Image recognition
Download as PPTX, PDF1 like3,495 views
Image recognition is a computer technology that identifies people, objects, and actions in images using algorithms and machine learning, integral to advancements in artificial intelligence and computer vision. Its applications span various fields, including autonomous vehicles, medical image analysis, and surveillance, leveraging techniques like convolutional neural networks to improve data processing. Challenges such as viewpoint and scale variations, occlusion, and limitations of traditional neural networks hinder its effectiveness, while trends from tech giants like Google and Apple are shaping the landscape of image recognition technology.
Image recognition
- 2. What is Image Recognition ? The recent advancement in artificial intelligence and machine learning has contributed to the growth of computer vision and image recognition concepts. From controlling a driver-less car to carrying out face detection for a biometric access, image recognition helps in processing and categorizing objects based on trained algorithms. Image recognition is a term for computer technologies that can recognize certain people, animals, objects or other targeted subjects through the use of algorithms and machine learning concepts. The term “image recognition” is connected to “computer vision,” which is an overarching label for the process of training computers to “see” like humans, and “image processing,” which is a catch-all term for computers doing intensive work on image data.
- 3. How is it used ? Computer views visuals as an array of numerical values and looks for patterns in the digital image, be it a still, video, graphic, or even live, to recognize and distinguish key features of the image. Computer vision uses Image Processing Algorithms to analyze and understand visuals from a single image or a sequence of images. With the advent of autonomous or semi autonomous cars, drones, wearables the potential of computer vision is growing and many organizations are investing in image recognition to interpret and analyze data coming primarily from visual sources for a number of uses such as medical image analysis, identifying objects in autonomous cars, face detection for security purpose, etc. Image recognition is the ability of a system or software to identify objects, people, places, and actions in images. It uses machine vision technologies with artificial intelligence and trained algorithms to recognize images through a camera system.
- 4. Techniques The use of convolutional neural networks to filter images through a series of artificial neuron layers. The convolutional neural network was specifically set up for image recognition and similar image processing. Through a combination of techniques such as max pooling, stride configuration and padding, convolutional neural filters work on images to help machine learning programs get better at identifying the subject of the picture. A digital image represents a matrix of numerical values. These values represent the data associated with the pixel of the image. The intensity of the different pixels, averages to a single value, representing itself in a matrix format. The information fed to the recognition systems is the intensities and the location of different pixels in the image. With the help of this systems learn to map out a relationship or pattern.
- 6. Limitations of Regular Neural Networks for Image Recognition The huge availability of data makes it difficult to process it due to the limited hardware availability. Difficulty in interpreting the model since the vague nature of the models prohibits its application in a number of areas. Development takes longer time and hence, the flexibility is compromised with the development time. Although the availability of libraries like Keras makes the development simple, it lacks flexibility in its usage. Also, the Tensorflow provides more control, but it is complicated in nature and requires more time in development.
- 7. Challenges of Image Recognition Viewpoint Variation: In a real world, the entities within the image are aligned in different directions and when such images are fed to the system, the system predicts inaccurate values. Scale Variation: Variations in size affect the classification of the object. The closer you view the object the bigger it looks in size and vice-versa Deformation: Objects do not change even if they are deformed. The system learns from the perfect image and forms a perception that a particular object can be in specific shape only. Inter-class Variation: Certain object varies within the class. They can be of different shape, size, but still represents the same class. For example, buttons, chairs, bottles, bags come in different sizes and appearances. Occlusion: Certain objects obstruct the full view of an image and result in incomplete information being fed to the system. It is necessary to devise an algorithm that is sensitive to these variations and consist of a wide range of samples of the data
- 8. Recognition Methods Optical Character Recognition Pattern Matching & Gradient Matching Face Recognition License Plate Matching Scene Identification or Scene Change Detection Image Recognition Using Machine Learning : A machine learning approach to image recognition involves identifying and extracting key features from images and using them as input to a machine learning model. An example of this is classifying digits using HOG features and an SVM Classifier. Image Recognition Using Deep Learning : A deep learning approach to image recognition may involve the use of a convolutional neural network to automatically learn relevant features from sample images and automatically identify those features in new images.
- 9. Role of Convolutional Neural Networks in Image Recognition Convolutional Neural Networks play a crucial role in solving the problems stated above. Its basic principles have taken the inspiration from our visual cortex. The inputs of CNN are not fed with the complete numerical values of the image. Instead, the complete image is divided into a number of small sets with each set itself acting as an image. A small size of filter divides the complete image into small sections. Each set of neurons is connected to a small section of the image.
- 10. Uses of Image Recognition Drones: Drones equipped with image recognition capabilities can provide vision-based automatic monitoring, inspection, and control of the assets located in remote areas. Manufacturing: Inspecting production lines, evaluating critical points on a regular basis within the premises. Monitoring the quality of the final products to reduce the defects. Military Surveillance: Detection of unusual activities in the border areas and automatic decision- making capabilities can help prevent infiltration and result in saving the lives of soldiers. Forest Activities: Unmanned Aerial Vehicles can monitor the forest, predict changes that can result in forest fires, and prevent poaching. It can also provide a complete monitoring of the vast lands, which humans cannot access easily. Autonomous Vehicles: Autonomous vehicles with image recognition can identify activities on the road and take necessary actions. Mini robots can help logistics industries to locate and transfer the objects from one place to another. It also maintains the database of the product movement history to prevent the product from being misplaced or stolen.
- 11. Latest Trends Google Vision : can detect whether you’re a cat or a human, as well as the parts of your face. It tries to detect whether you’re posed or doing something that wouldn’t be okay for Google Safe Search—or not. It even tries to detect if you’re happy or sad. Google Cloud Vision API PI enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API. Apple’s Face Recognition : Face ID uses a "TrueDepth camera system", which consists of sensors, cameras, and a dot projector at the top of the iPhone display in the notch to create a detailed 3D map of your face. It provides more privacy than Google Pixel and Samsung Galaxy S series mobile phones.
- 12. Threats of Image Recognition China facial recognition: Law professor sues Hangzhou Safari Park is violating consumer protection law by compulsorily collecting visitors individual characteristics, after it suddenly made facial recognition registration a mandatory requirement for visitor entrance. Mass surveillance fears as India readies facial recognition system : As India prepares to install a nationwide facial recognition system in an effort to catch criminals and find missing children, human rights and technology experts warn of the risks to privacy from increased surveillance. US government keeps its use of facial recognition tech secret : The ACLU is suing FBI the Department of Justice, and the DEA to get documents that explain how the US government is using facial recognition technology.
- 13. Latest Research in 2019 Automatic Video Surveillance System For Pedestrian Crossing using Digital Image Processing Remote Sensor Networks for Prediction of Chilli Crop Diseases using Infra-Red Image processing techniques. Natural Language Processing technique for Image Spam Detection Food Image Processing Techniques