In this paper, an uncompressed domain video watermarking scheme resilient to temporal adaptation is proposed for scalable video coding. In the proposed scheme, each temporal layer has been separately embedded with a d...
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ISBN:
(纸本)9781467385640
In this paper, an uncompressed domain video watermarking scheme resilient to temporal adaptation is proposed for scalable video coding. In the proposed scheme, each temporal layer has been separately embedded with a different watermark which is generated by DCT domain decomposition of a single watermark image. A zigzag sequence of block wise DCT coefficients of the watermark image is partitioned into non-overlapping sets and each set is embedded separately into different temporal layers. the base layer is embedded withthe first set of DCT coefficient (which includes DC coefficient of each block) and successive layers are embedded with successive nonoverlapping coefficient sets. the coefficients of each set is chosen in such a fashion that uniform energy distribution across all temporal layers can be maintained. Experimental results show that the proposed scheme is robust against temporal scalability and robustness of the watermark increases withthe addition of successive enhancement layers.
Conventionally, High Dynamic Range (HDR) images are generated by fusing multiple exposure Low Dynamic Range (LDR) images, where the HDR output often suffers from artifacts due to misalignment of camera and presence of...
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ISBN:
(纸本)9781467385640
Conventionally, High Dynamic Range (HDR) images are generated by fusing multiple exposure Low Dynamic Range (LDR) images, where the HDR output often suffers from artifacts due to misalignment of camera and presence of dynamic objects in the scene. An efficient approach to overcome these issues is to use single shot HDR imaging. In this paper, we propose a method for generating an HDR image from a single LDR image. We first generate multiple exposures of the given scene using histogram separation by adopting varying bin sizes. the resulting LDR images are fused making use of the quality measures such as contrast, saturation and well - exposedness. the results show the effectiveness of the proposed approach which is verified qualitatively and in terms of various quantitative measures.
High quality depth map estimation is required for better visualization of 3D views as there is great impact of depth map quality on overall 3D image quality. If the depth is estimated from conventional ways using two ...
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ISBN:
(纸本)9781479915880
High quality depth map estimation is required for better visualization of 3D views as there is great impact of depth map quality on overall 3D image quality. If the depth is estimated from conventional ways using two or more images, some defects come into picture, mostly in regions without texture. We utilised Microsoft Kinect RGBD dataset to obtain input color images and depth maps which also includes some noise factors. We proposed a method to remove this noise and get quality depthimages. First the color and depthimages are aligned to each other using intensity based image registration. this method of image alignment is mostly used in medical field, but we applied this technique to correct kinect depth maps by which one can avoid cumbersome task of feature based point correspondence between images. there is no requirement of preprocessing or segmentation steps if we use intensity based image alignment method. Second, we proposed an algorithm to fill the unwanted gaps in kinect depth maps and upsampled it using corresponding high resolution color image. Finally we applied 9x9 median filtering on implementation results and get high quality and improved depth maps.
this paper discusses a novel fast approach for moving object detection in H.264/AVC compressed domain for video surveillance applications. the proposed algorithm initially segments out edges from regions with motion a...
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ISBN:
(纸本)9781479915880
this paper discusses a novel fast approach for moving object detection in H.264/AVC compressed domain for video surveillance applications. the proposed algorithm initially segments out edges from regions with motion at macroblock level by utilizing the gradient of quantization parameter over 2D-image space. A spatial median filtering of the segmented edges followed by weighted temporal accumulation accounts for whole object segmentation. To attain sub-macroblock (4 x 4) level precision, the size of macroblocks (in bits) is interpolated using a two tap filter. Partial decoding rules out the complexity involved in full decoding and gives fast foreground segmentation results. Compared to other compressed domain techniques, the proposed approach allows the video streams to be encoded with different quantization parameters across macroblocks thereby increasing flexibility in bit rate adjustment.
this paper proposes a new algorithm for restoration of gray scale images corrupted by salt and pepper noise(SPN). the proposed algoritm identifies a pixel as noisy if its intensity value is 0 or 255 and processes it u...
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ISBN:
(纸本)9781467385640
this paper proposes a new algorithm for restoration of gray scale images corrupted by salt and pepper noise(SPN). the proposed algoritm identifies a pixel as noisy if its intensity value is 0 or 255 and processes it using pixels in a 3 x 3 window. If the window consists of noisy and non-noisy pixels, then the pixel to be processed is replaced withthe trimmed median value of the non-noisy pixels. However, if only noisy pixels are there in the window then their mean value is used to process the pixel. the proposed method uses processed (i.e. the de-noised) pixels in the window while processingthe noisy pixels and shows significantly better performance, particularly at high noise density, as compared to various methods reported in literature. Experimental results show improvements both visually and quantitatively compared to other reported methods.
Automatic and reliable identification of pedestrians from multiple camera views is very important for video surveillance and can save a lot of manual effort. the significant variations in viewpoints, poses, illuminati...
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ISBN:
(纸本)9781467385640
Automatic and reliable identification of pedestrians from multiple camera views is very important for video surveillance and can save a lot of manual effort. the significant variations in viewpoints, poses, illumination and occlusions makes this problem very challenging. Most of the existing approaches addressing this problem handle drastic viewpoint change in a supervised way and thus require labelling new training data for a different pair of camera views. In this paper, we present a novel approach for pedestrian re-identification using stereo matching, which does not require any kind of training. the cost of the stereo matching of two images is used for evaluating the similarity of the images, without performing 3-D reconstruction. We show that this cost is robust to the large pose variations observed in the images captured from multiple cameras. the proposed pedestrian re-identification algorithm is built on top of a dynamic programming stereo matching algorithm. Experimental evaluation on the challenging VIPeR dataset shows the effectiveness of the proposed approach.
A mammography is a specific type of imaging that uses low-dose x-ray system to examine breasts. this is an efficient means of early detection of breast cancer. High resolution is a common characteristic of such images...
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Instance retrieval (IR) is the problem of retrieving specific instances of a particular object, like a monument, from a collection of images. Currently, the most popular methods for IR use Bag of words (BoW) features ...
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ISBN:
(纸本)9781467385640
Instance retrieval (IR) is the problem of retrieving specific instances of a particular object, like a monument, from a collection of images. Currently, the most popular methods for IR use Bag of words (BoW) features for retrieval. However, a prominent problem for IR remains the tendency of BoW based methods to retrieve near-identical images as most relevant results. In this paper, we define diversity in IR as variation of physical properties among most relevant retrieved results for a query image. To achieve this, we propose both an ITML algorithm that re-fashions the BoW feature space into one that appreciates diversity better, and a measure to evaluate diversity in retrieval results for IR applications. Additionally, we also generate 200 hand-labeled images from the Paris dataset, for use in further research in this area. Experiments on the popular Paris dataset show that our method outperforms the standard BoW model in many cases.
Compact representation of visual content has emerged as an important topic in the context of large scale image/video retrieval. the recently proposed Vector of Locally Aggregated Descriptors (VLAD) has shown to outper...
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ISBN:
(纸本)9781479915880
Compact representation of visual content has emerged as an important topic in the context of large scale image/video retrieval. the recently proposed Vector of Locally Aggregated Descriptors (VLAD) has shown to outperform other existing techniques for retrieval. In this paper, we propose two spatio-temporal features for constructing VLAD vectors for videos in the context of large scale video retrieval. Given a particular query video, our aim is to retrieve similar videos from the database. Experiments are conducted on UCF50 and HMDB51 datasets, which pose challenges in the form of camera motion, view-point variation, large intra-class variation, etc. the paper proposes the following two spatio-temporal features for constructing VLADs i) Local Histogram of Oriented Optical Flow (LHOOF), and ii) Space-Time Invariant Points (STIP). the performance of these proposed features are compared with SIFT based spatial feature. the mean average precision (MAP) indicates the better retrieval performance of the proposed spatio-temporal feature over spatial feature.
Stamps and logos are generally used for authenticating the source of a document. For automatic document processing, identification and segmentation of stamps and logos are essential. In the past, methods to detect sta...
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ISBN:
(纸本)9781467385640
Stamps and logos are generally used for authenticating the source of a document. For automatic document processing, identification and segmentation of stamps and logos are essential. In the past, methods to detect stamps and logos were limited to specific shapes, colors, or training data. However, stamps and logos can be of any shape or color. In this paper, we have proposed a novel stamp and logo detection technique. Our approach is based on the fact that stamps and logos, in general, are not the primary contents of a document. this fact motivates us to propose an outlier detection technique for the same purpose in a feature space. Based on some geometric features, the detected outliers are classified as stamps and logos. Our method shows good performance in case of separating them from text. Moreover, this technique is capable of detecting logos as well as chromatic and achromatic stamps.
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