Affine Illumination compensation on hyperspectral/multiangular remote sensing images

The huge amount of information some of the new optical satellites developed nowadays will create demands to quickly and reliably compensate for changes in the atmospheric transmittance and varying solar illumination conditions. In this paper three different forms of affine transformation models (general, particular and diagonal) are considered as candidates for rapid compensation of illumination variations. They are tested on a group of three pairs of CHRIS-PROBA radiance images obtained in a test field in Barrax (Spain), and where there is a difference in the atmospheric as well as in the geometrical acquisition conditions. Results indicate that the proposed methodology is satisfactory for practical normalization of varying illumination and atmospheric conditions in remotely sensed images required for operational applicationsThis work was supported by the Spanish Ministry of Science and Innovation under the projects Consolider Ingenio 2010 CSD2007 − 00018, EODIX AYA2008 − 05965 − C04 − 04/ESP and ALFI3D TIN2009 − 14103 − C03 − 01, by the Generalitat Valenciana through the project PROMETEO/2010/028 and by Fundació Caixa-Castellóthrough the project P1 1B2007 − 4

Coloring local feature extraction

International audienceAlthough color is commonly experienced as an indispensable quality in describing the world around us, state-of-the art local feature-based representations are mostly based on shape description, and ignore color information. The description of color is hampered by the large amount of variations which causes the measured color values to vary significantly. In this paper we aim to extend the description of local features with color information. To accomplish a wide applicability of the color descriptor, it should be robust to : 1. photometric changes commonly encountered in the real world, 2. varying image quality, from high quality images to snap-shot photo quality and compressed internet images. Based on these requirements we derive a set of color descriptors. The set of proposed descriptors are compared by extensive testing on multiple applications areas, namely, matching, retrieval and classification, and on a wide variety of image qualities. The results show that color descriptors remain reliable under photometric and geometrical changes, and with decreasing image quality. For all experiments a combination of color and shape outperforms a pure shape-based approach

Color image registration under illumination changes

The estimation of parametric global motion has had a significant attention during the last two decades, but despite the great efforts invested, there are still open issues. One of the most important ones is related to the ability to recover large deformation between images in the presence of illumination changes while kipping accurate estimates. Illumination changes in color images are another important open issue. In this paper, a Generalized least squared-based motion estimator is used in combination with color image model to allow accurate estimates of global motion between two color images under the presence of large geometric transformation and illumination changes. Experiments using challenging images have been performed showing that the presented technique is feasible and provides accurate estimates of the motion and illumination parameter

Semantic Image Segmentation Using Visible and Near-Infrared Channels

Recent progress in computational photography has shown that we can acquire physical information beyond visible (RGB) image representations. In particular, we can acquire near-infrared (NIR) cues with only slight modification to any standard digital camera. In this paper, we study whether this extra channel can improve semantic image segmentation. Based on a state-of-the-art segmentation framework and a novel manually segmented image database that contains 4-channel images (RGB+NIR), we study how to best incorporate the specific characteristics of the NIR response. We show that it leads to improved performances for 7 classes out of 10 in the proposed dataset and discuss the results with respect to the physical properties of the NIR response

RGBE vs Modified TIFF for encoding high dynamic range images

High Dynamic Range (HDR) imaging has become more widespread in consumer imaging in the past few years, due to the emergence of methods for the recovering HDR radiance maps from multiple photographs [10]. In the domain of HDR encoding, the RGBE radiance format (.hdr) is one of the most widely used. However, conventional image editing applications do not always support this encoding and those that do take considerable time to read or write HDR images (compared with more conventional formats) and this hinders workflow productivity. In this paper we propose a simple, fast, and practical framework to extend the conventional 12 and 16-bit/channel integer TIFF gamma-encoded image format for storing such a wide dynamic range. We consider the potential of our framework for the tone-mapping application both by measuring the ?E S-CIELAB color difference between original and encoded image, and by conducting a psychophysical experiment to evaluate the perceptual image quality of the proposed framework and compare it with an RGBE radiance encoding. The preliminary results show that our encoding frameworks work well for all images of a 65 image dataset, and give equivalent results compared to RGBE radiance formats, while both consuming much less computational cost and removing the need for a separate image coding format. The results suggest that our method, used in the normal tone mapping workflow, is a good candidate for HDR encoding and could easily be integrated with the existing TIFF image library

Optimal global approximation to spatially varying tone mapping operators

Compared with spatially-varying tone-mapping operators, global tone maps have the advantage that the input is mapped to an output image without introducing spatial artifacts common to spatially-varying tone-mapping operators (e.g. halos and intensity inversions). However some local detail can be compressed (visually lost). In this work, we propose a global tone-mapping operator that optimally, in a sum of least-squares sense, approximates spatially-varying tone-mapping operators. Our method is based on a modification of the simple but elegant constrained optimization technique called Pool-Adjacent-Violators-Algorithm (PAVA). In a second step, we show how any lost local detail can be brought back through copying, in an edge sensitive manner, detail from the original input (an approach already developed in the literature). Our new global tone-curve approach has a specific advantage: we show it suffices to learn the tone-curve by processing a small thumbnail and then produce the final output by applying the tone-curve to the full resolution input. Not only does processing on thumbnails deliver excellent results we can, using this approach, significantly increase the speed of tone-mapping operators. To evaluate our method we carried out a paired comparison psychophysical experiment. Preference scores resulting from the experiment show that in general the perceived quality of our proposed operator is similar (equally preferred) to a range of spatially-varying tone-mapping operators