Author name: umang dayal

If you’re looking for extra security for your home via installation of facial recognition on your doorstep, you’re not alone. The good news? It’s possible. And that’s not all. Computer vision can do a lot more in every area of your life.

There have been constant developments in artificial intelligence, deep learning and neural networks in recent years. Computer vision has made it possible to detect and label objects, being able to accomplish tasks that humans can’t.

Seems like computers are our best friends and can make our lives easier, more entertaining and more secure. Let’s find out what computer vision is, how it works and how you can use it to enhance your everyday life.

What is Computer Vision?

Computer vision is a technology of computer science that focuses on human vision and its replication in order to help computers see and identify objects around them, just like human beings do. In simpler words, computer vision is like replicating the functions of the human eye in a computer.

Remember we talked about face recognition technology right at the beginning of the article? That’s one of the things computer vision enables. It allows phone companies and smart home devices to use facial recognition as a measure of security.

Where did it all begin? 1950s! Yes, that’s how old computer vision is but its growth in recent years has been phenomenal. Back in those days (70s and 80s), it was used to differentiate typed text from handwritten text.

How does it even work? How is computer vision able to detect objects? Let’s find the answer to this and put all curiosity to rest.

How Does Computer Vision work?

This question is like asking how the human brain works. The field of neuroscience has forever been intrigued by how complex our brains are and how they work. Machine learning asks the same question and works in the answer to develop this field of computer science.

Now we all know that brains aren’t easy to study and even science doesn’t have all the answers yet on the exact way images are processed in the brain. This is why computer vision works on what we do know: recognizing patterns.

So how does the computer learn to recognize an image? It all gets down to understanding the complexity of pixels and colors. In simple words, if you feed an algorithm with millions of images of a book, a set of machine learning algorithms will help it analyze the colors, shapes and the relative distance between objects. This helps the computer understand what a “book” is based on the types of data sets. Once done, this computer will be able to recognize books from images that are fed into it in the future.

Let’s break it down into steps. Here’s what a computer does:

• Acquire an image

• Process the image

• Understand the image

Advantages of Computer Vision

Computer vision benefits both the public and the private sector in various ways.

1. Better Searching Methods

   Let’s talk about the advertising industry. Digital advertising mainly relied on keywords and tags. While the method works it’s not cent percent efficient. After the introduction of computer vision to this sector, results got a lot better.Instead of relying on traditional tags, computer vision compares the actual physical characteristics of a specific image. Because of this, people are able to search for exactly what they’re looking for by using a photo to find “similar products”.

2. Better User Experience

   Those filters that transform your face on Snapchat and Instagram are a result of computer vision! With the use of facial mapping and augmentation, computer vision makes it possible to create such features on apps.

3. Patient Identification And Better Medical Procedures

   Computer vision improves patient identification thereby preventing wrong person procedures. One can also expect a more accurate diagnosis via medical imaging analysis. From surgery training assistance to patient rehabilitation assistance, computer vision helps the medical field to achieve goals that were once far-fetched.

   The contribution of computer vision to the medical field is quite a boon. Here are some examples of how it helps:

   • Patient rehabilitation assistance.

   • Medical students training.

   • Patient identification.

4. Better Security

   Computer vision works with cyber security systems to monitor any remote activity. This can be done from anywhere which makes it easier to recognize and analyze potential cyber threats and prevent them from happening.

   Here are some ways in which computer vision is used:
   • Biometrics for identification.
   • Security cameras.
   • Vehicle identification in instances of car theft.
   • AI fire detection that helps detect fires in buildings by taking images or videos.

5. Transport Safety

   Computer vision is trained and used to identify unauthorized and harmful objects such as guns, biological weapons, etc, before they are loaded on passenger transport vehicles like an aircraft.

   This technology isn’t just used by some airlines but is also used by other public transport such as trains and buses to minimize risks and maximize security for the travelers.

Types of Computer Vision

Image segmentation: Here, the image is divided into multiple regions that are examined separately.

1. Object detection: This pertains to identification of a specific object in one image. For instance, a book like we talked about earlier. With advanced object detection, your computer can recognize multiple objects in one image.

2. Facial recognition: Whether it’s human face recognition in general like in those app filters or recognition of a specific person like in a smartphone for unlocking, computer vision does it all.

3. Edge detection: This method identifies the outer edges of objects to identify what the image consists of.

4. Pattern detection: This technique helps with identification of colors, shapes, and other visual elements in images.

5. Image classification: Organizing images into various groups and categories.

6. Feature matching: This method helps match similarities in images to classify them.

While simple uses of computer vision might just require one of these techniques, more complex ones like self-driving cars may make the use of a combination of various types of computer vision.

Top 9 Computer Vision Applications

1. Self-driving cars
   Since dreams of self-driving cars are coming true, a lot of is can be attributed to computer vision. Tesla has already come up with autonomous vehicles and it’s just a matter of time before you can get around your city in a driverless car too!

2. Augmented Reality
   Augmented reality uses computer-generated augmentation to provide an experience of the natural surroundings. If you’ve played games that use AR, you know that they can make you feel like you’re actually in that virtual world while your actions here in the real world affect what’s going on inside the game! You swing your golf club here and the ball goes flying in the game. How cool is that?

3. Medical Imaging
   How does a doctor classify X-rays and MRIs into diseases like cancer and pneumonia? Computer vision is the core of early diagnosis in the medical field. It has helped save thousands of lives by enabling doctors to detect diseases early with the help of imaging.

4. Intelligent Video Analytics
   Identification techniques like pose estimation, face detection and object tracking have helped CCTV cameras in understanding a shopper’s interaction with various products in a retail shop, queue lengths at airports and malls and other such parameters in public places with large crowds.

5. Manufacturing and Construction
   Computer vision systems help in detection of defects and with safety inspections. This helps in a better manufacturing process with fewer chances of error. 3D vision systems make inspections far more superior and efficient in production lines.

6. Optical Character Recognition
   OCR goes back to 1974 but with the latest technology and Deep Learning systems, today’s OCR techniques can detect and translate text in natural environments without any human intervention.

   Read more: OCR in Machine Learning

7. Retail
   Nowadays there are AI stores like “Amazon-go” across the United States that are cashierless and customers can self-checkout after shopping. This shows that computer vision can revolutionize shopping experiences for both the store owners and the consumers.

8. Education
   There’s nothing better than providing a personalized learning experience to students because one size doesn’t fit all. Computer vision understands students’ learning behaviors to improve their learning experiences. The technology also helps assess students’ papers to reduce the burden on teachers.

9. Sports and Fitness
   Computer vision can help fitness apps capture performance data. This can not only help the person using the app but also help coaches in training sessions. In sports, computer vision can track objects and ball movements to improve referees’ decision-making.

Top Industries Using Computer Vision

Since we already saw the application of computer vision, it’s not difficult to understand what industries benefit the most from it. Here are the industries that use computer vision the most and how the technology helps each industry.

1. Agriculture

   • Helps identify pests with greater accuracy to optimize chemical application.

   • Automation of livestock management to reduce the need for human intervention in the field.

   • Helps monitor crop development to have a better quality yield.

   • Automotive enables self-driving cars with intelligence to detect objects.

   • Helps create a seamless and driverless experience with no human error.

   • Reduces the chances of accidents.

2. Retail and E-commerce

   • Minimizes human interactions in stores thereby enhancing safety.

   • Personalizing customer experience that leads to more upselling and cross-selling opportunities.

   • The advanced in-store analytics prevent “out of stock” scenarios and enhance store layouts.

     Read more: The Future Of Retail: How Computer Vision Is Modernizing Retail

3. Sports Analytics

   • Better referee decisions because of accurate ball/object and human position captures.

   • Accurate and personalized fitness plans or goals via apps that monitor various bodily functions.

4. Medical Institutions

   • Improved and early diagnosis of illnesses in patients via 3D imaging.

   • Real-surgery and training assistance for more effective outcomes.

   • Improved patient logs with better identification to avoid confusion.

FAQ’s

Computer Vision Is The Future

As you can see, almost everything becomes easier, quicker, more effective and more secure with the help of computer vision. The best part is that it can be applied to every field and industry, helping not just professionals and businesses but also consumers and common people too. Everyone can enjoy the benefits that come with it.

If you’d like your business or setup to grow faster with more effective interactions with your consumers, you must go for the best computer vision services. Get futuristic today!

Autonomous driving as a field is booming. As many automotive manufacturers integrate autonomous technologies in their vehicles, fully autonomous cars are becoming a mere stone’s toss away.

Regulations for autonomous driving typically focus on two key areas: safety and performance. This article is mostly focused on the regulatory and legislative hurdles regarding safety of automated driving and autonomous vehicles.

1. Liability and Autonomous Vehicles

No means of transportation is without its hiccups. And unfortunately, autonomous driving has had numerous fatal accidents, with eleven recorded in 2022 alone. Currently, all autonomous auto manufacturers are required to report accidents to the National Highway Traffic Safety Administration.

The points of failure in an autonomous vehicle are a little more nebulous, and concerns have surfaced about who is liable in an accident. Since the cars are not fully autonomous, the accident could be from driver carelessness. Or they could be a result of software malfunctions or mechanical failures. As the technology improves and cars become more autonomous, the accident liability will shift toward the manufacturers and developers. There is no clear-cut solution yet, as the issue has yet to mature.

A Problem of Interwoven Pieces

Autonomous vehicles are complex. There’s a lot of interconnectivity between the various pieces that power and control them. Some speculate that as liability shifts to developers and manufacturers, it will pose some severe hurdles to overcome per incident.

Those making the AVs must analyze every component of the vehicle and perhaps even divulge the proprietary software suites that power the car while assisting law enforcement.

2. Federal and State Regulations

The first road safety initiatives began years before computer chips ever graced automobiles. Much has changed in automotive technology since, but the regulatory bodies are slower to catch up. Currently, there isn’t a wide-sweeping federal regulation governing fully autonomous vehicles.

The NHTSA has made some provisions regarding autonomous vehicles and specific safety feature requirements. This is a positive sign since the safety features that auto manufacturers must include are congruent with autonomous vehicle technologies.

State Laws

Only 43 states out of 50 have legislation regarding automated vehicles. Some are restrictive, while others depend on each vehicle’s SAE automation level. Liability insurance factors into most of these laws since every state save Hawaii and Virginia requires it.

The other seven states haven’t enacted laws regarding autonomous vehicles, and there is no indication of when legislation might be drafted. Multiple states also require licensure for mandated drivers, adding another logistics drain onto larger fleet deployments.

Federal Laws

The only federal-level agency providing some oversight over autonomous driving is the previously mentioned NHTSA. Federal regulation currently stipulates safety features, not the deployment of large commercial autonomous vehicle fleets. This isn’t necessarily bad, but a lack of an overarching baseline may cause future headaches for manufacturers.

Limited federal regulations also mean manufacturers must consider various state laws when developing and deploying autonomous vehicles.

3. Cybersecurity of Autonomous Vehicles

Tech magnates worldwide have bolstered their cybersecurity after hard-learned lessons, including cyber attacks, extreme platform compromises, and significant money lost due to offline systems. Yet the nascent autonomous driving space hasn’t fully accounted for their lack of protected systems. And if a server goes down and a vehicle is compromised, the effects would not only be money and time lost but potentially lives.

Despite the technological marvels surrounding AVs, there isn’t much cybersecurity support. These vehicles have diverse means of connectivity, leaving many open attack vectors. For example, the Internet of Things (IoT) has long been a highly vulnerable method of communication. Many AVs communicate with smart devices in the home, and security measures haven’t yet been fully developed to address potential attacks.

Much could be done to bolster and harden the systems around autonomous vehicles. Encrypted digital transmission has been present in IoT for quite some time. Hardened entry points requiring user authentication could mitigate possible actions and deter bad actors.

A clear and effective incident response to a systems breach is now a necessity, as it provides a blueprint for how to respond to a compromised vehicle.

4. Data Usage and Privacy Concerns

Along with the lack of security, there is the question of what data auto manufacturers collect and how they use it.

We can expect manufacturers to collect performance metrics, but gathering personalized data presents grave privacy concerns. Regulatory bodies have already addressed the data collected in the medical, financial, and educational sectors. So perhaps it’s a matter of time before additional regulations develop regarding manufacturers’ collection and safeguarding of personal data.

Other concerns arise regarding what the companies do with the data collected from their autonomous vehicles. Location data gives a glimpse at the patterns and lifestyle of the operator of any autonomous vehicle, and it would be a simple step to leverage that data into marketing materials and betray the trust of a potential customer.

Current American legislature regulations regarding data collection could be adapted to provide some degree of security for user data. New legislature and regulations could further impact how manufacturers use the data gathered by AVs.

How to Position Your Enterprise at the Forefront of AD Policies

With all these concerns, how would you move forward?

Here are some steps that you can take to move forward and position your enterprise at the forefront of these policies and regulations.

1. Liability: Your organization can handle self-reporting, which helps maintain paper trails for all incidents and prepares your staff to respond appropriately to any incidents.

2. Federal and State Regulations: Maintaining liaisons with regulatory bodies nationwide could benefit your enterprise. It’s also best to adhere to good practices and industry-standard software stacks when approaching the development of these platforms.

3. Cybersecurity: Cybersecurity has many glaring issues, but you could strengthen your organization by adopting some of the principles AI and ML companies use.

4. Data: Software stacks could and should adhere to ISO standards regarding intelligent transport systems, like ISO 22737:2021. Data usage should be self-regulated, as there aren’t provisions for the safest practices concerning the protection of customer data.

Are you looking to integrate standard software solutions for your autonomous driving firm? Digital Divide Data provides data annotation services with SOC 2 Type 2 and ISO 27001 certification.

Part of the challenge of building machine learning models is that no two are the same. Train the same machine learning algorithm against different sets of data, and you end up with a different model.

If the quality of the raw data is high and the training data sampling is done well, the models shouldn’t vary a lot… but those are all big “if”s. Which is why data preprocessing, the actual data preparation process, is critically important.

A Forbes survey revealed that data scientists spend nearly 80% of their time on data prep, a quarter of that on data collection, and the other three quarters on data cleaning. Other survey results that indicate that real-world data science isn’t everything these practitioners thought it would be; clearly, data collection and data cleaning are not how they imagined they’d spend their working hours.

Data preparation is so time consuming because it is so important. The adage – or more appropriately in this setting, admonition – “Garbage In, Garbage Out” very much applies to data preparation for machine learning, which, in extreme cases, can involve the entire lifecycle of data collection to data cleaning and feature engineering. Missteps at any point in this process will result in low-confidence model predictions, or even a model that just misperforms.

Beyond their importance, training data sets for machine learning algorithms are also voluminous – many millions of data items in the case of complex problem spaces – and much of the data prep work demands human involvement (although much of this work is often repetitive and requires only contextual training to perform).

Finally, data preprocessing usually involves a variety of technologies, both for doing the actual work of preparing the data and for managing quality in the context of volume. If the problem space is simple – say, structured data with duplicates, null values and some lack of standardization – the technology needn’t be complex. But complex problem spaces – say, identifying and tracking video objects with complex taxonomies – can require specialized technology, much of it open source, with very particular feature sets.

In recent years, numerous solution providers have sprung up to fill the human and technological gaps that data scientists confront in preparing quality training data at scale. Some offload the human labor side of data prep. Others provide technology for cleaning and labeling training data sets. And yet, some provide both.

Data science teams would be well-advised to choose carefully when evaluating data preparation partners. DDD has “inherited” more than a few customers whose first vendor fell down, invariably on one or both of these two dimensions:

Something less than full lifecycle data prep. Recall that the Forbes survey indicated 60% of data scientists’ time is spent in data cleaning. Most of today’s data preparation vendors emphasize training data labeling and annotation. They presume that they will be given data that has already been cleaned.

If your data needs cleaning, i.e.,

• it has not been de-duped

• it is missing information

• it is inconsistently presented from different sources

• it requires entity resolution

• it is image data of uneven quality or perspective

• it is handwritten and requires transcription

If you don’t have data, or don’t have enough data, and need data collected or created, these vendors are not a good fit for you.

Reliance on a single technology platform. Every ML project is unique, with unique data. No single set of proprietary tools can possibly match up to every machine learning algorithm and training data set. Data science teams need to know that whoever is doing their data preparation is technology platform agnostic. They need to know that whoever is doing their data preparation has the flexibility and freedom to choose the best tool(s) for the project at hand and is not trying to shoehorn data into an inappropriate tool or trying to jerry-rig a third-party tool onto their platform for the sake of the team’s project.