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Focal Modulation Based End-to-End Multi-Label Classification for Chest X-Ray image Classification

Focal Modulation Based End-to-End Multi-Label Classification...

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IEEE Signal processing and communications Applications (SIU)

作者： Şaban Öztürk Tolga Çukur Elektrik ve Elektronik Mühendisligi Bölümü Bilkent Üniversitesi Ankara Türkiye Elektrik ve Elektronik Mühendisligi Bölümü Amasya Üniversitesi Amasya Türkiye Ulusal Manyetik Rezonans Araştırma Merkezi (UMRAM) Bilkent Üniversitesi Ankara Türkiye

Chest X-ray imaging is of critical importance in order to effectively diagnose chest diseases, which are increasing today due to various environmental and hereditary factors. Although chest X-ray is the most commonly used device for detecting pathological abnormalities, it can be quite challenging for specialists due to misleading locations and sizes of pathological abnormalities, visual similarities, and complex backgrounds. Traditional deep learning (DL) architectures fall short due to relatively small areas of pathological abnormalities and similarities between diseased and healthy areas. In addition, DL structures with standard classification approaches are not ideal for dealing with problems involving multiple diseases. In order to overcome the aforementioned problems, firstly, background-independent feature maps were created using a conventional convolutional neural network (CNN). Then, the relationships between objects in the feature maps are made suitable for multi-label classification tasks using the focal modulation network (FMA), an innovative attention module that is more effective than the self-attention approach. Experiments using a Chest x-ray dataset containing both single and multiple labels for a total of 14 different diseases show that the proposed approach can provide superior performance for multi-label datasets.

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A Deep Learning Based System for Automatic Sorting and Quality Grading of Citrus Fruits

A Deep Learning Based System for Automatic Sorting and Quali...

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International conference on Emerging Trends in Networks and Computer communications (ETNCC)

作者： Nidhi Kundu Sonam Geeta Rani Vijaypal Singh Dhaka Computer Science Sri Karan Narendra Agriculture University Jobner Jaipur India Computer Science Engg. Manipal University Jaipur Jaipur India Computer and Communication Engg. Manipal University Jaipur Jaipur India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The citrus industry plays a pivotal role in the global agricultural sector, contributing significantly to the economy. Approximately 161 million tonnes of citrus fruits were produced worldwide. One third of the total production is used in processing industry. The price of citrus fruits is highly dependent on its quality parameters such as size, citrus color index, maturity index, diseases etc. Therefore, grading and sorting of citrus fruits based on the quality parameters is demand of the market to maximize the profit. Manual grading and sorting are labour-intensive, time-consuming, and often prone to human error. Automatic grading and sorting of citrus fruits can resolve the above-stated issues. In this paper, we utilize the pattern recognition and image processing potential of deep learning techniques and propose a deep learning based automated system to revolutionize the citrus fruits grading process. The system utilizes Convolutional Neural Networks (CNNs) to extract high-level features from lemon, orange images, allowing for accurate classification based on quality parameters such as size, color, and visual defects. We curated a dataset comprising diverse lemon, orange samples from Kaggle to evaluate the performance of the developed model. Additionally, our system incorporates real-time processing capabilities, ensuring a swift and continuous flow of fruits through the sorting process. Thus, it offers a robust and scalable solution for the citrus industry to enhance profit in the global citrus market.

关键词： Industries Deep learning visualization Accuracy image color analysis Real-time systems Convolutional neural networks Reliability Indexes Sorting

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Human-robot interaction based on Haar-like features and eigenfaces

Human-robot interaction based on Haar-like features and eige...

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IEEE International conference on Robotics and Automation (ICRA)

作者： J. Barreto P. Menezes J. Dias Institute of Systems and Robotics University of Coimbra Coimbra Portugal

This paper describes a machine learning approach for visual object detection and recognition which is capable of processing images rapidly and achieving high detection and recognition rates. This framework is demonstrated on, and in part motivated by, the task of human-robot interaction. There are three main parts on this framework. The first is the person's face detection used as a preprocessing system to the second stage which is the recognition of the face of the person interacting with the robot, and the third one is the hand detection. The detection technique is based on Haar-like features introduced by Viola et al. and then improved by Lienhart et al. The eigenimages and PCA are used in the recognition stage of the system. Used in real-time human-robot interaction applications the system is able to detect and recognise faces at 10.9 frames per second in a PIV 2.2 GHz equipped with a USB camera.

关键词： Face detection Object detection image recognition Face recognition Machine learning Robots Principal component analysis Real time systems Universal Serial Bus Cameras

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An Improved image Super-Resolution Algorithm for Percutaneous Endoscopic Lumbar Discectomy 8th

An Improved Image Super-Resolution Algorithm for Percutaneo...

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8th International conference on Cognitive Systems and Information processing, ICCSIP 2023

作者： Li, Xue Zhou, Zihan Wang, Kaifeng Liu, Haiying Qian, Yalong Duan, Xingguang Li, Changsheng Beijing Institute of Technology 5 South Zhong Guan Cun Street Haidian District Beijing China The Department of Spinal Surgery Peking University People’s Hospital No. 11 Xizhimen South Street Xicheng District Beijing China

ISBN: (纸本)9789819980178

High-resolution (HR) spinal endoscopic images are essential to enhance the surgeon’s visual presence for the guidance of surgical procedures. However, available image super-resolution methods, especially deep learning methods, are mostly trained with open-source life scene datasets which possess limited medical image features. To address this issue, we have proposed an improved SRGAN model for the visual enhancement of percutaneous endoscopic lumbar discectomy (PELD) surgical images. Specifically, a residual dense block (RDB) and a dynamic RELU function are introduced. We validate the proposed method on PELD datasets. Quantitative and qualitative comparisons are carried out by comparing methods. The method proposed in this paper improves PSNR by 2.8% and SSIM by 6% compared with the original SRGAN, which proves the superiority of this methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Endoscopy

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A real-time multi-threaded system to detect patient motion in SPECT imaging using multiple optical cameras

A real-time multi-threaded system to detect patient motion i...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： J.D. Morgenstern M.A. Gennert S. Nadella N. Kumar G.C. Speckert P.P. Bruyant M.A. King Internet Video & Imaging Uxbridge MA Worcester Polytechnic Institute Worcester MA USA Speck Technology Inc. USA SpeckTech Inc. University of Massachusetts Worcester MA USA University of Massachusetts Worcester MA

Patient motion, which causes artifacts in reconstructed images, can be a serious problem in SPECT imaging. If patient motion can be detected and quantified, the reconstruction algorithm can compensate for the motion. In previous work, we described a prototype system for tracking patient motion. In this paper, we present a real-time multithreaded visual tracking system (VTS) that will be suitable for deployment in clinical trials. The VTS tracks patients using multiple video images and image processing techniques, calculating patient motion in three-dimensional space. High performance is achieved by acquiring and processing images in parallel. Our system will incorporate up to five optical cameras monitoring the patient. Each camera is associated with a thread of control within the VTS. Thus, video images can be acquired asynchronously. As images are acquired, they are stored in buffers until needed. Another processing thread is responsible for requesting images from a specific time, and processing those images as a stereo set. The SPECT system operates in list mode. In this mode, all detected events are stored in a long list together with an event timestamp. Thus, it is possible to temporally register the time-stamped images with the detected activity in order to rebin the SPECT data to compensate for patient motion

关键词： Real time systems Motion detection Optical imaging Cameras Optical buffering Tracking Yarn Buffer storage image reconstruction Reconstruction algorithms

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Improved ORB Algorithm Based on Binocular Vision

Improved ORB Algorithm Based on Binocular Vision

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IEEE International conference on Computer and communications

作者： Chao Li Yongxing Jia Huali Wang Chuanzhen Rong Ying Zhu Coege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLACoege of Counicions Engineering Ary Engineering Universiy of PLA

ISBN: (数字)9781728147437

ISBN: (纸本)9781728147444

In digital image processing, feature extraction is a very important step, and ORB algorithm is the best one in realtime performance, but it is not up to the standard in the system with higher real-time requirements. Through binocular vision calibration, we will delve into how to speed up the operation of ORB algorithm with ensuring accuracy. First, the overlapping regions of the left and right images are extracted, and then the images are subjected to morphological operations, and corner detection is performed at the edges. Secondly, the original description method is improved, and the description sub-selects the window value based on the area ring, which is more in line with the characteristics of the human eye and improves the operation speed. Then, a hierarchical matching strategy is adopted, combined with binocular visual constraints, the matching range is defined, and the accuracy is effectively improved. Finally, construct an experimental platform to process two images and compare them with existing algorithms. The experimental results show that the proposed method has much less computational time consumption than the original algorithm under the condition of improved accuracy, and is suitable for fast matching environment.

关键词： Feature extraction image edge detection Real-time systems Calibration Cameras Morphological operations Robustness

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Accurate Recognition of Kiwifruit Based on Improved YOLOv5

Accurate Recognition of Kiwifruit Based on Improved YOLOv5

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Natural Language processing (ICNLP), International conference on

作者： Sun Wei Sun Yi Jun Li Zhao Chen Guo Jing School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

In order to meet the urgent needs of automation and intelligent picking of kiwifruit, aiming at the problems of unreasonable construction of kiwifruit data set, low fruit recognition accuracy and poor spatial positioning in the natural environment of orchard, a precise recognition and visual positioning method of kiwifruit based on improved Yolov5s was proposed. In view of the growth characteristics of kiwifruit in trellis orchards, a multi-type kiwifruit data set was first constructed. Furthermore, the attention mechanism and multi-scale module are combined to improve the Yolov5s network structure, identify kiwifruit and extract the center coordinates of the prediction box. The experimental results show that the average accuracy of the model for six kiwifruit types under different weather and light conditions is 98 %. The single image recognition time of $1280\times 720$ pixel is about 13.8 ms, and the weight is only 15.21 Mb. It can be seen that this study can provide technical support for the vision system of kiwifruit automatic picking robot, and provide reference for the intelligent recognition and positioning of other fruits (such as apples, mangoes and oranges).

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Defect detection in pattern texture analysis

Defect detection in pattern texture analysis

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International conference on communications and Signal processing

作者： Manimozhi Iyer S. Janakiraman subbaih Manonmaniam Sundaranar at Tirunelveli India Dept of Banking Technology in Pondicherry University Pondicherry

The detection of abnormalities is a very challenging problem in computer vision, especially if these abnormalities must be detected in images of textured surfaces. The cast extrusion manufacturing process is the initial step which enables the creation of the raw materials, such as clear polypropylene film, needed for the flexible packaging manufacturing process. The current methodology of controlling extrusion related defects occurrences is attempted by a combination of statistical Sampling and human inspection. However, the defects are small in size and hard to visualize in a clear thin film 3 m in width moving at a speed of 50m/min/.This resulted in poor product quality and high return ratio from customers. This problem becomes even more complicated in case when testing is possible only with one surface of the part. Texture analysis plays an important role in the automated visual inspection of texture images to detect their defects. This automated classification method helps us to acquire knowledge about the pattern of defect within a very short period of time. This research investigates possible defect detection methodologies and has subsequently proposed a system that is capable of real time monitoring of defects on the cast extrusion manufacturing process.

关键词： Monitoring image edge detection Random access memory Performance evaluation

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A context-based privacy preserving framework for wearable visual lifeloggers

A context-based privacy preserving framework for wearable vi...

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IEEE Annual conference on Pervasive Computing and communications Workshops (PerCom)

作者： Eisa Zarepour Mohammadreza Hosseini Salil S. Kanhere Arcot Sowmya School of Computer Science and Engineering University of New South Wales Sydney Australia AICT Innovation Center Sharif University of Technology Tehran Iran

The ability of wearable cameras to continuously capture the first person viewpoint with minimal user interaction, has made them very attractive in many application domains. Wearable technology today is available and useful but not widely used and accepted due to various challenges mainly privacy concerns. In this paper, we introduce a novel efficient privacy-aware framework for wearable cameras that can protect all sensitive subjects such as people, objects (e.g, display screens, license plates and credit cards) and locations (e.g, bathrooms and bedrooms). It uses the contextual information obtained from the wearable's sensors and recorded images to identify the potential sensitive subjects in each image. Using image processing techniques, first the sensitive subjects are recognized and then blurred or eliminated. Finally the edited image is ready to publish. Using a practical case study, we experimentally evaluate the proposed framework. Our experimental results show that the proposed system can detect and blur the sensitive subjects among around 300 images with around 70% accuracy.

关键词： Privacy Credit cards Detectors Legged locomotion Neural networks Cameras image recognition

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Distributed volumetric scene geometry reconstruction with a network of distributed smart cameras

Distributed volumetric scene geometry reconstruction with a ...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shubao Liu Kongbin Kang Jean-Philippe Tarel David B. Cooper Division of Engineering Brown University Providence RI USA Laboratoire Central des Ponts et Chaussées Paris France

Central to many problems in scene understanding based on using a network of tens, hundreds or even thousands of randomly distributed cameras with on-board processing and wireless communication capability is the “efficient” reconstruction of the 3D geometry structure in the scene. What is meant by “efficient” reconstruction? In this paper we investigate this from different aspects in the context of visual sensor networks and offer a distributed reconstruction algorithm roughly meeting the following goals: 1. Close to achievable 3D reconstruction accuracy and robustness; 2. Minimization of the processing time by adaptive computing-job distribution among all the cameras in the network and asynchronous parallel processing; 3. Communication Optimization and minimization of the (battery-stored) energy, by reducing and localizing the communications between cameras. A volumetric representation of the scene is reconstructed with a shape from apparent contour algorithm, which is suitable for distributed processing because it is essentially a local operation in terms of the involved cameras, and apparent contours are robust to ourdoor illumination conditions. Each camera processes its own image and performs the computation for a small subset of voxels, and updates the voxels through collaborating with its neighbor cameras. By exploring the structure of the reconstruction algorithm, we design the minimum-spanning-tree (MST) message passing protocol in order to minimize the communication. Of interest is that the resulting system is an example of “swarm behavior”. 3D reconstruction is illustrated using two real image sets, running on a single computer. The iterative computations used in the single processor experiment are exactly the same as are those used in the network computations. Distributed concepts and algorithms for network control and communication performance are theoretical designs and estimates.

关键词： Layout Geometry Smart cameras image reconstruction Computer networks Reconstruction algorithms Robustness Algorithm design and analysis Wireless communication Context

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