Hand shredded content-less pages reassembly is a challenging task. This has applications in forensics and fun games. The process is even more tedious when the number of pages from which the fragments are obtained is u...
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Magnetic Resonance Imaging (MRI) system in recent times demands a high rate of acceleration in data acquisition to reduce the scanning time. The data acquisition rate can be accelerated to a significant order through ...
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Feature extraction is an essential step in many imageprocessing and computervision applications. It is quite desirable that the extracted features can effectively represent an image. Furthermore, the dominant inform...
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ISBN:
(纸本)9788132225386;9788132225379
Feature extraction is an essential step in many imageprocessing and computervision applications. It is quite desirable that the extracted features can effectively represent an image. Furthermore, the dominant information visually perceived by human beings should be efficiently represented by the extracted features. Over the last few decades, different algorithms are proposed to address the major issues of image representations by the efficient features. Gabor wavelet is one of the most widely used filters for image feature extraction. Existing Gabor wavelet-based feature extraction methodologies unnecessarily use both the real and the imaginary coefficients, which are subsequently processed by dimensionality reduction techniques such as PCA, LDA etc. This procedure ultimately affects the overall performance of the algorithm in terms of memory requirement and the computational complexity. To address this particular issue, we proposed a local image feature extraction method by using a Gabor wavelet. In our method, an image is divided into overlapping image blocks, and subsequently each of the image blocks are separately filtered out by Gabor wavelet. Finally, the extracted coefficients are concatenated to get the proposed local feature vector. The efficacy and effectiveness of the proposed feature extraction method is evaluated using the estimation of mean square error (MSE), peak signal-to-noise ratio (PSNR), and the correlation coefficient (CC) by reconstructing the original image using the extracted features, and compared it with the original input image. All these performance evaluation measures clearly show that real coefficients of the Gabor filter alone can effectively represent an image as compared to the methods which utilize either the imaginary coefficients or the both. The major novelty of our method lies on our claim-capability of the real coefficients of a Gabor filter for image representation.
Office automation is an active area of research. It involves archival and retrieval of official documents. For developing a system for this purpose, it is necessary to have an extensive benchmark dataset consisting va...
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In this article, we propose a novel multimodal Medical image Fusion (MIF) method based on a neuro-fuzzy technique in the transform (Non-Subsampled Shearlet Transform (NSST)) domain for spatially registered, multi-moda...
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An image captured in dark environment usually has ambient illumination, but the image looks dark and noisy. However, the use of flash can introduce unwanted artifacts such as sharp shadows at silhouettes, red eyes, an...
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ISBN:
(纸本)9781467385640
An image captured in dark environment usually has ambient illumination, but the image looks dark and noisy. However, the use of flash can introduce unwanted artifacts such as sharp shadows at silhouettes, red eyes, and non-uniform brightness in the image. We propose a new framework to enhance photographs captured in dark environments by combining the best features from a flash and a no-flash image. We use sparse and redundant dictionary learning based approach to denoise the no-flash image. A weighted least squares framework is used to transfer sharp details from the flash image into the no-flash image. We show that our approach is simple and able to generate better images than that of the state-of-the-art flash/no-flash fusion method.
The spread of global smartphone market in the past decade has resulted in exponential growth of unstructured data, particularly in the form of multimedia, in the domain of social networking. Consequently, it has made ...
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ISBN:
(纸本)9781467385640
The spread of global smartphone market in the past decade has resulted in exponential growth of unstructured data, particularly in the form of multimedia, in the domain of social networking. Consequently, it has made data retrieval cumbersome, specially for the users. This has also posed as a major challenge in the development of new algorithms and technologies. This paper presents a context based search technique for personalized image retrieval, based on Logical Item-set Mining on image Hash-Tags given by the users. The tests were performed on Instagram image datasets of two different users, collected through a crawler and show promising results.
This paper addresses the problem of segmenting handwritten annotations on scientific research papers. The motivation of this work is to geometrically segment the complex cases of handwritten annotations, including mar...
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ISBN:
(纸本)9781467385640
This paper addresses the problem of segmenting handwritten annotations on scientific research papers. The motivation of this work is to geometrically segment the complex cases of handwritten annotations, including marks, cuts and special symbols along, with the regular text. Our work particularly focuses on documents that have multi-oriented handwritten [1] annotations rather than annotations in controlled scenario [2]. Spectral Partitioning is adopted as the segmentation scheme to separate the printed text and annotations. A new feature Envelope Straightness is developed and included in our feature set. This leads to an improvement of accuracy over the state-of-the-art features. The experiments are performed on two datasets: 40 documents authored by two writers from IAM dataset, comprising only printed and handwritten text and a self created dataset of 40 scientific papers from various proceedings annotated by a reader, comprising varied types of annotations. In the framework of spectral partitioning, our feature set has achieved a recall of 98.39% for printed text and precision of 85.40% for handwritten annotations on our dataset. For IAM dataset our feature set has achieved a recall of 81.89% for printed text and a precision of 69.67% for handwritten annotations. The results achieved on both dataset are better compared with results obtained using [3] [1].
images taken in bad weather conditions like haze and fog suffer from loss of contrast and color shift. The object radiance is attenuated in the atmosphere and the atmospheric light is added to the scene radiance creat...
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ISBN:
(纸本)9781467385640
images taken in bad weather conditions like haze and fog suffer from loss of contrast and color shift. The object radiance is attenuated in the atmosphere and the atmospheric light is added to the scene radiance creating a veil like semi-transparent layer called airlight. The methods proposed till now assumes that the atmospheric light is constant throughout the image domain, which may not be true always. Here we propose a method that works under the relaxed assumption that the color of atmospheric light is constant but its intensity may vary in the image. We use the color line model to estimate the contribution of airlight in each patch and interpolate at places where the estimate is not reliable. We apply reverse operation to recover the haze free image.
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