The image passed to an Amazon Rekognition Image operation can be stored in an Amazon S3 bucket. If you are not using the AWS CLI, you can also pass Base64 encoded images bytes directly to an Amazon Rekognition operation. With Rekognition Image, you can locate faces within images and analyze face attributes, such as whether or not the face is smiling or the eyes are open. When analyzing an image, Rekognition Image will return the position and a rectangular frame for each detected face.
Image Recognition is a term used to describe a set of algorithms and technologies that attempt to analyse images and understand the hidden representations of features behind them and apply these learned representations for different tasks like classifying images into different categories automatically, understanding which objects are present and where in an image, etc. These technologies leverage various traditional computer vision methods as well as machine learning and deep learning algorithms to achieve required results for solving such problems.
Gathering data in image recognition
- Image recognition is one of the problems being solved within the computer vision field. Computer vision is a broader set of practices that solve such issues as: Image classification with localization – identifying an object in the image and showing where it's located by drawing a bounding box around it.
- Image recognition is the process of identifying an object or a feature in an image or video. It is used in many applications like defect detection, medical imaging, and security surveillance.
Data gathering is one of the first steps in image recognition. The more data we have, the better our models perform. Data can be gathered by different means like scraping from the web, gathering from third-party sources or you could even buy datasets from re-sellers etc. Some open-source labeled dataset repositories are as follows:
- Common Objects in Context (COCO) - cocodataset.org
- ImageNet - image-net.org
- Google's Open Images
- Mldata.org
- CV datasets on GitHub
Annotating data in Image recognition
Annotation is the second step after data gathering. Annotation means labeling the data like image video etc. for further references of data. It is done by assigning some sort of keywords on the required area of text, image, video etc. Once you have the data, you need to label it. There are primarily 2 things you need to be concerned about here:
- How do you label the data?
- Who labels the data?
There are different types of annotation done on images like:
- Bounding box annotation
- Polygon annotation
- Semantic annotation
- Key point annotation
- Redaction
Tools used for Image Annotation:
- LabeIMG - github.com/tzutalin/labelImg
- Annotorious - annotorious.github.io
- Comma Colouring
- VGG Image Annotator(VIA)
Processing data in Image recognition
Data preprocessing describes any type of processing that is performed on raw data to ready it for another processing procedure. Hence, preprocessing is the initial step which transforms the data into a format that will be more easily and effectively processed. In order to achieve higher recognition rates, it is important to have an effective preprocessing stage, therefore using effective preprocessing algorithms. Preprocessing techniques are needed on color, binary document or grey-level images containing text and/or graphics. Following are some of the techniques for character recognition systems:
- Image Enhancement Technique: To remove noise or correct the contrast in the image.
- Thresholding Technique: To remove the background containing any scenes, watermarks etc.
- Page Segmentation Technique: To separate graphics from text.
- Character Segmentation: To separate characters from each other.
- Morphological Preprocessing: To enhance the characters in cases.
There are a lot more preprocessing techniques that will be used according to the need of a given system.
Computers can use machine vision technologies in combination with a camera and artificial intelligence software to achieve image recognition.
Feature Extraction Technology
Feature extraction involves reducing the number of resources required to describe a large set of data. When doing analysis of complex data one of the major problems are created from the number of variables involved. Analysis with a very large number of variables usually requires a large amount of computation power and memory, also it may cause a classification algorithm to over fit to training samples and generalize poorly to new samples. Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy. This approach is useful when image sizes are large and a minimum feature representation is required to quickly complete tasks such as image retrieval and image matching.
Histogram of Oriented Gradients (HOG) Technology
A histogram of oriented gradients (HOG) is used in image processing applications for the detection of objects present in an image or video, which by definition is a feature descriptor. It uses a sliding detection window which is then moved around the image. This descriptor is then shown to trained SVM, which then classifies it as 'person' or 'not a person'.
High Altitude Aerial Reconnaissance (HAAR) Technology
Haar-like features are digital image features used in object recognition. A Haar-like feature considers adjacent rectangular regions at a specific location in a detection window, sums up the pixel intensities in each region and calculates the difference between these sums. Some HAAR features are as follows:
- Edge Features: Used to detect edges.
- Line Feature: Used to detect lines.
- Four Rectangle Feature: Used to detect a slanted line.
Convolutional Neural Network (CNN) Technology
Convolutional neural networks (CNN) is a class of deep learning neural networks. CNNs represent a huge innovation in image recognition. They're most used to analyze visual imagery and are frequently working behind the scenes in image classification. A CNN model is a combination of two components: feature extraction part and the classification part. The convolution + pooling layers perform feature extraction.
Machine Learning Models
A machine learning model is a mathematical representation of a real-world process. In the training data the learning algorithm finds patterns such that the input parameters mapped to the target. The output of the training process is a machine learning model which is used to make predictions. Some machine learning models are as follows:
- Regression Algorithms
- Instance-based Algorithms
- Regularization Algorithms
- Decision Tree Algorithms
- Bayesian Algorithms
- Clustering Algorithms
- Artificial Neural Network Algorithms
- Deep Learning Algorithms
Image Classification
Image classification is a process of grouping pixels into several classes. Image classification refers to the labelling of images into one of a number of predefined categories. Classification includes image sensors, image pre-processing, object detection, object segmentation, feature extraction and object classification. Following are the approaches of image classification:
- Supervised Classification
- Unsupervised Classification
- Parameter Classification
- Non Parameter Classification
- Pre pixel Classification
- Sub pixel Classification
- Hard Classification
- Soft Classification
Object Detection
Object detection is used for detection of instances of objects from a specific class in an image. The goal of object detection is to detect all instances of objects from a known class, such as cars, people, trees, buildings or faces in digital images and videos. It is widely used for computer vision tasks like face recognition, face detection, video object. It can also be used to track objects like tracking a ball in a football match, tracking the movement of cricket bat or tracking a person moving in a video. Object detection can be carried out by using the following methods:
- Machine Learning Approaches
- Deep Learning Approaches
Object detection is widely used from personal security to productivity in the workplace. Object detection is applied in many areas of computer vision for example image retrieval, surveillance, security, machine inspection and automated vehicle systems. Following are some present and future applications of object detection:
- Optical character recognition
- Self driving cars
- Tracking objects
- Face detection and recognition
- Identity verification through iris code
- Object detection in real time
- Emotion detection
- Medical imaging
- Ball tracking in sports
Image Recognition in Manufacturing
Predictive Maintenance:
It is the process of using machine learning devices to monitor data on machinery and components, often using sensors, to collect data points and identify signals or take corrective actions before assets or components break down.
Packaging Inspection:
It is very difficult for the companies to count tablets or capsules before placing in the container. To solve this issue, Pharmacy Packaging Systems has developed a solution that is using computer vision to check for broken tablets. Pictures are taken of tablets and transferred to a dedicated PC where the images are analyze using software to check if the tablets are right in color, length, width, and whole.
Reading Barcodes:
A machine vision based solution called PanelScan is developed to read the barcodes-which are the unique identifiers of each circuit that is present on the PCN panel.
Defect Reduction:
Machine vision is based on inspection system called WebSPECTOR that identifies defects and stores images. As the item fall in the production line, defects get classified according to their type.
Improving Safety:
A combination of real-time cameras and video analytics allows the equipment to run with greater efficiency and improved safety. The idea is to also apply deep learning based artificial intelligence to track people and predict the movement of equipment to help avoid dangerous interactions thereby improving safety.
Image Recognition in Insurance
**[Real-time car damage assessment](https://nanonets.com/drone/):**
The car owner can take pictures immediately after the car accident to the claims department. The compensability of the damages can be determined according to the detected car parts. The information regarding repair costs and logistics can be subsequently provided to both the insurer and the car owner. The technology is expected to shorten the process significantly from weeks to one day.
Use satellite images for agricultural insurance pricing:
The use of satellite images helps to survey and monitor a large agricultural area day and night. The satellite images allow insurers to receive real-time updates of potential perils in the fields. The data from the images, with the boundary of the insured, will help insurance to price risks more accurately.
Use drones to take photos of house roofs:
A drone can take hundreds of images in 10 to 20 minutes for quoting purposes. The use of drones provides speed and service. Also, this way is safer for insurance company employees to mitigate the risks of claiming roofs.
Risk modeling with image data:
Rekognition-image-search-engine
Facebook uses its imaging technology to identify and remove fake accounts. Such image-based fake-identification has immense potential in banking and insurance.
Radiological imaging diagnostics:
Some leading healthcare organizations are beginning to apply deep learning to identify pathologies in radiological images such as bone fractures and potentially cancerous lesions.
Image Recognition in Surveillance
Intelligent video surveillance:
It is the cutting edge video technology that records criminal activity in homes, businesses, and municipalities based on the preferences of the user.
Intelligent Video Analytics:
It supports basic alerts and compound alerts. Video Analytics: It automatically analyzes video to detect events, count people and recognize license plate numbers among many other things. The simplest form of this technology is motion detection.
Google Photo Recognition Search
Image Recognition in News and Media
Sifting Through the Clutter:
When you're even a bit successful, you will deal with hundreds, if not thousands, of mentions, comments, and interactions daily. The posting flood can easily overwhelm any social media team as a result. AI helps with this immensely. AI can sift through the haystack, find the most important interactions, weed out spam, and help you focus your attention. It can tell you who you should respond to first for best results.
Consumer Insights for Digital Marketing:
Artificial intelligence can also be helpful for gathering consumer insights. You know how important it is to understand what your audience is thinking and feeling, and their posts with images, in particular, are big clues.
Social Media Marketing and Customer Service: Netbeans eclipse shortcuts.
Artificial intelligence can also help automate the customer service you provide through social media. For instance, if a user posts about a problem they're having with your service, you can respond quickly and fix it.
Discovering Perfectly Matched Influencers: AI can search through hundreds of millions of influencers on multiple social networks and analyze how they use these platforms. AI can then find people that match up best with your brand. It looks at elements like what they're posting, the conversations they're having, and their characteristics, like demographics, location, and interests.
Image Recognition in E-commerce
Finding inappropriate content:
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Inappropriate content on e-commerce sites could be detected and removed using image recognition technology. One way of doing this is through logo recognition in which the legitimate brand can find fake logos of counterfeit products and remove any inappropriate or explicit content falsely associated with that brand.
A.R. for advertising:
A reader could snap a photo using their smartphone of an image within a magazine which would then prompt an ad that would take the reader to a brand's website. If this was an online retailer, the reader could take a photo of an outfit they like in the magazine which would prompt an ad and take the reader to an e-commerce site.
References
10 Best Image Recognition Tools - Talkwalker. Retrieved September 7, 2019, from https://www.talkwalker.com/blog/best-image-recognition-tools
Facial Recognition: 16 Industries The Tech Could Transform. Retrieved September 7, 2019, from https://www.cbinsights.com/research/facial-recognition-disrupting-industries/
Image Pre-processing - Towards Data Science. Retrieved September 7, 2019, from https://towardsdatascience.com/image-pre-processing-c1aec0be3edf
Image Processing in Industry 4.0 | Vision Campus. Retrieved September 7, 2019, from https://www.baslerweb.com/en/vision-campus/markets-and-applications/image-processing-industry-4-0/
Image Recognition: Image Recognition And How It's Used Today . Retrieved September 7, 2019, from http://gumgum.com/what-is-image-recognition
Understanding SVMs': For Image Classification - DataTurks: Data . Retrieved September 7, 2019, from https://medium.com/@dataturks/understanding-svms-for-image-classification-cf4f01232700
Image Recognition Technology
Acronis true image free version. Video & Image Data Collection Services | Data Collection for AI. Retrieved September 7, 2019, from https://www.globalme.net/video-image-data-collection
Tip: Deep learning techniques are popular for image recognition because they provide highly accurate and robust results. Deep learning tends to work best with a large amount of training data, and techniques such as transfer learning can simplify the image recognition workflow. Deep Learning Toolbox™ provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps.
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.
- Train Data: You start with a collection of images and compile them into their associated categories.
- Extract Features: Select the relevant features in each image. A feature extraction algorithm might extract edge or corner features that can be used to differentiate between classes in your data.
- Create a Machine Learning Model: These features are added to a machine learning model, which will separate these features into their distinct categories, and then use this information when analyzing and classifying new objects.
