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Detection of Palmprint from Contactless Smartphone-Based Video Hand Dataset 8th

Detection of Palmprint from Contactless Smartphone-Based Vid...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Chodvadiya, Charulkumar Singh, Kinshuk Gaurav Vyas, Ritesh Pandit Deendayal Energy Univ Gandhinagar Gujarat India

ISBN: (纸本)9789819752119;9789819752126

A fast-expanding topic is the study of palmprint biometric identification in contactless scenario, which uses techniques from computer vision and machine learning to identify and authenticate people. In this study, we utilized a handcrafted video dataset with 60 distinct classes, each labelled as either a left or right hand, to investigate palmprint detection and matching tasks. the dataset showcases various variations in palmprint patterns, like distance from the sensor, orientation, finger positioning, and deformation, making it an ideal candidate for the development of robust and accurate palmprint recognition models. the major goal of the study is to identify palmprints in the video collection and match them with the right class or pattern. To accomplish this task, different machine learning (ML) and deep learning (DL) models were trained and evaluated. To find the best method for palmprint identification in a contactless manner, the accuracy of each model was tested. In conclusion, our study adds to the expanding body of knowledge on biometric palmprint identification and introduces a fresh handmade video dataset that can be used to compare the effectiveness of various models.

关键词： Palmprint biometric identification computer vision Deep learning model Palmprint patterns Transfer learning

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Cross-domain image Translation and Feature Extraction for Large FoV Aerial Crop NIR image Creation 8th

Cross-domain Image Translation and Feature Extraction for La...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Rathore, Sudeep Shukla, Ankit Upadhyay, Avinash Sharma, Manoj Yadav, Ajay Bennett Univ Greater Noida UP India

ISBN: (纸本)9789819752119;9789819752126

Near-Infrared Spectroscopy (NIR) is valuable for getting thorough information the human eye cannot capture in agricultural operations and investigations. A special multispectral camera is required to get NIR spectral data, which provides low-resolution images compared to RGB (Red-Green-Blue) data. NIR spectral data contains low contrast and no colour information making it difficult to identify and match features in the NIR domain. We require more features for image stitching to achieve large FoV (Field of View) NIR images. We use Cycle Generative Adversarial Network (CycleGAN) for image-to-image translation to synthesise an RGB image from a NIR spectral band image to take advantage of both RGB and NIR bands for the feature extraction. Features are extracted from NIR and synthetic RGB images. then, we stitch the different NIR views using concatenated features of both domains to create large Field of View (FoV) NIR images. this augmented feature set yields superior stitching results compared to NIR features alone. the effectiveness of the proposed framework is demonstrated through experiments, showcasing the improvements in stitching quality.

关键词： NIR image stitching CycleGAN Deep learning Feature matching image blending

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processing 3D Point Clouds for High-throughput Plant Phenotyping 8th

Processing 3D Point Clouds for High-Throughput Plant Phenoty...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Dagar, Preety Arora, Alka Kumar, Sudhir Marwaha, Sudeep Jain, Rajni Chaurasia, Himanshushekhar Chinnusamy, Vishwanathan ICAR IASRI Div Comp Applicat Library Ave New Delhi 110012 India IARI Pusa ICAR IARI Div Plant Physiol New Delhi 110012 India ICAR CIRCOT Mumbai 400019 Maharashtra India

ISBN: (纸本)9789819752119;9789819752126

In recent years, measuring three-dimensional (3D) surface information has gained a great interest in plant phenotyping because it can represent the nature of plant architecture better than conventional 2D images. this paper presents an approach for processing 3D point clouds converted from 2D RGB images in the context of high-throughput plant phenotyping. High-resolution RGB multi-view imagery of a Chickpea plant was collected using a high-end camera. Based on these image sequences, 3D point cloud reconstruction of the canopy was conducted and analyzed. Later sophisticated 3D operations were performed on these images including 3D downsampling and after that clustering was performed on the processed point cloud. the information generated can help in the evaluation of crop traits and provide accurate statistics for the assessment of their growth parameters.

关键词： 3D image processing 2D to 3D image conversion Open3D 3D image processing for plant phenotyping

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A Deep Learning-Driven Contactless Finger Recognition System 8th

A Deep Learning-Driven Contactless Finger Recognition System

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Attris, Aman Bharat, Nagasai Anand, Vijay Kanhangad, Vivek Indian Inst Technol Indore Discipline Elect Engn Indore 453552 India

ISBN: (纸本)9789819752119;9789819752126

Fingerprint is one of the most widely explored biometric traits. Specifically, contact-based fingerprint recognition systems reign supreme due to their effectiveness, portability, and research advancements in this field. However, these are issues such as hygiene concerns, sensor degradation due to constant physical contact, and latent fingerprint threats. In this paper, we present the details of a fingerprint recognition system that captures finger photos without contact. the captured finger photos are then processed to obtain global and local (minutiae-based) features. Specifically, a Siamese convolutional neural network is designed to extract global features from a given finger photo. Finally, the matching scores computed using the global and local features are fused to generate the final matching score between the probe and the reference fingerprint templates. We have developed the proposed system using the Nvidia Jetson Nano development kit. the developed system performs contactless fingerprint recognition in real time with minimum latency and acceptable matching accuracy. the performance of the proposed system is evaluated on an in-house IITI contactless fingerprint dataset (IITI-CFD) containing 105 train and 100 test subjects. the proposed system achieves an equal error rate of 2.19% on IITI-CFD.

关键词： Contactless fingerprint Fingerprint recognition Siamese CNN

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MuSiC V1.0: A Software Solution for Automated Mustard Silique Count Using YOLOv5 8th

MuSiC V1.0: A Software Solution for Automated Mustard Siliqu...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Deb, Chandan Kumar Das, Madhurima Kumar, Mahesh Kumar, Sudhir Haque, Md. Ashraful Arora, Alka Marwaha, Sudeep Singh, Biswabiplab Raju, Dhandapani Chinnusamy, Viswanathan ICAR Indian Agr Stat Res Inst New Delhi India

ISBN: (纸本)9789819752119;9789819752126

Object detection is a challenging and important field in the domain of computer vision. Automated detection and counting of the number of siliques non-destructively, which is the most important yield component trait in indian mustard (B. juncea), which is important as well as widely cultivated oilseed in India, is done here. We have used Deep Learning based one-stage object detection model, YOLOv5 in the counting of mustard siliques from the images captured by a DSLR camera (SONY Alpha 7iii, 24.2MP) manually mounted on a tripod. A GUI-based desktop software, MuSiC v1.0 is developed using Tkinter, Python backed by YOLOv5 model developed from annotated training dataset using Roboflow annotator. Approximately 22,800 annotations were used to train the deep learning YOLOv5 model and the whole dataset was partitioned into training, testing and validation in the ratio of 8:1:1. the Confusion matrix, F1 score and mAP showed a moderate result of 66%, 0.51 and 0.38, respectively, with the handcrafted ground truth data. the deployed model in MuSiC v1.0 showed a promising result of approximately good accuracy for silique count. this is the first initiative of creation of a desktop application for mustard silique count non-destructively.

关键词： Silique count Non-destructive YOLOv5 Object detection Deep learning Mustard

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TaPaSe: Tanjore Paddy Seed Dataset 8th

TaPaSe: Tanjore Paddy Seed Dataset

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Sasithradevi, A. Vijayalakshmi, M. Varsini, S. R. Gabriel, Joshua R. Roomi, S. Mohamed Mansoor Prakash, P. Vellore Inst Technol Ctr Adv Data Sci Chennai Tamil Nadu India Vellore Inst Technol Sch Elect Engn Chennai Tamil Nadu India Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Thiagarajar Coll Engn Dept Elect & Commun Engn Madurai Tamil Nadu India Madras Inst Technol Dept Elect Engn Chennai Tamil Nadu India

ISBN: (纸本)9789819752119;9789819752126

Rice is a popular staple diet in India, and its demand has recently increased. thanjavur, located in the Cauvery Delta region, is known as the rice granary of South India. Due to recent technological advancements, digital farming and globalization have significantly impacted the agricultural industry. It is crucial to differentiate between types of rice grains to prevent fraudulent labeling during import and export. To achieve this, a dataset, namely "TaPaSe Dataset", comprising five varieties of rice, including MTU 1010, MTU 1290, Narmadha, Pacha Ponni, and Sonna Masur, which are mainly cultivated in thanjavur, has been collected. We designed an image acquisition system to capture the aforementioned varieties in real time. the captured paddy rice images are highly challenging in the sense that all the images are captured under illumination and scale variations. We evaluated existing deep learning models to understand their ability to classify paddy seed varieties. the existing pre-trained models attain remarkable recognition rates on the proposed paddy seed varieties dataset.

关键词： image classification Deep learning Pre-trained models

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MDFIDNet: Multi-domain Feature Integration Denoising Network 27th

MDFIDNet: Multi-domain Feature Integration Denoising Network

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27th International conference on Pattern Recognition, ICPR 2024

作者： Das, Debashis Maji, Suman Kumar Department of Computer Science and Engineering Indian Institute of Technology Bihar Patna801106 India

ISBN: (纸本)9783031781247

In the realm of computer vision, image denoising remains a formidable challenge with profound implications for fields like medical imaging, remote sensing, and photography. Despite notable advancements in deep learning, there are enduring challenges: current convolutional neural networks (CNNs) frequently struggle with training complexities due to their emphasis on increased network depth. At the same time, these networks often fail to adequately consider the crucial role of gradient information in the denoising process. Furthermore, there is a distinct gap in leveraging transform domain analysis in image denoising. this study addresses these limitations with MDFIDNet, a novel triple-phase attentive fusion network tailored for image denoising. MDFIDNet integrates three independent feature extraction pipelines: a frequency domain processing pipeline (FDP) enhanced by a multi-scale convolutional attention Block (MSCAB), a spatial domain processing pipeline (SDP) focusing on detail feature preservation, and a gradient-domain processing pipeline (GDP) driven by multidirectional gradient information. Experimental validation demonstrates that MDFIDNet surpasses existing benchmarks, exhibiting robust performance across diverse datasets. Comprehensive ablation studies underscore the individual contributions of each network component, elucidating the novel advancements that underpin MDFIDNet’s superior denoising efficacy. the source code and further details are available in the https://***/debashis15/MDFIDNet. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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International conference on computer graphics, Visualization, computer vision and image processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on computer Science and Information Systems, MCCSIS 2021

International Conference on Computer Graphics, Visualization...

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International conference on computer graphics, Visualization, computer vision and image processing 2021, CGVCVIP 2021, Connected Smart Cities 2021, CSC 2021 and Big Data Analytics, Data Mining and Computational Intelligence 2021, BIGDACI 2021 - Held at the 15th Multi-conference on computer Science and Information Systems, MCCSIS 2021

ISBN: (纸本)9789898704320

the proceedings contain 24 papers. the topics discussed include: using fast multidimensional projections to reveal band and circumplex patterns in reorderable matrices;a 3D spectral-spatial classification of hyperspectral remote sensing imagery using inception based network;3D face reconstruction from hard blended edges;capsule neural networks in classification of skin lesions;a single RGB image based 3D object reconstruction system;face features-based personality assessment;analysis of capsule networks for image classification;analysis and visual exploration of prediction algorithms for public bicycle sharing systems;and hello, my name is smarttram, human factors is on board, enjoy the ride! developing a human factors program for automatic trams.

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophis...

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4th International conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, thierry Chouireb, Fatima Department of Physics Laboratory of Applied Sciences and Didactics École Normale Supérieure de Laghouat Laghouat Algeria Department of Electronics Telecommunication Signals and Systems Laboratory University Amar Telidji of Laghouat Laghouat Algeria Department of Computer Science Lab. Mathématiques Image et Applications Université de La Rochelle La Rochelle France

ISBN: (纸本)9783031821523

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. therefore, special hardware that can acquire, control, or execute in parallel is required. these specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual processing Units VPUs, Tensor processing Units TPUs, Neural processing Units NPUs or graphics processing Units GPUs. this work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. this single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4× faster than the CPU for the HOG method and 2× for the Haar Cascade Classifier method. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： System-on-chip

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Automatic Skin Lesion Segmentation based on Saliency and Color 15

Automatic Skin Lesion Segmentation based on Saliency and Col...

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15th International Joint conference on computer vision, Imaging and computer graphics theory and Applications (VISIGRAPP) / 15th International conference on computer vision theory and Applications (VISAPP)

作者： Ramella, Giuliana CNR Inst Applicat Calculus M Picone Via P Castellino 111 I-80131 Naples Italy

ISBN: (纸本)9789897584022

Segmenting skin lesions in dermoscopic images is a key step for the automatic diagnosis of melanoma. In this framework, this paper presents a new algorithm that after a pre-processing phase aimed at reducing the computation burden, removing artifacts and improving contrast, selects the skin lesion pixels in terms of their saliency and color. the method is tested on a publicly available dataset and is evaluated both qualitatively and quantitatively.

关键词： Dermoscopic images Color image processing Saliency Map Skin Lesion Segmentation

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