A new computer vision (CV) software library has been launched for the development of "vision-enabled" applications targeting the mobile, home, PC, and automotive markets. Built by CEVA Inc, the new library is optimized for the firm's own CEVA-MM3101 imaging and vision platform. This is positioned as a tool for application developers to add vision capabilities to System-on-Chip (SoC) systems incorporating the CEVA-MM3101. NOTE: So-called "vision enabled" applications can be found in areas such as wireless (or wired) sensor networks, mobile computing scanning apps, PCs, smart TVs, natural user interface (NUI) devices, and advanced driver assistance systems (ADAS). CEVA-CV is based on OpenCV, a standard library of programming functions for computer vision processing.
This video course is a practical guide for developers who want to get started with building computer vision applications using Python 3. The video is divided into six sections: Throughout this video course, three image processing libraries: Pillow, Scikit-Image, and OpenCV are used to implement different computer vision algorithms. The course will help you build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that we look at in the course are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet. Saurabh Kapur is a computer science student at Indraprastha Institute of Information Technology, Delhi. His interests are in computer vision, numerical analysis, and algorithm design.
Our graphic design application--whose current prototype is built on top of the SchemePaint system [Eisenberg 1991]--includes a (still rudimentary) direct manipulation interface for selecting the type of charthat the user wishes to create (e.g., bar chart, line chart, scatter plot, and so forth); having selected this type of chart, the user is able to look at a variety of relevant examples built using the system, and employing language primitives (embedded in the Scheme programming language) appropriate to building this particular kind of chart. For instance, a user who wishes to create a bar chart is presented with a palette showing a variety of "specialty" bar charts (multicolored bars, bars with non-horizontal uppermost lines, bars going above and below the horizontal axis, etc.); the user can then access a language tutorial specifically geared to this particular type of chart (e.g., what language form was used to create this particular sample), and can access additional tutorials on the proper use and critiquing for this type of chart (that is, when this sort of graph might be useful, or counterproductive, and why). In presenting the user with a variety of examples (chosen for their pedagogical or illustrative value), the application is supplying some context-driven "feature" presentation; that is, the system is providing the user with some (presumably popular) alternatives for creating new charts. On the other hand, because the system presents not merely a choice of existing styles of work, but rather an extensive programming environment, it gives the user the (more difficult but ultimately more powerful) option of developing new types of charts on his or her own; moreover, it provides the user with assistance in developing expertise in the system-supplied language.
Ever since the dawn of the Industrial Revolution, people have been automating work to make it more efficient, drive down costs, and relieve employees from the drudgery of mundane tasks. And the automation of today's business applications is already quite sophisticated. Now we're on the eve of another Industrial Revolution as machine learning takes automation to another level, allowing computers to make decisions on our behalf. But how exactly does it work? Automation in conventional computer programming is rule-based: If x and y conditions are met, then z can occur.