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MODEL-BASED image-CODING

MODEL-BASED IMAGE-CODING

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conference on Visual Communications and image processing 94

作者： AIZAWA, K UNIV TOKYO DEPT ELECT ENGNBUNKYO KUTOKYO 113JAPAN

ISBN: (纸本)081941638X

Historically. progress in image coding tecliniqnes has been made by incorporating resnits from other fields SUCh as information theory. Most of the existing coding methods such as predictive coding. transform coding, and vector quantization belong to information-theory-based methods in which image signals are considered as ranidoni signals aiid compressed by exploiting their stochastic properties. Apart from these iiiformation-tlieoretic coding iniethods, research on new approaches such as modelbased coding (which is related to 1)0th image analysis and computer graphics) has recently intensified. An essential difference between conventional coding methods and these new approaches is the image model they assume. Contrary to conventional coding methods which efficiently represent 2-D waveforms of image signals model-based co ding represent iniage signals using structural image models which take into accomit the 3-D properties of the scene in some sense. there have been two major approaches to model-based image coding. One approach uses general niodels such as planar patches and sniooth surfaces to represent the 3-D properties of the oI)jects [11, 19, 28. 31, 33. 36, 39. 40, 43]. the other approach utilizes detailed 1)arameterized 3-D object models such as a parameterized facial model [2. 6, 13. 15. 17, 22. 24. 2G. 32, 35. 41. 47, 48. 49. 51. 54]. TIne former approach has been discussed in the context of advanced motion compensation. the latter approach is clearly different from the conventional approaches. since it uses an explicit 3—D model and encodes images with coniputer—vision— Oiieiite(il tecliniques an(I 1eJ)1()(luces iniages with colnj)uter—grapllics—oriented techniques . Both approaches will potentially achieves very low l)it rate video compression. Iii the following sectionis. unage co(ling schemes are described froni the point of view of their associated image IIli)delS. Amoiig our works related to these paradignins in the Univ. of Tokyo [2. 3, 4. 8, 9, 10,

关键词： 3D modeling Motion models image compression Model-based design 3D image processing Data modeling image analysis Motion estimation Solid modeling Systems modeling

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Real-Time Face Pose Estimation from Single Range images

Real-Time Face Pose Estimation from Single Range Images

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26th IEEE conference on computer vision and Pattern Recognition (CVPR 2008), vol.12

作者： Michael D. Breitenstein Daniel Kuettel thibaut Weise Luc van Gool Hanspeter Pfister Computer Vision Laboratory ETH Zurich Switzerland School of Engineering and Applied Sciences Harvard University Cambridge MA USA

We present a real-time algorithm to estimate the 3D pose of a previously unseen face from a single range image. Based on a novel shape signature to identify noses in range images, we generate candidates for their positions, and then generate and evaluate many pose hypotheses in parallel using modern graphics processing units (GPUs). We developed a novel error function that compares the input range image to precomputed pose images of an average face model. the algorithm is robust to large pose variations of ±90° yaw, ±45° pitch and ±30° roll rotation, facial expression, partial occlusion, and works for multiple faces in the field of view. It correctly estimates 97.8% of the poses within yaw and pitch error of 15° at 55.8 fps. To evaluate the algorithm, we built a database of range images with large pose variations and developed a method for automatic ground truth annotation.

关键词： Face detection Robustness Nose graphics Runtime computer vision Laboratories Shape image generation Error correction

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Scene Adaptive Cosmetic Makeup Transfer 18

Scene Adaptive Cosmetic Makeup Transfer

引用

Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Rishubh Parihar Aniket Dashpute Prem Kalra Indian Institute of Technology Delhi Hauz Khas New Delhi

ISBN: (纸本)9781450366151

Cosmetic makeup is an art in itself which people use to often enhance their beauty and express themselves. Putting on makeup is a tedious task, so who would not love to see themselves in makeup before physically applying it? In this work, we demonstrate the transfer of makeup from a reference makeup image to some subject image. Our technique involves generation and use of 3D face models of these images. Some nice features of our pipeline include: (1) process adapts to the lighting conditions of the subject image. (2) after makeup on subject image, it is possible to view it in different indoor and outdoor lighting conditions. (3) accessories can be added realistically with proper lighting so that they naturally fit in the subject's *** show that these also make our makeup look very realistic and improved compared to many existing *** main advantage of our novel pipeline is the requirement of only the subject and reference images as input. this is unlike other techniques using 3D models which require extensive data collection.

关键词： computer graphics

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Dynamic Gesture Recognition with Pose-based CNN Features derived from videos using LSTM 18

Dynamic Gesture Recognition with Pose-based CNN Features der...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Kankana Roy Rajiv R. Sahay Computational Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Computational Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Kharagpur

ISBN: (纸本)9781450366151

In this work, we address the problem of dynamic gesture recognition using a pose based video descriptor. the proposed approach takes as input video frames and extracts pose-specific image regions which are further processed by a pre-trained Convolutional Neural Network (CNN) to derive a pose-based descriptor for each frame. A Long Short Term Memory (LSTM) network is trained from scratch for dynamic gesture classification by learning long-term spatiotemporal relations among features. We also demonstrate that only using video data (RGB frames and optical flow) one can design an effective model for recognizing dynamic gestures. We utilize ChaLearn multi-modal gesture challenge dataset [13] and Cambridge hand gesture dataset [18] for evaluation of the proposed algorithm achieving an accuracy of 91.27% and 96% respectively using only RGB data.

关键词： Long short term memory

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HaraliCU: GPU-powered Haralick feature extraction on medical images exploiting the full dynamics of gray-scale levels 1

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15th International conference on Parallel Computing Technologies, PaCT 2019

作者： Rundo, Leonardo Tangherloni, Andrea Galimberti, Simone Cazzaniga, Paolo Woitek, Ramona Sala, Evis Nobile, Marco S. Mauri, Giancarlo Department of Radiology University of Cambridge Cambridge United Kingdom Cancer Research UK Cambridge Centre Cambridge United Kingdom Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Department of Haematology University of Cambridge Cambridge United Kingdom Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Hinxton United Kingdom Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute Cambridge United Kingdom Department of Human and Social Sciences University of Bergamo Bergamo Italy SYSBIO.IT Centre of Systems Biology Milano Italy Department of Biomedical Imaging and Image-guided Therapy Medical University Vienna Vienna Austria

ISBN: (数字)9783030256364

ISBN: (纸本)9783030256357

image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance since they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that can have a significant impact in the clinical practice. In this work, we focus on Haralick features, the most common and clinically relevant descriptors. these features are based on the Gray-Level Co-occurrence Matrix (GLCM), whose computation is considerably intensive on images characterized by a high bit-depth (e.g., 16 bits), as in the case of medical images that convey detailed visual information. We propose here HaraliCU, an efficient strategy for the computation of the GLCM and the extraction of an exhaustive set of the Haralick features. HaraliCU was conceived to exploit the parallel computation capabilities of modern graphics processing Units (GPUs), allowing us to achieve up to ~ 20× speed-up with respect to the corresponding C++ coded sequential version. Our GPU-powered solution highlights the promising capabilities of GPUs in the clinical research. © 2019, Springer Nature Switzerland AG.

关键词： Medical imaging

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Real-Time image Based Plant Phenotyping Using Tiny-YOLOv4 14th

Real-Time Image Based Plant Phenotyping Using Tiny-YOLOv4

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14th International conference on Intelligent Human computer Interaction, IHCI 2022

作者： Jain, Sonal Mahapatra, Dwarikanath Saini, Mukesh Indian Institute of Technology Ropar Rupnagar India Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031271984

image-based plant phenotyping is getting considerable attention with the advancement in computer vision technologies. In the past few years, the use of deep neural networks (DNNs) is well-known for segmentation and detection tasks. However, most DNN-based methods require high computational resources, thus making them unsuitable for real-time decision-making. this study presents a real-time plant phenotyping system using leaf counting and tracking individual leaf growth. For leaf localization and counting, a Tiny-YOLOv4 network is utilized, which provides faster processing, and is easily deployable on low-end hardware. Leaf growth tracking is performed by active contour segmentation of leaf localized using the Tiny-YOLOv4 network. the proposed system is implemented for top-view RGB images of the Arabidopsis thaliana’ plants. And its performance for leaf counting is evaluated against Tiny-YOLOv3 and Faster R-CNN using the difference in count (DiC), accuracy, and F1-score measures. the model achieves an improved accuracy of 90%, absolute DiC of 0.42, F1-score of 96%, and inference time of 15 milliseconds. Further, the segmentation accuracy measures using Dice and Jaccard scores are 0.91 and 0.86, with a computing time of 0.96 s. these obtained results depict the effectiveness of the proposed system for real-time plant phenotyping. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Multimodal Egocentric Activity Recognition Using Multi-stream CNN 2018

Multimodal Egocentric Activity Recognition Using Multi-strea...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Javed Imran Balasubramanian Raman Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9781450366151

Egocentric activity recognition (EAR) is an emerging area in the field of computer vision research. Motivated by the current success of Convolutional Neural Network (CNN), we propose a multi-stream CNN for multimodal egocentric activity recognition using visual (RGB videos) and sensor stream (accelerometer, gyroscope, etc.). In order to effectively capture the spatio-temporal information contained in RGB videos, two types of modalities are extracted from visual data: Approximate Dynamic image (ADI) and Stacked Difference image (SDI). these image-based representations are generated both at clip level as well as entire video level, and are then utilized to finetune a pretrained 2D-CNN called MobileNet, which is specifically designed for mobile vision applications. Similarly for sensor data, each training sample is divided into three segments, and a deep 1D-CNN network is trained (corresponding to each type of sensor stream) from scratch. During testing, the softmax scores of all the streams (visual + sensor) are combined by late fusion. the experiments performed on multimodal egocentric activity dataset demonstrates that our proposed approach can achieve state-of-the-art results, outperforming the current best handcrafted and deep learning based techniques.

关键词： Stacked Difference image Dynamic image Egocentric Activity Recognition Convolutional Neural Network Multimodal Fusion

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A Bottom-Up and Top-Down Approach for image Captioning using Transformer 18

A Bottom-Up and Top-Down Approach for Image Captioning using...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Sandeep Narayan Parameswaran Sukhendu Das Indian Institute of Technology Madras Chennai Tamil Nadu India

ISBN: (纸本)9781450366151

Describing the contents of an image automatically has been a fundamental problem in the field of artificial intelligence and computer vision. Existing approaches are either top-down, which start from a simple representation of an image and convert it into a textual description; or bottom-up, which come up with attributes describing numerous aspects of an image to form the caption or a combination of both. Recurrent neural networks (RNN) enhanced by Long Short-Term Memory networks (LSTM) have become a dominant component of several frameworks designed for solving the image captioning task. Despite their ability to reduce the vanishing gradient problem, and capture dependencies, they are inherently sequential across time. In this work, we propose two novel approaches, a top-down and a bottom-up approach independently, which dispenses the recurrence entirely by incorporating the use of a Transformer, a network architecture for generating sequences relying entirely on the mechanism of attention. Adaptive positional encodings for the spatial locations in an image and a new regularization cost during training is introduced. the ability of our model to focus on salient regions in the image automatically is demonstrated visually. Experimental evaluation of the proposed architecture on the MS-COCO dataset is performed to exhibit the superiority of our method.

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Real-time Simulation of Human Musculature 2018

Real-time Simulation of Human Musculature

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Neeladrishekhar Kanjilal Parag Chaudhuri Dept. of Computer Science & Engg. IIT Bombay

ISBN: (纸本)9781450366151

We present a reduced model based on position based dynamics for real-time simulation of human musculature. We demonstrate our methods on the muscles of the human arm. Co-simulation of all the muscles of the human arm allow us to accurately track the development of stresses and strains in the muscles, when the arm is moved. We evaluate our method for accuracy by comparing it with gold standard simulation models based on finite volume methods, and demonstrate the stability of the method under flexion, extension and torsion.

关键词： Human muscle simulation position based dynamics shape matching

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Design and Implementation of Blind Assistance System Using Real Time Stereo vision Algorithms

Design and Implementation of Blind Assistance System Using R...

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International conference on VLSI Design

作者： Vaddi Chandra Sekhar Satyajit Bora Monalisa Das Pavan Kumar Manchi S. Josephine Roy Paily Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati

the main objective of this paper aims at designing an efficient blind assistance system for the visually impaired people using real time disparity estimation algorithm. the local window based matching algorithms known as sum of absolute differences (SAD) and zero-mean SAD (ZSAD) are used for the disparity estimation and efficient hardware architectures for those algorithms are implemented in FPGA. the SAD and ZSAD algorithms are implemented for the image resolution of 640x360 pixels with square window of size 8x8 pixels and with a disparity range of 0 to 99. An efficient line buffering scheme for the left and right window of the two camera images is implemented to support the parallel processing mechanism. To provide high frame rate per second (fps) to the artificial vision system, parallel processing architecture for disparity estimation is required, but it consumes huge amount of hardware resources which is not desirable for this application. So, semi-parallel architectures are implemented for SAD and ZSAD algorithms for a compromise between hardware resource utilization and speed. the FPGA resource utilization for the ZSAD and SAD algorithms for a frame rate of 30 fps are 43529 LUTs, 50144 FFs and 34548 LUTs, 37544 FFs respectively. ZSAD algorithm consumes around 30% more hardware resources compared to SAD, but to counter the randomized distortion caused by the non-ideal stereo cameras ZSAD is more preferred. Finally from the disparity, the distance of the nearest obstacle is estimated and blind person is alerted through audio device instructions.

关键词： Hardware Estimation Cameras computer architecture Distortion Real-time systems Radiometry

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