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.

Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory

Abstract: Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue segmentation. The proposed approach better handles intensity overlap between tissues without explicitly employing methods for removal of MR image corruptions (such as bias field). Adaptive tissue class priors are employed that combine probabilistic atlas maps with spatial contextual information obtained from Markov random fields to guide tissue segmentation. The energy function is minimized using a variational level-set-based framework, which has shown great promise for MR image analysis. We evaluate the proposed method on two well-established real MR datasets with expert ground-truth segmentations and compare our approach against existing segmentation methods. KDT has low-computational complexity and shows better segmentation performance than other segmentation methods evaluated using these MR datasets.

A stereo VPIC system

Abstract: A binocular stereo system for images coded by visual pattern image coding (VPIC) is presented and evaluated. The use of VPIC for stereo vision applications is motivated by the capability of its coding primitives to reflect meaningful physical properties of projected real-scene surfaces: high-information edge regions and uniform regions. An algorithm for spatial matching of VPIC primitives is proposed. It involves the solution of a complex optimization problem satisfying VPIC-related constraints. A dense disparity map is obtained and then used, together with one of the VPIC coded images of the stereo pair, to predict the second stereo image. The proposed stereo system allows a very high compression of the overall stereo information. Experimental results are presented to illustrate the performance of both stereo VPIC matching and predicting algorithms in natural images.

The Impact of Order Statistics on Signal Processing

Abstract: In signal processing, the use of order statistics has been quite profitable. Nonlinear filters based on order statistic techniques have enabled signal processors to enhance and restore corrupted digital information. The first such device, the median filter, improved upon linear filtering methods by providing signal impulse rejection without the destruction of important signal properties. More general order statistic filter paradigms were then developed that could be tailored to certain signal characteristics and noise processes. Because the basic order statistic filters ignore temporal and spatial signal ordering, extensions such as the stack, C, Ll, WMMR, and permutation filters were created. Finally, order statistics have been applied to several important signal processing problems such as image morphology, edge detection, signal enhancement and signal restoration. This contribution attempts to summarize a few of the landmark innovations in signal processing that have been made possible through the adoption of order statistics.

Cross-Layer Optimization for Downlink

Abstract: Cross-layer optimization for efficient multimedia communications is an important emerging issue towards pro- viding better quality-of-service (QoS) over capacity-limited wireless channels. This paper presents a cross-layer optimization approach that operates between the application and physical layers to achieve high fidelity downlink video transmission by optimizing with respect to a quality criterion termed "visual entropy" using Lagrangian relaxation. By utilizing the natural layered structure of wavelet coding, an optimal level of power allocation is determined, which permits the throughput of visual entropy to be maximized over a multi-cell environment. A the- oretical approach to optimization using the Shannon capacity and the Karush-Kuhn-Tucker (KKT) conditions is explored when coupling the application with the physical layers. Simulations show that the throughput gain for cross-layer optimization by visual entropy is increased by nearly 80% at the cell boundary as compared with peak signal-to-noise ratio (PSNR).

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