Water depth estimation with ERTS-1 imagery

Contrast-enhanced 9.5 inch ERTS-1 images were produced for an investigation on ocean water color. Such images lend themselves to water depth estimation by photographic and electronic density contouring. MSS-4 and -5 images of the Great Bahama Bank were density sliced by both methods. Correlation was found between the MSS-4 image and a hydrographic chart at 1:467,000 scale, in a number of areas corresponding to water depth of less than 2 meters, 5 to 10 meters and 10 to about 20 meters. The MSS-5 image was restricted to depths of about 2 meters. Where reflective bottom and clear water are found, ERTS-1 MSS-4 images can be used with density contouring by electronic or photographic methods for estimating depths to 5 meters within about one meter

Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding

This work addresses the problem of semantic scene understanding under dense fog. Although considerable progress has been made in semantic scene understanding, it is mainly related to clear-weather scenes. Extending recognition methods to adverse weather conditions such as fog is crucial for outdoor applications. In this paper, we propose a novel method, named Curriculum Model Adaptation (CMAda), which gradually adapts a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data. In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog. Our experiments show that 1) our fog simulation slightly outperforms a state-of-the-art competing simulation with respect to the task of semantic foggy scene understanding (SFSU); 2) CMAda improves the performance of state-of-the-art models for SFSU significantly by leveraging unlabeled real foggy data. The datasets and code are publicly available.Comment: final version, ECCV 201

On Measuring the Infrared Luminosity of Distant Galaxies with the Space Infrared Telescope Facility

The Space Infrared Telescope Facility (SIRTF) will revolutionize the study of dust-obscured star formation in distant galaxies. Although deep images from the Multiband Imaging Photometer for SIRTF (MIPS) will provide coverage at 24, 70, and 160 micron, the bulk of MIPS-detected objects may only have accurate photometry in the shorter wavelength bands due to the confusion noise. Therefore, we have explored the potential for constraining the total infrared (IR) fluxes of distant galaxies with solely the 24 micron flux density, and for the combination of 24 micron and 70 micron data. We also discuss the inherent systematic uncertainties in making these transitions. Under the assumption that distant star-forming galaxies have IR spectral energy distributions (SEDs) that are represented somewhere in the local Universe, the 24 micron data (plus optical and X-ray data to allow redshift estimation and AGN rejection) constrains the total IR luminosity to within a factor of 2.5 for galaxies with 0.4 < z < 1.6. Incorporating the 70 micron data substantially improves this constraint by a factor < 6. Lastly, we argue that if the shape of the IR SED is known (or well constrained; e.g., because of high IR luminosity, or low ultraviolet/IR flux ratio), then the IR luminosity can be estimated with more certainty.Comment: 4 pages, 3 figures (2 in color). Accepted for Publication in the Astrophysical Journal Letters, 2002 Nov

Tracking-Based Non-Parametric Background-Foreground Classification in a Chromaticity-Gradient Space

This work presents a novel background-foreground classification technique based on adaptive non-parametric kernel estimation in a color-gradient space of components. By combining normalized color components with their gradients, shadows are efficiently suppressed from the results, while the luminance information in the moving objects is preserved. Moreover, a fast multi-region iterative tracking strategy applied over previously detected foreground regions allows to construct a robust foreground modeling, which combined with the background model increases noticeably the quality in the detections. The proposed strategy has been applied to different kind of sequences, obtaining satisfactory results in complex situations such as those given by dynamic backgrounds, illumination changes, shadows and multiple moving objects

Kernel bandwidth estimation for moving object detection in non-stabilized cameras

The evolution of the television market is led by 3DTV technology, and this tendency can accelerate during the next years according to expert forecasts. However, 3DTV delivery by broadcast networks is not currently developed enough, and acts as a bottleneck for the complete deployment of the technology. Thus, increasing interest is dedicated to ste-reo 3DTV formats compatible with current HDTV video equipment and infrastructure, as they may greatly encourage 3D acceptance. In this paper, different subsampling schemes for HDTV compatible transmission of both progressive and interlaced stereo 3DTV are studied and compared. The frequency characteristics and preserved frequency content of each scheme are analyzed, and a simple interpolation filter is specially designed. Finally, the advantages and disadvantages of the different schemes and filters are evaluated through quality testing on several progressive and interlaced video sequences