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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Bhat, Aruna Jha, Rahul Kumar Kedia, Vansh Delhi Technological University Department of Computer Engineering Delhi India

ISBN: (纸本)9781665410281

An enormous progress in the fields of Artificial Intelligence, machine Learning, and Computer vision is witnessed over the last decade, owing to a doubling in processing capacity per year, as well as a massive rise in data internationally. Facial identity, biometrics, and face identification have gone from a rarefied issue to a prominent topic for academics in computer science these days. Due to the basic nature of the topic, breakthroughs in computer image processing and understanding studies will give insights into how the human brain operates, according to the common belief. Face detection and recognition are often employed in national defense, CCTV surveillance, criminal investigation, personal protection, law enforcement, biometrics, and a variety of other applications. For this project, OpenCV, Haar cascade, NumPy, and Matplotlib Python packages are utilized. Haar-Cascades will be utilized for face detection, and OpenCV provides three built-in face recognizers: Face recognition algorithms such as Eigenfaces, Fisher faces, and Local binary pattern histograms are used. The approach employed in this research is literature review, which entails reading and examining numerous books or literature relevant to mathematical ideas that underpin the development of algorithms to detect a human's face, which are subsequently followed by implementation in code, particularly Python code. © 2022 IEEE.

关键词： Face recognition

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vision-Based Autonomous Car Brake and Steering Assistance Using Deep Learning

Vision-Based Autonomous Car Brake and Steering Assistance Us...

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AIML-applications for Engineering & Technology (ICAET), International Conference on

作者： Anees H Pooja Agrawal School of Robotics Defence institute of advanced technology Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This paper presents a novel approach for enhancing vehicle safety and navigation through an integrated system for lane detection, vehicle alignment, and automatic braking using visual feedback. Our proposed system employs advanced deep learning and computer vision techniques with real-time processing to detect the exact boundaries of lane and ensures precise vehicle movement within the lane. The system continuously analyses lane markings and modifies the vehicle's position to ensure optimal lane adherence by utilizing a combination of machine learning algorithms and camera-based image processing. Additionally, the system incorporates an adaptive braking mechanism that identifies vehicles ahead using visual inputs. Furthermore, the jerks experienced during steering alignment can be greatly reduced by the suggested steering control system. The system's efficiency in various driving conditions is evidenced by its experimental simulation results, which also show improvements in collision avoidance and lane-keeping accuracy. This approach contributes to improved driving convenience and road safety by marking a substantial advancement in autonomous driving technologies.

关键词： YOLO Instance segmentation Deep learning Visualization Accuracy Simulation Real-time systems Road safety Automobiles Autonomous vehicles

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Unveiling the power of Haar frequency domain: Advancing small target motion detection in dim light

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Chen, Hao Sun, Xuelong Hu, Cheng Wang, Hongxin Peng, Jigen Guangzhou Univ Machine Life & Intelligence Res Ctr Guangzhou Peoples R China Guangzhou Univ Sch Math & Informat Sci Guangzhou Peoples R China

Visual small target motion detection finds successful applications in varied scenarios. However, dim-light conditions, such as the tunnel scenes and nighttime environments, present significant challenges to existing detection methods which mainly operate within the spatiotemporal domain. This is because the transmission of small target motion information suffers from the inevitable interference of image noise caused by dim light in the spatiotemporal domain, resulting in the detriment of extracting essential spatiotemporal features of the target motion. Given the significant obstacles posed by dim-light imaging to small target motion detection within the spatiotemporal domain, the exploration of an alternative observation domain for small target motion, alongside the development of a corresponding detection method, emerges as a viable solution. To address this, in this paper, we discovered the remarkable potential of the Haar frequency domain in characterizing the small target motion in dim light. To investigate the advantages of integrating Haar frequency processing in small target motion detection, we introduce a Haar-windowed summation mechanism into an existing bio-inspired small target motion detection model. The proposed mechanism integrates visual information in spatiotemporal windows regulated by frequency parameters of Haar wavelets and effectively discriminates the small target motion from the disturbance of random noise caused by dim light. Theoretical analysis and numerical experiments confirm the superior performance of integrating the Haar frequency processing. This study provides a new vision of small target motion detection through the lens of the frequency domain and extends the limits of existing bio-inspired models for practical applications in dim light.

关键词： Frequency analysis Small target motion detection Dim light vision Haar wavelet transformation Visual neural system

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Discrete aggregation hashing for image set classification

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EXPERT SYSTEMS WITH applications 2024年第PartC期237卷

作者： Sun, Yuan Peng, Dezhong Ren, Zhenwen Sichuan Univ Coll Comp Sci Chengdu 610044 Peoples R China Chengdu Ruibei Yingte Informat Technol Co Ltd Chengdu 610054 Peoples R China Southwest Univ Sci & Technol Coll Natl Def Sci & Technol Mianyang 621010 Peoples R China

With the development of vision technology, image set classification (ISC) has flourished in the image processing field. Different from the one-shot image classification, ISC focuses on the set rather than a one-shot image. Hence, ISC can synthesize the abundant set information to alleviate various appearance variations. Despite the great success of the existing ISC methods, there are still some problems: (1) They usually face an expensive time complexity, which directly limits the practical application;(2) They largely ignore the intrinsic relationships between different sets. In light of this, we propose a novel Discrete Aggregation Hashing (DAH) for fast ISC. To be specific, to extract more semantic information from each set and each sample, we adopt the same projection standard to embed dual semantic labels (i.e., sample label and set label) into instance and set hash codes. Then we regard set hash codes as set-specific centers. A hashing aggregation strategy is proposed to learn compact discriminative instance hash codes via iteratively aggregating intrinsic neighborhood representations around each central node. Therefore, instance hash codes can obtain greater intra-set compactness and inter -set separability. Extensive experiments demonstrate that our DAH can obtain promising performance and outperform these state-of-the-art ISC methods on four image set datasets.

关键词： image set classification Aggregation hashing Binary representation

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Enhanced Efficient image Dehazing for Onboard Satellite imagery processing

Enhanced Efficient Image Dehazing for Onboard Satellite Imag...

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machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： D. Sai Sreekar Vishesh Vatsal Adithya Kothandhapani Ramakrishnan Rajagopalan Computer Vision Team Hyspace Technologies Pvt. Ltd. Bangalore India Chief of Technology Hyspace Technologies Pvt. Ltd. Bangalore India Chief of Products Hyspace Technologies Pvt. Ltd. Bangalore India Former Group Director at Space Applications Center ISRO

Satellite imagery is often composed of diverse terrains like forest, desert, snow and exhibits haze, fog, thin clouds which require dehazing in order to make them analysisready. Onboard processing of satellite imagery requires the algorithm's parameters to be fine-tuned depending on the type of terrain encountered. From the atmospheric light scattering model, the estimation of atmospheric light and transmission map is performed in single image dehazing method. This paper focuses on tuning an existing method the “Efficient image Dehazing with Boundary Constraints and Contextual Regularization method for satellite imagery”. A new image quality assessment method is introduced to enable fine-tuning the exponent of the algorithm. With the onset of onboard processing requirements, parallel implementation and faster image processing methods are explored for small run-times.

关键词： image quality Satellites Atmospheric modeling Snow image processing Light scattering Forestry

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Alps: An Adaptive Load Partitioning Scaling Solution for Stream processing System on Skewed Stream 33rd

Alps: An Adaptive Load Partitioning Scaling Solution for Str...

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33rd International Conference on Database and Expert Systems applications (DEXA)

作者： Zou, Beiji Zhang, Tao Zhu, Chengzhang Xiao, Ling Zeng, Meng Chen, Zhi Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Coll Literature & Journalism Changsha 410083 Peoples R China Mobile Hlth Minist Educ China Mobile Joint Lab Changsha 410008 Peoples R China

ISBN: (纸本)9783031124266;9783031124259

The distributed stream processing system suffers from the rate variation and skewed distribution of input stream. The scaling policy is used to reduce the impact of rate variation, but cannot maintain high performance with a low overhead when input stream is skewed. To solve this issue, we propose Alps, an Adaptive Load Partitioning Scaling system. Alps exploits adaptive partitioning scaling algorithm based on the willingness function to determine whether to use a partitioning policy. To our knowledge, this is the first approach integrates scaling policy and partitioning policy in an adaptive manner. In addition, Alps achieves the outstanding performance of distributed stream processing system with the least overhead. Compared with state-of-the-art scaling approach DS2, Alps reduces the end-to-end latency by 2 orders of magnitude on high-speed skewed stream and avoids the waste of resources on low-speed or balanced stream.

关键词： Data streams Stream processing system Adaptive scaling policy

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Hyperspectral image Dimensionality Reduction Using Inter-Band Cross-Correlation and K-Means Clustering Algorithm

Hyperspectral Image Dimensionality Reduction Using Inter-Ban...

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Workshops on image processing Theory, Tools and applications, IPTA

作者： Henry Ndu Akbar Sheikh-Akbari Koushlendra K. Singh School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom Machine Vision and Intelligence Lab National Institute of Technology Jamshedpur India

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

Hyperspectral imaging has become crucial in various domains, especially for the accurate detection of human veins in medical diagnostics, though managing the extensive data from hyperspectral (HS) images remains a challenge. To improve data handling during analysis, dimensionality reduction methods are frequently utilized. This paper presents a dimensionality reduction method for HS images using HS image inter-band cross-correlation and the K-means clustering algorithm. The proposed method computes inter-band correlations across all bands of the input HS image, which form a 2D correlation matrix. Eigen-decomposition is applied to the resulting matrix, extracting its eigenvectors and eigenvalues. The k-mean clustering algorithm is then applied to a selection of eigenvectors representing the largest eigenvalues, splitting the eigenvectors into several clusters. The reduced HS image is generated by averaging each cluster's image bands. The proposed dimensionality reduction method together with the Support Vector machine (SVM) classifier was then used for vein detection in HS images. The HyperVein image dataset was used to generate experimental results. Experimental results were generated for the proposed method and Principal Component Analysis (PCA) and Folded PCA (FPCA). Results show the proposed method outperforms PCA and FPCA in most performance metrics.

关键词： Dimensionality reduction Support vector machines Correlation Accuracy Clustering algorithms Eigenvalues and eigenfunctions Medical diagnosis Principal component analysis Hyperspectral imaging Morphological operations

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Eye Blink to Voice Communication Aid for Paralyzed Patients 2nd

Eye Blink to Voice Communication Aid for Paralyzed Patients

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2nd International Conference on Data Science, machine Learning and applications, ICDSMLA 2020

作者： Gowramma, G.S. Arumugam, S.S. Pranav, K.P. Soundharya Lakshmi, V. Mithun Gowda, V. Don Bosco Institute of Technology Bengaluru India

ISBN: (纸本)9789811636899

Paralyzed is also known as Locked-in syndrome happens when an individual is a quadriplegic and the patient cannot speak or do a facial movement (*** ). The affected patient cannot communicate but he will be aware of his surrounding;the patient can hear and see has a normal person. The patient has thinking capacity and reasoning ability he can only use eye movement to communicate with others. Hence, the key purpose of this paper is to propose a real-time interactive system for a paralyzed patient using eye blink an eye motion detection algorithm by assisting them to communicate to caretakers to their needs through blink which is converted to voice. For example, clinical help, S.O.S, essential utility. This is done using a video outline that is handled by Open Computer vision libraries for image processing which is an open-source programming method which is aimed at real-time computer vision. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Evolving processing Pipelines for Industrial Imaging with Cartesian Genetic Programming

Evolving Processing Pipelines for Industrial Imaging with Ca...

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Autonomic Computing and Self-Organizing Systems (ACSOS),International Conference on

作者： Andreas Margraf Henning Cui Anthony Stein Jörg Hähner Digital Production and AI Fraunhofer IGCV Augsburg Germany Organic Computing Group University of Augsburg Augsburg Germany AI in Agricultural Engineering University of Hohenheim Stuttgart Germany

The reconfiguration of machine vision systems heavily depends on the collection and availability of large datasets, rendering them inflexible and vulnerable to even minor changes in the data. This paper proposes a refinement of Miller’s Cartesian Genetic Programming methodology, aimed at generating filter pipelines for image processing tasks. The approach is based on CGP-IP, but specifically adapted for image processing in industrial monitoring applications. The suggested method allows for retraining of filter pipelines using small datasets; this concept of self-adaptivity renders high-precision machine vision more resilient to faulty machine settings or changes in the environment and provides compact programs. A dependency graph is introduced to rule out invalid pipeline solutions. Furthermore, we suggest to not only generate pipelines from scratch, but store and reapply previous solutions and re-adjust filter parameters. Our modifications are designed to increase the likelihood of early convergence and improvement in the fitness indicators. This form of self-adaptivity allows for a more resource-efficient configuration of image filter pipelines with small datasets.

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machine Learning and Artificial Intelligence for the Prediction of Host-Pathogen Interactions: A Viral Case

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INFECTION AND DRUG RESISTANCE 2021年 14卷 3319-3326页

作者： Yakimovich, Artur Artificial Intelligence Life Sci CIC 9 High St London N8 7FJ England

The research of interactions between the pathogens and their hosts is key for understanding the biology of infection. Commencing on the level of individual molecules, these interactions define the behavior of infectious agents and the outcomes they elicit. Discovery of host-pathogen interactions (HPIs) conventionally involves a stepwise laborious research process. Yet, amid the global pandemic the urge for rapid discovery acceleration through the novel computational methodologies has become ever so poignant. This review explores the challenges of HPI discovery and investigates the efforts currently undertaken to apply the latest machine learning (ML) and artificial intelligence (AI) methodologies to this field. This includes applications to molecular and genetic data, as well as image and language data. Furthermore, a number of breakthroughs, obstacles, along with prospects of AI for host-pathogen interactions (HPI), are discussed.

关键词： virus deep learning virus-cell interaction sequence analysis computer vision natural language processing NLP DL

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