Estimating the salient regions of an image plays a key role in scene analysis and image understanding. We can also apply saliency-based image processing techniques to image compression, evaluation, and effective searching methods. One of the most difficult problems is estimating the regions before recognizing what is in the image -- a problem that can be solved by accessing information of the low-level structures of objects in the image. This paper describes a method for estimating salient regions in images based on the distribution stability of local extrema of luminance during image blurring. Under blurring conditions, if an object's region has a more stable structure compared to another area, it must be more salient, so the saliency of these regions must be defined based on their stability for blurring. In the developed method, the local extrema of images are used to describe the complexity of the image's objects and background. Salient regions are estimated based on the stability of the local extrema for the blurring parameter. Experiments were conducted to compare the estimated result of salient regions and the psychophysical result obtained from the analysis of eye movement recordings. Results show that our method successfully extracts salient regions of natural images.
We visually evaluated the accuracy of color prediction methods that derive the colorimetric values of an object from the sensor responses of a digital still camera. Although an objective measure such as colorimetric error is often used in such evaluations, subjective evaluation is also required to ensure the practicality and acceptability of image archiving for cultural heritage. We conducted visual experiments in which observers evaluated the color matching accuracy between original Hizen porcelain and nishiki-e (Japanese woodblock prints) and their printed reproductions. Results demonstrate that the metamer estimation method, a color prediction method previously proposed by the authors, outperforms conventional methods and is therefore suitable for practical use.
To build a music database efficiently, an automatic score recognition system is a critical component. Many previous methods are applicable only to simple music scores. In the case of complex music scores, it is difficult to detect symbols correctly because of noise and connection between symbols included in the scores. In this paper, we propose a score recognition method that is applicable to complex music scores. Symbol candidates were detected by template matching. From these candidates correct symbols were selected by considering their relative positions and mutual connections. In the presence of noise and connected symbols, the proposed method outperformed “Score Maker”, which is optical music score recognition software.