Business Model Alchemist. Ultimately, customers are the only relevant judges of your business model. However, even before you test your model in the market, you can assess its design with 7 questions that go well beyond the conventional focus on products and market segments. First things first. In order to assess your business model you should sketch it out on the Business Model Canvas outlined in the video below. Collection Functions Arrays or Objects each. Alias forEach Iterates over a list of elements, yielding each in turn to an iteratee. After several years on the market there are now multiple Business Model Canvas adaptations floating around. People sometimes ask me about them. Edge Based Template Matching Code' title='Edge Based Template Matching Code' />Setup. The goals of development and production builds differ greatly. In development, we want strong source mapping and a localhost server with live reloading or hot. If you want to know more about the Canvas and how to use it you can read Business Model Generation of which 7. Assessing the basics. Every business model has a product andor service at its center that focuses on a customers job to be done. I call this the Value Proposition. Rdio. New Rdio was developed from the ground up with a component based framework based on Backbone. Every component on the screen is dynamically loaded and. Cisco UCS Integrated Infrastructure for Big Data and Analytics. The Cisco UCS Integrated Infrastructure for Big Data and Analytics solution is based on Cisco UCS. Sinatra is a DSL for quickly creating web applications in Ruby with minimal effort myapp. Hello world end. Run Accesskey R Save Accesskey S Download Fresh URL Open Local Reset Accesskey X. MSDN Magazine Issues and Downloads. Read the magazine online, download a formatted digital version of each issue, or grab sample code and apps. Vol. 7, No. 3, May, 2004. Mathematical and Natural Sciences. Study on Bilinear Scheme and Application to Threedimensional Convective Equation Itaru Hataue and Yosuke. D2ci43VsI/TqkMuKKuxbI/AAAAAAAAAQs/L1WcvYAEOzM/s1600/sift1.JPG' alt='Edge Based Template Matching Code' title='Edge Based Template Matching Code' />So before even turning to your business model as a whole, you need to ask yourself some basic questions related to your Value Proposition and the Customer Segments that you are targeting. First, ask yourself how well your Value Proposition is getting your target customers job done. For example, if a user of a search engine is trying to find and purchase the latest Nike running shoe, the measure of success will be how well the search engine helps the user get this job done. Secondly, ask yourself how many people or companies there are with a similar job to be done. This will give you the market size. Thirdly, ask yourself how important this job really is for the customer and if she actually has a budget to spend on it. Thats it as to the basics. However, even the greatest products are having an increasingly hard time to achieve a long term competitive advantage. That is the reason why you need to shift your focus away from a pure productmarket segment oriented approach towards a more holistic business model approach. Below are eight questions to assess your business model design. Rank your business models performance on a scale of 0 bad to 1. How much do switching costs prevent your customers from churningThe time, effort, or budget a customer has to spend to switch from one product or service provider to another is called switching costs. The higher the switching costs, the likelier a customer is to stick to one provider rather than to leave for the products or services of a competitor. A great example of designing switching costs into a business model is Apples introduction of the i. Pod in 2. 00. 1. Do you remember how Steve Jobs heralded his new product with the catchphrase thousand songs in a pocket Well, that was more than a product innovation focusing on storage. It was a business model strategy to get customers to copy all their music into i. Tunes and their i. Pod, which would make it more difficult for them to switch to competing digital music players. In a time when little more than brand preferences were preventing people from switching from one player to another this was a smart move and laid the foundation for Apples subsequent stronghold on music and later innovations. How scalable is your business model Scalability describes how easy it is to expand a business model without equally increasing its cost base. Of course software and Web based business models are naturally more scalable than those based on bricks and mortar, but even among digital business models there are large differences. An impressive example of scalability is Facebook. With only a couple of thousand of engineers they create value for hundreds of millions of users. Only few other companies in the world have such a ratio of users per employee. A company that has pushed the limits even further is the social gaming company Zynga. By building games like Farmville or Cityville on the back Facebook, the worlds largest social network, they could benefit from Facebooks reach and scale without having to build it themselves. A company that quickly learned its lessons regarding scalability was peer to peer communication company Skype in its early days. Their customer relationship collapsed under the weight of large numbers, when they were signing up ten thousands of users per day. They quickly had to adapt their business model to become more scalable. Does your business model produce recurring revenuesRecurring revenues are best explained through a simple example. When a newspaper earns revenues from the sales at a newsstand they are transactional, while revenues from a subscription are recurring. Recurring revenues have two major advantages. Firstly, the costs of sales incur only once for repetitive revenues. Secondly, with recurring revenues you have a better idea of how much you will earn in the future. A nice example of recurring revenues is Redhat, which provides open source software and support to enterprises based on a continuous subscription basis. In this model clients dont pay for new software versions because it is continuously updated. In the world of Software as a Service Saas these types of subscriptions are now the norm. This contrasts with Microsoft, which sells most of its software in the form of licenses for every major release. However, there is another aspect to recurring revenues, which are additional revenues generated from an initial sales. For example, when you buy a printer, you continue to spend on cartridges, or when you buy a game console, youll continue to spend on games. Or have a look at Apple. While they still earn most of their revenues from hardware sales, the recurring revenues from content and apps is steadily growing. Do you earn before you spend This one goes without saying. The more you can earn before spending, the better. Dell pioneered this model in the computer hardware manufacturing industry. By assembling on order after selling directly they managed to escape the terrible inventory depreciation costs of the hardware industry. Results showed how powerful it is to earn before spending. How much do you get others to do the work This is probably one of the least publicized weapons of mass destruction in business model design. What could be more powerful than getting others to do the work while you earn the money In the bricks and mortar world IKEA gets us to assemble the furniture we buy from them. We do the work. They save money. On the web Facebook gets us to post photos, create and participate in conversations, and like stuff. Thats the real value of Facebook, entirely created by users, while they simply provide the platform. We do the work. They earn the sky high valuations of their shares. Previously mentioned Redhat crafted another smart business model based on other peoples work. Their entire business model is built on top of software developed by the open source software development community. Easy 3D Character Modeling Program on this page. This allowed them to substantially reduce their development costs and compete head on with larger companies like Microsoft. A more malicious business model in which others do the work is the one practiced by so called patent trolls. In this model patents are purchased with the sole intention of suing successful companies to extract payments from them. Does your business model provide built in protection from competition A great business model can provide you with a longer term protection from competition than just a great product. Apples main competitive advantage arises more from its powerful business model than purely from its innovative products. Its easier for Samsung, for instance, to copy the i. Phone than to build an ecosystem like Apples appstore, which caters to developers and users alike and hosts hundred thousands of applications. Corner detection Wikipedia. Output of a typical corner detection algorithm. Corner detection is an approach used within computer vision systems to extract certain kinds of features and infer the contents of an image. Corner detection is frequently used in motion detection, image registration, video tracking, image mosaicing, panorama stitching, 3. D modelling and object recognition. Corner detection overlaps with the topic of interest point detection. FormalizationeditA corner can be defined as the intersection of two edges. A corner can also be defined as a point for which there are two dominant and different edge directions in a local neighbourhood of the point. An interest point is a point in an image which has a well defined position and can be robustly detected. This means that an interest point can be a corner but it can also be, for example, an isolated point of local intensity maximum or minimum, line endings, or a point on a curve where the curvature is locally maximal. In practice, most so called corner detection methods detect interest points in general, and in fact, the term corner and interest point are used more or less interchangeably through the literature. As a consequence, if only corners are to be detected it is necessary to do a local analysis of detected interest points to determine which of these are real corners. Examples of edge detection that can be used with post processing to detect corners are the Kirsch operator and the Frei Chen masking set. Corner, interest point and feature are used interchangeably in literature, confusing the issue. Specifically, there are several blob detectors that can be referred to as interest point operators, but which are sometimes erroneously referred to as corner detectors. Moreover, there exists a notion of ridge detection to capture the presence of elongated objects. Corner detectors are not usually very robust and often require large redundancies introduced to prevent the effect of individual errors from dominating the recognition task. One determination of the quality of a corner detector is its ability to detect the same corner in multiple similar images, under conditions of different lighting, translation, rotation and other transforms. A simple approach to corner detection in images is using correlation, but this gets very computationally expensive and suboptimal. An alternative approach used frequently is based on a method proposed by Harris and Stephens below, which in turn is an improvement of a method by Moravec. Moravec corner detection algorithmeditThis is one of the earliest corner detection algorithms and defines a corner to be a point with low self similarity. The algorithm tests each pixel in the image to see if a corner is present, by considering how similar a patch centered on the pixel is to nearby, largely overlapping patches. The similarity is measured by taking the sum of squared differences SSD between the corresponding pixels of two patches. Audra Mcdonald Go Back Home'>Audra Mcdonald Go Back Home. A lower number indicates more similarity. If the pixel is in a region of uniform intensity, then the nearby patches will look similar. If the pixel is on an edge, then nearby patches in a direction perpendicular to the edge will look quite different, but nearby patches in a direction parallel to the edge will result in only in a small change. If the pixel is on a feature with variation in all directions, then none of the nearby patches will look similar. The corner strength is defined as the smallest SSD between the patch and its neighbours horizontal, vertical and on the two diagonals. The reason is that if this number is high, then the variation along all shifts is either equal to it or larger than it, so capturing that all nearby patches look different. If the corner strength number is computed for all locations, that it is locally maximal for one location indicates that a feature of interest is present in it. As pointed out by Moravec, one of the main problems with this operator is that it is not isotropic if an edge is present that is not in the direction of the neighbours horizontal, vertical, or diagonal, then the smallest SSD will be large and the edge will be incorrectly chosen as an interest point. The Harris Stephens Plessey ShiTomasi corner detection algorithmseditHarris and Stephens4 improved upon Moravecs corner detector by considering the differential of the corner score with respect to direction directly, instead of using shifted patches. This corner score is often referred to as autocorrelation, since the term is used in the paper in which this detector is described. Cab Installer Windows Ce 6.0. However, the mathematics in the paper clearly indicate that the sum of squared differences is used. Without loss of generality, we will assume a grayscale 2 dimensional image is used. Let this image be given by Idisplaystyle I. Consider taking an image patch over the area u,vdisplaystyle u,v and shifting it by x,ydisplaystyle x,y. The weighted sum of squared differences SSD between these two patches, denoted Sdisplaystyle S, is given by Sx,yuvwu,vIux,vyIu,v2displaystyle Sx,ysum usum vwu,v,leftIux,vy Iu,vright2Iux,vydisplaystyle Iux,vy can be approximated by a Taylor expansion. Let Ixdisplaystyle Ix and Iydisplaystyle Iy be the partial derivatives of Idisplaystyle I, such that. Iux,vyIu,vIxu,vxIyu,vydisplaystyle Iux,vyapprox Iu,vIxu,vxIyu,vyThis produces the approximation. Sx,yuvwu,vIxu,vxIyu,vy2,displaystyle Sx,yapprox sum usum vwu,v,leftIxu,vxIyu,vyright2,which can be written in matrix form Sx,yxyAxy,displaystyle Sx,yapprox beginpmatrixx yendpmatrixAbeginpmatrixxyendpmatrix,where A is the structure tensor,Auvwu,vIxu,v2. Ixu,vIyu,vIxu,vIyu,vIyu,v2Ix. Ix. IyIx. IyIy. Asum usum vwu,vbeginbmatrixIxu,v2 Ixu,vIyu,vIxu,vIyu,v Iyu,v2endbmatrixbeginbmatrixlangle Ix2rangle langle IxIyrangle langle IxIyrangle langle Iy2rangle endbmatrixThis matrix is a Harris matrix, and angle brackets denote averaging i. If a Box filter is used the response will be anisotropic, but if a Gaussian is used, then the response will be isotropic. A corner or in general an interest point is characterized by a large variation of Sdisplaystyle S in all directions of the vector xydisplaystyle beginpmatrixx yendpmatrix. By analyzing the eigenvalues of Adisplaystyle A, this characterization can be expressed in the following way Adisplaystyle A should have two large eigenvalues for an interest point. Based on the magnitudes of the eigenvalues, the following inferences can be made based on this argument If 10displaystyle lambda 1approx 0 and 20displaystyle lambda 2approx 0 then this pixel x,ydisplaystyle x,y has no features of interest. If 10displaystyle lambda 1approx 0 and 2displaystyle lambda 2 has some large positive value, then an edge is found. If 1displaystyle lambda 1 and 2displaystyle lambda 2 have large positive values, then a corner is found.