Abstract: A screwing tool provided with a high-torsion handle including a screwing tool body and a screwing tool handle is provided. An inner hole in an irregular shape is formed in the screwing tool handle, and the shape of the top end of the screwing tool body is the same as that of the inner hole. The screwing tool body and the screwing tool handle are fixed to each other via high-strength glue, and the inner hole of the screwing tool handle is a semi-cylindrical inner hole, a triangular inner hole, a quadrilateral inner hole, a hexagonal inner hole, an octagonal inner hole or a decagonal inner hole. Since the inner hole of the handle is machined into the irregular polygon, all types of screwing tools including drill bits can be fixed to the screwing tool handle via the high-strength glue.

Abstract: The present invention relates to a method and system for foreground segmentation in which frames of a video sequence are analyzed in the transform domain to determine one or more features. The features are used to model the background. The background can be modeled as a single Gaussian model with a mean and variance of the features. A current frame is segmented by determining if one or more features of the current frame analyzed in the foreground domain satisfy a threshold between the background model. The threshold value can be based on the mean and/or variance of features. During the segmentation, the mean and variance can be updated based on previous corresponding values and current features to adaptively update the background model. In one embodiment, the frames are divided into a plurality of blocks. A transform is used to analyze the blocks in the transform domain. For example, the transform can be a discrete cosine transform (DCT).

Abstract: The present invention relates to a method and system for foreground segmentation in which frames of a video sequence are analyzed in the transform domain to determine one or more features. The features are used to model the background. The background can be modeled as a single Gaussian model with a mean and variance of the features. A current frame is segmented by determining if one or more features of the current frame analyzed in the foreground domain satisfy a threshold between the background model. The threshold value can be based on the mean and/or variance of features. During the segmentation, the mean and variance can be updated based on previous corresponding values and current features to adaptively update the background model. In one embodiment, the frames are divided into a plurality of blocks. A transform is used to analyze the blocks in the transform domain. For example, the transform can be a discrete cosine transform (DCT).