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Mandala as computational art: Vectorization and beyond 14

Mandala as computational art: Vectorization and beyond

引用

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

作者： Sarkar, Tusita Bhowmick, Partha Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798400716256

this paper introduces a novel technique of computational art with mandala-an iconic heritage of indian folk art. Its novelty lies in several fundamental steps. the first one is fixing the asymmetries and the imperfections in a hand-drawn piece of art based on the notion of a primitive map. the primitive map is described using a novel concept of geometric salience-a set of well-defined salient points on the frontier polygon of a primitive-characterizing the concavities and convexities present in the primitive. the primitive map is also used for the vectorization of a mandala and its succinct representation as a mandala sector graph (MSG), which eventually results in efficient graph operations on an existing artwork to create a new piece of art. the use of frontier polygons in different steps of the algorithm makes it robust and efficient. Experimental results on various datasets demonstrate the potential and versatility of the proposed technique. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Arts computing

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A text recognition augmented deep learning approach for logo identification 10

A text recognition augmented deep learning approach for logo...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Medhi, Moushumi Sinha, Shubham Sahay, Rajiv Ranjan Computational Vision Lab Department of Computer Science and Engineering Indian Institute of Technology Kharagpur KharagpurWest Bengal721302 India Department of Computer Science and Technology Indian Institute of Engineering Science and Technology ShibpurWest Bengal711103 India Department of Electrical Engineering Indian Institute of Technology Kharagpur KharagpurWest Bengal721302 India

Logo/brand name detection and recognition in unstructured and highly unpredictable natural images has always been a challenging problem. We notice that in most natural images logos are accompanied with associated text. therefore, we address the problem of logo recognition by first detecting and isolating text of varying color, font size and orientation in the input image using affine invariant maximally stable extremal regions (MSERs). Using an off-the-shelf OCR, we identify the text associated with the logo image. then an effective grouping technique is employed to combine the remaining stable regions based on spatial proximity of MSERs. Deep learning has the advantage that optimal features can be learned automatically from image pixel data. this motivates us to feed the clustered logo candidate image regions to a pre-trained deep convolutional neural network (DCNN) to generate a set of complex features which are further input to a multiclass support vector machine (SVM) for classification. We tested our proposed logo recognition system on 32 logo classes, and a non-logo class obtained by combining FlickrLogos-32 and MICC logo databases, amounting to a total of 23582 training and testing images. Our method yields robust recognition performance, outperforming state-of-the-art techniques achieving 97.8% precision, 95.7% recall and 95.7% average accuracy on the combined MICC and FlickrLogos-32 datasets and a precision of 98.6%, recall of 97.9% and average accuracy of 99.6% on only the FlickrLogos-32 dataset. © Springer International Publishing AG 2017.

关键词： Deep neural networks

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Distributed video coding using local rank transform 14

Distributed video coding using local rank transform

引用

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

作者： Mallick, Kallol Mukherjee, Jayanta Indian Institute of Technolgy Kharagpur Kharagpur India

ISBN: (纸本)1595930361

In today's world, with advanced technology and easily available portable gadgets as enabler, video has become an important medium of communication. While, the standard H.264/AVC has produced extremely good results, it has not been suitable for this application domain as it is computationally intensive and unsuitable for low-power resources. In distributed video coding (DVC), an encoder requires less computation than the decoder, which usually runs at sites of higher computational resources. Our approach is to find out a novel approach in DVC, to reduce encoder complexity, using local rank transform (LRT). LRT relies on the relative ordering of local intensity values for application on visual correspondence problem. Use of LRT in DVC, to the best of our knowledge, has not been reported in literature before. First, we have developed techniques for image reconstruction using LRT and then design a DVC codec using it. We show analytically and by experimental results, that, in power-rate-distortion model, LRT encoder outperforms standard encoder (LDPCA in Stanford architecture) specially in low bit rate condition. Copyright 2014 ACM.

关键词： image reconstruction

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Interpolating sporadic data 7th

Interpolating sporadic data

引用

7th European conference on computer vision (ECCV 2002)

作者： Noakes, L Kozera, R Univ Western Australia Dept Math & Stat Crawley WA 6009 Australia Univ Western Australia Dept Comp Sci & Software Engn Crawley WA 6009 Australia

ISBN: (纸本)3540437444

We report here on the problem of estimating a smooth planar curve(a) gamma : [0,T] --> R-2 and its derivatives from an ordered sample of interpolation points {gamma(t(0)),gamma(t(1)),..., gamma(t(i-1)), gamma(t(i)),..., gamma(t(m-1)), gamma(t(m))}, where 0 = t(0) < t(1) <... < t(i-1) < t(i) <... < t(m-i) < t(m) = T, and the ti are not known precisely for 0 < i < m. Such situtation may appear while searching for the boundaries of planar objects or tracking the mass center of a rigid body with no times available. In this paper we assume that the distribution of ti coincides with more-or-less uniform sampling. A fast algorithm, yielding quartic convergence rate based on 4-point piecewise-quadratic interpolation is analysed and tested. Our algorithm forms a substantial improvement (with respect to the speed of convergence) of piecewise 3-point quadratic Lagrange intepolation [19] and [20]. Some related work can be found in [7]. Our results may be of interest in computer vision and digital image processing [5], [8], [13], [141, [17] or [24], computer graphics [1], [4], [9], [10], [21] or [23], approximation and complexity theory [3], [6], [16], [22], [26] or [27], and digital and computational geometry [2] and [15].

关键词： shape image analysis and features curve interpolation

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FaSTIP: A new method for detection and description of space-time interest points for human activity classification 12

FaSTIP: A new method for detection and description of space-...

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

作者： Samanta, Soumitra Chanda, Bhabatosh Indian Statistical Institute Ellectronics and Communications Sciences Unit 203 B. T. Road Kolkata - 700108 India

ISBN: (纸本)9781450316606

this paper presents a new method to detect space time interest point (STIP) from video data. We use three dimensional facet model to detect STIP and call it as facet space-time interest point or FaSTIP. the proposed algorithm detects all the desired interest points efficiently in different scales compared to other existing methods. A video clip is described as a collection of 3D wavelet base features computed at these interest points. Finally, multi-channel SVM with χ2- kernel is used to classify human actions. Our contribution here are two fold: first, we present a new algorithm for interest point detection in video data, and second, we propose a new descriptor for general human activity classification. Experimental results show the accuracy of the detected interest points and the power of descriptor compared to the state-of-the-art. © 2012 ACM.

关键词： Video recording

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computer vision, Pattern Recognition, image processing, and graphics 1st ed. 2018

引用

丛书名： Communications in computer and Information Science

2018年

作者： Renu Rameshan Chetan Arora Sumantra Dutta Roy

ISBN: (数字)9789811300202

ISBN: (纸本)9789811300196

this book constitutes the refereed proceedings of the 6th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2017, held in Mandi, India, in December 2017. the 48 revised full papers presented in this volume were carefully reviewed and selected from 147 submissions. the papers are organized in topical sections on video processing; image and signal processing; segmentation, retrieval, captioning; pattern recognition applications.

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A Novel Approach for Neuromorphic vision Data Compression based on Deep Belief Network 14

A Novel Approach for Neuromorphic Vision Data Compression ba...

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

作者： Khaidem, Sally Nevatia, Abhipraay Sharma, Mansi Indian Institute of Technology Madras TN Chennai India

ISBN: (纸本)9798400716256

A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. they are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS records asynchronous per-pixel brightness changes, resulting in a stream of events that encode the time, location, and polarity of brightness change. DVS consumes little power and can capture a wider dynamic range with no motion blur and higher temporal resolution than conventional frame-based cameras. Despite yielding a lower bit rate compared to conventional video capture, the present approach of event capture demonstrates enhanced compressibility. Hence, we introduce a novel deep learning-based compression methodology tailored for event data. the proposed technique employs a deep belief network (DBN) to condense the high-dimensional event data into a latent representation, which is subsequently encoded utilising an entropy-based coding method. Notably, our proposed scheme represents one of the initial endeavours to integrate deep learning methodologies for event compression. It achieves a high compression ratio while maintaining good reconstruction quality outperforming state-of-the-art event data coders and other lossless benchmark techniques. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cameras

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Human gait recognition using depth camera: A covariance based approach 12

Human gait recognition using depth camera: A covariance base...

引用

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

作者： Kumar, M. S. Naresh Babu, R. Venkatesh Video Analytics Lab. SERC Indian Institute of Science Bangalore India

ISBN: (纸本)9781450316606

Gait is an important biometric modality for recognizing humans. Unlike other biometrics, human gait can be captured at a distance which makes it an unobtrusive method for recognition. In this paper, an unrestricted gait recognition algorithm is proposed which uses 3D skeleton information and trajectory covariance of joint points. 3-D skeleton is generated from the depth images that are captured using Kinect sensor. the temporal tracking of skeleton points is used for gait analysis. the covariance measure between these skeleton point trajectories are computed and the covariance matrices form the gait model. the gait is recognized by computing the minimum dissimilarity measure between the gait models of the training data and the testing data. Recognition accuracy of over 90% has been achieved for a data set consisting of fixed and moving camera scenarios of 20 subjects. © 2012 ACM.

关键词： Biometrics

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A beta distribution based novel scheme for detection of changes in crowd motion 10

A beta distribution based novel scheme for detection of chan...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Pal, Soumyajit Mondal, Sounak Saha, Sanjoy Kumar Chanda, Bhabatosh Department of Computer Science and Engineering Jadavpur University Kolkata India Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

An automated system for crowd behaviour analysis has gained significance in the context of surveillance and public management. Detecting the changes in the crowd behaviour demarcates one activity or event from another. thus, change detection is a fundamental step that enables the subsequent characterisation of the activities and analysis of the transition from one state to another. Proposed work deals with high density crowd. Global motion is an important cue for studying the behaviour of such crowd. In this work, crowd motion is modelled using beta distribution. Change in the distribution parameter is an indicator for change in crowd motion pattern. Proposed methodology has been tested with number of synthetic and natural video sequences and the performance is satisfactory. © Springer International Publishing AG 2017.

关键词： Automation

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Deep Learning Models for Medical image Analysis: Challenges and Future Directions 1

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7th International conference on Big Data analytics (BDA)

作者： Agrawal, R. K. Juneja, Akanksha Jawaharlal Nehru Univ New Delhi 110067 India

ISBN: (数字)9783030371883

ISBN: (纸本)9783030371883;9783030371876

Artificial neural network (ANN) introduced in the 1950s, is a machine learning framework inspired by the functioning of human neurons. However, for a long time the ANN remained inadequate in solving real problems, because of - the problems of overfitting and vanishing gradient while training a deep architecture, dearth of computation power, and non-availability of enough data for training the framework. this concept has lately re-emerged, in the form of Deep Learning (DL) which initially developed for computer vision and became immensely popular in several other domains. It gained traction in late 2012, when a DL approach i.e. convolutional neural network won in the imageNet Classification - an acclaimed worldwide computer vision competition. thereafter, researchers in practically every domain, including medical imaging, started vigorously contributing in the massively progressing field of DL. the success of DL methods can be owed to the availability of data, boosted computation power provided by the existing graphics processing units (GPUs), and ground-breaking training algorithms. In this paper, we have overviewed the area of DL in medical imaging, including (1) machine learning and DL basics, (2) cause of power of DL, (3) common DL models, (4) their applications to medical imaging and (5) challenges and future work in this field.

关键词： Medical image analysis Machine learning Deep Learning computer-aided diagnosis

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