The science of color and color vision

A survey of color science and color vision

Knowledge-based vision for space station object motion detection, recognition, and tracking

Computer vision, especially color image analysis and understanding, has much to offer in the area of the automation of Space Station tasks such as construction, satellite servicing, rendezvous and proximity operations, inspection, experiment monitoring, data management and training. Knowledge-based techniques improve the performance of vision algorithms for unstructured environments because of their ability to deal with imprecise a priori information or inaccurately estimated feature data and still produce useful results. Conventional techniques using statistical and purely model-based approaches lack flexibility in dealing with the variabilities anticipated in the unstructured viewing environment of space. Algorithms developed under NASA sponsorship for Space Station applications to demonstrate the value of a hypothesized architecture for a Video Image Processor (VIP) are presented. Approaches to the enhancement of the performance of these algorithms with knowledge-based techniques and the potential for deployment of highly-parallel multi-processor systems for these algorithms are discussed

Ground-based, Near-infrared Exospectroscopy. II. Tentative Detection of Emission From the Extremely Hot Jupiter WASP-12b

We report the tentative detection of the near-infrared emission of the Hot Jupiter WASP-12b with the low-resolution prism on IRTF/SpeX. We find a K-H contrast color of 0.137% +/- 0.054%, corresponding to a blackbody of temperature 2400 (+1500/-500) K and consistent with previous, photometric observations. We also revisit WASP-12b's energy budget on the basis of secondary eclipse observations: the dayside luminosity is a relatively poorly constrained (2.0-4.3) x 10^30 erg/s, but this still allows us to predict a day/night effective temperature contrast of 200-1,000 K (assuming A_B=0). Thus we conclude that WASP-12b probably does not have both a low albedo and low recirculation efficiency. Our results show the promise and pitfalls of using single-slit spectrographs for characterization of extrasolar planet atmospheres, and we suggest future observing techniques and instruments which could lead to further progress. Limiting systematic effects include the use of a too-narrow slit on one night -- which observers could avoid in the future -- and chromatic slit losses (resulting from the variable size of the seeing disk) and variations in telluric transparency -- which observers cannot control. Single-slit observations of the type we present remain the best option for obtaining lambda > 1.7 micron spectra of transiting exoplanets in the brightest systems. Further and more precise spectroscopy is needed to better understand the atmospheric chemistry, structure, and energetics of this, and other, intensely irradiated planet.Comment: ApJ accepted. 16 pages, 15 figure

Lip segmentation using adaptive color space training

In audio-visual speech recognition (AVSR), it is beneficial to use lip boundary information in addition to texture-dependent features. In this paper, we propose an automatic lip segmentation method that can be used in AVSR systems. The algorithm consists of the following steps: face detection, lip corners extraction, adaptive color space training for lip and non-lip regions using Gaussian mixture models (GMMs), and curve evolution using level-set formulation based on region and image gradients fields. Region-based fields are obtained using adapted GMM likelihoods. We have tested the proposed algorithm on a database (SU-TAV) of 100 facial images and obtained objective performance results by comparing automatic lip segmentations with hand-marked ground truth segmentations. Experimental results are promising and much work has to be done to improve the robustness of the proposed method

Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project

The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system

Person re-identification via efficient inference in fully connected CRF

In this paper, we address the problem of person re-identification problem, i.e., retrieving instances from gallery which are generated by the same person as the given probe image. This is very challenging because the person's appearance usually undergoes significant variations due to changes in illumination, camera angle and view, background clutter, and occlusion over the camera network. In this paper, we assume that the matched gallery images should not only be similar to the probe, but also be similar to each other, under suitable metric. We express this assumption with a fully connected CRF model in which each node corresponds to a gallery and every pair of nodes are connected by an edge. A label variable is associated with each node to indicate whether the corresponding image is from target person. We define unary potential for each node using existing feature calculation and matching techniques, which reflect the similarity between probe and gallery image, and define pairwise potential for each edge in terms of a weighed combination of Gaussian kernels, which encode appearance similarity between pair of gallery images. The specific form of pairwise potential allows us to exploit an efficient inference algorithm to calculate the marginal distribution of each label variable for this dense connected CRF. We show the superiority of our method by applying it to public datasets and comparing with the state of the art.Comment: 7 pages, 4 figure