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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1646 条记录，以下是461-470 订阅

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Copy-Move image Forgery Detection Using Gray-Tones with Texture Description 3rd

Copy-Move Image Forgery Detection Using Gray-Tones with Text...

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3rd international conference on computer vision and image processing (cvip)

作者： Dixit, Anuja Bag, Soumen Indian Sch Mines Dept Comp Sci & Engn Indian Inst Technol Dhanbad Bihar India

ISBN: (纸本)9789813292918;9789813292901

Copy-move forgery is a well-known image forgery technique. In this image manipulation method, a certain area of the image is replicated and affixed over the same image on different locations. Most of the times replicated segments suffer from multiple post-processing and geometrical attacks to hide sign of tampering. We have used block-based method for forgery detection. In block-based proficiencies, image is parted into partially overlapping blocks. Features are extracted corresponding to blocks. In the proposed scheme, we have computed Gray-Level Co-occurrence Matrix (GLCM) for blocks. Singular Value Decomposition (SVD) is applied over GLCM to find singular values. We have calculated Local Binary Pattern (LBP) for all blocks. The singular values and LBP features combinedly construct feature vector corresponding to blocks. These feature vectors are sorted lexicographically. Further, similar blocks discovered to identify replicated section of image. To ensure endurance of the proposed methods, Detection Accuracy (DA), False Positive Rate (FPR), and F-Measure are calculated and compared with existing methods. Experimental results establish the validity of proposed scheme for precise detection, even when meddled region of image sustain distortion due to brightness change, blurring, color reduction, and contrast adjustment.

关键词： Copy-move image forgery Feature extraction image forensics Local binary pattern Singular value decomposition

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Future Frame Prediction Using Deep Learning 3rd

Future Frame Prediction Using Deep Learning

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3rd international conference on Recent Trends in Advanced Computing - Artificial Intelligence and Technologies, ICRTAC-AIT 2020

作者： Itagi, Siddharth Gowda, Sinchana Udupa, Tanmaya Shylaja, S.S. CSE Department PES University Bangalore India

ISBN: (纸本)9789811664472

In today’s era, the usage of videos has led to major contributions in the technological world. Be it CCTV footage captured in apartments and at traffic intersections or real-time recordings in the medical industry, videos help in discerning various patterns and uncovering hidden details, accounting for its extensive usage. Future frame prediction involves using the analysis of videos to predict what could happen next in the video. This is an emerging field of deep learning and computer vision whose advancement would provide large potential to enable faster decision making processes in areas like autonomous driving cars, anomaly detection, and aggression in people and falls in the elderly. Due to the proven effectiveness of deep learning in image processing, our goal is to develop a generative model capable of producing a future frame given a set of sequential frames as input. Generative adversarial networks (GANs) and autoencoders are the models used in implementing and achieving the target results. This paper presents the analysis and results of different proposed architectures that take multiple frames as input and aim to produce accurate future frames with the use of multiple efficiency measures. A structural similarity index (SSIM) score of 0.875 was obtained for the predicted frame. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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Harnessing CNNs for Accurate Skin Disease Identification

Harnessing CNNs for Accurate Skin Disease Identification

引用

Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Parveen Dhoundiyal Vikrant Sharma Satvik Vats Karan Singh Department of Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India School of Computer and System Sciences JNU New Delhi New Delhi India

Skin disorders are common and can be brought on by several things, including viruses, bacteria, allergies, or fungi. The speed and precision of detecting skin diseases have increased because of developments in laser and photonics-based medical technologies. The expense of such a diagnostic is still considerable, though, which restricts accessibility. An automated early-stage dermatological screening system has been developed using image processing techniques to solve this issue. Effective skin disease classification requires the extraction of key information from pictures, and many detection algorithms rely on computer vision. DenseNet169 and NASNetMobile are some of the methods that were employed in the study. Graphs were used to display the results as the researchers compared the algorithms' performance based on training accuracy and confusion matrix. Keywords: Skin Disease, Medical Diagnosis, Deep Learning, Transfer Learning, CNNs

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Visually Impaired People Navigation System using Sensors and Neural Network 3

Visually Impaired People Navigation System using Sensors and...

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3rd IEEE international conference on Human-Machine Systems, ICHMS 2022

作者： Saber, Hakar Mohsin Al-Salihi, Nawzad Kameran Omer, Rebaz Mohammed Dler University of Kurdistan Hewlêr Computer Engineering Kurdistan Region Erbil Iraq

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Without a personal assistant or a trained dog, it is difficult and somehow risky for visually impaired individuals to navigate indoors and outdoors. Navigation for individuals with visual impairment is now an active area of research, and numerous systems have been created. Developing a reliable and robust system will give the visually impaired more awareness about their surroundings, provides more independence in their lives, and reduces the risks they might face. This paper proposes the design and implementation of a prototype for a wearable system that enables blind and visually impaired individuals to navigate easily and safely by using computer vision and deep learning to identify objects and sensors to detect obstacles. Using an attached camera, the system uses image processing to recognize objects around the user. It identifies the objects based on a trained deep learning dataset and audibly communicates the object names to the user. Ultrasonic sensors detect nearby obstructions and alert the user by vibrating. Upon request, a Global Positioning System (GPS) receiver transmits the user's location via Short Message Service (SMS) to a caregiver. All the sensors and devices are connected to and controlled by an Arduino Mega microcontroller that has been programmed with efficient and dependable algorithms to perform tasks precisely and rapidly. Object recognition and reading are performed using a computer equipped with the necessary MATLAB software and libraries. © 2022 IEEE.

关键词： Global positioning system

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computer vision and image processing the Challenges and Opportunities for new technologies approach: A paper review 3

Computer Vision and Image Processing the Challenges and Oppo...

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3rd international Scientific conference of Engineering Sciences and Advances Technologies, IICESAT 2021

作者： Hussien, Reem M. Al-Jubouri, Karrar Q. Gburi, Mohaimen Al Hussein Qahtan, Al Gburi Duaa Jaafar, Al Hamami Department of Civil Engineering Al-Farabi University College Baghdad Iraq Department of Production Engineering and Metallurgy University of Technology Iraq Dep of Electrical and Computer Engineering Altinbaş University Istanbul Turkey Administrative Affairs ID Dep Ministry of Education Baghdad Iraq

Digital image processing has numerous applications in many sectors of the world. It expands from initial information registration into methods and thoughts combining pattern recognition, computer vision, and machine learning. The wide utilization has pulled in many researchers to integrate with a range of related specializations. This work gives a study of the latest development and theoretical ideas clarifying an improvement of computer vision particularly with pattern recognition and image processing, utilizing various regions of their field implementation. Digital image processing causes researchers to analyze images to get important data and comprehend information. It utilized a technique of multi-range implementation and huge information analysis. This work aims to focus on the latest studies related to image processing, pattern recognition, and computer vision. In this paper, computer vision standard has been categorized into groups. For example, pattern recognition, image processing, and AI. Additionally, we give a short clarification of the forward-thinking data about the methods and their realization. This survey is restricted to computer vision, and more research can include detecting the conduct and properties of the object including people actions. © Published under licence by IOP Publishing Ltd.

关键词： computer vision

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Robustly Clipped Sub-equalized Histogram-Based Cosine-Transformed Energy-Redistributed Gamma Correction for Satellite image Enhancement 3rd

Robustly Clipped Sub-equalized Histogram-Based Cosine-Transf...

引用

3rd international conference on computer vision and image processing (cvip)

作者： Singh, Himanshu Kumar, Anil Balyan, L. K. Indian Inst Informat Technol Design & Mfg Jabalpur India

ISBN: (纸本)9789813292918;9789813292901

In this paper, a new proposal is reported for image quality enhancement. Here, statistically clipped, bi-histogram equalization-based adaptive gamma correction along with its cosine-transformed energy redistribution is introduced for improvement of low-contrast dark images. This approach computes the clipping limit adaptively by observing stretched histogram bins, mean, and median values for each sub-histogram. Considering the clipping limit as the lowest of these three values, this limit ensures the conservation of information content of the image to a great extent. This adaptive clipping limit selection also resolves the issue of over-emphasization of high-frequency bins during sub-histogram equalization. For harvesting more information and better illumination, gamma correction is imparted by using the adaptive gamma value-set. The corresponding gamma value-set is itself derived from the previously sub-equalized interim intensity channel. In addition to this, two-dimensional (2D) discrete cosine transformation (DCT) for gamma-corrected channel is also employed for incorporating the energy-based textural enhancement framework. Validation is performed here by analyzing the enhancement for various remotely sensed dark satellite images by evaluating standard performance indices.

关键词： Adaptive gamma correction Bi-histogram equalization image quality enhancement Optimal histogram clipping Remotely sensed images

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Moldy garlic recognition based on machine vision 3

Moldy garlic recognition based on machine vision

引用

3rd international Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Wang, Aojie Zhao, Jiangming Zhengzhou University School of Mechanical and Power Engineering Zhengzhou China

ISBN: (纸本)9781665402675

In order to further process and sell garlic, moldy garlic needs to be recognized quickly and effectively. Garlic images were acquired by machine vision, and features were collected from moldy and normal garlic images by image processing, and a classifier model was built using machine learning and tested on a test set. Segmentation of images by fixed thresholds in saturated color space can effectively segment the outer skin of garlic, extended histogram descriptors can better describe garlic moldy features compared to other feature collectors, and LDA-k-NN model can recognized moldy garlic with 97.5% correct rate in 51.4 seconds, and the combined correct rate and efficiency is higher than BPNN, KPCA- SVM, CNN, light-GBM and other classification algorithms. Moldy garlic identification based on machine vision is feasible to guide the deep processing and marketing of garlic, can also effectively improve product competitiveness and food safety © 2021 IEEE.

关键词： computer vision

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Smart Surveillance Object Detection System

Smart Surveillance Object Detection System

引用

Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Gaurav Chaudhary Shreya Pathak Vikas Choudhary Love Chauhan Abhishek N Tripathi School of Electronics Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

This paper presents an integrated Internet of Things based approach to smart surveillance and urban parking management systems, emphasizing robust object detection and precise parking occupancy monitoring. Our solution adopts cost-effective hardware like the ESP32 CAM module, leveraging its built-in camera and powerful processor for real-time image processing. For surveillance applications, we implement a custom object detection model built with OpenCV, a renowned Python library. This model, finely-tuned for efficient person detection, runs directly on the ESP32 CAM, eliminating the need for external processing units. Additionally, the ESP32 CAM’s Wi-Fi connectivity enables seamless integration with existing networks, simplifying deployment. This integrated approach, combining the cost-effectiveness of ESP32 CAM modules with the image processing capabilities of OpenCV, offers a comprehensive solution for smart surveillance and urban parking management. By utilizing readily available hardware and open-source software, the system promotes widespread adoption and affordability, making it a valuable tool for creating smarter and more sustainable cities.

关键词： computer vision Surveillance Object detection Real-time systems Libraries Hardware Internet of Things

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An Alternate Solution for Smartphone-Based Urinalysis 3

An Alternate Solution for Smartphone-Based Urinalysis

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3rd international conference on Electrical and Electronic Engineering, ICEEE 2021

作者： Harun, Abir Ebna Habib, Mohammad Ashfak Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9781665482813

Urinalysis is a common medical test that can be costly and inconvenient in medical facilities. The use of point-of-care(POC) test devices, smartphones, manifolds, and other additional tools can make urinalysis easier in a home-based environment. In this paper, we are proposing a new system that can be used to performing a laboratory-free urinalysis with the help of a urine test strip and a smartphone device. Our system contains several image pre-processing steps and an artificial neural network mapping model to analyze the color pixels of the urine test strip. By following our proposed solution, the user can acquire an accurate computer vision integrated urinalysis result. © 2021 IEEE.

关键词： Smartphones

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Local Roughness Binary Pattern for Texture Classification 3rd

Local Roughness Binary Pattern for Texture Classification

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3rd international conference on Data, Engineering, and Applications, IDEA 2021

作者： Gupta, Sumit Kumar Yadav, Susheel Singh, Dhirendra Pratap Choudhary, Jaytrilok Department of Computer Science and Engineering MANIT MP Bhopal India

ISBN: (纸本)9789811946868

The texture is an essential property of an image that tells about the arrangements of pixels of different intensities in an image matrix. Researchers have used many descriptors for texture classification of the images in computer vision and image processing. Local Binary Pattern (LBP) is one of the descriptors, which is computationally efficient and straightforward for texture classification. In recent years, researchers have made many enhancements to the basic LBP method to improve the quality of extracted features for an image. This paper presents a Local Roughness Binary Pattern (LRBP) descriptor for feature extraction of an image to increase LBP discriminative capability. This new descriptor extracts features from the local region of an image called a partial feature vector and concatenates these local features to obtain a feature vector of an image. This feature vector is used as input to a classifier to classify the class of an image. We tested the proposed descriptor for the images available on CUReT and KTHTIPS2b databases with Support Vector Machine (SVM) and k-nearest Neighbors (KNN) classifiers. We have presented the experimental results obtained from the analysis process regarding classification accuracy and confusion matrix. Finally, we have presented a performance comparison between the proposed method, basic LBP descriptor, and its variants. Results show that the proposed descriptor gives much better results than other descriptors on the KTHTIPS2b Database when we use the KNN classifier. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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