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检索条件"任意字段=Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing"

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Machine Learning Approaches to Classify indian Mango Varieties 1st

Machine Learning Approaches to Classify Indian Mango Varieti...

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1st International conference on Recent Advancements in Materials Science and Technology, ICRAMST 2024

作者： Febin, U. Pradeep, R. Nishath, S. M. Haji Arafat, I. Sheik Department of ECE College of Engineering Trivandrum Kerala Thiruvananthapuram India Department of AI DS Mohamed Sathak Engineering College Tamil Nadu Kilakarai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031699658

Mango is one of the most common tropical fruits and is known as king of fruits. It is the most loved fruit in India. Currently, the classification of mangoes is still primarily done through manual inspection, which makes it difficult to automate, time-consuming, and unreliable. So, the classification of mango fruit using image processing, computer vision, and artificial intelligence methods is needed. This methodology helps the mango industry in increasing productivity, profitability, and export potential. In this study, we introduce an effective machine vision system for better classification which can reduce the wastage and at the same time increase the marketing of mango export. In this paper, the mangoes are classified using a convolutional neural network (CNN). In this study, we classify mango images into 10 categories: Alphonso, Banganapalli, Chausa, Dasheri, Kesar, Malgova, Mallika, Neelam, Raspuri, and Totapuri. Natural image preprocessing is essential to image processing since unprocessed natural images might lead to inaccurate classification if they are fed straight into the CNN model. Thus, an improper classification can result. In the experiment section, the natural photos of mangoes were first preprocessed, and the convolution neural network was then fed with these preprocessed images as input for the classification. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fruits

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Effect of Particle image Velocimetry processing on CT Dicom images in a deep learning based pipeline for lung nodule segmentationg± 13

Effect of Particle Image Velocimetry processing on CT Dicom ...

引用

13th indian conference on computer vision, graphics, and image processing, ICVGIP 2022

作者： Suji, R Jenkin Godfrey, W Wilfred Dhar, Joydip ABV-IIITM Gwalior Madhya Pradesh Gwalior India

ISBN: (纸本)9781450398237

Segmentation of lung nodules is critical to computer-aided diagnosis systems for lung cancer diagnosis. In recent times, with the application of deep learning in medical image processing, novel architectures have been proposed for segmentation. However, most of these architectures work on a single modality and work on a single image or whole image volume. In this work, we present Particle image Velocimetry approach as a preprocessing technique by processing sequence of two input images. We used with and without PIV processed images on a deep learning-based pipeline to extract features from these images for candidate lung nodule detection. The results show that the use of particle image velocimetry processing significantly improves the performance of the deep learning pipeline. © 2022 ACM.

关键词： Pipelines

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Dense captioning for Text-image ReID 14

Dense captioning for Text-Image ReID

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Subramanyam, A.V. Sundararajan, Niranjan Dubey, Vibhu Lall, Brejesh IIITD India IITD India

ISBN: (纸本)9798400716256

Text-to-image (T2I) ReID has attracted a lot of attention in the recent past. CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of identities from MSMT17 but due to limited number of unique persons, the diversity is limited. On the other hand, CUHK-PEDES comprises of 13,003 identities but has relatively shorter text description on average. Further, these datasets are captured in a restricted environment with limited number of cameras. In order to further diversify the identities and provide dense captions, we propose a novel dataset. Our dataset comprises of 20,000 unique identities captured in the wild and provides a rich dataset for text-to-image ReID. With a minimum of 26 words for a description, each image is densely captioned. We further synthetically generate images and fine-grained captions using Stable-diffusion and BLIP models trained on our dataset. We perform elaborate experiments using state-of-art text-to-image ReID models and vision-language pre-trained models and present a comprehensive analysis of the dataset. Our experiments also reveal that synthetically generated data leads to a substantial performance improvement in both same dataset as well as cross dataset settings. We will release the code and dataset. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Benchmark Synthetic data Text-to-image ReID

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A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single image Dehazing✱ 22

A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet...

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proceedings of the thirteenth indian conference on computer vision, graphics and image processing

作者： Chippy M Manu Sreeni K G College of engineering trivandrum Kerala India IN College of Engineering Trivandrum India

ISBN: (纸本)9781450398220

Dehazing is a difficult process because of the damage caused by the non-uniform fog and haze distribution in images. To address these issues, a Multi-Scale Residual dense Dehazing Network (MSRDNet) is proposed in this paper. A Contextual feature extraction module (CFM) for extracting multi-scale features and an Adaptive Residual Dense Module (ARDN) are used as sub-modules of MSRDNet. Moreover, all the hierarchical features extracted by each ARDN are fused, which helps to detect hazy maps of varying lengths with multi-scale features. This framework outperforms the state-of-the-art dehazing methods in removing haze while maintaining and restoring image detail in real-world and synthetic images captured under various scenarios.

关键词： Dehazing Dilated Convolution image restoration

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UTILIZING vision-BASED OBJECT DETECTION ALGORITHMS IN RECOGNIZING UNCOMMON OPERATING CONDITIONS FOR CNC MILLING MACHINE 18

UTILIZING VISION-BASED OBJECT DETECTION ALGORITHMS IN RECOGN...

引用

ASME 18th Int Mfg Sci and Engn Conf held jointly with the 51st N Amer Res Conf / Japan-Soc-of-Mech-Engineers Int Conf on Leading Edge Mfg/Mat and Proc

作者： Choudhari, Sahil J. Sujay, B. J. Singh, Swarit Anand Desai, K. A. Indian Inst Technol Dept Mech Engn Jodhpur Rajasthan India

ISBN: (纸本)9780791887240

Although computer Numerical Control (CNC) machines were designed to perform tasks with the least human intervention, operator involvement is mandatory to ensure fault-free operations. Numerous technological solutions utilizing Artificial Intelligence, sensor fusion, Internet of Things (IoT), machine vision, etc., have been developed for process, component, and machine monitoring to impart smartness and autonomous operating abilities. The primary focus of these solutions is to monitor process faults such as tool wear, chatter, static deflections, and cutting forces to assist the operator in minimizing the consequences. The present work develops a vision-based solution for identifying uncommon process abnormalities like improper coolant flow, chip clogging, and tool breakage during CNC milling. The proposed solution replicates the task of a machine operator in identifying these faults and assists in fault-free operations. The study explores the feasibility of utilizing classical and deep learning-based object detection algorithms while developing these solutions. The classical image processing algorithm is ineffective during dynamic process conditions. The deep learning-based algorithm, with an average precision of about 0.75, showed proficiency in abnormalities detection. A Graphical User Interface (GUI) has been developed and integrated with the CNC milling machine to provide an interactive in-process monitoring tool. It is demonstrated that the proposed solution can reduce dependence on a machine operator while monitoring these faults.

关键词： CNC Machine Industry 4.0 Object Detection Machine vision image processing Deep Learning

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Designing a Sequential Hybrid BCI Speller on Fusion of Motor imagery and Steady-State Visually Evoked Potentials EEG Data 14

Designing a Sequential Hybrid BCI Speller on Fusion of Motor...

引用

14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Nama, Tutan Samanta, Debasis Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798400716256

Brain-computer interfaces (BCIs) enable direct communication between the human brain and external devices, interpreting signals like Electroencephalogram (EEG) to translate user intentions into commands. While EEG-based BCIs have potential, they face accuracy and user training challenges. Hybrid BCIs (hBCIs) integrate various input modalities to enhance robustness of BCI applications. Based on the operation mode hBCIs are two types, sequential and simultaneous. In simultaneous hBCI two or more input modalities work together to achieve a single goal. Sequential hBCIs, where modalities work in sequence, offer intuitive interactions. This work introduces a hybrid BCI speller combining MI and SSVEP in a sequential operation mode of hybrid BCI. Two-class MI EEG signals navigate through character groups at the initial stage and initiate SSVEP-based spelling. The interface integrates letters, punctuation, space, error correction operators, and digits. Promising results show an average entry rate of 8.28 char/min with 92.35% accuracy among six participants. This study showcases the potential of hybrid BCI spellers, enhancing communication for individuals with motor impairments. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Brain computer interface

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Controllable image Synthesis via Feature Mask Coupling using Implicit Neural Representation✱ 22

Controllable Image Synthesis via Feature Mask Coupling using...

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proceedings of the thirteenth indian conference on computer vision, graphics and image processing

作者： Sonam Gupta Arti Keshari Sukhendu Das Indian Institute of Technology Madras India

ISBN: (纸本)9781450398220

Implicit neural representation (INR) has emanated as a powerful paradigm for 2D image representation. Recent works like INR-GAN have successfully adopted INR for 2D image synthesis. However, these lack explicit control on the generated images as achieved by their 3D-aware image synthesis counterparts like GIRAFFE. Our work investigates INRs for the task of controllable image synthesis. We propose a novel framework that allows for manipulation of foreground, background and their shape and appearance in the latent space. To achieve effective control over these attributes, we introduce a novel feature mask coupling technique that leverages the foreground and background masks for mutual learning. Extensive quantitative and qualitative analysis shows that our model can disentangle the latent space successfully and allows to change the foreground and/or background’s shape and appearance. We further demonstrate that our network takes lesser training time than other INR-based image synthesis methods.

关键词： Generative adversarial networks

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A Novel Statistical High Density Salt-and-Pepper Noise Removal Algorithm for Brain Magnetic Resonance images 22

A Novel Statistical High Density Salt-and-Pepper Noise Remov...

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proceedings of the thirteenth indian conference on computer vision, graphics and image processing

作者： Amiya Halder Rudrajit Choudhuri Pritam Bhattacharya Apurba Sarkar St Thomas College of Engineering and Technology India St Thomas' College of Engineering & Technology India IIEST Shibpur India

ISBN: (纸本)9781450398220

Brain Magnetic Resonance Imaging (MRI) is a non-invasive technique that produces high quality images of the brain and is most suitable for analysis and diagnosis. However, these images can be soiled with noise during image acquisition or transmission. The paper is targeted at removing high density salt and pepper noise from such medical images using a denoising technique based on centroidal mean formulation. The presented method is tested on various noisy brain MRI images and the obtained results are promising even for images with high density corruptions, which is suggestive of the resiliency of the algorithm.

关键词： Statistical Denoising Technique Brain MRI images. Medical image Enhancement Centroidal Mean

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ColCompNeT: Deep Learning-Based Colorization-Based Coding Network 1

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4th International conference on Machine Intelligence and Signal processing, MISP 2022

作者： Mishra, Dipti Singh, Satish Kumar Mahindra University Hyderabad500043 India Indian Institute of Information Technology Allahabad Allahabad211012 India

ISBN: (数字)9789819900855

ISBN: (纸本)9789819900848

Converting a grayscale image to a visually plausible and perceptually meaningful color image is an exciting research topic in computer vision and graphics. However, predicting the chrominance channels from a grayscale image is an ill-posed problem, especially when the objective is to produce the same color as in the ground truth image, like for the application of compression using colorization or video/image restoration. Compression using colorization efficiently reduces the storage space and transmission bandwidth by two-thirds and maintains the image’s visual quality. However, colorizing decompressed gray images exactly as ground truth with maximum accuracy is a challenging task. In this paper, learning-based CNN network ColCompNeT has been proposed in which concept of parallel training of colorization and compression network is utilized. With the proposed algorithm, a maximum bit saving of 36.45% is achieved with the improved objective and subjective performance when compared with the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network coding

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Unique Identity Generation with Global Features from Multimodal Biometric Data 14

Unique Identity Generation with Global Features from Multimo...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Dash, Priyabrata Samanta, Debasis Sarma, Monalisa Indian Institute of Technology Kharagpur West Bengal Kharagpur India

ISBN: (纸本)9798400716256

The patterns in biometric data, such as fingerprints, iris, etc., are random and distinct from one individual to another, making them ideal for generating unique identities suitable for many applications. This work proposes creating users' unique identities based on their fingerprint and iris biometric data. The global feature descriptor, which is invariant to scale, rotation, and translation, is considered in this work. The global feature descriptors are obtained from fingerprint and iris images and fused to create a single feature vector. Next, feature selection based on the Laplacian score is applied to remove irrelevant features. Further, a metric-learning-based mechanism is introduced to enhance the feature vectors' intra-class similarity and inter-class separability. Finally, a novel quantization mechanism is used to generate a unique identity. Experiments with multimodal datasets show that the generated identities are distinct, random, revocable, and unlinkable. This identity generation scheme can be utilized in storage security, digital signature schemes, remote authentication protocols, blockchain security, etc. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Authentication

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