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GMM based single depth image super-resolution 6th

GMM based single depth image super-resolution

引用

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

作者： Balure, Chandra Shaker Kini, M. Ramesh Bhavsar, Arnav National Institute of Technology Karnataka Surathkal Mangalore India Indian Institute of Technology Mandi MandiH.P. India

ISBN: (纸本)9789811300196

Super-resolution (SR) is a technique to improve the resolution of an image from a sequence of input images or from a single image. As SR is an ill-posed inverse problem, it leads to many suboptimal solutions. Since modern depth cameras suffer from low-spatial resolution and are noisy, we present a Gaussian mixture model (GMM) based method for depth image super-resolution (SR). We train GMM from a set of high-resolution and low-resolution (HR-LR) synthetic training depth images to learn the relation between the HR and the LR patches in the form of covariance matrices. We use expectation-maximization (EM) algorithm to converge to an optimal solution. We show the promising results qualitatively and quantitatively in comparison to other depth image SR methods. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Covariance matrix

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Deep Automatic Licence Plate Recognition system 10

Deep Automatic Licence Plate Recognition system

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

作者： Jain, Vishal Sasindran, Zitha Rajagopal, Anoop Biswas, Soma Bharadwaj, Harish S. Ramakrishnan, K. R. Indian Inst Sci Bengaluru Karnataka India Myntra Designs Bengaluru Karnataka India

ISBN: (纸本)9781450347532

Automatic License Plate Recognition (ALPR.) has important applications in traffic surveillance. It is a challenging problem especially in countries like in India where the license plates have varying sizes, number of lines, fonts etc. the difficulty is all the more accentuated in traffic videos as the cameras are placed high and most plates appear skewed. this work aims to address ALPR. using Deep CNN methods for real-time traffic videos. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall. these proposals are fed to a CNN classifier for License Plate detection obtaining high precision. We then use a CNN classifier trained for individual characters along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic videos with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition. We also make available the dataset for further research on this topic.

关键词： Convolution Neural Network Spatial Transformer Network image Transformation Pursuit Automatic License Plate Recognition

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DP-GAN: Dual Pathway Generative Adversarial Network for Face Recognition in Degraded Scenarios 11

DP-GAN: Dual Pathway Generative Adversarial Network for Face...

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11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Bhattacharjee, Avishek Banerjee, Samik Das, Sukhendu Indian Inst Technol Madras Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781450366151

Face Recognition (FR) using Convolutional Neural Network (CNN) based models have achieved considerable success in constrained environments. they however fail to perform well in unconstrained scenarios, especially when the images are captured using surveillance cameras. these probe samples suffer from degradations such as noise, poor illumination, low resolution, blur as well as aliasing, when compared to the rich training (gallery) set, comprising mostly of mugshot images captured in laboratory settings. these images in the training (gallery) set are crisp and have high contrast, compared to the probe samples. To cope with this scenario, we propose a novel dual-pathway generative adversarial network (DP-GAN) which maps low resolution images captured using surveillance camera into their corresponding high resolution images, which are gallery-like, using a novel combination of multi-scale reconstruction and Jensen-Shannon divergence based loss. these images thus obtained are then used to train a deep domain adaptation (deep-DA) network to perform the task of FR. the proposed network achieves superior results (>90%) on four benchmark surveillance face datasets, evident from the rank-1 recognition rates when compared with recent state-of-the-art CNN-based techniques.

关键词： Face recognition

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Robust Registration of Mouse Brain Slices with Severe Histological Artifacts 16

Robust Registration of Mouse Brain Slices with Severe Histol...

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

作者： Agarwal, Nitin Xu, Xiangmin Gopi, M. Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Anat Neurobiol Irvine CA USA

ISBN: (纸本)9781450347532

Brain mapping research is facilitated by first aligning digital images of mouse brain slices to standardized atlas framework such as the Allen Reference Atlas (ARA). However, conventional processing of these brain slices introduces many histological artifacts such as tears and missing regions in the tissue, which make the automatic alignment process extremely challenging. We present an end-to-end fully automatic registration pipeline for alignment of digital images of mouse brain slices that may have histological artifacts, to a standardized atlas space. We use a geometric approach where we first align the bounding box of convex hulls of brain slice contours and atlas template contours, which are extracted using a variant of Canny edge detector. We then detect the artifacts using Constrained Delaunay Triangulation (CDT) and remove them from the contours before performing global alignment of points using iterative closest point (ICP). this is followed by a final non-linear registration by solving the Laplace's equation with Dirichlet boundary conditions. We tested our algorithm on 200 mouse brain slice images including slices acquired from conventional processing techniques having major histological artifacts, and from serial two-photon tomography (STPT) with no major artifacts. We show significant improvement over other registration techniques, both qualitatively and quantitatively, in all slices especially on slices with significant histological artifacts.

关键词： Non-Linear Registration image Artifacts Histology Mouse Brain Allen Reference Atlas Point Correspondences

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Autonomous vehicle control for lane and vehicle tracking by using deep learning via vision 6

Autonomous vehicle control for lane and vehicle tracking by ...

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6th International conference on Control Engineering and Information Technology, CEIT 2018

作者： Olgun, Masum Celil Baytar, Zakir Akpolat, Kadir Metin Koray Sahingoz, Ozgur Department of Computer Engineering Istanbul Kultur University Istanbul Turkey Computer Engineering Department Istanbul Kultur University Istanbul34158 Turkey

ISBN: (纸本)9781538676417

Camera-based lane detection and vehicle tracking algorithms are one of the keystones for many autonomous systems. the navigational process of those systems is mainly focused on the output of detection algorithms. However, detection algorithms for lane detection need more pre-processing time and computational effort. they are also affected by environmental conditions and must regularly be improved. In this paper machine learning techniques and computer vision algorithms are utilized for the tasks of the lane and vehicle tracking of an autonomous vehicle control scenario. With the nature of used learning algorithm, the proposed system can handle complex image problems. the vehicle, on which we implement our algorithms, can manage to carry out the following tasks autonomously;tracking the lanes, following another vehicle, and stopping in necessary conditions. For that, one of the primary purposes is image-based lane tracking methodology by using learning algorithms. Data augmentation is applied to create diversity for the dataset. Application in this methodology has been discussed. For lane tracking Convolutional Neural Network architecture which is based on NVIDIA's PilotNet is preferred. For detecting objects and vehicles, the system is trained on the faster region-based convolutional neural network (Faster R-CNN) to identify traffic light and stop sign are by Haar Cascade Classifier. All these learning models are trained on NVIDIA GTX 1070 graphics processing Unit (GPU) to reduce training time. Experimental results showed that the proposed system gives a favorable result to autonomously control vehicles for lane and vehicle tracking purposes by vision. © 2018 IEEE.

关键词： graphics processing unit

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image and Signal processing: 6th International conference, ICISP 2014, Cherbourg, France, June 20 -- July 2, 2014, Proceedings

Image and Signal Processing: 6th International Conference, I...

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2014年

作者： Abderrahim Elmoataz Olivier Lézoray Fathallah Nouboud

ISBN: (纸本)9783319079998

this book constitutes the refereed proceedings of the 6th International conference, ICISP 2014, held in June/July 2014 in Cherbourg, France. the 76 revised full papers were carefully reviewed and selected from 164 submissions. the contributions are organized in topical sections on multispectral colour science, color imaging and applications, digital cultural heritage, document image analysis, graph-based representations, image filtering and representation, computer vision and pattern recognition, computer graphics, biomedical, and signal processing.

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Single noisy image super resolution by minimizing nuclear norm in virtual sparse domain 6th

Single noisy image super resolution by minimizing nuclear no...

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

作者： Mandal, Srimanta Rajagopalan, A.N. Image Processing and Computer Vision Lab Department of Electrical Engineering IIT Madras Chennai600036 India

ISBN: (纸本)9789811300196

Super-resolving a noisy image is a challenging problem, and needs special care as compared to the conventional super resolution approaches, when the power of noise is unknown. In this scenario, we propose an approach to super-resolve single noisy image by minimizing nuclear norm in a virtual sparse domain that tunes with the power of noise via parameter learning. the approach minimizes nuclear norm to explore the inherent low-rank structure of visual data, and is further augmented with coarse-to-fine information by adaptively re-aligning the data along the principal components of a dictionary in virtual sparse domain. the experimental results demonstrate the robustness of our approach across different powers of noise. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Optical resolving power

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Saliency map improvement using edge-aware filtering 6th

Saliency map improvement using edge-aware filtering

引用

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

作者： Patel, Diptiben Raman, Shanmuganathan Electrical Engineering Indian Institute of Technology Gandhinagar GandhinagarGujarat382355 India

ISBN: (纸本)9789811300196

Content-aware applications in computational photography define the relative importance of objects or actions present in an image using a saliency map. Most saliency detection algorithms learn from the human visual system and try to find relatively important content as a salient region(s). this paper attempts to improve the saliency map defined by these algorithms using an iterative process. the saliency map of an image generated by an existing saliency detection algorithm is modified by filtering the image after segmenting into foreground and background. In order to enhance the saliency map values present in the salient region, the background is filtered using an edge-aware guided filter and the foreground is enhanced using a local Laplacian filter. the number of iterations required varies according to the image content. We show that the proposed framework enhances the saliency maps generated using the state-of-the-art saliency detection algorithms both qualitatively and quantitatively. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Iterative methods

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Sparse Signal Recovery for Multiple Measurement Vectors with Temporally Correlated Entries: A Bayesian Perspective 11

Sparse Signal Recovery for Multiple Measurement Vectors with...

引用

11th indian conference on computer vision, graphics and image processing (ICVGIP)

作者： Sharma, Shruti Chaudhury, Santanu Jayadeva, Jayadeva Bhagat, Snigdha Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9781450366151

Bayesian Sparse Signal Recovery (SSR) for Multiple Measurement Vectors, when elements of each row of solution matrix are correlated, is addressed in the paper. We propose a standard linear Gaussian observation model and a three-level hierarchical estimation framework, based on Gaussian Scale Mixture (GSM) model with some random and deterministic parameters, to model each row of the unknown solution matrix. this hierarchical model induces heavy-tailed marginal distribution over each row which encompasses several choices of distributions viz. Laplace distribution, Student's t distribution and Jeffery prior. Automatic Relevance Determination (ARD) phenomenon introduces sparsity in the model. It is interesting to see that Block Sparse Bayesian Learning framework is a special case of the proposed framework when induced marginal is Jeffrey prior. Experimental results for synthetic signals are provided to demonstrate its effectiveness. We also explore the possibility of using Multiple Measurement Vectors to model Dynamic Hand Posture Database which consists of sequence of temporally correlated hand posture sequence. It can be seen that by exploiting temporal correlation information present in the successive image samples, the proposed framework can reconstruct the data with less linear random measurements with high fidelity.

关键词： Matrix algebra

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image and Video Technology – PSIVT 2013 Workshops 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Fay Huang Akihiro Sugimoto

ISBN: (数字)9783642539268

ISBN: (纸本)9783642539251

this book constitutes the thoroughly refereed post-conference proceedings of the four workshops on Photographic Aesthetics and Non-Photorealistic Rendering (PAESNPR13), Geometric Properties from Incomplete Data (GPID), Quality Assessment and Control by image and Video Analysis (QACIVA) and Geometric Computation for computer vision (GCCV2013), held in conjunction with the 6th Pacific-Rim Symposium on image and Video Technology (PSIVT) in Guanajuato, Mexico during October 28-November 1, 2013. the 38 revised full papers presented were carefully selected from numerous submissions and cover all aspects of Imaging and graphics Hardware and Visualization, image/Video Coding and Transmission; processing and Analysis; Retrieval and Scene Understanding, but also Applications of image and Video Technology, Biomedical image processing and Analysis, Biometrics and image Forensics, Computational Photography and Arts, computer and Robot vision, Pattern Recognition and Video Surveillance.

关键词： image processing and computer vision Artificial Intelligence Pattern Recognition computer graphics Algorithm Analysis and Problem Complexity Information Systems Applications (incl. Internet)

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