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Chart classification: An empirical comparative study of different learning models 21

Chart classification: An empirical comparative study of diff...

引用

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

作者： thiyam, Jennil Singh, Sanasam Ranbir Bora, Prabin K. IIT Guwahati India

ISBN: (纸本)9781450391276

Charts are powerful tools for visualizing and comparing data. Representation of information through charts grows with time due to its easy and aesthetically attractive structure. With the increase in the number of documents with various chart types, chart classification has become an important task for downstream applications such as chart data recovery, chart replenishment, etc. though there have been various studies reported in the literature on chart classification using different classification methods, three of the important concerns are small dataset size, a small number of chart types, and inconsistencies in the performance reported in different studies. Motivated by the above concerns, this paper curates a large dataset of real chart images (110k samples) with a large number of chart types (24 charts types) and evaluates 21 different machine learning models. To the best of our knowledge, this is the largest (in sample size and chart types) real chart dataset reported in the literature to date. We further study - (i) the effect of dataset size on the classification model, (ii) the nature of chart noises and their influences on classification performance, and (iii) confusing chart pairs leading to misclassification. © 2021 ACM.

关键词： image classification

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Decomposition of branching volume data by tip detection

Decomposition of branching volume data by tip detection

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IEEE International conference on image processing

作者： Wei Ma Bo Xiang Xiaopeng Zhang Hongbin Zha Key Laboratory of Machine Perception (Ministry of Education) Peking University China LIAMA-NLPR CAS Institute of Automation China

We present an approach to decomposing branching volume data into sub-branches. First, a metric is proposed for evaluating local convexities in volumetric data, and it is a criterion for global selection of tip points. Second, a multi-path growing strategy is adopted to segment the volumes based on a DFS transformation starting from the tips. Experiments show that this approach is capable of generating desirable components and reasonable segmentation boundaries of a volume.

关键词： computer vision Detection algorithms Shape Skeleton Data mining Face detection Laboratories Content addressable storage Automation computer graphics

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A novel unsupervised thresholding technique for landsat image change detection 21

A novel unsupervised thresholding technique for landsat imag...

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

作者： Gupta, Neha Ari, Samit Mishra, Anoop Kumar VIT-AP University Andhra Pradesh Amaravati India National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9781450391276

thresholding is the most widely used change detection technique for identifying the changes in remote sensing images. However, most of the thresholding methods would generate isolated spots in the final change map, which are reduced by applying postprocessing step. this paper proposes a novel thresholding technique to address the aforesaid problem without applying the postprocessisng operation. the proposed technique uses two thresholds simultaneously to generate the change map, which is the key point of this method. Above-stated thresholds are calculated by initial indicators that are generated by local neighborhood mutual information. In addition, this approach compares the local statistics of the interested pixel with the derived thresholds instead of the pixel itself, which enhances the robustness of this technique. Particularly, the method resides on three things: generation of initial indicators, use of two thresholds simultaneously, and comparison of local statistics with thresholds. Finally, the method is tested on multitemporal multispectral images of different sensors, and experimental results validate the effectiveness of the proposed method. © 2021 ACM.

关键词： K-means clustering

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Table extraction from document images using fixed point model 14

Table extraction from document images using fixed point mode...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Bansal, Anukriti Harit, Gaurav Dutta Roy, Sumantra IIT Delhi India IIT Jodhpur India

ISBN: (纸本)1595930361

the paper presents a novel learning-based framework to identify tables from scanned document images. the approach is designed as a structured labeling problem, which learns the layout of the document and labels its various entities as table header, table trailer, table cell and non-table region. We develop features which encode the foreground block characteristics and the contextual information. these features are provided to a fixed point model which learns the inter-relationship between the blocks. the fixed point model attains a contraction mapping and provides a unique label to each block. We compare the results with Condition Random Fields(CRFs). Unlike CRFs, the fixed point model captures the context information in terms of the neighbourhood layout more efficiently. Experiments on the images picked from UW-III (University of Washington) dataset, UNLV dataset and our dataset consisting of document images with multi-column page layout, show the applicability of our algorithm in layout analysis and table detection. Copyright 2014 ACM.

关键词： image analysis

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Automatic synthesis of boolean expression and error detection from logic circuit sketches 6th

Automatic synthesis of boolean expression and error detectio...

引用

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

作者： Dhiman, Sahil Garg, Pushpinder Sharma, Divya Chattopadhyay, Chiranjoy Indian Institute of Technology Jodhpur JodhpurRajasthan India

ISBN: (纸本)9789811300196

Automatic techniques to recognize and evaluate digital logic circuits are more efficient and require less human intervention, as compared to, traditional pen and paper methods. In this paper, we propose LEONARDO (Logic Expression fOrmatioN And eRror Detection framewOrk), a hierarchical approach to recognize boolean expression from hand drawn digital logic gate diagram. the key contributions in the proposed approach are: (i) a novel hierarchical framework to synthesize boolean expression from a hand drawn logic circuit diagram;and (ii) identification of anomalies in drawing. Extensive experimentation was performed through qualitative and quantitative analysis. Results were also compared with existing techniques proposed on the similar problem. Upon experimentation and analysis, our system proved to be more robust to user variability in design and yielded an accuracy of 95.2 %, which is a 4 % gain over others. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Error detection

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Detecting Coronavirus (COVID -19) Disease Cues from Chest Radiography images 13

Detecting Coronavirus (COVID -19) Disease Cues from Chest Ra...

引用

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

作者： Vinod, Athira Shilna Sreeni, K.G. Purushothaman, Anurenjan Jeena, R.S. Computer Vision Lab Dept. of Electronics and Communication College of Engineering Trivandrum Kerala Trivandrum India Government Engineering College Kerala Idukki India Rajiv Gandhi Institute of Technology Kerala Kottayam India Affiliated to APJ Abdul Kalam Technological University Kerala Trivandrum India

ISBN: (纸本)9781450398237

this paper proposes a deep learning-based approach to detect COVID-19 infections in lung tissues from chest Computed Tomography (CT) images. A two-stage classification model is designed to identify the infection from CT scans of COVID-19 and Community Acquired Pneumonia (CAP) patients. the proposed neural model named, Residual C-NiN uses a modified convolutional neural network (CNN) with residual connections and a Network-in-Network (NiN) architecture for COVID-19 and CAP detection. the model is trained with the Signal processing Grand Challenge (SPGC) 2021 COVID dataset. the proposed neural model achieves a slice-level classification accuracy of 93.54% on chest CT images and patient-level classification accuracy of 86.59% with class-wise sensitivity of 92.72%, 55.55%, and 95.83% for COVID-19, CAP, and Normal classes, respectively. Experimental results show the benefit of adding NiN and residual connections in the proposed neural architecture. Experiments conducted on the dataset show significant improvement over the existing state-of-the-art methods reported in the literature. © 2022 ACM.

关键词： COVID-19

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Flow-free video object segmentation 6th

Flow-free video object segmentation

引用

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

作者： Vora, Aditya Raman, Shanmuganathan Electrical Engineering Indian Institute of Technology Gandhinagar GandhinagarGujarat382355 India

ISBN: (纸本)9789811300196

Segmenting foreground object from a video is a challenging task because of large deformations of objects, occlusions, and background clutter. In this paper, we propose a frame-by-frame but computationally efficient approach for video object segmentation by clustering visually similar generic object segments throughout the video. Our algorithm segments object instances appearing in the video and then performs clustering in order to group visually similar segments into one cluster. Since the object that needs to be segmented appears in most part of the video, we can retrieve the foreground segments from the cluster having maximum number of segments. We then apply a track and fill approach in order to localize the object in the frames where the object segmentation framework fails to segment any object. Our algorithm performs comparably to the recent automatic methods for video object segmentation when benchmarked on DAVIS dataset while being computationally much faster. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Motion compensation

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Dense captioning for Text-image ReID 14

Dense captioning for Text-Image ReID

引用

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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IGKS (abstract only): an integrated image processing and graphics environment 87

IGKS (abstract only): an integrated image processing and gra...

引用

Proceedings of the 15th annual conference on computer Science

作者： George Grinstein

ISBN: (纸本)9780897912181

this paper describes a part of a large project at the University of Lowell to develop an integrated environment for the unified treatment of image processing and graphics with interfaces to pictorial databases. the overall systems consists of a knowledge-based user interface containing three orthogonal kernels:an image processing and vision kernel system (IKS) andthe graphical kernel system (GKS)several pictorial databasesOne can think of a kernel as a set of procedures, tools and utilities that are coherent, orthogonal and fundamental. In our environment, all kernels are closely linked. there is a very strong link between the image/vision kernel and the graphics one. this is specifically referred to as the image and graphics Kernel System (IGKS) and this is the focus of this paper. this system is interfaced to the user through a knowledge-base and rule-based system (referred to symbolically as ).

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Enhancing label transfer in non-parametric scene parsing by superpixel-based dense alignment 21

Enhancing label transfer in non-parametric scene parsing by ...

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

作者： Bhowmick, Alexy Saharia, Sarat Hazarika, Shyamanta M. Dept. of Computer Science and Engineering Assam Don Bosco University Assam Guwahati781017 India Dept. of Computer Science and Engineering Tezpur University Assam Tezpur784028 India Biomimetic Robotics and Artificial Intelligence Lab Dept. of Mechanical Engineering IIT Guwahati Assam Guwahati781039 India

ISBN: (纸本)9781450391276

Contemporary (parametric) scene parsing methods are learning-based and mostly operate in a closed-universe scenario. We introduce a non-parametric scene parsing framework that is model-free, data-driven, and scales naturally to growing data. the scene parsing performance in the non-parametric approach depends on reliable dense correspondence or alignment across scenes for label transfer. Incorrect correspondence is known to affect the scene parsing results adversely. We propose a label transfer approach that relies on the dense correspondence of super-pixel pairs (in a query and candidate image) matched by a homogeneous kernel map to guide semantic label transfer. the aggregation (fusing) of multiple labels is done through a simple heuristic aggregation scheme (simple majority voting). the Markov Random Field (MRF) provides a principled probabilistic framework for combining the disparate information in the smoothing stage and ensures plausible labeling results. Evaluation results show that our non-parametric system obtains competitive scene parsing performance on the standard SIFT Flow and MSRC-21 datasets. © 2021 ACM.

关键词： Superpixels

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