Low-cost monitoring cameras/webcams provide unique visual information. To take advantage of the vast image dataset captured by a typical webcam, we consider the problem of retrieving weather information from a database of still images. The task is to automatically label all images with different weather conditions (e.g., sunny, cloudy, and overcast), using limited human assistance. To address the drawbacks in existing weather prediction algorithms, we first apply image segmentation to the raw images to avoid disturbance of the non-sky region. Then, we propose to use multiple kernel learning to gather and select an optimal subset of image features from a certain feature pool. To further increase the recognition performance, we adopt multi-pass active learning for selecting the training set. The experimental results show that our weather recognition system achieves high performance.
In this paper, we show that a better performance can be achieved by training a keypoint detector to only find those points that are suitable to the needs of the given task. We demonstrate our approach in an urban environment, where the keypoint detector should focus on stable man-made structures and ignore objects that undergo natural changes such as vegetation and clouds. We use Wald-Boost learning with task specific training samples in order to train a keypoint detector with this capability. We show that our aproach generalizes to a broad class of problems where the task is known beforehand.
We explore two recent methods for measuring the Modeling Transfer Function of a printing system 12 . We investigate the dependency on the amplitude when using the sinusoidal patches of the method proposed in 1 and show that for too small amplitudes the measurement of the MTF is not trustworthy. For the method proposed in 2 we discuss the underlying theory and in particular the use of a significance test for a statistical analysis. Finally we compare both methods with respect our application -the processing and printing of photographic images.
Preliminary experiments have shown that the quality of printed images depends on the capacity of the printing system to accurately reproduce details.1 We propose to improve the quality of printed images by compensating for the Modulation Transfer Function (MTF) of the printing system. The MTF of the printing system is measured using the method proposed by Jang and Allebach, 2 in which test pages consisting of series of patches with different 1D sinusoidal modulations (modified to improve the accuracy of the results 3 ) are printed, scanned and analyzed. Then the MTF is adaptively compensated in the Fourier domain, depending both on frequency and local mean values. Results of a category judgment experiment show significant improvement as the printed MTF compensated images obtain the best scores.
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