Three Existing Interactive Applications That Use Computer Vision - Essay Example

Thrее ехisting intеrасtivе аррliсаtiоns thаt usе соmрutеr visiоn Your name: Institution name: 1.0 Overview Computer vision is a discipline of science that “teaches machines how to see” things. This field of science can be dated back more than four decades ago, but the recent explosive growth of digital imaging technology has made the problems of automated image interpretation more relevant and exciting than before. There are two major themes that are involved in the computer vision literature: #D geometry and recognition. The first theme is about using as a source of metric 3D information: given one or more picture of a scenario that has been taken by a camera with known or unknown parameters. The second theme, is all about vision as a source of semantic information: can we recognize people, objects, or activities pictured in the images, and understand the relationships and structure of different scene components just as human beings would. This paper will review three interactive applications that used computer vision. Mobile Robot Fig 1: Mobile Robot Application Description Mobile Robot is a class of robot that has the ability to transport themselves in 3 dimensional space. While the controller need not to be physically present on the mobile platform (John, 2015). Mobile robots are mostly used in research on exploration and navigation, with applications for autonomous guided vehicles. Working Mechanic The centrepiece of the robotic control system is the control board (John, 2015). The controllers will make the decision that are based on the available sensor data and then the instruction will be send to the motors to control the robot’s movements (John, 2015). There are subsystems that are required for robotic movements: I/O Interface- the robot will communicate with variety of actuators and sensors such as LIDAR and GPS that are commonly use a serial interface or USB, while motors will require a CAN interface or digital port (John, 2015). Low-level control- state-space equations or PUD loops will be implemented to perform processing based on sensor feedback. Autonomous navigation system- this is used for planning and perception (John, 2015). Once the robot perceives and understands its sensor data, the data will then passed to a higher-level planning module. User Interface- it is used to display data or information on the robot’s health, such as hardware failures or power consumption levels. Fig 2 basic flow of information around the robot system Significance of computer vision Computer vision is significant important in a mobile robot because its ability to go to specific locations in one’s environment in a timely manner is important in creating a full autonomous robotic system (John, 2015). To do so, a camera must be installed to the robot are able to acquire information from the environment. In addition, cameras will assist during remote controlling the robot. Strength/Weakness of the application While robot localization and navigation have made a lot of strides on the road and indoors. The computer vision have helped robots to recognize their environments (John, 2015). However, one disadvantage of computer vision is that because of the complexity of the camera image, vision may come with a lot of challenges such as perspective, lighting, and occlusions. Social impact of the application Robots have been used to guide the disabled people in their movements; and this will make the visual impaired person live an ordinary life (John, 2015). In addition, the environmental conditions inside a greenhouse is characterised by high humidity and temperatures, sometimes hazardous for people especially when they are using toxins (John, 2015). Nowadays, robots are used to perform this tasks thus minimising accidents to works in the greenhouses. 2.0 Medical Image Analysis Fig 3 MRI scan Application Description In medical sector, computer vision will allow doctors or physician to make extensive use of medical imaging data to provide doctors with better diagnosis, predication of diseases and treatment (Zhang, 2012). Computer vision has been able to exploit shape, texture, contour and prior knowledge along with contextual information from image sequences and provide both 4D and 3D information that will help doctors understand information well (Zhang, 2012). Many powerful tools and equipment have been available through machine learning, image segmentation, reconstruction, tracking, and pattern classification to bring much needed quantitative information that is not easily available by trained human specialists. CT scan Operations Magnet fields are created by passing electric current the MRI scanner wire loops. While other coils in the magnet will send and receive radio waves (Payne, 2012). This will trigger protons in the patient body to align themselves. Once the protons have aligned themselves they will be absorbed by the protons, which will stimulate spinning. Energy that is released after “the exciting” of the molecules will emits energy signals that will be picked by the coil (Payne, 2012). This information is then sent to the computer which processes all the signals and in returns it generates the image (Payne, 2012). The final product will be a 3-D image that represent an area that is being examined by the doctor. Fig 4: MRI System block diagram Computer vision significance Computer vision plays a significant role in the field of MRI because this field is characterised by the extraction of information from image data for the purpose of making a diagnosis of a patient (Payne, 2012). The image is usually in the form of a microscopy images which is then turned into an imaged which can be understood well by doctors. Strength/Weakness of the application The advantage of a MRI scanner is that the human soft tissues contrast is depicted into greater details (Payne, 2012). In addition, structures that may be obscured by artifacts from bones can be evaluated with ease. While the weakness of an MRI is that is expensive which cannot be afforded by a common person. Social Impact MRI has been recognized as a valuable medical tool because it is painless and non-invasive diagnosis of illness and monitoring of therapy (Zhang, 2012). In addition, it has been seen to support surgical treatment and medical planning. 3.0 Cinematography Fig 5: Image distortion Description of Cinematography In Cinematography computer vision is used to capture the actor’s movement, which is then combined with computer generated graphics to create a new character such as Gollum in the Lord of the Rings Movie (Clegg, 2007). Detail working mechanic behind cinematography A camera lens is repeatedly used to focus the emitted or reflected light from objects into real images on the light-sensitive surface that is found inside the camera during a timed exposure and this will create many images (Clegg, 2007). When electronic image sensor are employed, it will produce an electrical charge at each image pixel, which will then be electronically be produced and stored in a file which subsequently processing or display (Landecker, 2006). The result of this concept will be photographic emulsion which will developed into a visible pictures. Fig 6: computer image processing Strength/Weakness The strength of computer vision in cinematography is that is used to enable film maker to model and manipulate the environment and people by using visual sensing (Clegg, 2007). In addition, it improves movie or film creativity by creating existing characters. The weakness of cinematography is expensive and require a lot of input to create one character. Social Impact of application Cinematography has had one significant cultural role for film, it has expanded the horizons for films viewers beyond their concerns, their communities and even their countries (Clegg, 2007). This has enable people to see directly into different worlds (Landecker, 2006). For many years many movies have served powerful mechanism for humanitarian and social change. References Clegg, Brian (2007). The Man Who Stopped Time. Joseph Henry Press. Landecker, Hannah (2006). "Microcinematography and the History of Science and Film". Isis. John Daintith. (2015). "mobile robotics." A Dictionary of Computing. 2004. Encyclopedia.com. . Payne, Emma Marie (2012). "Imaging Techniques in Conservation". Journal of Conservation and Museum Studies (Ubiquity Press): 17–29 Zhang Y. (2012). Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm. IEEE Trans. Med. Imag. 20(1):45–57 Read More