Alan C. Bovik

Bio: Alan C. Bovik is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Image quality & Video quality. The author has an hindex of 102, co-authored 837 publications receiving 96088 citations. Previous affiliations of Alan C. Bovik include University of Illinois at Urbana–Champaign & University of Sydney.

Making long-wave infrared face recognition robust against image quality degradations

Abstract: Face identification systems that operate on long wave infrared (LWIR) images are able to overcome some of the limitations of approaches based on visible images, such as dealing effectively with ill...

Automatic inspection of textured surfaces by support vector machines

Abstract: Automatic inspection of manufactured products with natural looking textures is a challenging task. Products such as tiles, textile, leather, and lumber project image textures that cannot be modeled as periodic or otherwise regular; therefore, a stochastic modeling of local intensity distribution is required. An inspection system to replace human inspectors should be flexible in detecting flaws such as scratches, cracks, and stains occurring in various shapes and sizes that have never been seen before. A computer vision algorithm is proposed in this paper that extracts local statistical features from grey-level texture images decomposed with wavelet frames into subbands of various orientations and scales. The local features extracted are second order statistics derived from grey-level co-occurrence matrices. Subsequently, a support vector machine (SVM) classifier is trained to learn a general description of normal texture from defect-free samples. This algorithm is implemented in LabVIEW and is capable of processing natural texture images in real-time.

A hierarchical Bayesian-map approach to computational imaging

Abstract: We present a novel approach to inverse problems in imaging based on a Hierarchical Bayesian-MAP (HB-MAP) formulation. In this paper we specifically focus on the difficult and basic inverse problem of multi-sensor (tomographic) imaging wherein the source image of interest is viewed from multiple directions by independent sensors. We employ a Probabilistic Graphical Modeling extension of the Compound Gaussian (CG) distribution as a global image prior into a Hierarchical Bayesian inference procedure. We first demonstrate the performance of the algorithm on Monte-Carlo trials followed by empirical data involving natural (optical) images. We demonstrate how our algorithm outperforms many of the previous approaches in the literature including Filtered Back-projection (FBP) and a variety of state-of-the-art compressive sensing (CS) algorithms.

Efficient User-Generated Video Quality Prediction

Abstract: Blind video quality assessment of user-generated content (UGC) has become a trending, challenging, unsolved problem. Accurate and efficient video quality predictors suitable for this content are thus in great demand to achieve intelligent analysis and processing of UGC videos. However, previous video quality models are either incapable or inefficient for predicting the quality of complex, diverse UGC videos in practical applications. Here we introduce an effective and efficient video quality model for UGC content, which we dub the Rapid and Accurate Video Quality Evaluator (RAPIQUE), which we show performs comparably to state-of-the-art models but with orders-of-magnitude faster runtime. Our experimental results on recent large-scale UGC video quality databases show that RAPIQUE delivers top performances on all datasets at a considerably lower computational expense. An implementation of RAPIQUE is online: https://github.com/vztu/RAPIQUE.

Image quality assessment: from error visibility to structural similarity

Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

A wavelet tour of signal processing

Abstract: Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

Image-to-Image Translation with Conditional Adversarial Networks

Abstract: We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Moreover, since the release of the pix2pix software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.

Image-to-Image Translation with Conditional Adversarial Networks

Abstract: We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These