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检索条件"任意字段=1st International Conference on Digital Image Processing and Pattern Recognition, DPPR 2011"

共 114 条记录，以下是21-30 订阅

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Gathering Identification Using image Metrics for Intelligent Situation Awareness System in Real Time Scenarios 1st

Gathering Identification Using Image Metrics for Intelligent...

引用

1st international conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Gautam, Nandita Das, Debdoot Khatua, Sunirmal Saha, Banani Computer Science Department University of Calcutta Kolkata700098 India

ISBN: (纸本)9789811390418

The advancement of image processing in the field of Artificial Intelligence has created various research prospects in the area of object detection, pattern recognition, etc. Face detection technology is applied for biometric authentication systems for face recognition and verification. This work aims at developing a tool that can be used in an Intelligent Situation Awareness System for a given Region of Interest. This has been done by identifying human face as objects from digital video frames through real time video streaming by using Recurrent Convolution Neural Network (RCNN) classifiers. The key frames from a video are identified and machine learning algorithm is being applied on it for performing the object identification. After the facial objects are being identified in a given frame, this can be utilized for performing semantic analysis in a given spatiotemporal scenario. The metrics identified in this work include object count, hand gestures, relative distance as well as density of objects for developing a robust system that could function in real time. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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A pattern Analysis-based Segmentation to Localize Early and Late Blight Disease Lesions in digital images of Plant Leaves

A Pattern Analysis-based Segmentation to Localize Early and ...

引用

IEEE international conference on Signal and image processing Applications (IEEE ICSIPA)

作者： Abdu, Aliyu Muhammad Mokji, Musa Mohd Sheikh, Usman Ullah Univ Technol Malaysia Sch Elect Engn Skudai 81310 Malaysia

ISBN: (纸本)9781728133775

This study reports a disease symptom classification algorithm using a proposed pattern recognition approach to individually localize early and late blight visual disease symptoms. The algorithm uses the pathological analogy hierarchy of the diseases to produce a novel homogeneous pattern localization, more informative to extract features that would be utilized for a machine learning system to classify the two diseases in digital photographs of vegetable plants. One of the most significant advantages of the proposed pattern analysis is localizing symptomatic and necrotic regions based on pathological disease analogy using soft computing, with which the pattern of each disease manifestation along the leaf surface can be tracked and quantified for characterization. In the 1st phase of the experiment, individual symptomatic (R-S), necrotic (R-N), and blurred (R-B, in-between healthy and symptomatic) regions were identified, segmented, and quantified. The 2nd phase focuses on the extraction of pattern features for classification and severity estimation with a machine learning classifier. The obtained results are encouraging, successfully localizing and quantifying individual disease lesions. This also indicates the enhanced applicability of the proposed approach discriminating the two diseases based on their dissimilarity. It is also envisaged that the algorithm can be extended to other plant disease symptoms. Moreover, it provides opportunities for early identification and detection of subtle changes in plant growth, disease stage, and severity estimation to assisting crop diagnostics in precision agriculture.

关键词： pattern analysis Computer vision Segmentation Plant disease lesion Plant disease detection

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Minimizing Training Data for Reliable Writer Identification in Medieval Manuscripts 2nd

Minimizing Training Data for Reliable Writer Identification ...

引用

2nd international Workshop on Recent Advances in digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep Learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

作者： Cilia, Nicole Dalia De stefano, Claudio Fontanella, Francesco Molinara, Mario Scotto di Freca, Alessandra University of Cassino and Southern Lazio Via G. Di Biasio 43 Cassino FR Italy

ISBN: (纸本)9783030307530

Palaeography aims to study ancient documents and the identification of the people who participated in the handwriting process of a given document is one of the most important problems. To this aim, expert paleographers typically analyze handwriting features such as letter heights and widths, distances between characters and angles of inclination. With the aim of achieving more precise measures and also thanks to the availability of high-quality digital images, paleographers are starting to use digital tools. In this context, in previous studies, we proposed a pattern recognition system for distinguishing the writers of mediaeval books and also investigated which is the minimum amount of training data needed to achieve satisfactory results in terms of accuracy. In this paper, we present a reject option that allows us to implement a highly-reliable system for writer identification, trained on a reduced set of data. The experimental results, performed on two sets of digital images from medieval Bibles, show that rejecting only a few samples it is possible to strongly reduce the error rate. © 2019, Springer Nature Switzerland AG.

关键词： digital devices

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A Cockpit of Measures for image Quality Assessment in digital Film Restoration 2nd

A Cockpit of Measures for Image Quality Assessment in Digita...

引用

作者： Plutino, Alice Lecca, Michela Rizzi, Alessandro Dip. di Informatica Milano Università degli Studi di Milano Milan Italy Fondazione Bruno Kessler ICT Center Trento Italy

ISBN: (纸本)9783030307530

We present an alternative approach for the quality assessment of the digital restoration of a degraded film. Instead of summarizing the film quality by an unique value, here we propose a set of basic measures that account for different film visual features. These measures describe over time global and local properties of the film, like brightness, contrast, color distribution entropy, color variations and perceptual intra-frame color changes. They are relevant to estimate the level of readability of the visual content of the film and to quantify the perceptual differences between the original and restored film. The measures proposed here are viewed as the parameters showed in a car or airplane cockpit and necessary to control the machine status and performance. The idea of cockpit would like to contribute to the automation of the digital restoration process and of its evaluation that are currently still performed for the most part manually by video editors and curators and thus often biased by subjective issues. © 2019, Springer Nature Switzerland AG.

关键词： image enhancement

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1st international Symposium on Signal and image processing, ISSIP 2017

1st International Symposium on Signal and Image Processing, ...

引用

1st international Symposium on Signal and image processing, ISSIP 2017

ISBN: (纸本)9789811088629

The proceedings contain 21 papers. The special focus in this conference is on Signal and image processing. The topics include: Analysis of picosecond pulse for ATR using ultra-wideband RADAR;Detection of endangered gangetic dolphins from underwater videos using improved hybrid frame detection technique in combination with LBP-SVM classifier;Automatic lip extraction using DHT and active contour;early started hybrid denoising technique for medical images;intelligent tutoring by diagram recognition;Color MRI image segmentation using quantum-inspired modified genetic algorithm-based FCM;multi-verse optimization clustering algorithm for binarization of handwritten documents;3D object recognition based on data fusion at feature level via principal component analysis;digital image watermarking through encryption and DWT for copyright protection;medav filter—Filter for removal of image noise with the combination of median and average filters;speech and non-speech audio files discrimination extracting textural and acoustic features;speaker recognition using occurrence pattern of speech signal;classification of metamaterial-based defected photonic crystal structure from band-pass filter characteristics using soft computing techniques;Sparse encoding algorithm for real-time ECG compression;wavelet based fractal analysis of solar wind speed signal;Class discriminator-based EMG classification approach for detection of neuromuscular diseases using discriminator-dependent decision rule (D3R) approach;a cooperative co-evolutionary approach for multi-objective optimization;automatic license plate recognition;s-shaped binary whale optimization algorithm for feature selection.

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A Comparative Analysis of Two Commercial digital Photogrammetry Software for Cultural Heritage Applications 2nd

A Comparative Analysis of Two Commercial Digital Photogramme...

引用

作者： Kingsland, Kaitlyn Institute for Digital Exploration University of South Florida TampaFL33620 United States

ISBN: (纸本)9783030307530

The paper seeks to evaluate the literature on digital photogrammetry processing software, comparing Agisoft Photoscan (now known as Agisoft Metashape, beginning with version 1.5) RealityCapture and to test the two programs in a real-world situation to further examine the difference in the software and resulting model. Tests were carried out for the evaluation of Photoscan, and RealityCapure—two European based digital photogrammetry programs—using the same artifact and data in all three tests. The artifact is part of the Farid Karam Collection of Antiquities, housed at the University of South Florida (USF) Libraries Tampa Special Collections. The chosen artifact for study, the aryballos inv. no. 68, was captured in ideal conditions to better evaluate multiple programs with the same data. The digital artifact was examined based upon knowledge and photographs of the physical artifact and evaluated relatively and comparatively between the programs. © 2019, Springer Nature Switzerland AG.

关键词： Photogrammetry

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Improving Ancient Cham Glyph recognition from Cham Inscription images Using Data Augmentation and Transfer Learning 2nd

Improving Ancient Cham Glyph Recognition from Cham Inscripti...

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作者： Nguyen, Minh-Thang Schweyer, Anne-Valérie Le, Thi-Lan Tran, Thanh-Hai Vu, Hai Computer Vision Department MICA International Research Institute Hanoi University of Science and Technology Hanoi Viet Nam French National Center for Scientific Research Paris France

ISBN: (纸本)9783030307530

Ancient Cham glyphs have mostly appeared in inscriptions on stones at some museums in Vietnam. Unfortunately, these inscriptions are being abrasive by the time. To conserve Cham heritage as well as to make them widely accessible and readable by users, digitization and recognition of ancient Cham glyphs become necessary. In our previous work, we have built the first dataset of champ inscription images, manually segmented them in glyphs and annotated by an ancient Cham expert. We adapted some automatic recognition methods and conducted experiments on the manually denoised dataset. The aim of this paper is to extend that earlier research to work on noising data. To this end, we face two main issues. Firstly, the current pre-built dataset is still small which is usually a main drawback for deep learning based methods. Therefore, some data augmentation techniques will be evaluated and investigated to increase the number and variation of samples in the dataset. Second, even with the augmented dataset, the fact of training a deep model from scratch could be very long and sometimes cannot meet a good local minimum. Therefore, we use a simple transfer learning procedure which inherits knowledge from similar or of the same family language. Experiments on both the raw test set and its denoised version show very promising results and of F1-score on two test sets respectively). © 2019, Springer Nature Switzerland AG.

关键词： image classification

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Improving Multi-scale Face recognition Using VGGFace2 2nd

Improving Multi-scale Face Recognition Using VGGFace2

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作者： Massoli, Fabio Valerio Amato, Giuseppe Falchi, Fabrizio Gennaro, Claudio Vairo, Claudio ISTI-CNR via G. Moruzzi 1 Pisa56124 Italy

ISBN: (纸本)9783030307530

Convolutional neural networks have reached extremely high performances on the Face recognition task. These models are commonly trained by using high-resolution images and for this reason, their discrimination ability is usually degraded when they are tested against low-resolution images. Thus, Low-Resolution Face recognition remains an open challenge for deep learning models. Such a scenario is of particular interest for surveillance systems in which it usually happens that a low-resolution probe has to be matched with higher resolution galleries. This task can be especially hard to accomplish since the probe can have resolutions as low as 8, 16 and 24 pixels per side while the typical input of state-of-the-art neural network is 224. In this paper, we described the training campaign we used to fine-tune a ResNet-50 architecture, with Squeeze-and-Excitation blocks, on the tasks of very low and mixed resolutions face recognition. For the training process we used the VGGFace2 dataset and then we tested the performance of the final model on the IJB-B dataset;in particular, we tested the neural network on the 1:1 verification task. In our experiments we considered two different scenarios: (1) probe and gallery with same resolution;(2) probe and gallery with mixed resolutions. Experimental results show that with our approach it is possible to improve upon state-of-the-art models performance on the low and mixed resolution face recognition tasks with a negligible loss at very high resolutions. © 2019, Springer Nature Switzerland AG.

关键词： Face recognition

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A study of English Neologisms Through Large-Scale Probabilistic Indexing of Bentham’s Manuscripts 2nd

A Study of English Neologisms Through Large-Scale Probabilis...

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作者： Toselli, Alejandro H. Romero, Verónica Vidal, Enrique Sánchez, Joan Andreu Seaward, Louise Schofield, Philip PRHLT Universitat Politècnica de València Camí de Vera s/n València46022 Spain Bentham Project UCL Laws 4-8 Endsleigh Gardens LondonWC1H 0EG United Kingdom

ISBN: (纸本)9783030307530

Probabilistic indexes (PI) are obtained from untranscribed handwritten text images by means of recently introduced lexicon-free, query-by-string, probabilistic keyword spotting techniques. PIs have proven to be a powerful tool that allow efficient, free textual searching in very large collections of handwritten historical documents. PIs convey uncertain information about the textual contents of the document images. However, text uncertainty is accurately modeled by the associated lexical probability distributions, which can be conveniently exploited in many applications. As an example of these applications, here we study the dating of a number of English neologisms in the large collection of Bentham’s manuscripts, which encompass images. The statistical techniques used for neologism dating are theoretically motivated and experiments on this collection are reported. Among other interesting contributions of this study, it provides sound evidence that some commonly assumed neologism introduction dates need to be revised. © 2019, Springer Nature Switzerland AG.

关键词： Probability distributions

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A pattern Analysis-based Segmentation to Localize Early and Late Blight Disease Lesions in digital images of Plant Leaves

A Pattern Analysis-based Segmentation to Localize Early and ...

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IEEE international conference on Signal and image processing Applications (ICSIPA)

作者： Aliyu Muhammad Abdu Musa Mohd Mokji Usman Ullah Sheikh School of Electrical Engineering University Technology Malaysia Johor Malaysia

This study reports a disease symptom classification algorithm using a proposed pattern recognition approach to individually localize early and late blight visual disease symptoms. The algorithm uses the pathological analogy hierarchy of the diseases to produce a novel homogeneous pattern localization, more informative to extract features that would be utilized for a machine learning system to classify the two diseases in digital photographs of vegetable plants. One of the most significant advantages of the proposed pattern analysis is localizing symptomatic and necrotic regions based on pathological disease analogy using soft computing, with which the pattern of each disease manifestation along the leaf surface can be tracked and quantified for characterization. In the 1st phase of the experiment, individual symptomatic (Rs), necrotic ( RN), and blurred ( RB, in-between healthy and symptomatic) regions were identified, segmented, and quantified. The 2nd phase focuses on the extraction of pattern features for classification and severity estimation with a machine learning classifier. The obtained results are encouraging, successfully localizing and quantifying individual disease lesions. This also indicates the enhanced applicability of the proposed approach discriminating the two diseases based on their dissimilarity. It is also envisaged that the algorithm can be extended to other plant disease symptoms. Moreover, it provides opportunities for early identification and detection of subtle changes in plant growth, disease stage, and severity estimation to assisting crop diagnostics in precision agriculture.

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