检索结果-内蒙古大学图书馆

Optimal KAZE and AKAZE Features for Facial Similarity Matching 1

Proceedings of the 7th International Conference on Advances in Computing and Data Sciences, ICACDS 2023

作者： Vinay, A. Vasu, Kishan Athirala Lodha, Pranav Yogi Natarajan, S. Sudarshan, T.S.B. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru560085 India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

Face recognition is one of the premier disciplines in the vast field of computer vision and image analysis. A popular method is the Gaussian scale space analysis which limits the performance by smoothing both the noise and natural boundaries in the same proportion. In order to prevent the loss of natural boundaries to smoothing we tap into nonlinear scale space techniques such as KAZE and Accelerated KAZE. KAZE is a multistage 2-D feature detection and description algorithm. It makes use of AOS schemes to develop the nonlinear scale space for analysis. Though the results are satisfactory, it is computationally intense as they solve a humongous module of linear equations. To ascertain the mentioned limitation of KAZE we make use of Accelerated-KAZE, which uses pyramidal structure with Fast Explicit Diffusion incorporated in it, thus minimizing the computation in the step of feature detection of nonlinear scale space. Also, with the use of M-LDB (Modified-Local Difference Binary) descriptor the problem of rotation is solved. Usage of RANSAC after processing in two methods had some disadvantages. It gave bad results when the inliers ratio in the dataset is low. Thus, Optimal RANSAC is employed which works well even when the inliers ratio is as low as 5%. The proposed methods are tested on many standard datasets and various performance parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Enhancement of Degraded CCTV Footage for Forensic Analysis 3rd

Enhancement of Degraded CCTV Footage for Forensic Analysis

引用

3rd International Conference on Innovative Computing and Communication, ICICC 2020

作者： Vinay, A. Lokesh, Aditya Kamath, Vinayaka R. Murty, K.N.B. Natarajan, S. Center for Pattern Recognition and Machine Intelligence PES University Bengaluru India

ISBN: (纸本)9789811551123

Forensic analysis has proved to be one of the most utilitarian tool in investigating crime. Forensic analysis provides evidence/basic information of the said crime through analysis of physical evidence. In this paper, we present a scintillating technique to enhance the image quality of CCTV video to assist in the investigation of criminal cases. This paper, address the issue of lack of a budget surplus for a high-quality image/video capturing device. The problem of enhancing images is addressed by pure image processing methods and machine learning techniques. The paper analyzes both techniques and further concludes that the machine learning approach produces a more efficient result. The applications of this technique can range from simple image filtering/beautification to forensic image processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Crime

来源：评论

学校读者我要写书评

暂无评论

Unconstrained face recognition using asurf and cloud-forest classifier optimized with VLAD

arXiv

引用

arXiv 2021年

作者： Vinay, A. A., Joshi H.M., Surana H., Garg K.N.B., Murthy S., Natarajan Center for Pattern Recognition and Machine Intelligence PES University Bangalore560085 India

The paper posits a computationally-efficient algorithm for multi-class facial image classification in which images are constrained with translation, rotation, scale, color, illumination and affine distortion. The proposed method is divided into five main building blocks including Haar-Cascade for face detection, Bilateral Filter for image preprocessing to remove unwanted noise, Affine Speeded-Up Robust Features (ASURF) for keypoint detection and description, Vector of Locally Aggregated Descriptors (VLAD) for feature quantization and Cloud Forest for image classification. The proposed method aims at improving the accuracy and the time taken for face recognition systems. The usage of the Cloud Forest algorithm as a classifier on three benchmark datasets, namely the FACES95, FACES96 and ORL facial datasets, showed promising results. The proposed methodology using Cloud Forest algorithm successfully improves the recognition model by 2-12% when differentiated against other ensemble techniques like the Random Forest classifier depending upon the dataset used. © 2021, CC BY.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Residual and Ensemble Learning on Locally Derived Features for Face recognition 1

引用

5th International Symposium on Intelligent Systems Technologies and Applications, ISTA 2019

作者： Vinay, A. Gupta, Abhijay Bharadwaj, Aprameya Srinivasan, Arvind Balasubramanya Murthy, K.N. Natarajan, S. Centre for Pattern Recognition and Machine Intelligence PES University BengaluruKA560085 India

ISBN: (数字)9789811539145

ISBN: (纸本)9789811539138

In this paper, an efficient approach toward face recognition is presented. The proposed model is invariant to pose, expression, scale, illumination, and translation with the application of different techniques and implementation of their algorithms. The images are first preprocessed using different techniques like CS-LBP, XCS-LBP, CS-LDMP, etc. to remove noise and make it illumination invariant. In one implementation, these are fed as input to different descriptors. The keypoints detected are invariant to lighting condition. A wide variety of descriptors are applied to the detected keypoints in order to compare the quality of features computed. These descriptors are quantized to a single vector, representing the most salient features of the facial image. The vectors are then fed into the stacking classifier. In the other method, these preprocessed images are directly fed as input into the residual network. A comparison of the results by these two pipelines is studied on two benchmark face datasets, namely FACES94 and Grimace. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Network Based Intelligent Handwritten Document recognition

引用

Computers, Materials & Continua 2022年第3期70卷 4563-4581页

作者： Sagheer Abbas Yousef Alhwaiti Areej Fatima Muhammad A.Khan Muhammad Adnan Khan Taher M.Ghazal Asma Kanwal Munir Ahmad Nouh Sabri Elmitwally School of Computer Science National College of Business administration and EconomicsLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Bangi 43600SelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Department of Computer Science GC UniversityLahorePakistan Department of Computer Science Faculty of Computers and Artificial IntelligenceCairo University12613Egypt

This paper presents a handwritten document recognition system based on the convolutional neural network *** today’s world,handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired *** technology is also helpful for the automatic data entry *** the proposed systemprepared a dataset of English language handwritten character *** proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten *** this research,multiple experiments get very worthy recognition *** proposed systemwill first performimage pre-processing stages to prepare data for training using a convolutional neural *** this processing,the input document is segmented using line,word and character *** proposed system get the accuracy during the character segmentation up to 86%.Then these segmented characters are sent to a convolutional neural network for their *** recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given *** proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%,and for validation that accuracy slightly decreases with 90.42%.

关键词： Convolutional neural network segmentation skew cursive characters recognition

来源：评论

学校读者我要写书评

暂无评论

SOM-VLAD Based Feature Aggregation for Face recognition Using Keypoint Fusion 2nd

SOM-VLAD Based Feature Aggregation for Face Recognition Usin...

引用

2nd International Conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2018

作者： Vinay, A. Cholin, Ajaykumar S. Bhat, Aditya D. Deshpande, Arnav Ajay Murthy, K. N. Balasubramanya Natarajan, S. Centre for Pattern Recognition and Machine Intelligence PES University 100 Feet Ring Road BSK Stage III BengaluruKarnataka560085 India

ISBN: (纸本)9789811391835

Over the years, quite a lot of research has been done on using different machine learning techniques for Face recognition (FR) for identifying the faces of different people. Current FR techniques are still not accurate enough for real world scenarios and pose a lot of problems against varying illumination levels, pose variations, noise and occlusion in the image. Thus, a single keypoint extraction technique may not be suitable for all cases. Hence, in this paper a novel technique is proposed for Keypoint Fusion (KF) obtained by fusing SIFT, SURF and ORB keypoints which is more accurate and suitable for real time application. The paper is also focused on proposing a novel technique of using a Self-Organizing Map (SOM) and Vector of Locally Aggregated Descriptors (VLAD) for image clustering. VLAD is used to extend the SOM’s ability to cluster keypoint descriptors. Image classification is carried out using a SGD (Stochastic Gradient Descent) based SVM (Support Vector machine) classifier. The performance of classification of the proposed framework on benchmark datasets (Grimace, Faces95 and Faces96) has been tabulated and compared with other standard techniques. It is seen that the proposed framework performs better than the BOW (Bag Of Words) model and the KF technique was accurate and quick enough to beat the traditional keypoint extraction techniques. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Self organizing maps

来源：评论

学校读者我要写书评

暂无评论

Effective Utilization of Whitening for Person Identification

SSRN

引用

SSRN 2019年

作者： Vinay, A. Kailasam, Siddharth Bharadwaj, Aprameya Balasubramanya, K.N. Natarajan, Murthy S. Center for Pattern Recognition and Machine Intelligence PES University Bangalore India

The growth of importance of Person Identification(PI) is so drastic these days, since it helps support the growth of many other fields like surveillance, robotics etc. Computer Vision Enthusiasts are working on making this robust to various factors such as noise, scale, pose, intensity, illumination, translation, expression and many more. Whitening is a pre-processing technique which makes an image robust to noise, intensity and illumination. There are many feature detectors such as FLANN matching algorithms, which use relatively lesser resources, but are not robust to one or more of the above mentioned problems. However, in general whitening is usually used with machine learning algorithms, which are already robust to these problems. This paper hence compares results obtained using SVD whitening and ZCA whitening to without pre-processing on four distinct pipelines. The first two pipelines use SIFT extracted keypoints and SURF extracted keypoints matched by KD-Tree FLANN matcher and classified using confidence probability matching. These pipelines as we know are not robust to the problems solved by whitening. They are implemented on ORL, grimace and faces 95 database. The third and fourth pipeline includes machine learning algorithms to classify the features extracted by Spatial Pyramid Matching and Earth Mover’s Distance on Dense SIFT keypoints. They were implemented on ORL, faces 95 and FEI database. Results show that SVD whitening and ZCA whitening increases the accuracy more when FLANN matcher is used than when machine learning algorithms are used to classify the images. In both cases, we encounter a scenario where the accuracy is slightly lesser than the accuracy obtained without whitening. However, we find that the decrease in accuracy is more when machine learning algorithms are used than when FLANN matchers are used. © 2019, The Authors. All rights reserved.

关键词： Pipelines

来源：评论

学校读者我要写书评

暂无评论

Vocabulary trees with OSS detectors 1

Vocabulary trees with OSS detectors

引用

1st International Conference on Pervasive Computing Advances and Applications, PerCAA 2019

作者： Vinay, A. Acharya, Harshith R. Singh, Harshita Satyanarayana, Vibha Murthy, K.N.B. Natarajan, S. Deptartment of Computer Science PES University Banashankari 3rd Stage Bangalore Karnataka560085 India Center for Pattern Recognition and Machine Intelligence PES University Banashankari 3rd Stage Bangalore Karnataka560085 India

In today’s diversified world, with every person having their own idiosyncratic identity, the field of face detection and recognition has received significant scope. Since birth, humans develop and harvest similar facial features, making their recognition a challenging problem. Thus, the development of faster, more accurate and robust algorithms is of prime importance. The most critical requirements in developing a reliable face recognition algorithm is a large database of facial images and a well-developed systematic procedure to evaluate the system. In this paper, we bring out an approach to implement quick facial recognition using the concept of vocabulary trees in accordance with ORB, SIFT and SURF detectors and compare their performance. The model is trained using the Grimace, Face95 and ORL databases. The experiments are conducted by employing various state-of-the-art clustering algorithms and the results are compared for accuracy. The results and comparisons obtained on the databases show that the ORB detector clustered using Spectral clustering algorithm to construct the Vocabulary Tree outperforms the other techniques. © 2019 The Authors. Published by Elsevier Ltd.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks

arXiv

引用

arXiv 2023年

作者： Zohrevand, Abbas Imani, Zahra Sadri, Javad Suen, Ching Y. Dept. of Computer Engineering Kosar University of Bojnord Bojnord Iran Camp in Labs Foundation 1 Westmount Square Bureau 1001 WestmountQCH3Z 2P9 Canada Dept. of Computer Science and Software Engineering Faculty of Engineering and Computer Science Concordia University MontrealQC Canada Center for Pattern Recognition and Machine Intelligence Concordia University MontrealH3G 1M8 Canada

Most of the methods on handwritten recognition in the literature are focused and evaluated on Black and White (BW) image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), as eye simulator, we investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten databases that have all three color modalities of handwritings. To answer this question, we selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected datasets are divided into training, validation, and testing sets. Afterwards, similar conventional CNN models are trained with the training samples. While the experimental results on the testing set show that CNN on the BW digit and word images has a higher performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modalities. Also, comparisons of training times in three color modalities show that recognition of handwritten digits and words in BW images using CNN is much more efficient. © 2023, CC BY.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

DisenE: Disentangling knowledge graph embeddings

arXiv

引用

arXiv 2020年

作者： Kou, Xiaoyu Lin, Yankai Li, Yuntao Xu, Jiahao Li, Peng Zhou, Jie Zhang, Yan Department of Machine Intelligence Peking University Beijing China Pattern Recognition Center WeChat AI Tencent Inc. China

Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently. However, the existing research is mainly based on the black-box neural models, which makes it difficult to interpret the learned representation. In this paper, we introduce DisenE, an end-to-end framework to learn disentangled knowledge graph embeddings. Specially, we introduce an attention-based mechanism that enables the model to explicitly focus on relevant components of entity embeddings according to a given relation. Furthermore, we introduce two novel regularizers to encourage each component of the entity representation to independently reflect an isolated semantic aspect. Experimental results demonstrate that our proposed DisenE investigates a perspective to address the interpretability of KGE and is proved to be an effective way to improve the performance of link prediction tasks. The code and datasets are released on https://***/KXY-PUBLIC/DisenE. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：