Motion segmentation is a crucial step for video analysis and has many applications. This paper proposes a method for motion segmentation, which is based on construction of statistical background model. Variance and Covariance of pixels are computed to construct the model for scene background. We perform average frame differencing with this model to extract the objects of interest from the video frames. Morphological operations are used to smooth the object segmentation results. The proposed technique is adaptive to the dynamically changing background because of change in the lighting conditions and in scene background. The method has the capability to relearn the background to adapt these variations. The immediate advantage of the proposed method is its high processing speed of 30 frames per second on large sized (high resolution) videos. We compared the proposed method with other five popular methods of object segmentation in order to prove the effectiveness of the proposed technique. Experimental results demonstrate the novelty of the proposed method in terms of various performance parameters. The method can segment the video stream in real-time, when background changes, lighting conditions vary, and even in the presence of clutter and occlusion
Food preservation has been practised in many parts of the world for thousands of years, and it applies to a wide range of foods, including fruits, vegetables, cereals, and meat. Food preservation techniques are canning, freezing, pickling, curing (smoking or salting), and drying. Food spoilage caused by moisture is caused by the growth of mould, yeast, bacteria, and enzymes in the food. The drying process removes enough moisture from food to significantly reduce the humidity level’s likelihood of these adverse outcomes. The material's content measures how much moisture is present in that substance. The moisture content of fresh food can range from 20 to 90 percent depending on the type of food consumed. There will be no signs of moisture in food that has been thoroughly dried before being chopped. We used the experimental analysis in this study to create a mathematical model that could be used to determine which parameter was most important in the design of a solar dryer. In the future, the model is expected to be a helpful design tool for estimating the short- and long-term performance of a solar dryer under load and overload scenarios. The simulation of a solar dryer system has been performed under conditions such as the gap between the glass and the absorber plate, and the impact of hole size. The optimal hole size and spacing between the glass and the absorber plate are determined.
Abstract. Maritime surveillance represents a challenging scenario for moving object segmentation due to the complexity of the observed scenes. The waves on the water surface, boat wakes, and weather issues contribute to generate a highly dynamic background. Moving object segmentation using change detection under maritime environment is a challenging problem for the maritime surveillance system. To address these issues, a fast and robust moving object segmentation approach is proposed which consist of seven steps applied on given video frames which include wavelet decomposition of frames using complex wavelet transform; use of change detection on detail coefficients (LH, HL, HH); use of background modeling on approximate co-efficient (LL sub-band); cast shadow suppression; strong edge detection; inverse wavelet transformation for reconstruction; and finally using closing morphology operator. For dynamic background modeling in the water surface, we have used background registration, background difference, and background difference mask in the complex wavelet domain. For shadow detection and suppression problem in water surface, we exploit the high frequency sub-band in the complex wavelet domain. A comparative analysis of the proposed method is presented both qualitatively and quantitatively with other standard methods available in the literature for seven datasets. The various performance measures used for quantitative analysis include relative foreground area measure (RFAM), misclassification penalty (MP), relative position based measure (RPM), normalized cross correlation (NCC), Precision (PR), Recall (RE), shadow detection rate (SDR), shadow discrimination rate, execution time and memory consumption. Experimental results indicate that the proposed method is performing better in comparison to other methods in consideration for all the test cases as well as addresses all the issues effectively.
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