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SIEDOB: Semantic image Editing by Disentangling Object and Background

SIEDOB: Semantic Image Editing by Disentangling Object and B...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Wuyang Luo Su Yang Xinjian Zhang Weishan Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University School of Computer Science and Technology China University of Petroleum

Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map. In this task, the features of the foreground objects and the backgrounds are quite different. However, all previous methods handle backgrounds and objects as a whole using a monolithic model. Consequently, they remain limited in processing content-rich images and suffer from generating unrealistic objects and texture-inconsistent backgrounds. To address this issue, we propose a novel paradigm, Semantic image Editing by Disentangling Object and Background (SIEDOB), the core idea of which is to explicitly leverages several heterogeneous subnetworks for objects and backgrounds. First, SIEDOB disassembles the edited input into background regions and instance-level objects. Then, we feed them into the dedicated generators. Finally, all synthesized parts are embedded in their original locations and utilize a fusion network to obtain a harmonized result. Moreover, to produce high-quality edited images, we propose some innovative designs, including Semantic-Aware Self-Propagation Module, Boundary-Anchored Patch Discriminator, and Style-Diversity Object Generator, and integrate them into SIEDOB. We conduct extensive experiments on Cityscapes and ADE20K-Room datasets and exhibit that our method remarkably outperforms the baselines, especially in synthesizing realistic and diverse objects and texture-consistent backgrounds. Code is available at https://***/WuyangLuo/SIEDOB.

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Improving Deep Learning-based Automatic Checkout System Using image Enhancement Techniques

Improving Deep Learning-based Automatic Checkout System Usin...

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IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Long Hoang Pham Duong Nguyen-Ngoc Tran Huy-Hung Nguyen Hyung-Joon Jeon Tai Huu-Phuong Tran Hyung-Min Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University South Korea

The retail sector has experienced significant growth in artificial intelligence and computer vision applications, particularly with the emergence of automatic checkout (ACO) systems in stores and supermarkets. ACO systems encounter challenges such as object occlusion, motion blur, and similarity between scanned items while acquiring accurate training images for realistic checkout scenarios is difficult due to constant product updates. This paper improves existing deep learning-based ACO solutions by incorporating several image enhancement techniques in the data pre-processing step. The proposed ACO system employs a detect-and-track strategy, which involves: (1) detecting objects in areas of interest; (2) tracking objects in consecutive frames; and (3) counting objects using a track management pipeline. Several data generation techniques—including copy-and-paste, random placement, and augmentation—are employed to create diverse training data. Additionally, the proposed solution is designed as an open-ended framework that can be easily expanded to accommodate multiple tasks. The system has been evaluated on the AI City Challenge 2023 Track 4 dataset, showcasing outstanding performance by achieving a top-1 ranking on test-set A with an F1 score of 0.9792.

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TransGait: vision Transformer Based Gait recognition Network

TransGait: Vision Transformer Based Gait Recognition Network

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image processing, computer vision and Machine Learning (ICICML), International conference on

作者： Kaixuan Li Steven Meng Computer Science Blair Academy Blairstown USA Electronics Engineering and Computer Science Peking University Beijing China

Gait, the distinctive walking pattern of an individual, stands out as a human modality that can be effortlessly captured even with low-resolution cameras from a considerable distance. Gait patterns are unique to each individual and hard to fake. Gait recognition is a cutting-edge biometric technology that focuses on identifying individuals based on their distinctive walking patterns. Therefore, its fields of application span a number of domains, encompassing healthcare, robotics, automation, as well as security and access control. The origin of this investigation stems from challenges faced by high school and college residents contending with forgotten identification credentials, compounded by the intricacies of facial recognition in the context of the COVID-19 pandemic, especially in instances involving mask-obscured facial features. This paper proposes Transformer based Gait recognition Network (Occlusion-TransGait), which combining temporal vision Transformer (VIT) and Swin Transformer based structure and occlusion masked datasets. Utilizing the Swin Transformer model, the study achieved enhanced precision, with improvements of 0.4% for NM, 1.0% for BG, and 1.4% for CL. Furthermore, this paper presents a novel open-set detection technique for recognizing potential threats arising from unidentified individuals. Finally, this research embraces an inventive strategy that integrates human detection, tracking, and segmentation to create an end-to-end pipeline well-suited for implementation on the NVIDIA Jetson platform. This research promises a transformative impact on gait recognition systems, enhancing their effectiveness and security, and paving the way for stronger and more efficient biometric solutions.

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Revolutionizing livestock management: a multiscale approach to cow recognition system

Revolutionizing livestock management: a multiscale approach ...

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International conference on computer vision and Internet of Things 2023 (ICCVIoT'23)

作者： R. Newlin Shebiah S. Arivazhagan Centre for Image Processing and Pattern Recognition Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Sivakasi Tamilnadu 626005 India

The implementation of a cow recognition system has significantly transformed the field of livestock management through the automation of identification and monitoring procedures. By employing sophisticated computer vision, this system is capable of distinguishing individual cows, facilitating accurate health monitoring, reproductive management, as well as customized diet. This study presents a multiscale framework inspired by Googlenet architecture that effectively captures intricate information at many levels of abstraction within an image. The complexity of cow identification, influenced by factors like posture, size, and unique characteristics, necessitates a multiscale technique. The proposed Methodology was tested using a dataset consisting of 13 classes of Holstein cow images, resulting in an accuracy rate of 97.97%. In order to assess the credibility of the model, the dataset and experimental settings were maintained at a constant level and subsequently compared to the outcomes obtained by VGG16, AlexNet, GoogLeNet, MobileNet, ResNet, SqueezeNet, and EfficientNet.

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On Data Scaling in Masked image Modeling

On Data Scaling in Masked Image Modeling

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conference on computer vision and pattern recognition (CVPR)

作者： Zhenda Xie Zheng Zhang Yue Cao Yutong Lin Yixuan Wei Qi Dai Han Hu Tsinghua University Microsoft Research Asia Xi'an Jiaotong University

Scaling properties have been one of the central issues in self-supervised pre-training, especially the data scalability, which has successfully motivated the large-scale self-supervised pre-trained language models and endowed them with significant modeling capabilities. However, scaling properties seem to be unintentionally neglected in the recent trending studies on masked image modeling (MIM), and some arguments even suggest that MIM cannot benefit from large-scale data. In this work, we try to break down these preconceptions and systematically study the scaling behaviors of MIM through extensive experiments, with data ranging from 10% of imageNet-1K to full imageNet-22K, model parameters ranging from 49-million to one-billion, and training length ranging from 125K to 500K iterations. And our main findings can be summarized in two folds: 1) masked image modeling remains demanding large-scale data in order to scale up computes and model parameters; 2) masked image modeling cannot benefit from more data under a non-overfitting scenario, which diverges from the previous observations in self-supervised pre-trained language models or supervised pre-trained vision models. In addition, we reveal several intriguing properties in MIM, such as high sample efficiency in large MIM models and strong correlation between pre-training validation loss and transfer performance. We hope that our findings could deepen the understanding of masked image modeling and facilitate future developments on largescale vision models. Code and models will be available at https://***/microsoft/SimMIM.

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3DSAINT Representation for 3D Point Clouds

3DSAINT Representation for 3D Point Clouds

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IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab. Computer and Information Sciences University of Delaware Newark Delaware

This paper introduces a Sphere-based representation to model a 3D scene and show its performance on various tasks, including Structure from Motion (SfM) and 3D scene classification. A significant target application of this work is Mixed Reality, where 3D data can be efficiently represented, and synthetic and real data can be mixed for an immersive experience. Over the past few decades, 3D big data has garnered increased attention in computer vision. Acquiring, representing, reconstructing, querying, classifying, and visualizing 3D models for Mixed Reality has become crucial for many applications, such as medicine, architecture, entertainment, and bioinformatics. With the ever-increasing amount of data that the 3D scanners produce, storing, processing, and transmitting the data becomes challenging. Techniques that exploit the shape information need to be developed to model, classify and visualize the data. Our work offers a novel multi-scale surface representation based on spheres, with the ultimate goal of helping scientists to see and work with 3D data in Mixed Reality more effectively and efficiently.

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Evolved Part Masking for Self-Supervised Learning

Evolved Part Masking for Self-Supervised Learning

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conference on computer vision and pattern recognition (CVPR)

作者： Zhanzhou Feng Shiliang Zhang National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Peng Cheng Laboratory

Existing Masked image Modeling methods apply fixed mask patterns to guide the self-supervised training. As those patterns resort to different criteria to mask local regions, sticking to a fixed pattern leads to limited vision cues modeling capability. This paper proposes an evolved part-based masking to pursue more general visual cues modeling in self-supervised learning. Our method is based on an adaptive part partition module, which leverages the vision model being trained to construct a part graph, and partitions parts with graph cut. The accuracy of partitioned parts is on par with the capability of the pretrained model, leading to evolved mask patterns at different training stages. It generates simple patterns at the initial training stage to learn low-level visual cues, which hence evolves to eliminate accurate object parts to reinforce the learning of object semantics and contexts. Our method does not require extra pretrained models or annotations, and effectively ensures the training efficiency by evolving the training difficulty. Experiment results show that it substantially boosts the performance on various tasks including image classification, object detection, and semantic segmentation. For example, it outperforms the recent MAE by 0.69% on imageNet-1K classification and 1.61% on ADE20K segmentation with the same training epochs.

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Simulation of computer image processing Model Based on Random Forest Algorithm

Simulation of Computer Image Processing Model Based on Rando...

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3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME), International conference on

作者： Haotian Liu University of Edinburgh Edinburgh UK

In essence, image recognition, as a pattern classification problem, can also be processed by classical pattern classification algorithms. Under the background of network information technology, it is need to use computer vision algorithm to process the image and realize the three-dimensional reconstruction of the image for the distortion of the true three-dimensional display image of intelligent interactive system. Random Forest (RF) is a new ensemble learning algorithm developed in recent years, which has good classification accuracy. In this article, a computer image processing model based on RF algorithm is proposed to enhance the ability of depth feature learning. The experimental results show that this image processing technology is more accurate than BP neural network and can be widely used in image processing. High-level target feature representation can often improve the recognition accuracy. However, the extraction of high-level target features generally has certain computational complexity. Compared with traditional methods, the accuracy of the proposed method is greatly improved, and the generalization performance is superior, which can identify images that have been beautified and repaired.

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Design of IoT based Automatic Number Plate recognition

Design of IoT based Automatic Number Plate Recognition

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Technology & Engineering Management conference - asia Pacific (TEMSCON-ASPAC), IEEE

作者： Manjunath R Kounte Muhammed Ziyan Nr Vishwas Joyson K Sunny School of Electronics and Communication Engineering REVA University Bengaluru India

ISBN: (数字)9798350384659

ISBN: (纸本)9798350384666

New opportunities for several applications in the fields of security and transportation have emerged because of developments in computer vision and image processing techniques. One of these well-known uses is automatic number plate detection (ANPD), which is essential for traffic control, toll collection, parking management, and law enforcement. This paper focuses on creating and implementing a productive ANPD system using the Internet of Things based Raspberry Pi *** paper includes the summary of the methods currently in use and the difficulties involved in number plate recognition. We have also highlighted the benefits of the Internet of Things based approaches like Raspberry Pi as the hardware platform for our system, including its cost, adaptability, and computational power. The need of using computer vision methods, such as edge detection, contour analysis, and morphological operations to preprocess the video frames and extract pertinent characteristics is also highlighted. Traditional computer vision methods and deep learning models are combined to accomplish accurate number plate identification.

关键词： computer vision Law enforcement image edge detection Transportation Traffic control Hardware Internet of Things

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12th International Congress in Telematics and Computing, WITCOM 2023

12th International Congress in Telematics and Computing, WIT...

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12th International Congress in Telematics and Computing, WITCOM 2023

ISBN: (纸本)9783031453151

The proceedings contain 35 papers. The special focus in this conference is on Telematics and Computing. The topics include: A Novel Method Based on Gunnar Farneback Method, Mathematical Morphology, and Artificial vision for Flow Analysis in Electrochemical Reactors;search Space Reduction in Road Networks for the Ambulance Location and Allocation Optimization Problems: A Real Case Study;development of a Web-Based Calculator to Simulate Link Budget for Mobile Communications Systems at Urban Settlements;scientific Information Management System for Multidisciplinary Teams;air Quality Prediction in Smart Cities Using Wireless Sensor Network and Associative Models;User Interface of Digital Platforms Used TDHA Patients: Case Study in Educational Environment;recognition of Pollen-Carrying Bees Using Convolutional Neural Networks and Digital image processing Techniques;enhancing Air Quality Monitoring in Mexico City: A Hybrid Sensor-Machine Learning System;multi-labeling of Malware Samples Using Behavior Reports and Fuzzy Hashing;APOS is Not Enough: Towards a More Appropriate Way to Estimate Computational Complexity in CIC Decimation Architectures;Computational Simulation Applied to 3.5 GHz Band Microstrip Yagi Array Antenna Design for 5G Technology Mobile Wireless Device;3D Point Cloud Outliers and Noise Reduction Using Neural Networks;Development and Coding of a Data Framing Protocol for IoT/LPWAN Networks Based on 8-Bit processing Architectures;performance Analysis of Variable Packet Transmission Policies in Wireless Sensor Networks;about a Sentiment Analysis Technique on Twitter and Its Application to Regional Tourism in Quintana Roo;comparative Study of pattern recognition Techniques in the Classification of Vertebral Column Diseases;security Verification of Instant Messaging Cryptographic Protocols;Elaboration of an Information System to Improve Operational Discipline in PEMEX with LAGS Methodology;ICIS: A Model for Context-Based Classification of Sensitive Personal In

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