In Innovation as Usual: How to Help Your People Bring Great Ideas to Life (2013), Miller and Wedell-Wedellsborg discuss the importance of establishing systems within organizations that promote not only the creativity that results in innovation, but also make it possible for employees to bring innovative ideas to fruition. Miller and Wedell-Wedellsborg argue that a leaderâs primary job “is not to innovate; it is to become an innovation architect, creating a work environment that helps . . . people engage in the key innovation behaviors as part of their daily work” (page 4). Such a work environment must be reinforced by innovation architectureâthe structures within an organization that support an innovation, from the brainstorming phase to final realization. The more well developed the architecture and the simpler the processes involved, the more likely employees are to be innovators.
For this assignment, you will research the innovation architecture of at least three companies that are well-known for successfully supporting a culture of innovation. Write a 1,500-word paper that addresses the following:
What particular elements of each organizationâs culture, processes, and management systems and styles work well to support innovation?
Why do you think these organizations have been able to capitalize on innovation and intrapreneurship while others have not?
Based on what you have learned, what processes and systems might actually stifle innovation and intrapreneurship?
Imagine yourself as an innovation architect. What structures or processes would you put in place to foster a culture of innovation within your own organization?
Sample Solution
A few examples of the many methods and algorithm that can be used within the field of facial recognition are: Geometric Feature Based Methods, Template Based Methods, Correlation Based Methods, Matching, Pursuit Based Methods, Singular Value Decomposition Based Methods, The Dynamic Link, Matching Methods, Illumination Invariant Processing Methods, Support Vector Machine Approach, Karhunen- Loeve Expansion Based Methods, Feature Based Methods, Neural Network Based Algorithms and Model Based Methods [25]. Later on, one of the most known methods will be discussed in a detailed way. The facial recognition methods that can be used, all have a different approach. Some are more frequently used for facial recognition algorithms than others. The use of a method also depends on the needed applications. For instance, surveillance applications may best be served by capturing face images by means of a video camera while image database investigations may require static intensity images taken by a standard camera. Some other applications, such as access to top security domains, may even necessitate the forgoing of the nonintrusive quality of face recognition by requiring the user to stand in front of a 3D scanner or an infrared sensor[15]. Consequently, there can be concluded that there can be made a division of three groups of face recognition techniques, depending on the wanted type of data results, i.e. methods that compare images, methods that look at data from video cameras and methods that deal with other sensory data, like 3D pictures or infrared imagery. All of them can be used in different ways, to prevent crime from happening or recurring.>
A few examples of the many methods and algorithm that can be used within the field of facial recognition are: Geometric Feature Based Methods, Template Based Methods, Correlation Based Methods, Matching, Pursuit Based Methods, Singular Value Decomposition Based Methods, The Dynamic Link, Matching Methods, Illumination Invariant Processing Methods, Support Vector Machine Approach, Karhunen- Loeve Expansion Based Methods, Feature Based Methods, Neural Network Based Algorithms and Model Based Methods [25]. Later on, one of the most known methods will be discussed in a detailed way. The facial recognition methods that can be used, all have a different approach. Some are more frequently used for facial recognition algorithms than others. The use of a method also depends on the needed applications. For instance, surveillance applications may best be served by capturing face images by means of a video camera while image database investigations may require static intensity images taken by a standard camera. Some other applications, such as access to top security domains, may even necessitate the forgoing of the nonintrusive quality of face recognition by requiring the user to stand in front of a 3D scanner or an infrared sensor[15]. Consequently, there can be concluded that there can be made a division of three groups of face recognition techniques, depending on the wanted type of data results, i.e. methods that compare images, methods that look at data from video cameras and methods that deal with other sensory data, like 3D pictures or infrared imagery. All of them can be used in different ways, to prevent crime from happening or recurring.>
