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

作者： Goyal, Manik Rajpura, Param Bojinov, Hristo Hegde, Ravi Varanasi Varanasi221005 India Indian Institute of Technology Gandhinagar Gandhinagar382355 India Innit Inc. Redwood City94063 United States

ISBN: (纸本)9789811300196

Although Deep Convolutional Neural Networks trained with strong pixel-level annotations have significantly pushed the performance in semantic segmentation, annotation efforts required for the creation of training data remains a roadblock for further improvements. We show that augmentation of the weakly annotated training dataset with synthetic images minimizes both the annotation efforts and also the cost of capturing images with sufficient variety. Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU from 52.80% to 55.47% by adding just 100 synthetic images per object class. Our approach is thus a promising solution to the problems of annotation and dataset collection. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Semantic Segmentation

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InMDb: indian Movie Database for Emotion Analysis 23

InMDb: Indian Movie Database for Emotion Analysis

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

作者： Ritik Garg Rushil Thareja Manak Bisht Manavjeet Singh Sarthak Arora Jainendra Shukla IIIT Delhi IN IIIT-Delhi IN

ISBN: (纸本)9798400716256

Cinematic experiences, characterized by intricate audio-visual stimuli, foster profound emotional engagement. However, the correlation between audience emotions, physiological responses, film genres, and ratings, particularly in the underexplored Bollywood context, remains largely uncharted. Understanding this intricate interplay can provide filmmakers valuable insights for content adaptation. Addressing this research gap, we introduce "InMDB: indian Movie DataBase," a comprehensive multimodal dataset that examines emotional responses elicited by Bollywood trailers, using both self-reported measures and physiological data. Our meticulous statistical analysis of the dataset deepens the understanding of how emotions and their subsequent physiological responses correlate with, and potentially influence, film ratings and categories, offering novel insights into emotional engagement in the cinematic context.

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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...

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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...

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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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DePondFi'23 Challenge on Real-Time Pond Environment: Methods and Results

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IEEE ACCESS 2024年 12卷 157975-157987页

作者： Sasithradevi, A. Suganya, R. Prakash, P. Mohamed Mansoor Roomi, S. Vijayalakshmi, M. Nathan, Sabari Kasthuri, P. Persiya, J. Brighty Ebenezer, L. Jain, Sparsh Verma, Sshubam Balasubramanian, S. Sai Subramaniam, M. Sai Sriram, T. Pranav Phanindra Sai, M. Raj, Chandan Yadav, Amandeep Payak, Ritik Paul Choudhury, Suman Singh, Rohit Vellore Inst Technol Ctr Adv Data Sci Chennai 600127 India Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India Indian Inst Technol Madras Dept Elect Engn Chennai 600044 India Thiagarajar Coll Engn Dept Elect & Commun Engn Madurai 625015 India Vellore Inst Technol Sch Elect Engn Chennai 600127 India Couger Inc Tokyo Japan Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi 110086 India Sri Sathya Sai Inst Higher Learning Dept Math & Comp Sci Anantapur 515134 Andhra Pradesh India IIT Roorkee Dept Elect Engn Roorkee 247667 India SAHAJ AI Bangalore 560034 India

This article summarizes the Detection of Pond Fish Challenge (DePondfi'23 Challenge), held during the National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG 2023). The main goal of the challenge was to find the most effective methods for detecting pond fish in underwater images, overcoming obstacles such as poor visibility, variations in turbidity, and environmental shifts. Sixty participants registered, with 15 teams submitting results for phase 1. The challenge concluded with four teams earning top honors based on mAP (mean Average Precision) score and time complexity. The mAP scores achieved by toppers are as follows: DETECTRON - 38.93%, DMACS SAI - 36.65%, Pondvision - 31.63%, and Sahajeevis - 29.06%. This article describes the toppers method and discusses the detection results. Our challenge event is in line with Sustainable Development Goal 14, which focuses on the conservation and sustainable utilization of ponds and marine resources for sustainable development.

关键词： Aquaculture underwater challenges detection models mAP computational time.

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Intelligent Computing Methodologies 1

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丛书名： Lecture Notes in computer Science

1000年

作者： De-Shuang Huang Abir Hussain Kyungsook Han M. Michael Gromiha

ISBN: (数字)9783319633152

ISBN: (纸本)9783319633145

This three-volume set LNCS 10361, LNCS 10362, and LNAI 10363 constitutes the refereed proceedings of the 13th International conference on Intelligent Computing, ICIC 2017, held in Liverpool, UK, in August 2017. The 212 full papers and 20 short papers of the three proceedings volumes were carefully reviewed and selected from 612 submissions. This third volume of the set comprises 67 papers. The papers are organized in topical sections such as Intelligent Computing in Robotics; Intelligent Computing in computer vision; Intelligent Control and Automation; Intelligent Agent and Web Applications; Fuzzy Theory and Algorithms; Supervised Learning; Unsupervised Learning; Kernel Methods and Supporting Vector Machines; Knowledge Discovery and Data Mining; Natural Language processing and Computational Linguistics; Advances of Soft Computing: Algorithms and Its Applications - Rozaida Ghazali; Advances in Swarm IntelligenceAlgorithm; Computational Intelligence and Security for image Applications in Social;Network; Biomedical image Analysis; Information Security; Machine Learning; Intelligent Data Analysis and Prediction.

关键词： Artificial Intelligence Information Systems Applications (incl. Internet) Information Storage and Retrieval image processing and computer vision Data Mining and Knowledge Discovery Algorithm Analysis and Problem Complexity

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ViLP: Knowledge Exploration using vision, Language, and Pose Embeddings for Video Action Recognition 23

ViLP: Knowledge Exploration using Vision, Language, and Pose...

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

作者： Soumyabrata Chaudhuri Saumik Bhattacharya IIT Bhubaneswar IN E&ECE Indian Institute of Technology Kharagpur IN

ISBN: (纸本)9798400716256

Video Action Recognition (VAR) is a challenging task due to its inherent complexities. Though different approaches have been explored in the literature, designing a unified framework to recognize a large number of human actions is still a challenging problem. Recently, Multi-Modal Learning (MML) has demonstrated promising results in this domain. In literature, 2D skeleton or pose modality has often been used for this task, either independently or in conjunction with the visual information (RGB modality) present in videos. However, the combination of pose, visual information, and text attributes has not been explored yet, though text and pose attributes independently have been proven to be effective in numerous computer vision tasks. In this paper, we present the first pose augmented vision-language model (VLM) for VAR. Notably, our scheme achieves an accuracy of 92.81% and 73.02% on two popular human video action recognition benchmark datasets, UCF-101 and HMDB-51, respectively, even without any video data pre-training, and an accuracy of 96.11% and 75.75% after kinetics pre-training.

关键词： Action recognition vision-language model multimodal training

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Segmentation of Bangla words in scene images 12

Segmentation of Bangla words in scene images

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Banik, Prakriti Bhattacharya, Ujjwal Parui, Swapan K. Indian Statistical Institute Kolkata 700108 India

ISBN: (纸本)9781450316606

Some studies on extraction of Bangla texts from scene images are available in the literature. Also, OCR of printed Bangla texts has been extensively studied. However, the performance of available Bangla OCR on scene texts is not acceptable. In this article, we present our recent study of segmentation of characters or their parts from Bangla texts extracted from scene images. The proposed approach detects the background and text by a combination of two algorithms: unsupervised learning algorithm K-means clustering and Otsu's threshold selection. We propose a criterion to choose an optimal K value for K-means clustering. The text segmentation is based on region growing and extraction of both headline and baseline of such texts. These two lines divide a Bangla word into three horizontal zones. The present algorithm segments characters or their parts in each individual zone. This zone-based segmentation approach helps to reduce the number of symbols to be handled by the classifier in the next stage of the OCR system. Our algorithm can also detect an image having only numerals, avoiding zone detection in that case. Extracted scene texts are often affected by artifacts and our segmentation algorithm can remove them efficiently. Our algorithm has been tested on 2460 Bangla words extracted from 260 scene images. © 2012 ACM.

关键词： Optical character recognition

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Spectrum-inspired Low-light image Translation for Saliency Detection 13

Spectrum-inspired Low-light Image Translation for Saliency D...

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

作者： Varghese, Kitty Rajagopalan, Sudarshan Lamba, Mohit Mitra, Kaushik Indian Institute of Technology Madras India

ISBN: (纸本)9781450398237

Saliency detection methods are central to several real-world applications such as robot navigation and satellite imagery. However, the performance of existing methods deteriorate under low-light conditions because training datasets mostly comprise of well-lit images. One possible solution is to collect a new dataset for low-light conditions. This involves pixel-level annotations, which is not only tedious and time-consuming but also infeasible if a huge training corpus is required. We propose a technique that performs classical band-pass filtering in the Fourier space to transform well-lit images to low-light images and use them as a proxy for real low-light images. Unlike popular deep learning approaches which require learning thousands of parameters and enormous amounts of training data, the proposed transformation is fast and simple and easy to extend to other tasks such as low-light depth estimation. Our experiments show that the state-of-The-Art saliency detection and depth estimation networks trained on our proxy low-light images perform significantly better on real low-light images than networks trained using existing strategies. © 2022 ACM.

关键词： Satellite imagery

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Automatic Perspective Camera Calibration Based on an Incomplete Set of Chessboard Markers

Automatic Perspective Camera Calibration Based on an Incompl...

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indian conference on computer vision, graphics & image processing (ICVGIP)

作者： Alessandro Bevilacqua Alessandro Gherardi Ludovico Carozza ARCES University of Bologna Bologna Italy

The measurement in metric units from a perspective view of a real world object is an important task in many computer vision applications. The goal of the perspective calibration is to map the reference coordinates of the 3D object into the 2D image coordinates. This is usually done by recovering the perspective transformation parameters by presenting to the camera a view of a known calibration pattern. Many of the available algorithms require to process all the points of the calibration pattern in order to recover the transformation parameters effectively. The approach we present for the computation of the inverse perspective transformation does not require to consider all the grid points. Rather, a grid matching between the 3D points projection and the model is performed exploiting a graph based approach. Extensive experiments have proved the efficacy of the algorithm in different application fields such as camera calibration and automotive headlight beam characterization.

关键词： Cameras Calibration Layout Pattern matching Robot kinematics Navigation computer vision Automotive engineering Robotics and automation Optical computing

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