Abstract: Evidence shows that start-ups success is positively
correlated with the launch of the first product. However, new ventures
are seldom able to acquire abundant resources for new product
development (NPD), which means that entrepreneurs may depend on
personal creativity instead of physical investments to achieve and
accelerate innovation speed. This study accentuates the role of
entrepreneurial bricolage, which defined as making do by applying
combinations of the resources at hand to new problems and
opportunities, in the relations of creative self-efficacy and innovation
speed. This study uses the multiple regression analysis to test the
hypotheses in a sample of 203 start-ups operating in various creative
markets in Taiwan. Results reveal that creative self-efficacy is
positively and directly associated with innovation speed, whereas
entrepreneurial bricolage plays a full mediator. These findings offer
important theoretical and practical implications.
Abstract: Facial features are frequently used to represent local
properties of a human face image in computer vision applications. In
this paper, we present a fast algorithm that can extract the facial
features online such that they can give a satisfying representation of a
face image. It includes one step for a coarse detection of each facial
feature by AdaBoost and another one to increase the accuracy of the
found points by Active Shape Models (ASM) in the regions of interest.
The resulted facial features are evaluated by matching with artificial
face models in the applications of physiognomy. The distance measure
between the features and those in the fate models from the database is
carried out by means of the Hausdorff distance. In the experiment, the
proposed method shows the efficient performance in facial feature
extractions and online system of physiognomy.