The process of strength-power training and the subsequent adaptation is a multi-factorial process. These factors range from the genetics and morphological characteristics of the athlete to how a coach selects, orders,...The process of strength-power training and the subsequent adaptation is a multi-factorial process. These factors range from the genetics and morphological characteristics of the athlete to how a coach selects, orders, and doses exercises and loading patterns. Consequently, adaptation from these training factors may largely relate to the mode of delivery, in other words, programming tactics. There is strong evidence that the manner and phases in which training is presented to the athlete can make a profound difference in performance outcome. This discussion deals primarily with block periodization concepts and associated methods of programming for strength-power training within track and field. 2015 Production and hosting by Elsevier B.V. on behalf of Shanghai University of Sport.展开更多
The paper proposes a new method of ＂Separated Same Rectangle Feature （SSRF）＂ for face detection. Generally, Haar-like feature is used to make an Adaboost training algorithm with strong classifier. Haar-like featur...The paper proposes a new method of ＂Separated Same Rectangle Feature （SSRF）＂ for face detection. Generally, Haar-like feature is used to make an Adaboost training algorithm with strong classifier. Haar-like feature is composed of two or more attached same rectangles. Inefficiency of the Haar-like feature often results from two or more attached same rectangles. But the proposed SSRF are composed of two separated same rectangles. So, it is very flexible and detailed. Therefore it creates more accurate strong classifier than Haar-like feature. SSRF uses integral image to reduce execuive time. Haar-like feature calculates the Sanl of intmsities of pixels on two or more rectangles. But SSRF always calculates the stun of intensities of pixels on only two rectangles. The weak classifier of Ariaboost algorithm based on SSRF is fastex than one based on Haar-like feature. In the experiment, we use 1 000 face images and 1 000nm- face images for Adaboost training. The proposed SSRF shows about 0.9% higher acctwacy than Haar-like features.展开更多
文摘The process of strength-power training and the subsequent adaptation is a multi-factorial process. These factors range from the genetics and morphological characteristics of the athlete to how a coach selects, orders, and doses exercises and loading patterns. Consequently, adaptation from these training factors may largely relate to the mode of delivery, in other words, programming tactics. There is strong evidence that the manner and phases in which training is presented to the athlete can make a profound difference in performance outcome. This discussion deals primarily with block periodization concepts and associated methods of programming for strength-power training within track and field. 2015 Production and hosting by Elsevier B.V. on behalf of Shanghai University of Sport.
基金supported by the Korea Research Foundation Grant funded by the Korean Government(MOEHRD),the MKE(The Ministry of Knowledge Economy,Korea)the ITRC(Information Technology Research Center)support program(NIPA-2009-(C1090-0902-0007))
文摘The paper proposes a new method of ＂Separated Same Rectangle Feature （SSRF）＂ for face detection. Generally, Haar-like feature is used to make an Adaboost training algorithm with strong classifier. Haar-like feature is composed of two or more attached same rectangles. Inefficiency of the Haar-like feature often results from two or more attached same rectangles. But the proposed SSRF are composed of two separated same rectangles. So, it is very flexible and detailed. Therefore it creates more accurate strong classifier than Haar-like feature. SSRF uses integral image to reduce execuive time. Haar-like feature calculates the Sanl of intmsities of pixels on two or more rectangles. But SSRF always calculates the stun of intensities of pixels on only two rectangles. The weak classifier of Ariaboost algorithm based on SSRF is fastex than one based on Haar-like feature. In the experiment, we use 1 000 face images and 1 000nm- face images for Adaboost training. The proposed SSRF shows about 0.9% higher acctwacy than Haar-like features.
