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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...

引用

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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Improved ORB Algorithm Based on Binocular Vision

Improved ORB Algorithm Based on Binocular Vision

引用

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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EyeRis: visual image Recognition using Machine Learning for the visually-Impaired

EyeRis: Visual Image Recognition using Machine Learning for ...

引用

International conference on Electronics, Information and communications (ICEIC)

作者： Alliah May Eugenio Marian Jowie Patulot Lykha Jane Seguiro Angelica Nicole Tuazon Shekinah Lor Huyo-a Mideth Abisado Gabriel Avelino Sampedro Department of Computer Science Technological University of the Philippines Manila Philippines Philippine Coding Camp Research and Development Center Manila Philippines College of Computing and Information Technologies National University Manila Philippines Faculty of Information and Communication Studies University of the Philippines Open University Laguna Philippines

visually impaired people struggle daily and have difficulty recognizing and distinguishing objects around them. Thus, they mainly depend on supervision from other people to assist them. Since smartphones have become a necessity in this modern world, the researchers formulated a solution to help the visually impaired through a machine learning-based mobile application for object recognition. Nowadays, software applications can provide accurate findings in picture classification and processing processes thanks to machine learning techniques and algorithms. In this study, the researchers use a convolutional neural network (CNN)-based system on TensorFlow Lite to create a mobile version of a visual information system employing a machine learning strategy and deep learning framework. The main objectives of the smartphone application, EyeRis, is to recognize and categorize items in real-time and to separate photographs from the user-selected scenarios. Results were analyzed and contrasted based on the app’s obtained recognition accuracy data. It demonstrated the utility of CNN as a model for image recognition algorithms.

关键词： visualization image recognition Machine learning algorithms Software algorithms Neural networks Software Real-time systems

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Deep learning-based Object Detection in Underwater communications System 2

Deep learning-based Object Detection in Underwater Communica...

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2nd International conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Sangari, M. Siva Thangaraj, K. Vanitha, U. Srikanth, N. Sathyamoorthy, J. Renu, K. KPR Institute of Engineering and Technology Department of CSE Coimbatore India Sona College of Technology Department of Information Technology Salem India Sri Krishna College of Engineering and Technology Department of ECE Coimbatore India SASI Institute of Technology and Engineering Department of Electronics and Communication Technology Andhra Pradesh Tadepalligudem India Bannari Amman Institute of Technology Department of CSE Erode India GST Department of EECE GITAM Deemed to Be University A.P Visakhapatnam -45 India

ISBN: (纸本)9798350397635

Being at the nexus of robotics and ocean engineering, underwater robots have been a developing research area. They can be used for deep sea infrastructure inspections, oceanographic mapping, and environmental monitoring. Autonomous navigation skills are essential for doing these activities successfully, especially given the poor communication conditions in underwater locations. Autonomous navigation technologies, such as path planning and tracking, have been one of the fascinating but difficult issues in the field of study due to the extremely dynamic and three-dimensional settings. Due to their short detection ranges and poor visibility, cameras have not received much attention as an underwater sensor. However, using visual data from cameras is still a popular technique for underwater sensing, and it works particularly well for close-range detections. In this study, the enhancement of underwater vision is achieved by combining the max-RGB and shades of grey methods. Then, to solve the problem of poorly illuminated underwater images, a technique known as RCNN (Region-based Convolutional Neural Network) is proposed. This procedure tells the mapping relationship how to create the illumination map. Following image processing, an RCNN strategy for underwater detection and classification is recommended. Two improved strategies are then used to change the RCNN structure in accordance with the properties of underwater vision. In order to deal with the challenges of object tracking and detection in underwater communication, a correlation filter tracking algorithm (CFTA) method was created. The properties of the invariant moment and area were looked at after the object's region had been extracted using a threshold segment and morphological technique. The findings show that the suggested method is effective for underwater target tracking based on RCNN-CFTA in the aquatic environment. Simulated evaluation of these methods' performance demonstrates the potency of the suggested

关键词： Cameras

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Alpha model based mixed pixel processing for view synthesis

Alpha model based mixed pixel processing for view synthesis

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3DTV conference

作者： Hariprasad Kannan Kiran N Iyer Kausik Maiti Devendra Purbiya Ajit Bopardikar Anshul Sharma Samsung India Software Operations Private Limited Bangalore India

The commercial success and acceptability of 3D technology will critically depend on the overall visual quality of the rendered images. Therefore, view synthesis techniques like Depth image Based Rendering (DIBR) are crucial components of the 3D system chain. In this paper, we propose a fast α-model based approach that enables high quality DIBR. Given an image and depth map, a point based representation of the scene is obtained, where each point is associated with color, transparency and depth. A low complexity method estimates these attributes, making the system suitable for deployment in consumer electronics.

关键词： Pixel Rendering (computer graphics) Layout Color Equations Consumer electronics Three dimensional TV Software quality Multimedia communication communications technology

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Multi-mode Kernel-Based Minimum Mean Square Error Estimator for Accelerated image Error Concealment

Multi-mode Kernel-Based Minimum Mean Square Error Estimator ...

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Data Compression conference (DCC)

作者： Ján Koloda Jürgen Seiler Antonio M. Peinado André Kaup Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Germany Dpt. of Signal Theory Universidad de Granada Spain

Summary form only given. In this paper, we propose a novel multi-mode error concealment algorithm that aims at obtaining high quality reconstructions with reduced computational burden. Block-based coding schemes in packet loss-environment are considered. The proposed technique exploits the excellent reconstructing abilities of the kernel-based minimum mean square error (K-MMSE) estimator [1]. The complexity of our technique is dynamically adapted to the visual complexity of the area being reconstructed. The technique outperforms other state of the art algorithms and produces high quality reconstructions, equivalent to K-MMSE, while requiring less than one fourth of its computational time.

关键词： image reconstruction Multimedia communication Estimation Mean square error methods Acceleration Streaming media Data compression

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Lightweight image Classification Algorithm Based on Dual Knowledge Distillation 23

Lightweight Image Classification Algorithm Based on Dual Kno...

引用

Proceedings of the 2023 6th International conference on Artificial Intelligence and Pattern Recognition

作者： Peng Xiao Ying Liu Yu Hao Center for Image and Information Processing School of Communications and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Deep learning models perform exceptionally well in image processing; however, the increasing number of parameters and computational burdens make it difficult to apply these models on mobile devices. To address this issue, a widely adopted approach is using knowledge distillation to compress and optimize deep learning models. Traditional knowledge distillation trains a student network with soft targets from a teacher network. This paper proposes a Novel Dual Knowledge Distillation (NDKD) network by integrating Convolutional Block Attention Module (CBAM) distillation and feature distillation. Specifically, a new CBAM module and a hash layer are first added to improve the teacher-student network. Next, attention distillation loss is computed to transfer CBAM knowledge from the teacher module to the student network to enhance the student network's ability to capture low-level visual features. Finally, feature distillation loss is constructed by hashing layers to transfer semantic knowledge from the teacher network to the student network to enhance the student network's high-level semantic ability. Our method is compared with other knowledge distillation methods on benchmarking datasets including the CIFAR-10, SVNM, EMNIST, and Tire Pattern image dataset CIIP-TPID. Experimental results show that our method outperforms other methods on adapted datasets.

关键词： Convolutional Block Attention Module Deep learning image processing knowledge distillation

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Landmine detection in high resolution 3D GPR images

Landmine detection in high resolution 3D GPR images

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International conference on Microwaves, Radar and Wireless communications

作者： E.E. Ligthart A.G. Yarovoy F. Roth L.P. Ligthart International Research Centre for Telecommunications-transmission and Radar (IRCTR) Delft University of Technology CD Delft The Netherlands International Research Centre for Telecommunications-transmission and Radar (IRCTR) Delft University of Technnology Delft Netherlands Int. Res. Center for Telecommun.-transmission & radar Delft Univ. of Technol. Netherlands

This work describes a novel landmine detection and classification algorithm for high resolution 3D ground penetrating radar (GPR) images. The algorithm was tested on data measured with a video impulse radar (VIR) system developed by the International Research Centre for Telecommunications-transmission and Radar (IRCTR). The algorithm detected all landmines (including the difficult to detect M14 mines) and classified almost all specific landmines features correctly with a reasonably low false alarm rate. The feature on a small height object demonstrates an effective reduction of false alarms.

关键词： Landmine detection image resolution Ground penetrating radar Radar imaging Radar detection Object detection Radar measurements Clutter image processing Radar polarimetry

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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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