Lecture 1 and 2 PDF

Human Visual System: consists of two parts: eye and brain. human eye: acts as a receptor of images by capturing light and converting it into signals. The human eye is analogous to a camera. These signals are then transmitted to the brain for further analysis. Eyes and brain work in combination t...

Human Visual System: consists of two parts: eye and brain. human eye: acts as a receptor of images by capturing light and converting it into signals. The human eye is analogous to a camera. These signals are then transmitted to the brain for further analysis. Eyes and brain work in combination to form a picture. Image acquisition: aim to obtain the digital image from the object. SUN Retinal Image from lens The basic lens equation is 𝟏 𝟏 𝟏 + = 𝒖 𝒗 𝒇 𝑢 → distance between object and lens 𝑣 → distance between lens and retinal image Note → the distance between lens and retina varies from 14 to 17 mm. 𝑓 → Focal length 𝒖𝑴 𝒇= 𝑴+𝟏 M is Magnification Factor. Magnification factor is defined as the ratio of size of image to the size of object. 𝑶 𝑰 = 𝒖 𝒗 𝑂 → Object size 𝐼 → Retina Image size Drill Problem: An Image is 2400 pixels wide and 2400 pixels high. The image was scanned at 300 dpi. What is the physical size of the image. Physical size of the image = No. of pixels in heights (i.e. resolution) × = No. of pixels in widths (i.e. resolution) 2400 2400 Physical size of the image = × = 8′ × 8′ 300 300 Components of an Image Processing System: set of devices for acquiring, storing, manipulating and transmitting digital images. The main components of an image processing system are (1) Sensing device, (2) Image processing elements, (3) Storage device and (4) Display device. Sensing devices (OR Image Sensor): are used to capture the image. The sensing device senses the energy radiated by the object and converts it into digital form. For example, a digital camera senses the light intensity and converts into the digital image form. Moreover, video camera and scanners use some types of sensors for capturing the image. Image processing elements are used to perform various operations on a digital image. It requires a combination of hardware and software. Storage is a very important part of an image processing system. The size of an image or video file is very large. For instance, an 8-bit image having 1024 x 1024 pixels requires 1 megabyte of storage space. Therefore, mass storage devices are required in image processing systems. Display devices: are required to display the images. These can be a computer monitor, mobile screen, projector for display or hardcopy devices, such as printers. A communication channel is also essential for sending and receiving images. Applications of Digital Image Processing: Digital image processing techniques are now used in a number of applications; some common applications are given below. In medicine: Several medical tools use image processing for various purposes, such as image enhancement, image compression, object recognition, etc. X-radiation (X- rays), computed tomography scan (CT scan), positron-emission tomography (PET), Single-photon emission computed tomography (SPECT), nuclear magnetic resonance (NMR) spectroscopy and Ultra-Sonography are some popular pieces of medical equipment based on image processing. In agriculture: Image processing plays a vital role in the field of agriculture. Various paramount tasks such as weed detection, food grading, harvest control and fruit picking are done automatically with the help of image processing. In weather forecasting: Image processing also plays a crucial role in weather forecasting, such as prediction of rainfall, hailstorms, flooding. Meteorological radars are widely used to detect rainfall clouds and, based on this information, systems predict immediate rainfall intensity. In photography and film: Retouched and spliced photos are extensively used in newspapers and magazines for the purpose of picture quality enhancement. In movies, many complex scenes are created with image and video editing tools which are based on image and video processing operations. Image processing-based methods are used to predict the success of upcoming movies. In entertainment and social media: Face detection and recognition are widely used in social networking sites where, as soon as a user uploads the photograph, the system automatically identifies and gives suggestion to tag the person by name. In security: Biometric verification systems provide a high level of authenticity and confidentiality. Biometric verification techniques are used for recognition of humans based on their behaviours or characteristics. To create alerts for particularly undesirable behaviour, video surveillance systems are being employed in order to analyze peoples’ movements and activities. Several banks and other departments are using these image processing-based video surveillance systems in order to detect undesired activities. In banking and finance: The use of image processing-based techniques is rapidly increasing in the field of financial services and banking. ‘Remote deposit capture’ is a banking facility that allows customers to deposit checks electronically using mobile devices or scanners. The data from the check image is extracted and used in place of a physical check. Face detection is also being used in the bank customer authentication process. Some banks use ‘facial-biometric’ to protect sensitive information. Signature verification and recognition also plays a significant role in authenticating the signature of the customers. However, a robust system used to verify handwritten signatures is still in need of development. This process has many challenges because handwritten signatures are imprecise in nature, as their corners are not always sharp, lines are not perfectly straight, and curves are not necessarily smooth. In marketing and advertisement: Some companies are using image-sharing through social media in order to track the influence of the company’s latest products/ advertisement. The tourist department uses images to advertise tourist destinations. In defence: Image processing, along with artificial intelligence, is contributing to defence based on two fundamental needs of the military: one is autonomous operation and the other is the use of outputs from a diverse range of sophisticated sensors for predicting danger/threats. In the Iran-Iraq war, remote sensing technologies were employed for the reconnaissance of enemy territory. Satellite images are analyzed in order to detect, locate and destroy weapons and defence systems used by enemy forces. In industrial automation: An unprecedented use of image processing has been seen in industrial automation. The ‘Automation of assembly lines’ system detects the position and orientation of the components. Bolting robots are used to detect the moving bolts. Automation of inspection of surface imperfection is possible due to image processing. The main objectives are to determine object quality and detect any abnormality in the products. Many industries also use classification of products by shape automation. In forensics: Tampered documents are widely used in criminal and civil cases, such as contested wills, financial paper work and professional business documentation. Documents like passports and driving licenses are frequently tampered with in order to be used illegally as identification proof. Forensic departments have to identify the authenticity of such suspicious documents. Identifying document forgery becomes increasingly challenging due to the availability of advanced document editing tools. The forger uses the latest technology to perfect his art. Computer scan documents are copied from one document to another to make them genuine. Forgery is not only confined to documents; it is also gaining popularity in images. Imagery has a remarkable role in various areas, such as forensic investigation, criminal investigation, surveillance systems, intelligence systems, sports, legal services, medical imaging, insurance claims and journalism. Almost a decade ago, Iran was accused of doctoring an image from its missile tests; the image was released on the official website, Iran’s Revolutionary Guard, which claimed that four missiles were heading skyward simultaneously. Almost all the newspaper and news magazine published this photo including, The Los Angeles Times, The Chicago Tribune and BBC News. Later on, it was revealed that only three missiles were launched successfully, one missile failed. The image was doctored in order to exaggerate Iran’s military capabilities. In underwater image restoration and enhancement: Underwater images are often not clear. These images have various problems, such as noise, low contrast, blurring, non-uniform lighting, etc. In order to restore visual clarity, image enhancement techniques are utilized. Digital image processing: the acquisition and processing of visual information by computer. It can be divided into two main application areas: (1) computer vision and (2) human vision, with image analysis being a key component of both. Computer vision applications: imaging applications where the output images are for computer use. Human vision applications: imaging applications where the output images are for human consumption. Digital image processing: concerns with techniques to perform processing on an image, to get an enhanced image or to extract some useful information from it to make some decisions based on it. Digital image processing techniques are growing at a very fast speed. Three levels of image processing operations are defined: Low-Level image processing: Primitive operations on images (e.g., contrast enhancement, noise reduction, etc.) are under this category, where both the input and the output are images. Mid-Level image processing: In this category, operations involving extraction of attributes (e.g., edges, contours, regions, etc.), from images are included. High-Level image processing: This category involves complex image processing operations related to analysis and interpretation of the contents of a scene for some decision making. Image processing involves many disciplines, mainly computer science, mathematics, psychology and physics. Other areas, such as artificial intelligence, pattern recognition, machine learning, and human vision, are also involved in image processing. Typical Image Processing Operations: Image processing involves a number of techniques and algorithms. The most representative image processing operations are: Binarization: Many image processing tasks can be performed by converting a color image or a grayscale image into binary in order to simplify and speed up processing. Conversion of a color or grayscale image to a binary image having only two levels of gray (black and white) is known as binarization. Smoothing: A technique that is used to blur or smoothen the details of objects in an image. Sharpening: Image processing techniques, by which the edges and fine details of objects in an image are enhanced for human viewing, are known as sharpening techniques. Noise Removal and De-blurring: Before processing, the amount of noise in images is reduced using noise removal filters. Image removal technique can sometimes be used, depending on the type of noise or blur in the image. Edge Extraction: To find various objects before analysing image contents, edge extraction is performed. Image restoration: the process of taking an image with some known, or estimated, degradation, and restoring it to its original appearance. Image enhancement: improving an image visually. Image compression: reducing the amount of data needed to represent an image. Image analysis: the examination of image data to solve an image processing problem.  Image segmentation: used to find higher level objects from raw image data. The process of dividing an image into various parts is known as segmentation. For object recognition and classification segmentation is a pre-processing step. Feature extraction: acquiring higher level information, such as shape or color of objects. To find various objects before analysing image contents, edge extraction is performed. Image transforms: may be used in feature extraction to find spatial frequency information. Pattern classification: used for identifying objects in an image.