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Distance Estimation for Endoscopy Based on Depth Detection in the Small Intestine Using image processing techniques 6

Distance Estimation for Endoscopy Based on Depth Detection i...

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6th International conference on Signal processing and Intelligent Systems (ICSPIS)

作者： Ramedani, Majid Majd, Vahid J. Tarbiat Modares Univ Dept Elect Engn Tehran Iran

ISBN: (纸本)9781728186290

In recent years, three-dimensional measurement techniques have been widely used in medical sciences, and thus, depth detection in an image plays an important role in computer vision applications. In this paper, we discuss the estimation of the distance between the head of an endoscope and the small intestine septum and its problems. the main objective is to detect the depth of the small intestine to estimate distance. images were collected through video sampling, and then the data are preprocessed. Morphological reconstruction, bounding box, Convex Hull, and Euclidean distance are employed to update the mentioned distance. At the end of this process, the outputs are simulated, and we are given the output distance in centimeters. this method will assist the endoscope to move inside the small intestine without injuries.

关键词： image processing depth detection Euclidean distance morpholog

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Single-scan Run Length Algorithm for Real-time Fiducial Marker Detection Based on FPGA Devices 22

Single-scan Run Length Algorithm for Real-time Fiducial Mark...

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Proceedings of the 2022 6th International conference on Video and image processing

作者： Farhana Binte Sufi Fernando Rincón Calle Jesús Barba Romero Juan Carlos López López Department of Electrical and Electronic Engineering University of Rajshahi Bangladesh Department of Technologies and Information Systems University of Castilla-La Mancha Spain

ISBN: (纸本)9781450397568

image processing pipelines involved in detecting markers from real-time video employ contour detection algorithms. Field Programmable Gate Arrays (FPGA) can provide greater computing power due to parallel processing and pipelining of the different stages in these algorithms. However, FPGA implementation of different contour detection methods remains challenging due to resource limitation and timing constraints. In this work a novel approach for a complete pipeline has been presented based on FPGA. the method used for the detection of fiducial markers from real-time video streams is based on a single-scan approach. Implementation of such pipeline into reconfigurable technology is specially challenging compared to software-oriented implementation as pointers, dynamic structures, efficient use of resources, etc. need to be considered. A driver is the need to fit the design into low-cost reconfigurable devices. To help face the related challenges of a hardware implementation High Level Synthesis (HLS) technology has been adopted, which implies reengineering of the reference algorithms. the results show a successful detection of markers over a dataset acquired in a controlled indoor environment with different resolutions at different distances. the proposal has been prototyped and tested on a Zedboard with Xilinx Zynq®-7000 SoC.

关键词： Single-scan run length algorithm Contour detection algorithm FPGA Stream processing Edge computer vision Fiducial markers HLS

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Exploring Pre-processing Approaches for Deep Learning-based Fingerprint Spoof Detection Mechanisms 6

Exploring Pre-Processing Approaches for Deep Learning-based ...

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6th International conference on Trends in Electronics and Informatics (ICOEI)

作者： Singh, Samridhi Selwal, Arvind Sharma, Deepika Cent Univ Jammu Dept Comp Sci & Informat Technol Jammu India

ISBN: (纸本)9781665483285;9781665483292

Fingerprint-based human authentication being most widely deployed systems are also exposed to many security threats. Among all, spoofing attacks is widely attempted that involves circumventing the sensor module of the system by presenting a fake replica of the original trait of a genuine user. A counter measuring mechanism is deployed in these systems that intelligently measure the vitality characteristics of a presented fingerprint image. these sub-modules are integrated besides sensing device of the system and popularly known as anti-spoofing methods or fingerprint spoof detection (FSD) mechanisms. the evaluation of data-driven enabled computer vision paradigm has facilitated researchers to design intelligent FSD. Pre-processing data to train these FSD models is required to improve the overall quality of the input images. this paper presents a focused review of pre-processing approaches used in deep learning-based FSD models. the proposed research study has clearly observed that image pre-processing is a vital step to significantly improve the overall accuracy and efficiency of FSD approaches.

关键词： Spoof attacks pre-processing fingerprint anti-spoofing normalization label encoding

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Experimentation with NMT models on low resource Indic languages 6

Experimentation with NMT models on low resource Indic langua...

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6th International conference on image Information processing, ICIIP 2021

作者： Bansal, Nikunj Datta, Goutam Singh, Anupam University of Petroleum and Energy Studies Informatics School of Computer Science Dehradun248007 India

ISBN: (纸本)9781665433617

Today's Artificial Intelligence (AI) is data centric. Unlike earlier rule based systems where we used to write many rules to solve any specific problem, these days, we need to train our machine learning models with the help of huge corpus (data set). In this paper, we have discussed one of the important applications of AI and computational linguistic i.e. Machine Translation (MT) which translates one natural language to another *** industry has passed through different phases since its earlier popular approach such as statistical machine translation (SMT) systems and its other version such as phrase based SMT. Performance wise NMT always outperformed SMT on various aspects. However, this holds true only for the languages having large parallel corpora. For low-resource languages, it still remains suboptimal. In this paper, we have applied NMT to low resources indian languages, i.e. English-Hindi. We used a basic LSTM based Seq2Seq model and an attention-based Seq2Seq model with fixed vocabulary size. We merged the corpus collected from various sources and preprocessed them for further use. We used the BLEU metric score for evaluation. We also evaluated the Google Translator to compare our experimental results with it. © 2021 IEEE.

关键词： Computational linguistics

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S3DMT-Net: Improving soft sharing based multi-task CNN using task-specific distillation and cross-task interactions 21

S3DMT-Net: Improving soft sharing based multi-task CNN using...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Jha, Ankit Banerjee, Biplab Chaudhuri, Subhasis Indian Institute of Technology Bombay India

ISBN: (纸本)9781450391276

We deal with the problem of multi-task learning (MTL) in the context of performing multiple related visual dense prediction tasks from single image inputs. the soft-sharing-based deep MTL Convnets (CNN) have separate networks for each of the tasks with additional constraints on the model parameters. Although such MTL models have shown convincing performances for tasks including semantic segmentation, depth estimation, and surface normal estimation from monocular images, they have two inherent bottlenecks: i) the constraints imposed on such models do not in general leverage the inter-task information which otherwise can boost the joint training, and ii) the performances of the individual task-specific networks are not explicitly optimized. We hypothesize that the MTL performance can be enhanced comprehensively if the aforesaid issues are taken into account in soft-sharing-based MTL models. To this end, we introduce a novel MTL architecture in this paper called S3 DMT-Net, where i) the task-specific networks are trained under the notion of self-distillation, which aims at inheriting the features from the deeper layers into the shallower layers, thus enriching the capacity of the network, and ii) the idea of cross-task interactions are utilized where the feature-maps of the task-specific encoders are shared amongst each other. We validate the model performance on two scenes: indoor (NYUv2 and Mini Taskonomy) and urban (CityScapes and ISPRS), where substantial improvements are recorded for all the tasks over the existing literature. © 2021 ACM.

关键词： Learning systems

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image Definition Evaluation Function Based on Improved Maximum Local Variation and Focusing Window Selection 1

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6th International conference on Data Mining and Big Data, DMBD 2021

作者： Li, Shiyun Chen, Jian Wan, Jiaze Li, Zuoyong Lin, Li School of Electronic Electrical Engineering and Physics Fujian University of Technology Xueyuan Road Fujian Fuzhou350118 China College of Computer and Control Engineering Minjiang University No. 200 Xiyuangong Road Fujian Fuzhou350121 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (数字)9789811675027

ISBN: (纸本)9789811675010

Aiming at the problem that commonly used image definition evaluation functions in the focusing process are sensitive to noise, we propose a new image definition evaluation function based on improved maximum local variation and focusing window selection. Firstly, the focusing window is selected by gradient accumulation of a 4-directional Scharr operator in order to reduce the calculation complexity and improve the accuracy of evaluation results. Secondly, an improved 3-neighbors method based on the maximum local variation is proposed to decrease the change in scores for noisy images. Finally, the standard deviation of the improved maximum local variation distribution is used as the measure of clarity. the experimental results show that compared with the method using maximum local variation, the proposed method has better unbiasedness and sensitivity. Compared with the commonly used evaluation functions, the proposed method has better noise immunity and high sensitivity. Compare with other no-reference image quality assessment algorithms, it has better monotonicity, unimodality, unbiasedness, sensitivity and real-time performance as well. the proposed method is suitable for the fine focusing stage with high real-time performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

6th National Conference on Computer Vision, Pattern Recognit...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

ISBN: (纸本)9789811300196

the proceedings contain 48 papers. the special focus in this conference is on computer vision, Pattern Recognition, image processing and graphics. the topics include: Parametric reshaping of humans in videos incorporating motion retargeting;enhanced aggregated channel features detector for pedestrian detection using parameter optimisation and deep features;unsupervised segmentation of speech signals using Kernel-Gram matrices;Design of biorthogonal wavelet filters of DTCWT using factorization of halfband polynomials;single noisy image super resolution by minimizing nuclear norm in virtual sparse domain;near real-time correction of specular reflections in flash images using no-flash image prior;A method for detecting JPEG anti-forensics;an end-to-end deep learning framework for super-resolution based inpainting;saliency map improvement using edge-aware filtering;classification of human actions using 3-D convolutional neural networks: A hierarchical approach;A generative adversarial network for tone mapping HDR images;efficient clustering-based noise covariance estimation for maximum noise fraction;GMM based single depth image super-resolution;patch similarity in transform domain for intensity/range image denoising with edge preservation;multi-modal image analysis for plant stress phenotyping;source classification using document images from smartphones and flatbed scanners;Homomorphic incremental directional averaging for noise suppression in SAR images;An EEG-based image annotation system;multimodal registration of retinal images;Dynamic class learning approach for smart CBIR;SmartTennisTV: Automatic indexing of tennis videos;exploring memory and time efficient neural networks for image captioning;dataset augmentation with synthetic images improves semantic segmentation;deep neural network for foreground object segmentation: An unsupervised approach;document image segmentation using deep features;flow-free video object segmentation.

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image Classification Based On improved VGG Network

Image Classification Based On improved VGG Network

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IEEE International conference on Signal and image processing (ICSIP)

作者： Li Tang School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665446006

image classification is a fundamental problem in the field of computer vision. In this paper, we propose an improved VGG-16 model suitable for small data sets such as cifar10. the model reduces the fully connected layer, adds batch normalization (BN) and a stronger regularizer (Dropout) as well as improving the loss function. For the cifar10 data set, this paper uses four data augmentation methods: random flip, random translation, random erasure, and mix up to improve the generalization ability of the model. Experimental results show that the training parameters of the improved VGG model are reduced to 10.65% of the original model, and the average classification accuracy rate is increased to 94.43%.

关键词： Training computer vision Computational modeling image processing conferences Data models image classification

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Anti-Collision System for Accident Prevention in Underground Mines using computer vision 22

Anti-Collision System for Accident Prevention in Underground...

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Proceedings of the 6th International conference on Advances in Artificial Intelligence

作者： Mohamed Imam Karim Baïna Youness Tabii Intissar Benzakour Youssef Adlaoui El Mostafa Ressami El Hassan Abdelwahed Alqualsadi Research Team Rabat IT Center ENSIAS Mohammed V University in Rabat ENSIAS Morocco and Digitalization & Microelectronic Smart Devices - MAScIR Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Morocco Alqualsadi Research Team Rabat IT Center ENSIAS Mohammed V University in Rabat ENSIAS Morocco Information Retrieval and Data Analytics (IRDA) Research Team Rabat IT Center ENSIAS ENSIAS Morocco Reminex Research and Engineering center MANAGEM Group REMINEX Morocco Digitalization & Microelectronic Smart Devices - MAScIR Moroccan Foundation for Advanced Science Innovation and Research (MAScIR) Morocco Laboratory of Computer Systems Engineering (LISI) FSSM Cadi Ayyad University FSSM Morocco

ISBN: (纸本)9781450396943

Underground prospecting operations are often characterized by critical safety issues mainly due to poor visibility and blind spots around large vehicles and equipment. this can result in vehicle-to-vehicle collisions, as well as vehicle-to-pedestrian or structural-element collisions, resulting in accidents. In this article, we discuss an anti-collision system for pedestrian identification in deep mines under the premise that we are looking to prevent collisions with moving machinery. this study presents the findings from testing an image processing module and sensory system based on deep learnig in the context of "smart connected mine" project.

关键词： Yolov5 Pedestrian detection Deep Learning Underground mining

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image processing and pattern recognition-based car classification for intelligent transportation system

Image processing and pattern recognition-based car classific...

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6th Smart Cities Symposium (SCS 2022)

作者： V. Dange S. Pophale N. Raut F. D. Shadrach S. Chowdhury G. Ramya P. Dutta Vishwakarma Institute of technology Pune India KPR Institute of Engineering and Technology Coimbatore India Dept. of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India MCA Department Sri Venkateswara College of Engineering and Technology Chittoor India SITE School VIT Vellore Vellore India School of Engineering and Technology Amity University Kolkata Kolkata India

the combination of car model classification and accelerated advances in computer vision appears to have the potential to significantly revolutionize automated transportation systems. Pattern recognition and image processing-based car classification methods have been proposed in recent years to improve the performance of smart highway toll collection and monitoring of traffic systems. Unfortunately, all these algorithms are trained on a inadequate number of features derived from tiny datasets, which do not considered for real-time road traffic circumstances. To address these difficulties, proposed system presents a classification model for vehicle based on convolutional neural networks to enhance the resilience of car classifier model in real-time applications. To achieve real-time robustness in classification systems, this proposed system offers a Stanford car dataset of 16,185 images classified into 157 common vehicle classes. To begin, the car dataset was trained using Inception-v3, VGG16, and ResNet50 to evaluate their accuracy based on performance. As a result, a comparative study was carried out to assess the effectiveness of the proposed system.

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