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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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A Low Complexity Algorithm for Eye Detection and Tracking in Energy-Constrained Applications

A Low Complexity Algorithm for Eye Detection and Tracking in...

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1st international conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Ando, Tomoaki Moshnyaga, Vasily G. OKI Semiconductors Japan

ISBN: (纸本)9781467328210;9781467328203

This paper presents a low-complexity algorithm of non-intrusive real time eye-tracking by a single camera. The algorithm employs the feature-based approach, scanning a seven-segment rectangular filter over the input image to find the Between-The-Eyes pattern of human face and locating eyes within the pattern. Unlike related methods it neither uses computationally expensive cascades of Adaboost classifiers nor SVM template matching, while detecting eye with high accuracy. We implemented the algorithm and present the results of its experimental evaluation.

关键词： cameras computer vision eye filters learning (artificial intelligence) support vector machines

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Super-resolution reconstruction of image sequences compressed with DWT-based techniques

Super-resolution reconstruction of image sequences compresse...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Zhu, Xiang Yuan, Jie Du, Si-Dan Nanjing Univ Dept Elect Sci & Engn Nanjing 210093 Peoples R China

ISBN: (纸本)9781424410651

Spatial domain based super-resolution reconstruction techniques, which were designed for uncompressed video to produce high-resolution image or image sequences, may not work well when applied directly to compressed videos, especially to those with severe quantization errors. Here a reconstruction approach designed for videos or image sequences, which were compressed using DWT-based techniques, is presented. This method utilizes the theory of projection onto convex sets (POCS) with a new projection operator based on Discrete Wavelet Transformation (DWT) for reducing blurring artifacts. It also applies maximum a posteriori (MAP) estimation techniques to remove ringing noises from restored images. Experimental results show that such approach could effectively recover objects' edges and details that were blurred during compressing process while, expanding image's, size simultaneously.

关键词： image processing super-resolution reconstruction POCS MAP JPEG2000 wavelet transform

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An Improved Pre-processing machine learning Approach for Cross-Sectional MR Imaging of Demented Older Adults 1

An Improved Pre-processing Machine Learning Approach for Cro...

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1st international conference of Intelligent Computing and Engineering, ICOICE 2019

作者： Khan, Afreen Zubair, Swaleha Al Sabri, Muaadhabdo Aligarh Muslim University Department of Computer Science Aligarh India

ISBN: (纸本)9781728144870

Data pre-processing is the foremost step employed in building any machine learning (ML) model. It has a significant effect on the generalization performance of the model. In the present study, we have attempted to present the data pre-processing techniques for analysis of cross-sectional Magnetic Resonance Imaging (MRI) data of demented and non-demented older adults. The MRI dataset consists of 434 MR sessions of 416 subjects, aged between 18 to 96 years. Before performing classification of MRI data and its pattern analysis, characteristics of the dataset, such as sampling, imbalanced dataset, missing values, outliers, incompleteness, and the presence of irrelevant features, had been addressed. We involved ten major steps to process dataset for the ML model building. Experimental results on the cross-sectional data indicated a significant relative improvement in the pattern analysis achieved by doing PCA (Principal Component Analysis). pattern analysis employing PCA resulted in noteworthy advancement in pattern recognition with an explained variance of 0.97377. © 2019 IEEE.

关键词： Magnetic resonance imaging

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An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN 24

An Efficient Approach for Polyps Detection in Endoscopic Vid...

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24th international conference on pattern recognition (ICPR)

作者： Mo, Xi Tao, Ke Wang, Quan Wang, Guanghui Univ Kansas Dept Elect Engn & Comp Sci Lawrence KS 66045 USA Jilin Univ Hosp 1 Changchun 130000 Jilin Peoples R China

ISBN: (纸本)9781538637883

Polyp has long been considered as one of the major etiologies to colorectal cancer which is a fatal disease around the world, thus early detection and recognition of polyps plays an crucial role in clinical routines. Accurate diagnoses of polyps through endoscopes operated by physicians becomes a chanllenging task not only due to the varying expertise of physicians, but also the inherent nature of endoscopic inspections. To facilitate this process, computer-aid techniques that emphasize on fully-conventional image processing and novel machine learning enhanced approaches have been dedicatedly designed for polyp detection in endoscopic videos or images. Among all proposed algorithms, deep learning based methods take the lead in terms of multiple metrics in evolutions for algorithmic performance. In this work, a highly effective model, namely the faster region-based convolutional neural network (Faster R-CNN) is implemented for polyp detection. In comparison with the reported results of the state-of-the-art approaches on polyps detection, extensive experiments demonstrate that the Faster R-CNN achieves very competing results, and it is an efficient approach for clinical practice.

关键词： Endoscopy

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Feature Visualization Based stacked Convolutional Neural Network for Human Body Detection in a Depth image 1st

Feature Visualization Based Stacked Convolutional Neural Net...

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1st Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Liu, Xiao Mei, Ling Yang, Dakun Lai, Jianhuang Xie, Xiaohua Sun Yat Sen Univ Guangzhou 510006 Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9783030033354;9783030033347

Human body detection is a key technology in the fields of biometric recognition, and the detection in a depth image is rather challenging due to serious noise effects and lack of texture information. For addressing this issue, we propose the feature visualization based stacked convolutional neural network (FV-SCNN), which can be trained by a two-layer unsupervised learning. Specifically, the next CNN layer is obtained by optimizing a sparse auto-encoder (SAE) on the reconstructed visualization of the former to capture robust high-level features. Experiments on SZU Depth Pedestrian dataset verify that the proposed method can achieve favorable accuracy for body detection. The key of our method is that the CNN-based feature visualization actually pursues a data-driven processing for a depth map, and significantly alleviates the influences of noise and corruptions on body detection.

关键词： Human detection Depth image Feature visualization Sparse auto-encoder Convolutional neural network

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Microscopic image Classification Using DCT for the Detection of Acute Lymphoblastic Leukemia (ALL)

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1st international conference on Computer Vision and image processing (CVIP)

作者： Mishra, Sonali Sharma, Lokesh Majhi, Bansidhar Sa, Pankaj Kumar Natl Inst Technol Dept Comp Sci & Engn Pattern Recognit Res Lab Rourkela 769008 India

ISBN: (纸本)9789811021046;9789811021039

Development of a computer-aided diagnosis (CAD) system for early detection of leukemia is very essential for the betterment of medical purpose. In recent years, a variety of CAD system has been proposed for the detection of leukemia. Acute leukemia is a malignant neoplastic disorder that influences a larger fraction of world population. In modern medical science, there are sufficient newly formulated methodologies for the early detection of leukemia. Such advanced technologies include medical image processing methods for the detection of the syndrome. This paper shows that use of a highly appropriate feature extraction technique is required for the classification of a disease. In the field of image processing and machine learning approach, Discrete Cosine Transform (DCT) is a well-known technique. Nucleus features are extracted from the RGB image. The proposed method provides an opportunity to fine-tune the accuracy for the detection of the disease. Experimental results using publicly available dataset like ALL-IDB shows the superiority of the proposed method with SVM classifier comparing it with some other standard classifiers.

关键词： Acute Lymphoblastic Leukemia Discrete Cosine Transform Watershed segmentation CAD system

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The Epistle to Cangrande Through the Lens of Computational Authorship Verification 2nd

The Epistle to Cangrande Through the Lens of Computational A...

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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： Corbara, Silvia Moreo, Alejandro Sebastiani, Fabrizio Tavoni, Mirko Istituto di Scienza e Tecnologie dell’Informazione Consiglio Nazionale delle Ricerche Pisa56124 Italy Dipartimento di Filologia Letteratura e Linguistica Università di Pisa Pisa56126 Italy

ISBN: (纸本)9783030307530

The Epistle to Cangrande is one of the most controversial among the works of Italian poet Dante Alighieri. For more than a hundred years now, scholars have been debating over its real paternity, i.e., whether it should be considered a true work by Dante or a forgery by an unnamed author. In this work we address this philological problem through the methodologies of (supervised) Computational Authorship Verification, by training a classifier that predicts whether a given work is by Dante Alighieri or not. We discuss the system we have set up for this endeavour, the training set we have assembled, the experimental results we have obtained, and some issues that this work leaves open. © 2019, Springer Nature Switzerland AG.

关键词： machine learning

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Boosting sparsity-induced autoencoder: A novel sparse feature ensemble learning for image classification 1st

Boosting sparsity-induced autoencoder: A novel sparse featur...

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1st Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Shi, Rui Ji, Jian Zhang, Chunhui Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9783030033378

As a kind of unsupervised learning model, the autoencoder is usually adopted to perform the pretraining to obtain the optimal initial value of parameter space, so as to avoid the local minimality that the nonconvex problem may fall into and gradient vanishment of the process of back propagation. However, the autoencoder and its variants have not taken the statistical characteristics and domain knowledge of the train set and also lost plenty of essential representaions learned from different levels when it comes to image processing and computer vision. In this article, we firstly add a sparsity-induced layer into the autoencoder to exploit and extract more representative and essential features exist in the input and then combining the ensemble learning mechanism, we propose a novel sparse feature ensemble learning method, named Boosting sparsity-induced autoencoder, which could make full use of hierarchical and diverse features, increase the accuracy and the stability of a single model. The classification results on different data sets illustrated the effectiveness of our proposed method.

关键词： Sparse representation sparsity-induced mechanism image denoising image classification

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A New Approach to Optimal structuring Element Extraction for Mst-Based Shapes Description

A New Approach to Optimal Structuring Element Extraction for...

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4th international conference on Computer Science and Informatics, JCIS 1998, 1st international Workshop on High Performance, 1st international Workshop on Computer Vision, pattern recognition and image processing Volume 4

作者： Jiang, Tianzi School of Mathematics University of New South Wales Sydney 2052 Australia Natl. Lab. of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China

The use of a genetic algorithm to select the optimal structuring element was discussed. An optimal structuring element extraction for mathematical morphology signature transform (Mst) based shapes was determined. The new method was found to have the ability to find the global optimum, keeping the advantages of genetic algorithms, and tabu search. A brief description of the Mst, its shape, and the application of the structuring element were also discussed.

关键词： Feature extraction

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Crop Disease Detection using CNN Architecture VGG16 1

Crop Disease Detection using CNN Architecture VGG16

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1st IEEE international conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Raj, Sandeep Kaur, Surinder Singh, Neetu Gupta, Monica Dronacharya College of Engineering Department of Artificial Intelligence and Machine Learning Haryana Farrukh Nagar India Bharati Vidyapeeth's College of Engineering Department of Information Technology New Delhi India Bharati Vidyapeeth's College of Engineering Department of Electronics and Communication Engineering New Delhi India

ISBN: (纸本)9798350356816

The Agriculture plays a major part in any nation for economy through the production of various crops and it's an essential source of income in India. The plant disease is one of the leading task of agriculture. When the infections occur in crops result in reduced quantity and quality of production of crops which will result in economic loss. Therefore, plague needs to be identified as quickly as possible for the healthy production of crops and plants. Various methods and solutions were proposed for crop disease detection by the researchers using image processing, machine learning, and deep learning. The deep learning plays an essential part in the field of image processing using convolution neural networks and their architecture. In this experiment, we have examined the CNN, Transfer learning VGG16 model on the plant-village dataset which was available on the Kaggle. The dataset(records) includes images of various potato, tomato and pepper crops of 15 different classes. The detection was done using the leaf samples. Our goal is to detect the disease in plants with accurate without taking so much time so that people can take care of their plants and apply the appropriate treatment. In future this VGG16 can be used to identify specific objects, animals, plants, and more in a picture. to identify, recognition and classification of image, that will use in many applications. Medical, Banking, Transportations well as Agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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