Algorithms for Image Processing and Computer Vision
What is computer vision?
In a basic sense, computer vision is the ability for a computer to see andunderstand what it sees in a similar way to humans.When you want to take a drink from a glass of water, multiple vision-relatedthings happen: 1. You have to recognize that the thing in front of you is a glass of water. 2. You have to know where your arm and the glass are, then move your arm in the direction of the glass. 3. You have to recognize when your hand is close enough to properly grab the glass. 4. You have to know where your face is, then pick up the glass and move it toward your face.Computer vision is the same thing… but for computers!Computer vision problems fall into a few different buckets. This is importantbecause different problems are solved with different methods.
Computer Vision and Image Processing:
These two distinct somewhere on a thin line. The process of generating newimage using an existing image is what we define as Image Processing. Multiplefilters can be added; Image can be manipulated in order to improve the qualityof image. Now this output image can be used for better understanding of theimages using computer vision. Majorly, Image Processing does not aim tounderstand the content of the image while computer vision has to. Imageprocessing is responsible for manipulating the image in way to let the machineidentify the objects and connect the dots.Although there are many factors contributing to improvements and groundbreaking changes in the field of Computer Vision, availability of trainingdata in massive amount, is primary.Recent developments resulting in advance Neural Networks and Deep Learningalgorithms have pushed the limits, producing State-of-the-art Algorithms withbetter outputs. Added computation power and point accuracy with latestalgorithms has made an impact on computer vision.
Applications of Computer Vision:
Being a sub field of Artificial Intelligence, Computer Vision comes with andprovide solutions to high quality products. To recognize objects in a picturewas good but specifically identifying a face, along with being able to addfilters is a big leap. Using Face Detection techniques, Facebook and Snapchatdetects live faces and improves the quality of pictures with filters.When we use google for Image Search, it simply understands the content of theimage we pass, learning the objects in the picture, it tries to match theobjects and the results are purely based content matching. When we talk aboutImage Classification, Convolutional Neural Networks (CNNs) is the top pic andwhile other models require enough training and inputs, CNN does it all byitself. A CNN algorithm simply takes an image as input, recognize the objectsinside and assign importance and finally compares the objects identified.Surveillance Cameras in public places can now keep an eye on suspiciousbehaviour and notify. Security features like Biometrics, Face matching andIRIS are setting new limits for security.Self-Driving cars are the future and various aspects of computer vision holdkeys to improve the driverless car. Medical Imaging, Optical CharacterRecognition, machine inspection, 3D Model Building, Motion Detection,healthcare etc. are few of many applications.
Where is computer vision in all of that?
Now, let’s concentrate on computer vision. You see, manufacturing companieshave to be able to capture and process visual information in real-time.Computer vision transfers images to algorithms and enables almost instantfeedback regarding the production process.As you already know from our past blog posts, computer vision algorithms relyon various instruments, lenses, image sensors, and vision processing software.This technology is commonly used in many sectors, just to mention theautomotive, pharmaceutical, and printing industries.Today, computer vision use cases become an integral part of every modernmanufacturing line. And thanks to machine learning and image processingtechniques, computer vision use cases are now more and more extensive. Thecomputer vision applications are no longer limited just to structured,repetitive tasks. Nowadays, they offer vital help that results in improvedefficiency, fewer errors, and a better understanding of data.At this point, we have to state that when it comes to the manufacturingprocess, we frequently speak about machine vision.
Computer Vision Applications in Use Today
Computer vision has many applications already in use today, several withsignificant social implications. For example, CV uses image recognition toenable self-driving cars to recognize pedestrians, road signs, and otherimportant features in their path. Medical professionals also leverage CV tosupport diagnoses from CT scans, radiology images, and other imaging tools.Many e-commerce organizations rely on CV for driving ad placement andidentifying unsafe brand content.Whatever the use case, enterprise companies are investing in computer visionto make predictions and decisions quickly and with high confidence. Manycompanies rely solely on computer vision for their AI solutions, an actionmade possible due to the large amounts of image data now available for machineprocessing.
Computer Vision: Deep Learning Vs. Machine Learning
Computer vision typically leverages either classic machine learning (ML)techniques or deep learning methods. With a standard ML approach, developersprogram small applications to identify patterns in images. A statisticallearning algorithm then classifies the images and detects objects within them.This is a vast improvement over the original method, where developers had tomanually code numerous unique rules into computer vision applications.Deep learning for computer vision offers a very different approach to ML. It’sbased on neural networks, which solve problems by identifying patterns inprovided examples. It requires an extensive amount of high-quality trainingdata and appropriate adjustments of variables, such as the number of neuralnetworks used. With enough examples, the neural network will learn to identifythe desired object (for example, a cancerous growth in a radiology image)without needing additional direction. Many computer vision applications usedeep learning techniques, as these tend to be easier to deploy than othermethods.
C++ and Python (numpy) implementation of algorithms discussed in RichardSzeliski’s ‘Computer Vision: Algorithms and Applications’To use C++ code, download CImg library from http://www.cimg.eu/ and place inbase directory
Algorithms for Image Processing and Computer Vision
J. R. ParkerA cookbook of algorithms for common image processing applications Algorithmsnow exist for a wide variety of sophisticated image processing applicationsrequired by software engineers and developers, advanced programmers, graphicsprogrammers, scientists, and related specialists This bestselling book hasbeen completely updated to include the latest algorithms, including 2D visionmethods in content-based searches, details on modern classifier methods, andgraphics cards used as image processing computational aids Saves hours ofmathematical calculating by using distributed processing and GPU programming,and gives non-mathematicians the shortcuts needed to program relativelysophisticated applications. Algorithms for Image Processing and ComputerVision, 2nd Edition provides the tools to speed development of imageprocessing applications.The file will be sent to your email address. It may take up to 1-5 minutesbefore you receive it.The file will be sent to your Kindle account. It may takes up to 1-5 minutesbefore you received it.Please note you need to add our firstname.lastname@example.org approved e-mail addresses.Read more.
Computer Vision vs. Image Processing: Understand the Difference
What separates computer vision from image processing? Image processing worksoff of rules-based engines, Goertz notes. For example, one can apply rules toa digital image to highlight certain colors or aspects of the image. Thoserules generate a final image.Computer vision, on the other hand, is fueled by machine learning algorithmsand AI principles. Rules do not govern the outcome of the image analysis —machine learning does. And with each processing of an image by the algorithmsthat underpin computer vision platforms, the computer refines its techniquesand improves. This, Goertz notes, means that computer vision results “in ahigher and higher probability of a correct interpretation the more times youuse it.”Solinger adds that the major difference between computer vision and imageprocessing is that image processing is actually a step in a computer visionprocess. The main difference, she says, is “the methods, not the goals.”Computer vision encompasses hardware and software. Image processing tools lookat images and pull out metadata, and then allow users to make changes to theimages and render them how they want. Computer vision uses image processing,and then uses algorithms to generate data for computer vision use, Solingersays.
Computer vision in healthcare helps in making diagnoses
Computer vision systems offer precise diagnoses minimizing false positives. Inmany instances, computer vision algorithms can be even more effective thanhuman physicians. That’s because these algorithms are trained on thousands ofmedical images presenting a given disease or anomaly. As a result, AIalgorithms can spot any irregularities with amazing precision. Not to mentionthat they are never tired, can work 24/7, never go on vacation, or sick leave.Computer vision in healthcare can significantly improve many fields of modernmedicine, i.a.: * X-ray radiography * Magnetic resonance imaging (MRI) * Ultrasound * Endoscopy * ThermographyThanks to the fact that computer vision algorithms are trained using a vastamount of training data, computer vision algorithms can detect even theslightest presence of an anomaly that may be missed out by human analysts. Theuse of computer vision in healthcare diagnosis can provide high levels ofprecision. And that happens even today!Consider LYmph Node Assistant (LYNA). It’s one of the most prominent deeplearning models in the computer vision field, developed at MIT. LYNA reviewssample slides and recognizes characters of tumors and metastases in a shorttimespan with a mind-boggling 99% rate of accuracy. And the good news is,your company can implement a similar model! But let’s take a look at some moreexamples.
Computer vision algorithms and hardware implementations: A survey
PublishedJournal Article (Review)© 2019 The Authors The field of computer vision is experiencing a great-leap-forward development today. This paper aims at providing a comprehensive surveyof the recent progress on computer vision algorithms and their correspondinghardware implementations. In particular, the prominent achievements incomputer vision tasks such as image classification, object detection and imagesegmentation brought by deep learning techniques are highlighted. On the otherhand, review of techniques for implementing and optimizing deep-learning-basedcomputer vision algorithms on GPU, FPGA and other new generations of hardwareaccelerators are presented to facilitate real-time and/or energy-efficientoperations. Finally, several promising directions for future research arepresented to motivate further development in the field.
Basics of Computer Vision
This course will give you more basic introduction to the computer vision. Itwill include all the fundamentals of the following. * * Image formation. * Camera imaging geometry. * Feature detection and matching. * Multiview geometry which will include the following. 1. Stereo. 2. Motion estimation. 3. Tracking. 4. Classification.