Digital video: Guides og brugervejledninger

Software quality is a development process which fulfills and satisfies the user¿s requirement or customers need. SQA is a continuous process of the software development life cycle (SDLC) that checks the developed product to ensure it meets prescribed quality measures. SQA helps make sure the development of high-quality software product. SQA practices are performed in all sorts of software product development, in spite of the underlying software product development model being used.

The book presents a comparative view of object oriented metrics, on the basis of different results of metrics, implement with two Object Oriented languages C++ and Java. The Java is more object-oriented and known as pure object oriented language where C++ is only object oriented language. Mainly the comparison is done on the basis of Size, Complexity, Re usability, Information Hiding, Coupling and Cohesion and also describes experimental results with the help of table and charts.

There has been an extensive demand in the use of databases for decision support in these days. This was mainly due to the fact that information, one of the most precious assets of an organization, can assist in decision making and this way considerably improves the value of an organization. This phenomenon is a result of the increased availability of new technologies to support capable storage and retrieval of large volumes of data, namely data warehousing. One of the most important requirements of a data warehouse server is the query performance. The principal aspect from the user perspective is how quickly the server processes a given query: ¿the data warehouse must be fast¿. The main focus of our research is finding adequate solutions to improve query response time of typical data ware house queries and improve scalability using an environment that takes advantage of characteristics specific to the data ware house context. Our propose model provides very good performance and scalability even on huge data warehouses.

For the last two decades ¿active contour¿ or ¿snake¿ has been effective as an interactive image segmentation tool in a wide range of applications. Although successful as an interactive segmentation tool, snake exhibits poor performances in various noteworthy image segmentation applications that require complete automation. This book presents a novel, completely automated snake/active contour algorithm for multiple blob-object delineation. The algorithm consists of three sequential steps: snake initialization, snake evolution and snake validation. Existing efforts towards snake automation have concentrated only on the succession of initialization and evolution steps and have practically overlooked the snake validation step. Here, we emphasize that we cannot skip the validation step, even though the initialization and evolution have performed well. Our proposed novel validation step, executed after complete convergence of a snake contour from a given initialization, classifies the evolved contour into desired object and non-object classes. In the validation step, we classify the snakes into object and non-object classes using a novel adaptive regularized boosting.