In Digital Subtraction Angiography (DSA), non-rigid registration of the mask and contrast images to reduce the motion artifacts is a challenging problem. In this paper, we have proposed a novel stratified registration...
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ISBN:
(纸本)9781450347532
In Digital Subtraction Angiography (DSA), non-rigid registration of the mask and contrast images to reduce the motion artifacts is a challenging problem. In this paper, we have proposed a novel stratified registration framework for DSA artifact reduction. We use quad-trees to generate the non-uniform grid of control points and obtain the sub-pixel displacement offsets using Random Walker (RW). We have also proposed a sequencing logic for the control points and an incremental LU decomposition approach that enables reuse of the computations in the RW step. We have tested our approach using clinical data sets, and found that our registration framework has performed comparable to the graph-cuts (at the same partition level), in regions wherein 95% artifact reduction was achieved. The optimization step achieves a speed improvement of 4.2 times with respect to graph-cuts.
Visualizing 2-D/3-D embeddings of image features can help gain an intuitive understanding of the image category landscape. However, popular visualization methods of visualizing such embeddings (e.g. color-coding by ca...
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ISBN:
(纸本)9781450347532
Visualizing 2-D/3-D embeddings of image features can help gain an intuitive understanding of the image category landscape. However, popular visualization methods of visualizing such embeddings (e.g. color-coding by category) are impractical when the number of categories is large. To address this and other shortcomings, we propose novel quantitative measures defined on image feature embeddings. Each measure produces a ranked ordering of the categories and provides an intuitive vantage point from which to view the entire set of categories. As an experimental testbed, we use deep features obtained from category-epitomes, a recently introduced minimalist visual representation, across 160 object categories. We embed the features in a visualization friendly yet similarity-preserving 2-D manifold and analyze the inter/intra-category distributions of these embeddings using the proposed measures. Our analysis demonstrates that the category ordering methods enable new insights for the domain of large-category object representations. Moreover, our ordering measure approach is general in nature and can be applied to any feature-based representation of categories.
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 quantif...
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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 computervision 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.
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 c...
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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.
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 r...
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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.
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...
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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.
In computervision, 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...
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ISBN:
(纸本)9781450347532
In computervision, 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.
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 surve...
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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.
Saliency computation is widely studied in computervision but not in medical imaging. Existing computational saliency models have been developed for general (natural) images and hence may not be suitable for medical i...
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ISBN:
(纸本)9781450347532
Saliency computation is widely studied in computervision but not in medical imaging. Existing computational saliency models have been developed for general (natural) images and hence may not be suitable for medical images. This is due to the variety of imaging modalities and the requirement of the models to capture not only normal but also deviations from normal anatomy. We present a biologically inspired model for colour fundus images and illustrate it for the case of diabetic retinopathy. The proposed model uses spatially varying morphological operations to enhance lesions locally and combines an ensemble of results, of such operations, to generate the saliency map. The model is validated against an average Human Gaze map of 15 experts and found to have 10% higher recall (at 100% precision) than four leading saliency models proposed for natural images. The F-score for match with manual lesion markings by 5 experts was 0.4 (as opposed to 0.532 for gaze map) for our model and very poor for existing models. The model's utility is shown via a novel enhancement method which employs saliency to selectively enhance the abnormal regions and this was found to boost their contrast to noise ratio by similar to 30%.
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