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Fragment-based real-time object tracking: A sparse representation approach

Fragment-based real-time object tracking: A sparse represent...

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2012 19th IEEE International conference on image processing, ICIP 2012

作者： Naresh Kumar, M.S. Parate, Priti Venkatesh Babu, R. Supercomputer Education and Research Centre Indian Institute of Science Bangalore - 560012 India

ISBN: (纸本)9781467325332

Real-time object tracking is a critical task in many computer vision applications. Achieving rapid and robust tracking while handling changes in object pose and size, varying illumination and partial occlusion, is a challenging task given the limited amount of computational resources. In this paper we propose a real-time object tracker in l1 framework addressing these issues. In the proposed approach, dictionaries containing templates of overlapping object fragments are created. The candidate fragments are sparsely represented in the dictionary fragment space by solving the l 1 regularized least squares problem. The non zero coefficients indicate the relative motion between the target and candidate fragments along with a fidelity measure. The final object motion is obtained by fusing the reliable motion information. The dictionary is updated based on the object likelihood map. The proposed tracking algorithm is tested on various challenging videos and found to outperform earlier approach. © 2012 IEEE.

关键词： Motion estimation

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Artificially Intelligent Game Framework Based on Facial Expression Recognition 7th

Artificially Intelligent Game Framework Based on Facial Expr...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Patidar, Itisha Modh, Karan Sanjay Chattopadhyay, Chiranjoy Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (纸本)9789811586965

During gameplay, a player experiences emotional turmoil. In most of the cases, these emotions directly reflect the outcome of the game. Adapting game features based on players’ emotions necessitates a way to detect the current emotional state. Researchers in the area of "video game user research" has studied biometric data as a way to address the diverse characteristics of players, their individual preferences, gameplay expertise, and experiences. Identification of the player’s current state is fundamental for designing a game, which interacts with the player adaptively. In this paper, we present an artificially intelligent game framework with smart features based on automatic facial expression recognition and adaptive game features based on the gamer’s emotion. The gamer’s emotions are recognized at run-time during gameplay using Deep Convolutional Neural Networks (CNN), and the game is adapted accordingly to the emotional condition. Once identified, these features directly modify critical parameters of the underlying game engine to make the game more exciting and challenging. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Algebraic Splats Representation for Point Based Models

Algebraic Splats Representation for Point Based Models

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

作者： Naveen Kumar Bolla P.J. Narayanan Center for Visual Information Technology IIIT-Hyderabad India

The primitives of point-based representations are independent but are rendered using surfels, which approximate the immediate neighborhood of each point linearly. A large number of surfels are needed to convey the exact shape. Higher-order approximations of the local neighborhood have the potential to represent the shape using fewer primitives, simultaneously achieving higher rendering speeds. In this paper, we propose algebraic splats as a basic primitive of representation for point based models. An algebraic splat based representation can be computed using a moving least squares procedure. We specifically study low order polynomial splats in this paper. Quadratic and cubic splats provide good quality and high rendering speed using far fewer primitives on a wide range of models. They can also be rendered fast using ray tracing on modern GPUs. We also present an algorithm to construct a representation of a model with a user-specified number of primitives. Our method to generates a hole-free representation parametrized by a smoothing radius. The hole-free representation reduces the number of primitives needed by a factor 20 to 30 on most models and by a factor of over 100 on dense models like David with little or no drop in visual quality. We also present a two-pass GPU algorithm that ray-traces the algebraic splats and blends them using a Gaussian weighting scheme for smooth appearance. We are able to render models like David at upwards of 200 fps on a commodity GPU using algebraic splats.

关键词： Least squares approximation Shape Piecewise linear approximation Polynomials Surface fitting Ray tracing computer vision computer graphics image processing Information technology

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Kannada Word Detection in Heterogeneous Scene images 10

Kannada Word Detection in Heterogeneous Scene Images

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10th International conference on Signal processing and Integrated Networks, SPIN 2023

作者： Shikha, N. Pranav, R. Singh, Nidhi R Umadevi, V. Hussain, Muzammil B.M.S. College of Engineering Dept. of Computer Science Bengaluru India B.M.S. College of Engineering Dept. of Electrical and Electronics Bengaluru India B.M.S. College of Engineering Dept. of Medical Electronics Bengaluru India Tika Data Services Pvt. Ltd. Bengaluru India

ISBN: (纸本)9781665490993

Text extraction from scene images has started gaining a lot of traction in recent years in the computer vision field as its applications is manifold. One of its sub-categories is scene text detection. Factors like complex backgrounds, curvature, orientation, image quality, and various font styles and sizes makes it a difficult task. Moreover, building a single language scene text detector in a multilingual setting, especially indian scripts, is more challenging in contrast to a general text detector. In this Work We attempt the task of Kannada text localization as Well as Word detection in natural scenes, Which is underexplored in comparison to other Well-known indian languages. To achieve this, We fine tune a You Only Look Once (YOLOv4) based object detection model on our Kannada scene text images dataset collected. Experiments on local boards and sign images captured on smartphone, showed a mean average precision of 93.5% and per image average detection speed of 30 milli-seconds;pointing towards real time use and integration With mobile applications. © 2023 IEEE.

关键词： Object detection

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An Algorithm to Calculate Retinal Vessel Diameter in Fundus images 14

An Algorithm to Calculate Retinal Vessel Diameter in Fundus ...

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

作者： Yoga Sri Varshan, V. Sahayam, Subin Jayaraman, Umarani Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamil Nadu Chennai India

ISBN: (纸本)9798400716256

Retinal blood vessels are the arteries and veins that supply blood to the human eye. Fundus images obtained through a fundus camera capture retinal information like, the macula, optic disc, cup, fovea, retinal blood vessels, and abnormalities. The retinal blood vessels are miniature and are usually measured in micrometers. It is difficult and time-consuming to study retinal vessels from a fundus image. Generally, a detailed vessel study requires costly equipment like Ocular Coherence Tomography (OCT) and is comparatively less available at hospitals than a fundus camera. Blood vessels are connected throughout the body. Studying retinal blood vessel health can help ophthalmologists and doctors understand the overall health of blood vessels across the body non-invasively. Narrowing of blood vessels can make a person prone to many diseases, like hypertension, cardiovascular disease, and stroke. A few methods aim to automate the task using computer Aided Diagnosis (CAD). The work proposes a novel and relatively less complex algorithm for calculating the diameter of a given retinal blood vessel at a given distance from the optic disc center in a fundus image. The approach aims to compute the artery-to-vein diameter ratio of retinal blood vessels at a given distance from the center of the optic disc. The proposed method has been tested on the LES-AV 2020 dataset and it achieved an approximate artery-to-vein diameter ratio (AVR) of 2:3, which is in line with the average healthy AVR in the literature. Therefore, the proposed method is accurate and can be extended to find AVR in various clinical scenarios to detect and study any abnormalities. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blood vessels

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Fusion of image quality assessment and transfer learning for COVID19 detection using CT scan image 21

Fusion of image quality assessment and transfer learning for...

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

作者： Kiruthika, S. Masilamani, V. Joshi, Pratik Indian Institute of Information Technology Design and Manufacturing Kancheepuram Tamilnadu Chennai India

ISBN: (纸本)9781450391276

One of the main challenges in controlling the spread of COVID19 pandemic is to diagnose infection early. The most reliable method RT - PCR takes several hours to give results. Although the Anti-Body (Serological) test gives the results in a few hours, it is not accurate, and hence it is not reliable. Moreover, they are invasive. Another issue with these methods is that the number of labs performing these tests are very limited. It will be beneficial if the already existing clinical infrastructure is used for diagnosing COVID19 accurately in real time. Recently chest CT images are used by researchers to diagnose the COVID19 with impressive accuracy. The state of the art method for detecting COVID19 using CT chest images involves Deep Learning. Deep Learning is expected to provide accurate and reliable results only when the model is trained on a large data set. Due to non-availability of a large data set the existing models have been trained on a smaller size data set. Therefore it would be better to design a model to give good accuracy with reliability. To achieve accuracy along with reliability we proposed a COVID19 detection model with the combination of deep learning model and the traditional machine learning model. The novelty of the proposed model is the fusion of image quality and deep learning. The proposed method outperformed the state of the art method in terms of accuracy, recall and F1 score (more than 99 % in almost all the metrics) on a benchmark data set. The efficacy of the selected features and also explainability of the method are demonstrated through various tests. © 2021 ACM.

关键词： Diagnosis

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A novel approach for delineation and feature extraction in QRS complex of ECG signal

A novel approach for delineation and feature extraction in Q...

引用

2011 International conference on image Information processing, ICIIP 2011

作者： Pramanik, Sayak Mitra, Rupendra Nath Mitra, Sucharita Chaudhuri, Bidyut Baran Department of Electronics and Communication Institute of Engineering and Management Kolkata India Network Planning and Optimization Nokia Siemens Networks Pvt. Ltd. Kolkata India Department of Electronics Netaji Nagar Day College Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781612848617

This paper reports the development of a software suite to be accessed in future with any General Packet Radio Service (GPRS) enabled mobile phone or Personal Digital Assistant (PDA) for the extraction and analysis of disease-related features from the images of paper based ECG records. In India and other developing countries, the cheaper paper based ECG machines are mainly used. Also in rural areas of these countries - where there is acute dearth of qualified cardiologists, the cardiac diseases are still the major silent killers. One way of addressing this problem is cellular medicine which necessitates an intelligent cardiac parameter extraction algorithm. In our bid to address exactly this requirement, a software is developed with the help of few image processing techniques. At first, the background noises i.e. the gridlines are removed by thresholding technique. Binarization and thinning has been done for extraction of digital time data and then Discrete Wavelet Transform (DWT) and water reservoir based pattern recognition technique are subsequently used to delineate other important ECG parameters for ECG interpretation. © 2011 IEEE.

关键词： Electrocardiograms

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ISIgraphy: A tool for online handwriting sample database generation

ISIgraphy: A tool for online handwriting sample database gen...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Arindam Das Ujjwal Bhattacharya Department of Computer Science & Engineering Narula Institute of Technology Kolkata India Computer Vision and Pattern Recognition Unit Indian Statistical Institute (ISI) Kolkata India

Online handwriting recognition research has recently received significant thrust. Specifically for indian scripts, handwriting recognition has not been focused much till in the near past. However, due to generous Government funding through the group on Technology Development for indian Languages (TDIL) of the Ministry of Communication & Information Technology (MC&IT), Govt. of India, research in this area has received due attention and several groups are now engaged in research and development works for online handwriting recognition in different indian scripts. An extensive bottleneck of the desired progress in this area is the difficulty of collection of large sample databases of online handwriting in various scripts. Towards the same, recently a user-friendly tool on Android platform has been developed to collect data on handheld devices. This tool is called ISIgraphy and has been uploaded in the Google Play for free download. This application is designed well enough to store handwritten data samples in large scales in user-given file names for distinct users. Its use is script independent, meaning that it can collect and store handwriting samples written in any language, not necessarily an indian script. It has an additional module for retrieval and display of stored data. Moreover, it can directly send the collected data to others via electronic mail.

关键词： Databases Handwriting recognition Character recognition Handheld computers Benchmark testing Text recognition

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A density based method for automatic hairstyle discovery and recognition

A density based method for automatic hairstyle discovery and...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Jyotikrishna Dass Monika Sharma Ehtesham Hassan Hiranmay Ghosh Electronics and Communication Engineering Indian Institute of Technology Guwahati India TCS Innovation Labs Delhi TATA Consultancy Services Limited Gurgaon India

This paper presents a novel method for discovery and recognition of hairstyles in a collection of colored face images. We propose the use of Agglomerative clustering for automatic discovery of distinct hairstyles. Our method proposes automated approach for generation of hair, background and face-skin probability-masks for different hairstyle category without requiring manual annotation. The probability-masks based density estimates are subsequently applied for recognizing the hairstyle in a new face image. The proposed methodology has been verified with a synthetic dataset of approximately thousand images, randomly collected from the Internet.

关键词： Hair Face image recognition Nose Face recognition image segmentation Internet

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Tracking of retinal microsurgery tools using late fusion of responses from convolutional neural network over pyramidally decomposed frames 10

Tracking of retinal microsurgery tools using late fusion of ...

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

作者： Mishra, Kaustuv Sathish, Rachana Sheet, Debdoot Indian Institute of Technology Kharagpur Kharagpur India

computer vision and robotic assistance are increasingly being used to improve the quality of surgical interventions. Tool tracking becomes critical in interventions viz. endoscopy, laparoscopy and retinal microsurgery (RM) where unlike open surgery the surgeons do not have direct visual and physical access to the surgical site. RM is performed using miniaturized tools and requires careful observation through a surgical microscope by the surgeon. Tracking of surgical tools primarily provides robotic assistance during surgery and also serves as a means to assess the quality of surgery, which is extremely useful during surgical training. In this paper we propose a deep learning based visual tracking of surgical tool using late fusion of responses from convolutional neural network (CNN) which comprises of 3 steps: (i) training of CNN for localizing the tool tip on a frame (ii) coarsely estimating the tool tip region using the trained CNN and (iii) a finer search around the estimated region to accurately localize the tool tip. Scale invariant tracking of tool is ensured by incorporating multi-scale late fusion where the CNN responses are obtained at each level of the Gaussian scale decomposition pyramid. Performance of the proposed method is experimentally validated on the publicly available retinal microscopy instrument tracking (RMIT) dataset (https://***. com/site/sznitr/code-and-datasets). Our method tracks tools with a maximum accuracy of 99.13% which substantiates the efficacy of the proposed method in comparison to existing approaches. © Springer International Publishing AG 2017.

关键词： Convolution

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