To predict the peak signal-to-noise ratio (PSNR) quality of decoded images in fractal image coding more efficiently and accurately, an improved method is proposed. After some derivations and analyses, we find that the linear correlation coefficients between coded range blocks and their respective best-matched domain blocks can determine the dynamic range of their collage errors, which can also provide the minimum and the maximum of the accumulated collage error (ACE) of uncoded range blocks. Moreover, the dynamic range of the actual percentage of accumulated collage error (APACE), APACEmin to APACEmax, can be determined as well. When APACEmin reaches a large value, such as 90%, APACEmin to APACEmax will be limited in a small range and APACE can be computed approximately. Furthermore, with ACE and the approximate APACE, the ACE of all range blocks and the average collage error (ACER) can be obtained. Finally, with the logarithmic relationship between ACER and the PSNR quality of decoded images, the PSNR quality of decoded images can be predicted directly. Experiments show that compared with the previous similar method, the proposed method can predict the PSNR quality of decoded images more accurately and needs less computation time simultaneously.
The human object segmentation and classification are main work in the applications of Intelligent Visual Surveillance
System or Passenger Flow Counting System. Traditional approaches to segment and classify human objects are usually
based on the face, leg motion and silhouette. These algorithms' performances and their applications have proved to be
effective in recent years. But these algorithms all assume that features can always be extracted. In complex situations,
however, features adopted in traditional algorithms might not be extracted, because human attitude and illumination
change greatly. In this case, if a definite feature is used, the algorithm's accuracy will fall. In this paper we propose an
approach to select the feature and the corresponding algorithm adaptively based on the human attitude and object
neighborhood illumination. The selected features can be used in the following tracking operation. Because this method
solves the human object segmentation and classification problem, it can broad the 3D recovery and behavior understanding
research results in simple situations to the application in complex situations.
In this paper, the algorithms are proposed for the human attitude and illumination detection, the feature selection strategies
in different situation are given. The experimental results show that the algorithm can detect the object lightness properly,
and can give the right attitude for feature selection. The algorithms have good performance and computation efficiency.
A texture segmentation algorithm based on HVS (Human Visual System) is proposed in this paper. Psychophysical and Neurophysiological conclusions have supported the hypothesis that the processing of afferent pictorial information in the HVS (the visual cortex in particular) involves two stages: the preattentive stage, and the focused attention stage. To simulate the preattentive stage of HVS, ring and wedge filtering methods are used to segment coarsely and the texture number in the input image is gotten. As texture is the repeating patterns of local variations in image intensity, we can use a part of the texture as the whole region representation. The inscribed squares in the coarse regions are transformed respectively to frequency domain and each spectrum is analyzed in detail. New texture measurements based on the Fourier spectrums are given. Through analyzing the measurements of the texture, including repeatability directionality and regularity, we can extract the feature, and determine the parameters of the Gabor filter-bank. Then to simulate the focused attention stage of HVS, the determined Gabor filter-bank is used to filter the original input image to produce fine segmentation regions. This approach performs better in computational complexity and feature extraction than the fixed parameters and fixed stages Gabor filter-bank approaches.