The Experts below are selected from a list of 18999 Experts worldwide ranked by ideXlab platform
Alan C. Bovik - One of the best experts on this subject based on the ideXlab platform.
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Towards a Closed Form Second-Order Natural Scene Statistics Model
IEEE Transactions on Image Processing, 2018Co-Authors: Zeina Sinno, Constantine Caramanis, Alan C. BovikAbstract:Previous work on Natural Scene statistics (NSS)-based image models has focused primarily on characterizing the univariate bandpass statistics of single pixels. These models have proven to be powerful tools driving a variety of computer vision and image/video processing applications, including depth estimation, image quality assessment, and image denoising, among others. Multivariate NSS models descriptive of the joint distributions of spatially separated bandpass image samples have, however, received relatively little attention. Here, we develop a closed form bivariate spatial correlation model of bandpass and normalized image samples that completes an existing 2D joint generalized Gaussian distribution model of adjacent bandpass pixels. Our model is built using a set of diverse, high-quality Naturalistic photographs, and as a control, we study the model properties on white noise. We also study the way the model fits are affected when the images are modified by common distortions.
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Depth estimation from monocular color images using Natural Scene statistics models
IVMSP 2013, 2013Co-Authors: Che-chun Su, Lawrence K. Cormack, Alan C. BovikAbstract:We consider the problem of estimating a dense depth map from a single monocular image. Inspired by psychophysical evidence of visual processing in human vision systems (HVS) and Natural Scene statistics (NSS) models of image and range, we propose a Bayesian framework to recover detailed 3D Scene structure by exploiting the statistical relationships between local image features and depth variations inherent in Natural images. By observing that similar depth structures may exist in different types of luminance/chrominance textured regions in Natural Scenes, we build a dictionary of canonical range patterns as the prior, and fit a multivariate Gaussian mixture (MGM) model to associate local image features to different range patterns as the likelihood. Compared with the state-of-the-art depth estimation method, we achieve similar performance in terms of pixel-wise estimated range error, but superior capability of recovering relative distant relationships between different parts of the image.
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Blind quality assessment of videos using a model of Natural Scene statistics and motion coherency
2012 Conference Record of the Forty Sixth Asilomar Conference on Signals Systems and Computers (ASILOMAR), 2012Co-Authors: Michele A. Saad, Alan C. BovikAbstract:We propose a no-reference algorithm for video quality evaluation. The algorithm relies on a Natural Scene statistics (NSS) model of video DCT coefficients as well as a temporal model of motion coherency. The proposed framework is tested on the LIVE VQA database, and shown to correlate well with human visual judgments of quality.
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MICA: A Multilinear ICA Decomposition for Natural Scene Modeling
IEEE Transactions on Image Processing, 2008Co-Authors: Alan C. BovikAbstract:We refine the classical independent component analysis (ICA) decomposition using a multilinear expansion of the probability density function of the source statistics. In particular, we introduce a specific nonlinear system that allows us to elegantly capture the statistical dependences between the responses of the multilinear ICA (MICA) filters. The resulting multilinear probability density is analytically tractable and does not require Monte Carlo simulations to estimate the model parameters. We demonstrate the MICA model on Natural image textures and envision that the new model will prove useful for analyzing nonstationarity Natural images using Natural Scene statistics models.
Jun Guo - One of the best experts on this subject based on the ideXlab platform.
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Text extraction from Natural Scene image: A survey
Neurocomputing, 2013Co-Authors: X. F. Zhang, Yi Zhe Song, Honggang Zhang, Kaili Zhao, Jun GuoAbstract:Abstract With the increasing popularity of portable camera devices and embedded visual processing, text extraction from Natural Scene images has become a key problem that is deemed to change our everyday lives via novel applications such as augmented reality. Text extraction from Natural Scene images algorithms is generally composed of the following three stages: (i) detection and localization, (ii) text enhancement and segmentation and (iii) optical character recognition (OCR). The problem is challenging in nature due to variations in the font size and color, text alignment, illumination change and reflections. This paper aims to classify and assess the latest algorithms. More specifically, we draw attention to studies on the first two steps in the extraction process, since OCR is a well-studied area where powerful algorithms already exist. This paper offers to the researchers a link to public image database for the algorithm assessment of text extraction from Natural Scene images.
Diane M. Beck - One of the best experts on this subject based on the ideXlab platform.
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Finding “good” features for Natural Scene classification
Journal of Vision, 2010Co-Authors: Eamon Caddigan, Dirk B. Walther, Diane M. BeckAbstract:References Fei-Fei, L., & Perona, P. (2005). A Bayesian Hierarchical Model for Learning Natural Scene Categories. IEEE Comp. Vis. Patt. Recog. Fei-Fei, L., VanRullen, R., Koch, C., & Perona, P. (2002). Rapid Natural Scene categorization in the near absence of attention. Proc. Natl. Acad. Sci, 99, 8378-8383. Hoiem, D., Efros, A. A., & Hebert, M. (2005). Geometric Context from a Single Image. ICCV. Lazebnik, S., Schmid, C. & Ponce, J. (2006). Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. IEEE Comp. Vis. Patt. Recog. Loschky, L. C., Sethi, A., Simons, D. J., Pydimarri, T. N.,Ochs, D., & Corbeille, J. L. (2007). The importanceof information localization in Scene gist recognition. Journal Of Experimental Psychology: Human Per-ception & Performance, 33, 1431–1450. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. Int. Journal of Computer Vision, 60 (2), 91-110. Oliva, A., & Torralba, A. (2001). Modeling the shape of the Scene: A holistic representation of the spatial envelope. Int. Journal of Computer Vision, 42, 145-175. Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381, 520-522. Torralbo, A., Chai, B., Caddigan, E., Walther, D., Beck, D., & Fei-Fei, L. (2009). Categorization of good and bad examples of Natural Scene categories. Presented at the 9th annual meeting of the Vision Sciences Society. Tversky, B., & Hemenway, K. (1983). Categories of Environmental Scenes. Cognitive Psychology, 15, 121-149. Feature localization • All features benefit from some localization • No improvement for any feature beyond level 6
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Natural Scene categories revealed in distributed patterns of activity in the human brain
The Journal of Neuroscience, 2009Co-Authors: Dirk B. Walther, Eamon Caddigan, Li Feifei, Diane M. BeckAbstract:Human subjects are extremely efficient at categorizing Natural Scenes, despite the fact that different classes of Natural Scenes often share similar image statistics. Thus far, however, it is unknown where and how complex Natural Scene categories are encoded and discriminated in the brain. We used functional magnetic resonance imaging (fMRI) and distributed pattern analysis to ask what regions of the brain can differentiate Natural Scene categories (such as forests vs mountains vs beaches). Using completely different exemplars of six Natural Scene categories for training and testing ensured that the classification algorithm was learning patterns associated with the category in general and not specific exemplars. We found that area V1, the parahippocampal place area (PPA), retrosplenial cortex (RSC), and lateral occipital complex (LOC) all contain information that distinguishes among Natural Scene categories. More importantly, correlations with human behavioral experiments suggest that the information present in the PPA, RSC, and LOC is likely to contribute to Natural Scene categorization by humans. Specifically, error patterns of predictions based on fMRI signals in these areas were significantly correlated with the behavioral errors of the subjects. Furthermore, both behavioral categorization performance and predictions from PPA exhibited a significant decrease in accuracy when Scenes were presented up-down inverted. Together these results suggest that a network of regions, including the PPA, RSC, and LOC, contribute to the human ability to categorize Natural Scenes.
Hongwei Hao - One of the best experts on this subject based on the ideXlab platform.
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robust text detection in Natural Scene images
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014Co-Authors: Xucheng Yin, Xuwang Yin, Kaizhu Huang, Hongwei HaoAbstract:Text detection in Natural Scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecting texts in Natural Scene images. A fast and effective pruning algorithm is designed to extract Maximally Stable Extremal Regions (MSERs) as character candidates using the strategy of minimizing regularized variations. Character candidates are grouped into text candidates by the single-link clustering algorithm, where distance weights and clustering threshold are learned automatically by a novel self-training distance metric learning algorithm. The posterior probabilities of text candidates corresponding to non-text are estimated with a character classifier; text candidates with high non-text probabilities are eliminated and texts are identified with a text classifier. The proposed system is evaluated on the ICDAR 2011 Robust Reading Competition database; the f-measure is over 76%, much better than the state-of-the-art performance of 71%. Experiments on multilingual, street view, multi-orientation and even born-digital databases also demonstrate the effectiveness of the proposed method.
X. F. Zhang - One of the best experts on this subject based on the ideXlab platform.
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Text extraction from Natural Scene image: A survey
Neurocomputing, 2013Co-Authors: X. F. Zhang, Yi Zhe Song, Honggang Zhang, Kaili Zhao, Jun GuoAbstract:Abstract With the increasing popularity of portable camera devices and embedded visual processing, text extraction from Natural Scene images has become a key problem that is deemed to change our everyday lives via novel applications such as augmented reality. Text extraction from Natural Scene images algorithms is generally composed of the following three stages: (i) detection and localization, (ii) text enhancement and segmentation and (iii) optical character recognition (OCR). The problem is challenging in nature due to variations in the font size and color, text alignment, illumination change and reflections. This paper aims to classify and assess the latest algorithms. More specifically, we draw attention to studies on the first two steps in the extraction process, since OCR is a well-studied area where powerful algorithms already exist. This paper offers to the researchers a link to public image database for the algorithm assessment of text extraction from Natural Scene images.
