Recently, a quantitative understanding of patient movement in 3-D space has become more important in medical rehabilitation science. In this study, a new personal computeraided system is developed for acquiring digital data of human movement from two CCD video cameras and a foot-force-plate sensor, which are configured to start and stop simultaneously. One problematic body joint will influence other body parts in daily movement. Medical therapy using taping and dynamic shoe insoles improves the alignment of all joints in movement and causes a natural recovery. Also, the feedback control in maintenance of standing balance is improved. This is an important index of recovery in medical rehabilitation. Using this system, timeseries digital data from 3-D image processing and photogrammetry provide effective information for biomechanical and spectrum analysis of human movement. After therapy, the fluctuation of COG (Center of Gravity) and COP (Center of Pressure), which was not even, instable in patients having lower limb disorders, were improved.
A neural network (NN) land cover classification model is proposed for multitemporal satellite image classification, which is drivel by co-occurrence matrix as spatial and spectral information source. The proposed method and the two kinds of conventional NN methods were evaluated by using the Landsat TM data set constituting four images observed in four seasons. As the result, the best performance was achived by the proposed model. The proposed model showed 93% of overall classification accuracy which was 4% to 8% higher than that of the conventional NN methods.
At devastated land around Asio copper mine, erosion control work to restore vegetation has been executed since 1957 in large expense. In this paper, to evaluate erosion control work, we develop a vegetation restoration model and simulate vegetation restoration using remote sensing and GIS. The vegetation restoration model is based on the Mitscherlich's growth curve that is applied to vegetation index. Model parameters were decided by analyzing remote sensing data as time series data with various ground data. We found that erosion control work makes restoration speed twice. We propose a new index PRV to describe proximity to remaining vegetation. The PRV has a highly positive relationship with restoration speed. It suggests that the vast forest destruction makes natural vegetation restoration to be difficult. Finally, restoration simulation for 200 years was executed under the several scenarios. As a result, a remarkable effect of erosion control work for vegetation restoration was recognized.
Automated house extraction from aerial photographs is one of the most critical steps in updating GIS databases or developing digital photogrammetric systems. In this paper, a method for extracting houses from the large-scale aerial imageries of urban areas mainly based upon both the region-based and line-based stereo matching schemes is proposed. Wavelet transform is utilized to detect edge features from images. Hough transform is employed to connect fragmentary line segments to improve reliablity in region/line-based stereo matching. Finally, houses/buildings are extracted by recognizing height difference between houses/buildings and terrain surface. The experimental results obtained with the method are demonstrated.