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Adaptive Artistic Stylization of images 16

Adaptive Artistic Stylization of Images

引用

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

作者： Deshpande, Ameya Raman, Shanmuganathan IIT Gandhinagar Gandhinagar Gujarat India

ISBN: (纸本)9781450347532

In this work, we present a novel non-photorealistic rendering method which produces good quality stylization results for color images. the procedure is driven by saliency measure in the foreground and the background region. We start with generating saliency map and simple thresholding based segmentation to get rough estimation of the foreground background mask. We improve this mask by using a scribble based method where the scribbles for foreground-background regions are automatically generated from the previous rough estimation. Followed by the mask generation, we proceed with an iterative abstraction process which involves edge preserving blurring and edge detection. the number of iterations of the abstraction process to be performed in the foreground and background regions are decided by tracking the changes in saliency measure in the foreground and the background regions. Performing unequal number of iterations helps to improve the average saliency measure in more salient region (foreground) while decreasing the average saliency measure in the non-salient region (background). Implementation results of our method shows the merits of this approach with other competing methods.

关键词： Non-photorealistic rendering Saliency image Abstraction Guided Filter

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Towards Quantifying the Amount of Uncollected Garbage through image Analysis 16

Towards Quantifying the Amount of Uncollected Garbage throug...

引用

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

作者： Suresh, Susheel Sharma, Tarun Prashanth, T. K. Subramaniam, V Sitaram, Dinkar Nirupama, M. PES Inst Technol Dept Comp Sci Bengaluru 560085 India

ISBN: (纸本)9781450347532

Civic authorities in many indian cities have a tough time in garbage collection and as a result there is a pile up of garbage in the cities. In order to manage the situation, it is first required to be able to quantify the issue. In this paper, we address the problem of quantification of garbage in a dump using a two step approach. In the first step, we build a mobile application that allows citizens to capture images of garbage and upload them to a server. In the second step, back-end performs analysis on these images to estimate the amount of garbage using computer vision techniques. Our approach to volume estimation uses multiple images of the same dump (provided by the mobile application) from different perspectives, segments the dump from the background, reconstructs a three dimensional view of the dump and then estimates its volume. Using our novel pipeline, our experiments indicate that with 8 different perspectives, we are able to achieve an accuracy of about 85 % for estimating the volume.

关键词： Automatic Segmentation and 3D Reconstruction Segmentation using Deep Learning Structure from Motion Volume Estimation of Garbage Dumps

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A Context Dependent Fragile Watermarking Scheme for Tamper Detection from Demosaicked Color images 16

A Context Dependent Fragile Watermarking Scheme for Tamper D...

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

作者： Manikandan, V. M. Masilamani, V. Indian Inst Informat Technol Design & Mfg Kanchee Madras 600127 Tamil Nadu India

ISBN: (纸本)9781450347532

Fragile watermarking schemes are very common in practice for tamper detection and image authentication. In this paper, we propose a new fragile watermarking scheme to detect common image tampering operations such as copy and paste attack and image splicing attack from color images. the proposed scheme is intended to do the watermarking with an image dependent authentication code during color image demosaicking procedure. We propose a new method to generate almost unique authentication code from an image by comparing RGB color components of the pixels. In the proposed scheme, every pixel will be watermarked with an encrypted authentication code derived from the same pixel. To embed the watermark a new method has been proposed in this paper, where the color filter array sampled components will not be considered during watermarking, and only the rebuilt color component values of every pixel will be modified according to the authentication code. the experimental study shows that the proposed watermarking scheme produces images with better visual quality as compared to the state-of-the-art, and is capable of detecting both copy and paste attacks and image splicing attacks more accurately than the existing schemes.

关键词： fragile watermarking image demosaicking tamper detection authentication code

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A Large Scale Dataset For Classification of Vehicles in Urban Traffic Scenes 16

A Large Scale Dataset For Classification of Vehicles in Urba...

引用

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

作者： Bharadwaj, Harish S. Biswas, Soma Ramakrishnan, K. R. Indian Inst Sci Dept Elect Engn Bengaluru India

ISBN: (纸本)9781450347532

Vehicle Classification has been a well-researched topic in the recent past. However, advances in the field have not been corroborated with deployment in Intelligent Traffic Management, due to non-availability of surveillance quality visual data of vehicles in urban traffic junctions. In this paper, we present a dataset aimed at exploring Vehicle Classification and related problems in dense, urban traffic scenarios. We present our on-going effort of collecting a large scale, surveillance quality, dataset of vehicles seen mostly on indian roads. the dataset is an extensive collection of vehicles under different poses, scales and illumination conditions in addition to a smaller set of Near Infrared spectrum images for night time and low light traffic surveillance. We will make the dataset available for further research in this area. We propose and evaluate few baseline algorithms for the task of vehicle classification on this dataset. We also discuss challenges and potential applications of the data.

关键词： Vehicle Classification Urban Traffic Junction NIR images Convolutional Neural Networks

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Detection and Segmentation of Mirror-like Surfaces Using Structured Illumination 16

Detection and Segmentation of Mirror-like Surfaces Using Str...

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

作者： Aggarwal, Rajat Namboodiri, Anoop M. Int Inst Informat Technol Hyderabad Kohli Ctr Intelligent Syst Hyderabad Andhra Pradesh India

ISBN: (纸本)9781450347532

In computer vision, many active illumination techniques employ Projector-Camera systems to extract useful information from the scenes. Known illumination patterns are projected onto the scene and their deformations in the captured images are then analyzed. We observe that the local frequencies in the captured pattern for the mirror-like surfaces is different from the projected pattern. this property allows us to design a custom Projector-Camera system to segment mirror-like surfaces by analyzing the local frequencies in the captured images. the system projects a sinusoidal pattern and capture the images from projector's point of view. We present segmentation results for the scenes including multiple reflections and inter-reflections from the mirror-like surfaces. the method can further be used in the separation of direct and global components for the mirror-like surfaces by illuminating the non-mirror-like objects separately. We show how our method is also useful for accurate estimation of shape of the non-mirror-like regions in the presence of mirror-like regions in a scene.

关键词： Structured Illumination Mirror-like surfaces Segmentation Projector- Camera

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Depth Estimation from Single image using Machine Learning Techniques 16

Depth Estimation from Single Image using Machine Learning Te...

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

作者： Chahal, Nidhi Pippal, Meghna Chaudhury, Santanu Indian Inst Technol Delhi India Cent Elect Engn Res Inst Pilani Pilani Rajasthan India

ISBN: (纸本)9781450347532

In this paper, the problem of depth estimation from single monocular image is considered. the depth cues such as motion, stereo correspondences are not present in single image which makes the task more challenging. We propose a machine learning based approach for extracting depth information from single image. the deep learning is used for extracting features, then, initial depths are generated using manifold learning in which neighborhood preserving embedding algorithm is used. then, fixed point supervised learning is applied for sequential labeling to obtain more consistent and accurate depth maps. the features used are initial depths obtained from manifold learning and various image based features including texture, color and edges which provide useful information about depth. A fixed point contraction mapping function is generated using which depth map is predicted for new structured input image. the transfer learning approach is also used for improvement in learning in a new task through the transfer of knowledge from a related task that has already been learned. the predicted depth maps are reliable, accurate and very close to ground truth depths which is validated using objective measures: RMSE, PSNR, SSIM and subjective measure: MOS score.

关键词： Monocular manifold sequential labeling

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Automated Brain Tractography Segmentation using Curvature Points 16

Automated Brain Tractography Segmentation using Curvature Po...

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

作者： Patel, Vedang Parmar, Anand Bhavsar, Arnav Nigam, Aditya BITS Pilani Hyderabad Dept Comp Sci Hyderabad Andhra Prades India Indian Inst Technol Mandi Sch Comp & Elect Engn Mandi India

ISBN: (纸本)9781450347532

Classification of brain fiber tracts is an important problem in brain tractography analysis. We propose a supervised algorithm which learns features for anatomically meaningful fiber clusters, from labeled DTI white matter data. the classification is performed at two levels: a) Grey vs White matter (macro level) and b) White matter clusters (micro level). Our approach focuses on high curvature points in the fiber tracts, which embodies the unique characteristics of the respective classes. Any test fiber is classified into one of these learned classes by comparing proximity using the learned curvature-point model (for micro level) and with a neural network classifier (at macro level). the proposed algorithm has been validated with brain DTI data for three subjects containing about 2,50,000 fibers per subject, and is shown to yield high classification accuracy (> 93%) at both macro and micro levels.

关键词： DTI Tractography Fiber Grey matter White matter Segmentation

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Reinforced Random Forest 16

Reinforced Random Forest

引用

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

作者： Paul, Angshuman Mukherjee, Dipti Prasad Indian Stat Inst 203 BT Rd Kolkata India

ISBN: (纸本)9781450347532

Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy. Our algorithm is initialized with a forest. then the entire training data is tested using the initial forest. In order to reinforce learning, we use mis-classified data points to grow certain number of new trees. A subset of the new trees is added to the existing forest using a novel graph-based approach. We show that addition of these trees ensures improvement in classification accuracy. this process is continued iteratively until classification accuracy saturates. the proposed RRF has low computational burden. We achieve at least 3% improvement in F-measure compared to random forest in three breast cancer datasets. Results on benchmark datasets show significant reduction in average classification error.

关键词： Random forest reinforcement learning classification accuracy shortest path

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Data-driven 2D Effects Animation 16

Data-driven 2D Effects Animation

引用

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

作者： Grover, Divyanshu Chaudhuri, Parag Indian Inst Technol Dept Comp Sci & Engn Bombay 400076 Maharashtra India

ISBN: (纸本)9781450347532

Making plausible, high quality visual effects, like water splashes or fire, in traditional 2D animation pipelines require an animator to draw many frames of phenomena that are very difficult to recreate manually. We present a technique that uses a database of video clips of such phenomena to assist the animator. the animator has to only input sample sketched frames of the phenomena at particular time instants. these are matched to frames of the video clips and a plausible sequence of frames is generated from these clips that contain the animator drawn frames as constraints. the colour style of the hand-drawn frames is used to render the generated frames, thus resulting in a 2D animation that follows the style and intent of the 2D animator. Our system can also create multi-layered effects animation, allowing the animator to draw interacting mixed phenomena, like water being poured on fire.

关键词： non-photorealistic animation non-photorealistic rendering sketch-based special effects

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Intrinsic image Decomposition using Focal Stacks 16

Intrinsic Image Decomposition using Focal Stacks

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

作者： Saini, Saurabh Sakurikar, Parikshit Narayanan, P. J. IIIT Hyderabad Kohli Ctr Intelligent Syst Ctr Visual Informat Technol Hyderabad Telangana India

ISBN: (纸本)9781450347532

In this paper, we presents a novel method (RGBF-IID) for intrinsic image decomposition of a wild scene without any restrictions on the complexity, illumination or scale of the image. We use focal stacks of the scene as input. A focal stack captures a scene at varying focal distances. Since focus depends on distance to the object, this representation has information beyond an RGB image towards an RGBD image with depth. We call our representation an RGBF image to highlight this. We use a robust focus measure and generalized random walk algorithm to compute dense probability maps across the stack. these maps are used to de fine sparse local and global pixel neighbourhoods, adhering to the structure of the underlying 3D scene. We use these neighbourhood correspondences with standard chromaticity assumptions as constraints in an optimization system. We present our results on both indoor and outdoor scenes using manually captured stacks of random objects under natural as well as artificial lighting conditions. We also test our system on a larger dataset of synthetically generated focal stacks from NYUv2 and MPI Sintel datasets and show competitive performance against current state-of-the-art IID methods that use RGBD images. Our method provides a strong evidence for the potential of RGBF modality in place of RGBD in computer vision.

关键词： Intrinsic image decomposition focal stacks generalized random walk RGBF RGBF-IID

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