• Journal of KIISE
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• v.43 no.12
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• pp.1356-1364
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• 2016

This paper proposes a novel aesthetic photo recomposition method using a deep convolutional neural network (DCNN). Previous recomposition approaches define the aesthetic score of photo composition based on the distribution of salient objects, and enhance the photo composition by maximizing the score. These methods suffer from heavy computational overheads, and often fail to enhance the composition because their optimization depends on the performance of existing salient object detection algorithms. Unlike previous approaches, we address the photo recomposition problem by utilizing DCNN, which shows remarkable performance in object detection and recognition. DCNN is used to iteratively predict cropping directions for a given photo, thus generating an aesthetically enhanced photo in terms of composition. Experimental results and user study show that the proposed framework can automatically crop the photo to follow specific composition guidelines, such as the rule of thirds.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.327-339
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• 2004

In this paper, we propose a new watermark algorithm for halftone images using iterative error minimizing method. In the proposed method, watermark bits are hidden at pseudo-random locations within a halftone image. To remove the distortions due to the inserted watermark bits and increase the invisibility of watermark, an iterative error minimizing technique is used. We define the halftoning error is defined as the difference between the original grayscale image and HVS-filtered printed halftone image. Then we iteratively find the pixel pattern with minimum halftoning error and displace the original pixel pattern with it. In order to be robust to geometrical modification like cropping or rotation, we insert the same watermark periodically into halftone images. Experiments using printed and scanned images show that the proposed method is a robust method to the geometrical modification and to hide the large amount of data within a halftone image without noticeable distortion.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.3
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• pp.15-21
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• 2009

In this paper, we proposed an iterative digital image watermarking technique using encrypted binary phase computer generated hologram in the discrete cosine transform(OCT) domain. For the embedding process of watermark, using simulated annealing algorithm, we would generate a binary phase computer generated hologram(BPCGH) which can reconstruct hidden image perfectly instead of hidden image and repeat the hologram and encrypt it through the XOR operation with key image that is ramdomly generated binary phase components. We multiply the encrypted watermark by the weight function and embed it into the DC coefficients in the DCT domain of host image and an inverse DCT is performed. For the extracting process of watermark, we compare the DC coefficients of watermarked image and original host image in the DCT domain and dividing it by the weight function and decrypt it using XOR operation with key image. And we recover the hidden image by inverse Fourier transforming the decrypted watermark. Finally, we compute the correlation between the original hidden image and recovered hidden image to determine if a watermark exits in the host image. The proposed watermarking technique use the hologram information of hidden image which consist of binary values and encryption technique so it is very secure and robust to the external attacks such as compression, noises and cropping. We confirmed the advantages of the proposed watermarking technique through the computer simulations.