Nature and origin of secondary mineral coatings on volcanic rocks of the Black Mountain, Stonewall Mountain, and Kane Springs Wash volcanic centers, southern, Nevada

The following subject areas are covered: (1) genetic, spectral, and LANDSAT Thematic Mapper imagery relationship between desert varnish and tertiary volcanic host rocks, southern Nevada; (2) reconnaissance geologic mapping of the Kane Springs Wash Volcanic Center, Lincoln County, Nevada, using multispectral thermal infrared imagery; (3) interregional comparisons of desert varnish; and (4) airborne scanner (GERIS) imagery of the Kane Springs Wash Volcanic Center, Lincoln County, Nevada

Learning Enriched Features for Real Image Restoration and Enhancement

With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography, medical imaging, and remote sensing. Recently, convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. Existing CNN-based methods typically operate either on full-resolution or on progressively low-resolution representations. In the former case, spatially precise but contextually less robust results are achieved, while in the latter case, semantically reliable but spatially less accurate outputs are generated. In this paper, we present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network and receiving strong contextual information from the low-resolution representations. The core of our approach is a multi-scale residual block containing several key elements: (a) parallel multi-resolution convolution streams for extracting multi-scale features, (b) information exchange across the multi-resolution streams, (c) spatial and channel attention mechanisms for capturing contextual information, and (d) attention based multi-scale feature aggregation. In a nutshell, our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Extensive experiments on five real image benchmark datasets demonstrate that our method, named as MIRNet, achieves state-of-the-art results for a variety of image processing tasks, including image denoising, super-resolution, and image enhancement. The source code and pre-trained models are available at https://github.com/swz30/MIRNet.Comment: Accepted for publication at ECCV 202

Combining audio-visual features for viewers' perception classification of Youtube car commercials

Proccedings of: 2nd International Workshop on Speech, Language and Audio in Multimedia. Penang, Malaysia, 11-12 September 2014.In this paper, we present a computational model capable of predicting the viewer perception of Youtube car TV commercials by using a set of low-level audio and visual descriptors. Our research goal relies on the hypothesis that these descriptors could reflect to some extent the objective value of the videos and, in turn, the average viewer's perception. To that end, and as a novel approach to this problem, we automatically annotate our video corpus, grouped into 2 classes corresponding to differ-ent satisfaction levels, by means of a regular k-means algorithm applied to the video metadata related to users feedback. Evaluation results show that simple linear logistic regression models based on the 10 best visual descriptors and on the 10 best audio descriptors individually perform reasonably well, achieving a classification accuracy of roughly 70% and 75%, respectively. Combination of audio and visual descriptors yields better performance, roughly 86% for the top-20 selected from the entire descriptor set, but tipping the balance in favor of the audio ones (i.e. 17 vs 3). Audio content bigger influence in this domain is also evidenced by a side analysis of the video comments.Publicad

Intrinsic Image Transfer for Illumination Manipulation

This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework consisting of three photo-realistic losses defined on the sub-layers factorized by an intrinsic image decomposition. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. Moreover, with a series of relaxations, all of them can be directly defined on images, giving a closed-form solution for image illumination manipulation. This new paradigm differs from the prevailing Retinex-based algorithms, as it provides an implicit way to deal with the per-pixel image illumination. We finally demonstrate its versatility and benefits to the illumination-related tasks such as illumination compensation, image enhancement, and high dynamic range (HDR) image compression, and show the high-quality results on natural image datasets

Third Earth Resources Technology Satellite Symposium. Volume 3: Discipline summary reports

Presentations at the conference covered the following disciplines: (1) agriculture, forestry, and range resources; (2) land use and mapping; (3) mineral resources, geological structure, and landform surveys; (4) water resources; (5) marine resources; (6) environment surveys; and (7) interpretation techniques

Experience-dependent plasticity in the auditory domain: effects of expertise and training on functional brain organization

The present dissertation aims at systematically investigating manifestations of experience-dependent plasticity in the auditory domain, resulting from intensive musical training, utilizing analytical tools from network neuroscience. The dissertation is based on data acquired in the course of a longitudinal study investigating structural and functional changes in the auditory domain due to music training. A group of aspiring professional musicians, attending preparatory courses for entrance exams at universities of arts, and a group of amateur musicians, actively practicing in their everyday life, completed up to 5 behavioral and neuroimaging assessments in the course of one year. The dissertation consists of three studies addressing cross-sectional and longitudinal aspects of functional plastic differences and changes, respectively, ranging from a specific auditory process over unconstrained music listening to longitudinal changes in functional organization

Local Low-light Image Enhancement via Region-Aware Normalization

In the realm of Low-Light Image Enhancement (LLIE), existing research primarily focuses on enhancing images globally. However, many applications require local LLIE, where users are allowed to illuminate specific regions using an input mask, such as creating a protagonist stage or spotlight effect. However, this task has received limited attention currently. This paper aims to systematically define the requirements of local LLIE and proposes a novel strategy to convert current existing global LLIE methods into local versions. The image space is divided into three regions: Masked Area A be enlightened to achieve the desired lighting effects; Transition Area B is a smooth transition from the enlightened area (Area A) to the unchanged region (Area C). To achieve the task of local LLIE, we introduce Region-Aware Normalization for Local Enhancement, dubbed as RANLEN. RANLEN uses a dynamically designed mask-based normalization operation, which enhances an image in a spatially varying manner, ensuring that the enhancement results are consistent with the requirements specified by the input mask. Additionally, a set of region-aware loss terms is formulated to facilitate the learning of the local LLIE framework. Our strategy can be applied to existing global LLIE networks with varying structures. Extensive experiments demonstrate that our approach can produce the desired lighting effects compared to global LLIE, all the while offering controllable local enhancement with various mask shapes

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

A landsat remote sensing study of vegetation growing on mineralized terrain

Imperial Users onl