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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Handwritten Digit Classification based on Ensemble Majority Voting Classifier 11

Handwritten Digit Classification based on Ensemble Majority ...

引用

11th ieee International conference on Systems and Control, ICSC 2023

作者： Alsharif, Mariam Abdo, Amina Bozed, Kenz A University of Benghazi Faculty of Information Technology Benghazi Libya Collage of Computer Technology-Benghazi Departemnt of Artificial Intelligence Benghazi Libya University of Benghazi Department of Computer System Design Benghazi Libya

ISBN: (纸本)9798350304886

Handwritten digit recognition is a significant challenge in the field of machine learning, particularly for pattern recognition and computer vision applications. It has found applications in various areas, such as identifying digits on utility maps, postal mail zip codes, bank check amounts, and more. Offline handwritten digits possess distinct characteristics, including size, orientation, position, and thickness. Each person's handwriting is unique, making the classification process more difficult. Additionally, the high similarity between certain digits and the over fitting problem with high-dimensional data can increase computational time and cost. Consequently, numerous researchers have developed and implemented different machine learning algorithms to effectively address the issue of recognizing handwritten digits. This paper introduces a novel approach to improve the classification performance and accuracy of handwritten digit recognition. The proposed method utilizes an ensemble majority vote classifier, which combines three classifiers in each experiment by utilizing a range of different algorithms;Naïve Bayes (NB), Adaptive Boosting Algorithm (AdaBoost), K-Nearest Neighbors (K-NN), Multi-layer Perceptron Classifier (MLP), eXtreme Gradient Boosting algorithm (XGBoost), and Decision Tree Algorithm (DT) to form a single ensemble classifier. To validate the approach, we used the MINST free public handwritten dataset, to ascertain the classifier with the highest rate of accuracy in this study. The results obtained from the experiments demonstrated that the full performance of Ensemble model classifiers MLP, XGBOOST, and DT is superior to AdaBoost, K-NN, and NB. It achieved the highest accuracy level, reaching 98% in the experiment. The experimental results of this research showed that the recognition accuracy was improved compared to previous works. © 2023 ieee.

关键词： Machine learning

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Deep Learning based Spatial-Temporal In-loop filtering for Versatile Video Coding

Deep Learning based Spatial-Temporal In-loop filtering for V...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Pham, Chi D. K. Fu, Chen Zhou, Jinjia Hosei Univ Tokyo Japan JST PRESTO Saitama Japan

ISBN: (纸本)9781665448994

The existing deep learning-based Versatile Video Coding (VVC) in-loop filtering (ILF) enhancement works mainly focus on learning the one-to-one mapping between the reconstructed and the original video frame, ignoring the potential resources at encoder and decoder. This work proposes a deep learning-based Spatial-Temporal In-Loop filtering (STILF) that takes advantage of the coding information to improve VVC in-loop filtering. Each CTU is filtered by VVC default in-loop filtering, self-enhancement Convolutional neural network (CNN) with CU map (SEC), and the reference-based enhancement CNN with the optical flow (REO). Bits indicating ILF mode are encoded under CABAC regular mode. Experimental results show that 3.78%, 6.34%, 6%, and 4.64% BD-rate reductions are obtained under All Intra, Low Delay P, Low Delay B, and Random Access configurations, respectively.

关键词： Video coding Optical filters Filtering Delays Decoding pattern recognition Convolutional neural networks

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RGB-D Local Implicit Function for Depth Completion of Transparent Objects

RGB-D Local Implicit Function for Depth Completion of Transp...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhu, Luyang Mousavian, Arsalan Xiang, Yu Mazhar, Hammad van Eenbergen, Jozef Debnath, Shoubhik Fox, Dieter Univ Washington Seattle WA 98195 USA NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9781665445092

Majority of the perception methods in robotics require depth information provided by RGB-D cameras. However, standard 3D sensors fail to capture depth of transparent objects due to refraction and absorption of light. In this paper, we introduce a new approach for depth completion of transparent objects from a single RGB-D image. Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed. Based on this representation, we present a novel framework that can complete missing depth given noisy RGBD input. We further improve the depth estimation iteratively using a self-correcting refinement model. To train the whole pipeline, we build a large scale synthetic dataset with transparent objects. Experiments demonstrate that our method performs significantly better than the current stateof-the-art methods on both synthetic and real world data. In addition, our approach improves the inference speed by a factor of 20 compared to the previous best method, ClearGrasp [43].

关键词： computer vision Three-dimensional displays Pipelines Robot vision systems Estimation Sensors pattern recognition

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Car recognition System Using Convolutional Neural Network 6

Car Recognition System Using Convolutional Neural Network

引用

6th International conference on Contemporary Computing and Informatics, IC3I 2023

作者： Chauhan, Shweta Kumar, Rahul Kumar, Bibek University Centre for Research and Development Chandigarh University Mohali India Vishweshwarya Group of Institutions Computer Science Engineering Delhi India

ISBN: (纸本)9798350304480

Car recognition systems have been gaining popularity in recent years due to their ability to identify the make, model, and year of cars from images. In this paper, we present the building of accurate car recognition systems by using neural networks. This paper proposes a car recognition system using the convolution neural network and VMMRdb dataset and the ResNet-152 model, which provides state-of-the-art performance in image classification tasks. The performance of the proposed model is compared with the GG16 model. The experimental results show that the developed car recognition system achieved high accuracy of 54.4% and efficiency in identifying the cars which has important applications in traffic analysis, theft prevention, and marketing research. We also compared the performance of our model with that of other state-of-the-art car recognition systems, including the popular VGG16 model. This object recognition system has practical applications in various fields, such as law enforcement, parking access control, and traffic monitoring, and can help identify stolen or wanted vehicles quickly and efficiently. © 2023 ieee.

关键词： CNNs computer vision Resnet152 Torch vision

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Understanding Failures of Deep Networks via Robust Feature Extraction

Understanding Failures of Deep Networks via Robust Feature E...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Singla, Sahil Nushi, Besmira Shah, Shital Kamar, Ece Horvitz, Eric Univ Maryland College Pk MD 20742 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665445092

Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances. We introduce and study a method aimed at characterizing and explaining failures by identifying visual attributes whose presence or absence results in poor performance. In distinction to previous work that relies upon crowdsourced labels for visual attributes, we leverage the representation of a separate robust model to extract interpretable features and then harness these features to identify failure modes. We further propose a visualization method aimed at enabling humans to understand the meaning encoded in such features and we test the comprehensibility of the features. An evaluation of the methods on the ImageNet dataset demonstrates that: (i) the proposed workflow is effective for discovering important failure modes, (ii) the visualization techniques help humans to understand the extracted features, and (iii) the extracted insights can assist engineers with error analysis and debugging.

关键词： Measurement Visualization computer vision Error analysis Aggregates Debugging Feature extraction

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Are Labels Always Necessary for Classifier Accuracy Evaluation?

Are Labels Always Necessary for Classifier Accuracy Evaluati...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Deng, Weijian Zheng, Liang Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781665445092

To calculate the model accuracy on a computer vision task, e.g., object recognition, we usually require a test set composing of test samples and their ground truth labels. Whilst standard usage cases satisfy this requirement, many real-world scenarios involve unlabeled test data, rendering common model evaluation methods infeasible. We investigate this important and under-explored problem, Automatic model Evaluation (AutoEval). Specifically, given a labeled training set and a classifier, we aim to estimate the classification accuracy on unlabeled test datasets. We construct a meta-dataset: a dataset comprised of datasets generated from the original images via various transformations such as rotation, background substitution, foreground scaling, etc. As the classification accuracy of the model on each sample (dataset) is known from the original dataset labels, our task can be solved via regression. Using the feature statistics to represent the distribution of a sample dataset, we can train regression models (e.g., a regression neural network) to predict model performance. Using synthetic meta-dataset and real-world datasets in training and testing, respectively, we report a reasonable and promising prediction of the model accuracy. We also provide insights into the application scope, limitation, and potential future direction of AutoEval.

关键词： Training computer vision Computational modeling Predictive models Rendering (computer graphics) pattern recognition Object recognition

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MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes

MagDR: Mask-guided Detection and Reconstruction for Defendin...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Zhikai Xie, Lingxi Pang, Shanmin He, Yong Zhang, Bo Xi An Jiao Tong Univ Xian Peoples R China Huawei Inc Shenzhen Peoples R China Tencent Blade Team Shenzhen Peoples R China

ISBN: (纸本)9781665445092

Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet. This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR starts with a detection module that defines a few criteria to judge the abnormality of the output of deepfakes, and then uses it to guide a learnable reconstruction procedure. Adaptive masks are extracted to capture the change in local facial regions. In experiments, MagDR defends three main tasks of deepfakes, and the learned reconstruction pipeline transfers across input data, showing promising performance in defending both black-box and white-box attacks.

关键词： Visualization Perturbation methods Pipelines Semantics Prediction algorithms pattern recognition Task analysis

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Learning View Selection for 3D Scenes

Learning View Selection for 3D Scenes

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sun, Yifan Huang, Qixing Hsiao, Dun-Yu Guan, Li Hua, Gang Univ Texas Austin Austin TX 78712 USA Wormpex AI Res Bellevue WA USA

ISBN: (纸本)9781665445092

Efficient 3D space sampling to represent an underlying 3D object/scene is essential for 3D vision, robotics, and beyond. A standard approach is to explicitly sample a dense collection of views and formulate it as a view selection problem, or, more generally, a set cover problem. In this paper, we introduce a novel approach that avoids dense view sampling. The key idea is to learn a view prediction network and a trainable aggregation module that takes the predicted views as input and outputs an approximation of their generic scores (e.g., surface coverage, viewing angle from surface normals). This methodology allows us to turn the set cover problem (or multi-view representation optimization) into a continuous optimization problem. We then explain how to effectively solve the induced optimization problem using continuation, i.e., aggregating a hierarchy of smoothed scoring modules. Experimental results show that our approach arrives at similar or better solutions with about 10 x speed up in running time, comparing with the standard methods.

关键词： Solid modeling computer vision Three-dimensional displays Neural networks Cameras pattern recognition Optimization

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Wildlife Species recognition Using Deep Learning

Wildlife Species Recognition Using Deep Learning

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2023 ieee International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Salomon, Sergio Bringas, Santos Duque, Rafael Montana, Jose L. Gonzalez, Avelino J. Axpe Consulting Department of Artificial Intelligence Camargo39600 Spain University of Cantabria Faculty of Science Department of Mathematics Statistics and Computer Science Santander39005 Spain University of Central Florida Department of Computer Science OrlandoFL32816 United States

ISBN: (纸本)9798350337020

The recent deep learning techniques of the last decade are opening new applications and innovations for numerous diverse fields. In this research, we study the problem of wildlife species recognition based on camera trap snapshots and automated methods. In comparison to conventional image classification, this poses a harder problem due to noise and information redundancy. To this end, we apply state-of-the-art techniques from computer vision, and we consider the characteristics of this problem in regard to heterogeneous noisy data. We design a preliminary approach, in the form of a data pipeline, based on techniques such as image preprocessing, data augmentation, transfer learning, and convolutional neural network models. We introduce in this work a case study for the Integrated Management System of the Natural Environment (known as 'SIGMedNat') that collects data about Cantabria's wildlife. We analyze several factors and challenges for this case, as well as results from our preliminary approach for species recognition. This application can be useful to facilitate and improve the monitoring and tracking of wildlife animals for the purposes of observation and preservation. © 2023 ieee.

关键词： computer vision Deep Learning Species recognition

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An Extended Moore-Neighbour Tracing Algorithm for Ear Segmentation in a Multi-Feature Ear recognition System

An Extended Moore-Neighbour Tracing Algorithm for Ear Segmen...

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2024 International conference on Advances in Modern Age Technologies for Health and Engineering Science, AMATHE 2024

作者： Adeniyi, Jide Kehinde Olanrewaju, Ahmed Babajide Adeniyi, Abidemi Emmanuel Brahma, Biswajit Awotunde, Joseph Bamidele Bhuyan, Hemanta Kumar Landmark University Department Of Computer Science Kwara State Omu-Aran Nigeria University Of Ibadan Department Of Computer Science Ibadan Nigeria Bowen University Department Of Computer Science Osun State Iwo Nigeria McKesson Corporation CA United States University Of Ilorin Department Of Computer Science Ilorin Nigeria Vignan's Foundation For SC Technology & Research Department Of It AP India

ISBN: (纸本)9798350371567

Ear recognition is an example of a biometric system that uses human biological traits for recognition. This kind of recognition has been recently examined due to its distinctive properties such as its invariant shape. When performing analysis on image processing or pattern recognition, one of the major problems encountered is the number of features involved. It is necessary to extract a well-defined feature to make the classification process more efficient. Hence, this paper aims to propose an ear recognition system that extracts two features from the human ear: textural and geometrical features. This is aimed at improving the accuracy of the biometric trait. The extracted features were saved as a template and used for matching. The proposed system was evaluated with two online ear image datasets (AMI Ear database and USTB Ear database) and it produced an accuracy of 98.15. © 2024 ieee.

关键词： Biometrics

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