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How much zoom is the right zoom from the perspective of Super-Resolution?

How much zoom is the right zoom from the perspective of Supe...

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6th indian conference on computer vision, graphics and image processing

作者： Arora, Himanshu Namboodiri, Anoop M. IIIT Ctr Visual Informat Technol Hyderabad 500032 Andhra Pradesh India

ISBN: (纸本)9781424442195

Constructing a high-resolution (HR) image from low-resolution (LR) image(s) has been a very active research topic recently with focus shifting from multi-frames to learning based single-frame super-resolution (SR). Multi-frame SR algorithms attempt the exact reconstruction of reality, but are limited to small magnification factors. Learning based SR algorithms learn the correspondences between. LR and HR patches. Accurate replacements or revealing the exact underlying information is not guaranteed in many scenarios. In this paper we propose an alternate solution. We propose to capture images at right zoom such that it has just sufficient amount of information so that further resolution enhancements can be easily achieved using an v off the shelf single-frame SR algorithm. this is true under the assumption that such a zoom factor is not very high, which is true for most man-made structures. the low-resolution. image is divided into small patches and ideal resolution is predicted for every patch. the contextual information is incorporated using a Markov Random Field based prior. Training data is generated from high-quality images and can use any single-frame SR algorithm. Several constraints are proposed to minimize the extent of zoom-in. We validate the proposed approach on synthetic data and real world images to show the robustness.

关键词： image enhancement

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Scalable Clustering and Applications 16

Scalable Clustering and Applications

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

作者： Shahid, K., I Chaudhury, Santanu Indian Inst Technol Delhi Dept Elect Engn Delhi India Inst Pilani Cent Elect Engn Res Pilani Rajasthan India

ISBN: (纸本)9781450347532

Laprge scale machine learning is becoming an active research area recently. Most of the existing clustering algorithms cannot handle big data due to its high time and space complexity. Among the clustering algorithms, eigen vector based clustering, such as Spectral clustering, shows very good accuracy, but it has cubic time complexity. there are various methods proposed to reduce the time and space complexity for eigen decomposition such as Nystrom method, Lanczos method etc. Nystrom method has linear time complexity in terms of number of data points, but has cubic time complexity in terms of number of sampling points. To reduce this, various Rank k approximation methods also proposed, but which are less efficient compare to the normalized spectral clustering. In this paper we propose a two step algorithm for spectral clustering to reduce the time complexity to O (nmk + m(2) k'), by combining both Nystrom and Lanczos method, where k is the number of clusters and k' is the rank k approximation of the sampling matrix (k < k' << m << n). It shows very good results, with various data sets, image segmentation problems and churn prediction of a telecommunication data set, even with very low sampling (for 10 Million x 10 Million matrix, sampled only 100 columns) with lesser time, which confirms the validity of the algorithm.

关键词： Large scale machine learning Segmentation and grouping

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Robust Registration of Mouse Brain Slices with Severe Histological Artifacts 16

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

引用

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

Sparse Signal Recovery for Multiple Measurement Vectors with...

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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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Segmentation Technique based on Information Redundancy Minimization 12

Segmentation Technique based on Information Redundancy Minim...

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12th International Joint conference on computer vision, Imaging and computer graphics theory and Applications (VISIGRAPP)

作者： Murashov, Dmitry RAS Fed Res Ctr Comp Sci & Control Vavilov St 40 Moscow 119333 Russia

ISBN: (纸本)9789897582257

In this paper, a problem of image segmentation quality is considered. the problem of segmentation quality is viewed as selecting the best segmentation from a set of images generated by segmentation algorithm at different parameter values. We use superpixel algorithm SLIC supplemented with the simple post-processing procedure for generating a set of partitioned images with different number of segments. A technique for selecting the best segmented image is proposed. We propose to use information redundancy measure as a criterion for optimizing segmentation quality. It is shown that proposed method for constructing the redundancy measure provides it with extremal properties. Computing experiment was conducted using the images from the Berkeley Segmentation Dataset. the experiment confirmed that the segmented image corresponding to a minimum of redundancy measure produces the suitable dissimilarity when compared with the original image. the segmented image that was selected using the proposed criterion, gives the highest similarity with the ground- truth segmentations, available in the database.

关键词： image Segmentation Segmentation Quality Redundancy Measure Superpixel

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vision-based Steering Angle Prediction by the Fusion of Depth and Intensity Deep Features 11

Vision-based Steering Angle Prediction by the Fusion of Dept...

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

作者： John, Vijay Rathour, Swarn Singh Mita, Seiichi Tehrani, Hossein Ishimaru, Kazuhisa Konishi, Masataka Chin, Hakusho Toyota Technol Inst Nagoya Aichi Japan DENSO CORP Kariya Aichi Japan SOKEN INC Tokyo Japan

ISBN: (纸本)9781450366151

In monocular camera-based end-to-end driving, the vehicle driving parameters such as steering angle, speed etc are directly estimated from the camera using deep learning. On the other hand, in traditional autonomous driving, these parameters are estimated using multiple modules such as sensing, behaviour generation, path planning and control. Owing to its ability to directly estimate the driving parameters, the end-to-end driving framework has received significant attention from the research community. In this paper, we present a novel stereo-based deep learning framework for end-to-driving, where the depth and appearance information generated using the stereo camera, are integrated to improve the steering angle prediction accuracy, especially for varying illumination conditions. Validation of the proposed algorithm is performed using multiple sequences of pre-defined driving routes with an expert driver. Each pre-defined driving route is acquired over multiple days with varying illumination conditions. Utilizing the acquired dataset, we show that the steering angle prediction accuracy of stereo-based end-to-end driving is better than monocular camera-based end-to-end driving.

关键词： Stereo vision End-to-End driving

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On-Demand Augmentation of Contour Trees 11

On-Demand Augmentation of Contour Trees

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

作者： Sharma, Mohit Natarajan, Vijay Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9781450366151

the contour tree represents the topology of level sets of a scalar function. Nodes of the tree correspond to critical level sets and arcs of the tree represent a collection of topologically equivalent level sets connecting two critical level sets. the augmented contour tree contains degree-2 nodes on the arcs that represent regular level sets. the degree-2 nodes correspond to regular points of the scalar function and other critical points that do not affect the number of level set components. the augmented contour tree is significantly larger in size and requires more effort to compute when compared to the contour tree. Applications of the contour tree to data exploration and visualization require the augmented contour tree. Current approaches propose algorithms to compute the contour tree and the augmented contour tree from scratch. Precomputing and storing the large augmented contour tree will not be necessary if the contour tree can be augmented on-demand. this paper poses the problem of computing the augmented contour tree given a contour tree as input. Computational experiments demonstrate that the on-demand augmentation can be computed fast while resulting in good memory savings.

关键词： Data visualization

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LEDNet: Deep Network for Single image Haze Removal 11

LEDNet: Deep Network for Single Image Haze Removal

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

作者： Dudhane, Akshay Murala, Subrahmanyam Dhall, Abhinav Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar India Indian Inst Technol Ropar Learning Affect & Semant Image Anal Grp Rupnagar India

ISBN: (纸本)9781450366151

Haze during the bad weather, degrades the visibility of the scene drastically. Degradation of scene visibility varies with respect to the transmission coefficient/map (T-c) of the scene. Estimation of accurate T-c is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the T-c. We, on the other hand, propose integration of local and global approaches to learn both point level and object level T-c. the proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level T-c using parallel convolutional filters and spatial invariance filtering. the second stage comprises of a two level encoder-decoder architecture which anticipates the object level T-c. We also propose, local air-light estimation (LAE) algorithm, which is able to obtain the air-light component of the outdoor scene. Combination of LEDNet and LAE improves the accuracy of haze model to recover the scene radiance. Structural similarity index, mean square error and peak signal to noise ratio are used to evaluate the performance of the proposed approach for single image haze removal. Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal.

关键词： Haze removal CNN transmission map

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SegFast : A Faster SqueezeNet based Semantic image Segmentation Technique using Depth-wise Separable Convolutions 11

SegFast : A Faster SqueezeNet based Semantic Image Segmentat...

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

作者： Pal, Anisha Jaiswal, Shourya Ghosh, Swarnendu Das, Nibaran Nasipuri, Mita Manipal Inst Technol Dept Informat & Commun Technol Manipal Karnataka India Manipal Inst Technol Dept Elect & Commun Engn Manipal Karnataka India Jadavpur Univ Dept Comp Sci & Engn Kolkata W Bengal India

ISBN: (纸本)9781450366151

Recent trends in image segmentation algorithms have shown various large scale networks with impressive performance for natural scene images. However most of the networks come with costly overheads such has large memory requirements or dependence on huge number of parallel processing units. In most cases costly graphics processing units or GPUs are used to boost computational capability. However for creating products in the real world we need to consider speed, performance as well as cost of deployment. We propose a novel "spark" module which is a combination of the "fire" module of SqueezeNet and depth-wise separable convolutions. Along with this modified SqueezeNet as an encoder we also propose the use of depth-wise separable transposed convolution for a decoder. the resultant encoder-decoder network has approximately 49 times lesser number of parameters than SegNet and almost 223 times lesser number of parameters than fully convolutional networks(FCN). Even in a CPU the network completes a forward pass for a single sample in approximately 0.39 seconds which is almost 5.1 times faster as compared to SegNet and almost 8.7 times faster compared to FCN.

关键词： Deep Learning image Segmentation Squeeze Net Depthwise separable convolution

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Objects from Animacy: Discovery in Joint Shape and Haar Feature Space

Objects from Animacy: Discovery in Joint Shape and Haar Feat...

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6th indian conference on computer vision, graphics and image processing

作者： Nandi, Sudipto Guha, Prithwijit Venkatesh, K. S. Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781424442195

We propose that appearance descriptors derived from the complete animacy of an object during its scene presence more comprehensively capture the essence of an object than descriptors that merely encode uncorrelated sets of its instantaneous appearances. During its frame presence, an object presents itself in many poses with differing frequencies, thus generating multiple modes of varying strengths in the appearance feature space. Further we utilize tracking information to extract the set of all appearances of the object, while excluding those intervals where the object is partly or fully occluded by other objects or back-ground entities. this allows for completely unsupervised computation of the descriptors that consist of time-indexed vectors from shape and Haar feature templates which are then clustered to obtain appearance modes. these lead to the construction of object-animacy models as probability distributions over the space of co-occurrent shape and Haar templates. these object models are clustered further in an unsupervised manner by using different spatial clustering algorithms with a Bhattacharya distance metric between object models. Unsupervised categorization results on simple (PETS2000) and complex traffic scenes consisting of a wide variety of objects show robust performance of the proposed approach.

关键词： Clustering algorithms

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