A brief introduction about Image Recognition and the recognition methods. Privacy Concerns and Advantages.

1. 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