non-Markovian constraints. the non-Markovian constraints regulate the matching in a wider scale, ...
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ISBN:
(纸本)9781479918058
Recently, the horizon of dynamic time warping (DTW) for matching two sequential patterns has been extended to deal with non-Markovian constraints. the non-Markovian constraints regulate the matching in a wider scale, whereas Markovian constraints regulate the matching only locally. the global optimization of the non-Markovian DTW is proved to be solvable in polynomial time by a graph cut algorithm. the main contribution of this paper is to reveal what is the best constraint for handwriting recognition by using the non-Markovian DTW. the result showed that the best constraint is not a Markovian but a totally non-Markovian constraint that regulates the matching between very distant points;that is, it was proved that the conventional Markovian DTW has a clear limitation and the non-Markovian DTW should be more focused in future research.
Increasingly, distributed systems are being constructed by composing a number of discrete components. this practice, termed composition, is particularly prevalent within the Web service domain. Here, enterprise system...
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the proceedings contain 23 papers. the topics discussed include: longer stay less priority: flow length approximation used in information-agnostic traffic scheduling in data center networks;where is the light(ning) in...
ISBN:
(纸本)9781665435383
the proceedings contain 23 papers. the topics discussed include: longer stay less priority: flow length approximation used in information-agnostic traffic scheduling in data center networks;where is the light(ning) in the taproot dawn? unveiling the bitcoin lightning (IP) network;using distributed tracing to identify inefficient resources composition in cloud applications;leveraging partial model extractions using uncertainty quantification;super-resolution on edge computing for improved adaptive http live streaming delivery;secure distributed storage on cloud-edge infrastructures;and a machine learning approach for service function chain embedding in cloud datacenter networks.
Internet of things (IoT) has a growing application in agriculture and smart farming. Different monitoring, controlling and tracking systems have been proposed for increasing the efficiency and quality of agricultural ...
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the paper presents preliminary results of data analysis and discusses the application of soft computing methods in the field of non-destructive tests. the main objective of developed diagnostic system are the automati...
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ISBN:
(纸本)9783642132315
the paper presents preliminary results of data analysis and discusses the application of soft computing methods in the field of non-destructive tests. the main objective of developed diagnostic system are the automatic detection and evaluation of damage. thus the system is composed of two signal processing techniques known as novelty detection and patternrecognition. For this purpose autoassociative as well as feed-forward neural networks are used. All the signals used for training the system are obtained from laboratory tests of strip specimens, where phenomenon of elastic wave propagation in solids was utilized. Computed parameters of time signals defines various types of input vectors used for training neural networks. the results finally obtained prove that the proposed diagnostic system made automation of structure testing possible and can be applied to Structural Health Monitoring.
In the present situation, researchers have the task of developing a high quality features based cloud computing system in the real world if service is desired on request. Quality of service (QoS) describes such parame...
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this book contains revised and extended versions of selected papers from the 10th and 11;internationalconference on patternrecognition, ICPRAM 2021 and 2022, held in February 2021 and 2022. Due to COVID-19 pandemic ...
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ISBN:
(数字)9783031245381
ISBN:
(纸本)9783031245374
this book contains revised and extended versions of selected papers from the 10th and 11;internationalconference on patternrecognition, ICPRAM 2021 and 2022, held in February 2021 and 2022. Due to COVID-19 pandemic the conferences were held virtually. Bothconferences received in total 204 submissions from which 8 full papers were carefully reviewed and selected for presentation in this volume. the papers span a wide range of investigation as well as development lines, which of course always reflect the last trends of research in the patternrecognition community.
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