computer vision

While language models in generative AI focus on textual data, vision language models (VLMs) bridge the gap between textual and visual data. Before we explore Moondream 2, let’s understand VLMs better.

Understanding vision language models

VLMs combine computer vision (CV) and natural language processing (NLP), enabling them to understand and connect visual information with textual data.

Some key capabilities of VLMs include image captioning, visual question answering, and image retrieval. It learns these tasks by training on datasets that pair images with their corresponding textual description. There are several large vision language models available in the market including GPT-4v, LLaVA, and BLIP-2.

However, these are large vision models requiring heavy computational resources to produce effective results, and that too at slow inference speeds. The solution has been presented in the form of small VLMs that provide a balance between efficiency and performance.

In this blog, we will look deeper into Moondream 2, a small vision language model.

What is Moondream 2?

Moondream 2 is an open-source vision language model. With only 1.86 billion parameters, it is a tiny VLM with weights from SigLIP and Phi-1.5. It is designed to operate seamlessly on devices with limited computational resources.

Let’s take a closer look at the defined weights for Moondream2.

SigLIP (Sigmoid Loss for Language Image Pre-Training)

It is a newer and simpler method that helps the computer learn just by looking at pictures and their captions, one at a time, making it faster and more effective, especially when training with lots of data. It is similar to a CLIP (Contrastive Language–Image Pre-training) model.

However, Moondream 2 has replaced softmax loss in CLIP with a simple pairwise sigmoid loss. The change ensures better performance because sigmoid loss only focuses on image-text pairs. Without the need for a global view of all pairwise data within a batch, the process becomes faster and more efficient.

Learn computer vision using Python

Phi-1.5

It is a small language model with 1.3 billion parameters and transformer-based architecture. Developed by Microsoft, the model was trained on 30 billion words, including information from its predecessor, Phi-1 and 20 billion words artificially created by another AI model, GPT-3.5.

With its unique training method, Phi-1.5 has mastered different skills required of a language model, like understanding common sense, following complex instructions, comprehending language, and retaining information. Moreover, it even outperforms its larger counterparts.

A closer look into Moondream 2

It is the latest and updated version of the model previously known as Moondream 1. Below is a quick comparison of the two models, highlighting the basic parameters of improvement.

Like typical vision language models, Moondream 2 interprets the visual data to provide textual responses based on the information. Some demos included in its repository highlight its capabilities. Let’s look at these demos below:

Demo 1

Moondream 2 was provided with the following image:

Based on this image, the VLM was asked some relevant questions.

Question: What is the girl doing?

Answer: The girl is sitting at a table and eating a large hamburger.

Question: What color is the girl’s hair?

Answer: The girl’s hair is white.

The interaction with Moondream 2 highlights that the VLM is capable of understanding the basic image properties like the girl’s hair color. Moreover, it can also interpret the actions of the actors in an image from their position in a specific instance.

Demo 2

The VLM was provided with another image.

The demo focused on asking the following:

Question: What is this?

Answer: The image features a computer server rack, which is a large metal structure designed to hold and organize multiple computer components, such as motherboards, cooling systems, and other peripherals. The rack is filled with various computer parts, including multiple computer chips, wires, and other electronic components. The rack is placed on a carpeted floor, and there is a couch in the background, suggesting that the setup is likely in a living or working space.

Question: What is behind the stand?

Answer: There is a brick wall behind the stand.

The above demo highlights the ability of Moondream 2 to explore and interpret complex visual outputs in great detail. The VLM provides in-depth textual information from the visual data. It also presents spacial understanding of the image components.

Hence, Moondream 2 is a promising addition to the world of vision language models with its refined capabilities to interpret visual data and provide in-depth textual output. Since we understand the strengths of the VLM, it is time to explore its drawbacks or weaknesses.

Here’s a list of  7 books you must explore when learning about computer vision

Limitations of Moondream 2

Before you explore the world of Moondream 2, you must understand its limitations when dealing with visual and textual data.

Generating inaccurate statements

It is important to understand that Moondream 2 may generate inaccurate statements, especially for complex topics or situations requiring real-world understanding. The model might also struggle to grasp subtle details or hidden meanings within instructions.

Presenting unconscious bias

Like any other VLM, Moondream 2 is also a product of the data is it trained on. Thus, it can reflect the biases of the world, perpetuating stereotypes or discriminatory views.

As a user, it’s crucial to be aware of this potential bias and to approach the model’s outputs with a critical eye. Don’t blindly accept everything it generates; use your own judgment and fact-check when necessary.

Mirroring prompts

VLMs will reflect the prompts provided to them. Hence, if a user prompts the model to generate offensive or inappropriate content, the model may comply. It’s important to be mindful of the prompts and avoid asking the model to create anything harmful or hurtful.

In conclusion…

To sum it up, Moondream 2 is a promising step in the development of vision language models. Powered by its key components and compact size, the model is efficient and fast. However, like any language model we use nowadays, Moondream 2 also requires its users to be responsible for ensuring the creation of useful content.

If you are ready to experiment with Moondream 2 now, install the necessary files and start right away! Here’s a look at what the VLM’s user interface looks like.

Computer vision is a rapidly growing field with a wide range of applications. In recent years, there has been a significant increase in the development of computer vision technologies, and this trend is expected to continue in the coming years. As computer vision technology continues to develop, it has the potential to revolutionize many industries and aspects of our lives.

One of the most promising applications of computer vision is in the field of self-driving cars. Self-driving cars use cameras and other sensors to perceive their surroundings and navigate without human input.

Computer vision is essential for self-driving cars to identify objects on the road, such as other cars, pedestrians, and traffic signs. It also helps them to track their location and plan their route.

Self-driving cars: A game-changer

Self-driving cars are one of the most exciting and promising applications of computer vision. These cars use cameras and other sensors to perceive their surroundings and navigate without human input. Computer vision is essential for self-driving cars to identify objects on the road, such as other cars, pedestrians, and traffic signs. It also helps them to track their location and plan their route.

Healthcare: Diagnosing and innovating

Computer vision is also being used in a variety of healthcare applications. For example, it can be used to diagnose diseases, such as cancer and COVID-19. Computer vision can also be used to track patient progress and identify potential complications. In addition, computer vision is being used to develop new surgical techniques and devices.

Manufacturing: Quality control and efficiency

Computer vision is also being used in manufacturing to improve quality control and efficiency. For example, it can be used to inspect products for defects and to automate tasks such as assembly and packaging. Computer vision is also being used to develop new manufacturing processes and materials.

Key applications of computer vision in 2023: DeepAI and cutting-edge technologies

DeepAI’s Mission

DeepAI is a research lab founded by Ilya Sutskever, a former research scientist at Google Brain. The lab’s mission is to “accelerate the development of artificial general intelligence (AGI) by making AI more accessible and easier to use.”

One of DeepAI’s main areas of focus is computer vision. Computer vision is a field of computer science that deals with the extraction of meaningful information from digital images or videos. DeepAI has developed a number of cutting-edge computer vision technologies, including:

DALL-E 2: Transforming text into images

DALL-E 2 is a neural network that can generate realistic images from text descriptions. For example, you can give DALL-E 2 the text description “a photorealistic painting of a cat riding a unicycle,” and it will generate an image that matches your description.

CLIP: Matching images and text

CLIP is a neural network that can match images with text descriptions. For example, you can give CLIP the image of a cat and the text description “a furry animal with four legs,” and it will correctly identify the image as a cat.

Clova Vision: extracting information from visual media

Clova Vision is a computer vision API that can be used to extract information from images and videos. For example, you can use Clova Vision to identify objects in an image, track the movement of objects in a video, or generate a summary of the contents of a video.

Applications of DeepAI’s Technologies

1. Artificial Intelligence

DeepAI’s computer vision technologies are being used to develop new artificial intelligence applications in a variety of areas, including:

• Self-driving cars: DeepAI’s computer vision technologies are being used to help self-driving cars see and understand the world around them. This includes identifying objects, such as other cars, pedestrians, and traffic signs, as well as understanding the layout of the road and the environment.
• Virtual assistants: DeepAI’s computer vision technologies are being used to develop virtual assistants that can see and understand the world around them. This includes being able to identify objects and people, as well as understand facial expressions and gestures.

2. Healthcare

DeepAI’s computer vision technologies are being used to develop new healthcare applications in a variety of areas, including:

• Medical imaging: DeepAI’s computer vision technologies are being used to develop new methods for analyzing medical images, such as X-rays, MRIs, and CT scans. This can help doctors to diagnose diseases more accurately and quickly.
• Disease detection: DeepAI’s computer vision technologies are being used to develop new methods for detecting diseases, such as cancer and Alzheimer’s disease. This can help doctors to identify diseases at an earlier stage, when they are more treatable.

Read more –> LLM Use-Cases: Top 10 industries that can benefit from using large language models

3. Retail

DeepAI’s computer vision technologies are being used to develop new retail applications in a variety of areas, including:

• Product recognition: DeepAI’s computer vision technologies are being used to develop systems that can automatically recognize products in retail stores. This can help stores to track inventory more efficiently and to improve the customer experience.
• Inventory management: DeepAI’s computer vision technologies are being used to develop systems that can automatically track the inventory of products in retail stores. This can help stores to reduce waste and to improve efficiency.

4. Security

DeepAI’s computer vision technologies are being used to develop new security applications in a variety of areas, including:

• Facial recognition: DeepAI’s computer vision technologies are being used to develop systems that can automatically recognize people’s faces. This can be used for security purposes, such as to prevent crime or to identify criminals.
• Object detection: DeepAI’s computer vision technologies are being used to develop systems that can automatically detect objects. This can be used for security purposes, such as to detect weapons or to prevent unauthorized access to a building.

DeepAI’s computer vision technologies are still under development, but they have the potential to revolutionize a wide range of industries. As DeepAI’s technologies continue to improve, we can expect to see even more innovative and groundbreaking applications in the years to come.

Are you ready to transform lives through computer vision?

Computer vision is a powerful technology with a wide range of applications. In 2023, we can expect to see even more innovative and groundbreaking uses of computer vision in a variety of industries. These applications have the potential to improve our lives in many ways, from making our cars safer to helping us to diagnose diseases earlier.

As computer vision technology continues to develop, we can expect to see even more ways that this technology can be used to improve our lives.

In this blog post, we will explore the technology behind self-driving toy cars and how computer vision can be used to enable them to navigate their environment. We will discuss the various computer vision techniques that can be implemented, including thresholding, edge detection, blob detection, optical flow, and machine learning.

Self-driving cars have been a hot topic in the technological world for quite some time now. But did you know that you can also create a self-driving toy car using computer vision? Self-driving cars are no longer just a thing of science fiction, they are rapidly becoming a reality.

The advancements in technology and computer vision have made it possible to create autonomous vehicles that can navigate their environment without human intervention. One of the most exciting applications of this technology is the ability to create self-driving toy cars using computer vision. 

We will also explore the hardware and software required to build a self-driving toy car and the challenges that need to be overcome to make it a reality. 

Requirements 

As this is a theoretical blog post, we will only discuss the necessary requirements and the overall process of building a self-driving toy car. To begin building our self-driving toy car, we will first need to gather the necessary hardware. The main components we will need are a Raspberry Pi, a camera module, a small toy car, and a few electronic components such as a motor driver and some wires.

The Raspberry Pi is a small computer that can be used to run various software and control hardware. It is perfect for our project because it is powerful enough to run computer vision algorithms and small enough to fit inside our toy car. The camera module is what will allow the car to “see” its surroundings and make decisions based on that information. 

Once we have all the hardware, we will need to set it up and install the necessary software. The Raspberry Pi runs on a Linux operating system, so we will need to install an image of the operating system on a microSD card and then insert it into the Raspberry Pi. Next, we will need to install the necessary software libraries for computer vision, such as OpenCV, on the Raspberry Pi. This will allow us to use the camera module and process the images it captures. 

Read more about computer vision with these top 7 books

Diving deeper 

Now we can start diving deeper into various computer vision techniques. This is where the fun begins! We will learn about image processing techniques such as thresholding and edge detection to identify the path that the car should follow. 

One of the key challenges in building a self-driving toy car is calibrating the camera module so that it can accurately detect the path that the car should follow. This can involve adjusting the camera’s focus, exposure, and other settings to optimize the image quality for the specific lighting and background conditions of the environment where the car will be operating. 

Another challenge is to accurately interpret the images captured by the camera and identify the path that the car should follow. This can involve using various image processing techniques to isolate the path from the background and then using that information to control the car’s motors. 

Once the car can accurately detect and follow a path, it can be further enhanced by adding additional functionality such as obstacle detection and avoidance. This can be done by using additional sensors such as ultrasonic sensors. 

Computer vision techniques 

Computer vision techniques are a set of algorithms and methods used to interpret and understand the images captured by a camera. These techniques can be used in a toy car to help it detect and follow a path, as well as to detect and avoid obstacles. 

Some of the most used computer vision techniques that can be implemented in a toy car include: 

1. Thresholding:

Thresholding is the process of converting an image into a binary image, where all pixels are either black or white. This can be done by applying a threshold value to each pixel in the image. Pixels with a value greater than the threshold are set to white, while pixels with a value less than the threshold are set to black. This can be useful for isolating the path from the background, as it allows the algorithm to easily identify the edges of the path. 

2. Edge detection:

Edge detection is the process of identifying and highlighting the edges of an object in an image. It is usually done by convolving the image with a kernel that detects edges, such as the Sobel operator, Prewitt operator or Canny operator. Each operator will have a different way of detecting the edges, and the best one to use depends on the image. 

3. Blob detection:

Blob detection is the process of identifying and tracking specific objects or regions in an image. This can be done using various techniques, such as connected component analysis, or by training a machine learning model to recognize specific objects. This is a useful technique for detecting and tracking the position of the car, as well as for detecting and avoiding obstacles. 

4. Optical flow:

Optical flow is the process of tracking the motion of objects in an image. It is typically done by analyzing the movement of pixels between consecutive frames in a video. This can be used to determine the direction and speed of the car, as well as to detect and avoid obstacles. 

5. Machine learning:

In addition to these traditional computer vision techniques, machine learning can also be used to train a model to recognize and identify objects and features in an image. This can be useful for detecting and avoiding obstacles, as well as for more advanced tasks such as object tracking and lane keeping. 

These are some of the basic computer vision techniques that can be implemented in a toy car to enable it to detect and follow a path, and to detect and avoid obstacles. There are other techniques, but these are considered the basics to get started. 

Learn in detail about Artificial Intelligence and Computer Vision for road safety

Are you ready to start your own computer vision project?

In conclusion, building a self-driving toy car using computer vision is a challenging but rewarding project that can be a great way to learn about autonomous vehicle technology and computer vision. By using techniques such as thresholding, edge detection, blob detection, optical flow and machine learning, you can create a car that can navigate its environment and avoid obstacles.

However, it is important to keep in mind that this is not a simple task and requires a good understanding of programming, computer vision and robotics. 

We hope that this blog post has provided you with the information and inspiration you need to start your own self-driving toy car project. Keep experimenting and have fun! 

In this blog, we will discuss how Artificial Intelligence and computer vision are contributing to improving road safety for people. 

Each year, about 1.35 million people are killed in crashes on the world’s roads, and as many as 50 million others are seriously injured, according to the World Health Organization. With the increase in population and access to motor vehicles over the years, rising traffic and its harsh effects on the streets can be vividly observed with the growing number of fatalities.

We call this suffering traffic “accidents” — but, in reality, they can be prevented. Governments all over the world are resolving to reduce them with the help of artificial intelligence and computer vision.  

Humans make mistakes, as it is in their nature to do so, but when small mistakes can lead to huge losses in the form of traffic accidents, necessary changes are to be made in the design of the system.

A technology deep-dive into this problem will show how a lack of technological innovations has failed to lower this trend over the past 20 years. However, with the adoption of the ‘Vision Zero’ program by governments worldwide, we may finally see a shift in this unfortunate trend.  

 Role of Artificial Intelligence for improving road traffic

AI can improve road traffic by reducing human error, speeding up the process of detection and response to accidents, as well as improving safety. With the advancement of computer vision, the quality of data and predictions made with video analytics has increased ten-folds.  

Artificial Intelligence is already leveraging the power of vision analytics in scenarios like identifying mobile phone usage by the driver on highways and recognize human errors much faster. But what lies ahead to be used in our everyday life? Will progress be fast enough to tackle the complexities self-driving cars bring with them? 

In recent studies, it’s been inferred through data that subtle distractions on a busy road are correlated to the traffic accidents there. Experts believe that in order to minimize the risk of an accident, the system must be planned with the help of architects, engineers, transport authorities, city planners and AI.  

With the help of AI, it becomes easier to identify the problems at hand, however they will not solve them on their own. Designing the streets in a way that can eliminate certain factors of accidents could be the essential step to overcome the situation at hand.  

AI also has a potential to help increase efficiency during peak hours by optimizing traffic flow. Road traffic management has undergone a fundamental shift because of the quick development of artificial intelligence (AI). With increasing accuracy, AI is now able to predict and manage the movement of people, vehicles, and goods at various locations along the transportation network.  

As we make advancements into the field, simple AI programs along with machine learning and data science, are enabling better service for citizens than ever before while also reducing accidents by streamlining traffic at intersections and enhancing safety during times when roads are closed due to construction or other events.  

Deep learning impact on improved infrastructure for road safety

Deep learning system’s capacity for processing, analyzing, and making quick decisions from enormous amounts of data has also facilitated the development of efficient mass transit systems like ride-sharing services. With the advent of cloud-edge devices, the process of gathering and analyzing data has become much more efficient.

Increase in the number of different sources of data collection has led to an increase of not only quality but quantity of variety of data as well. These systems leverage the data from real-time edge devices and can tackle them effectively by retrofitting existing camera infrastructure for road safety. 

Join our upcoming webinar

In our upcoming webinar on 29th November, we will summarize the challenges in the industry and how AI plays its part in making a safe environment by solutions catering to avoiding human errors.

References: 

1. https://www.nytimes.com/2022/04/19/technology/ai-road-car-safety.html 

1. https://www.clickworker.com/customer-blog/artificial-intelligence-road-traffic/ 

In this blog, we have gathered the top 7 computer vision books. Learning this subject is a challenge for beginners. Take your learning experience one step ahead with these seven computer vision books. Explore a range of topics, from Computer vision to Python. 

1. Learning openCV 4 computer vision with Python 3 book by Joe Minichino and Joseph Howse: 

This book will teach you how to create a computer vision system using Python. You will learn how to use the OpenCV library, which is a cross-platform library that has been used in many research and commercial projects. Joe and Joseph in this book introduces computer vision and OpenCV with Python programming language. 

Both novices and seasoned pros alike will find something of use in this book’s extensive coverage of the subject of CV. It explains how to use Open CV 4 and Python 3 across several platforms to execute tasks like image processing and video analysis and comprehension. Machine learning algorithms and their many uses will be covered in this course. With these ideas in hand, you may design your image and video object detectors!  ~ Adam Crossling, Marketing manager at Zenzero 

2. Multiple view geometry in computer vision book by Richard Hartley: 

This book discusses the use of geometry and algebra in image reconstruction, with applications to computer vision. In this book, Richard discusses the geometry of images and how they are processed in this area. The book covers topics such as image formation, camera models, image geometry, and shape from shading. 

The main goal of this book is to provide a comprehensive introduction to computer vision by focusing on the geometric aspects of images. This article describes a wide variety of tactics, from traditional to innovative, to make it very evident when particular approaches are being employed.  

Camera projection matrices, basic matrices (which project an image into 2D), and the trifocal tensor are all introduced, along with their algebraic representations, in this book. It explains how to create a 3D model using a series of photographs taken at various times or in different sequences.  

3. Principles, algorithms, applications, learning book by E. R. Davies: 

New developments in technology have given rise to an exciting academic discipline: computer vision. The goal of this field is to understand information about objects and their environment by creating a mathematical model from digital images or videos, which can be used to extract meaningful data for analysis or classification purposes.  

This book teaches its readers not just the basics of the subject but also how it may be put to use and gives real-world scenarios in which it might be of benefit.  

4. Deep learning for vision systems by Mohamed Elgendy: 

This book should be the go-to text for anyone looking to learn about how machine learning works in AI (Artificial Intelligence) and, fundamentally, how the computer sees the world. By using only the simplest algebra a high school student would be able to understand, they can demonstrate some overly complicated topics within the AI engineering world.  

Learn about deep learning using Python

Hands-on deep learning using Python in Cloud

Through illustrations as well as Elgendy’s expertise, the book is the most accurate yet simplest way to understand computer vision for the modern day. ~ Founder & CEO of Lantech 

5. Digital image processing by Rafael C. GONZALES and Richard E. Woods: 

Image processing is one of the topics that form the core of Computer Vision and DIP by Gonzalez is one of the leading books on the topic. It provides the user with a detailed explanation of not just the basics like feature extraction and image morphing but also more advanced concepts like wavelets and superpixels. It is good for both beginners and people who need to refresh their basics.

It also comes with MATLAB exercises to help the reader understand the concepts practically. Senior Machine Learning Developer, AltaML  Rafael C. GONZALES and Richard E. Woods wrote this book to provide an introduction to digital image processing for undergraduate students and professionals who are interested in this field.

The book covers the fundamentals of image formation, sampling and quantization, the design of analog-to-digital converters, image enhancement techniques such as filtering and edge detection, image compression techniques such as JPEG and MPEG, digital watermarking techniques for copyright protection purposes and more advanced topics like fractal analysis in texture synthesis. 

6. Practical machine learning for computer vision: End-to-end machine by Martin Görner, Ryan Gillard, and Valliappa Lakshmanan: 

Learning for Images. This tutorial shows how to extract information from images using machine learning models. ML (Machine Learning) engineers and data scientists will learn how to use proven ML techniques such as classification, object detection, autoencoders, image generation, counting, and captioning to solve a variety of image problems.  

You will find all aspects of deep learning from start to finish, including dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Valliappa Lakshmanan, Martin Görner, and Ryan Gillard of Google show how to use robust ML architecture to develop accurate and explainable computer vision ML models and put them into large-scale production in a flexible and maintainable manner. You will learn how to use TensorFlow or Keras to design, train, evaluate, and predict models. Senior IT Director at Propnex 

Further, this book provides a great introduction to deep end-to-end learning for computer vision, including how to design, train, and deploy models. You will learn how to select appropriate models for various tasks, preprocess images for better learnability, and incorporate responsible AI best practices. The book also covers how to monitor and manage image models after deployment. You will also learn how to put your models into large-scale production using robust ML architecture. The authors are Google engineers with extensive experience in the field, so you can be confident you are learning from the best. – Will Cannon, CEO, and Founder of Uplead   

7. Computer vision by Richard Szeliski:  

This book is all about algorithms and applications. This book is perfect for undergraduate students in computer science as it aims to provide a comprehensive course in computer vision. It is also known as the bible of computer vision. The focus of this book is on the algorithm, application, and techniques for image processing and recognition in CV.

It also helps one to get an understanding of the real-based applications and further discuss the implementation and practical challenges of techniques in computer vision. Co-Founder at Twiz LLC 

If you are interested in teaching senior-level courses in this subject, then this book is for you as it can help you to learn more techniques and enhance your knowledge about computer vision. 

Share more computer vision books with us 

If you have read any other interesting computer vision book, share with us in the comments below and let us help the learners to begin with computer vision. 

Confused about which machine learning conferences you should attend? Here are our top 10 picks for the remaining months of 2022.

For aspiring data scientists, machine learners, and researchers, conferences are a great way to network, highlight their own work, and learn from others. This article highlights the top 10 machine learning conferences that you should attend if you are in Asia or are planning to visit soon.

1. ACAIT 2022: The 6th Asian Conference on Artificial Intelligence Technology – Changzhou, China

Taking place in the southern Jiangsu province of China, on the 4th of November, the ACAIT is a joint endeavor of the Institute of Electrical and Electronics Engineers (IEEE), Chinese Association for Artificial Intelligence (CAAI), and Changzhou Institute of Technology (CIT).

The conference invites significant and original research work from the world of artificial intelligence. The main aim of the conference is to provide an international forum for researchers to share their ideas and achievements in the field of artificial intelligence.

The conference covers all major topics from AI-related brain and cognitive sciences to machine Cognition and Pattern Recognition, Big data and knowledge engineering, Robotics, swarm intelligence, and even the Internet of Things.

Further details regarding the conference can be found here.

2. 4th Asian Conference on Machine Learning (ACML 2022) – Hyderabad, India

Taking place between 12th to 14th December in Hyderabad, India, the ACML abides by the post-pandemic laws and will be conducted virtually, as well as allow in-person interactions.

Focusing on theoretical and practical aspects of machine learning, the conference encourages researchers from around the globe to join and be a part of the conversation.

The conference will cover general machine learning topics such as supervised learning and reinforcement learning, and even dive deeper into Deep Learning, Probabilistic Methods, theoretical frameworks, and much more.

Further details regarding the conference can be found here.

3. The 29th International Conference on Computational Linguistics – Gyeongju, Republic of Korea

One of the most popular conferences on natural language processing and computational linguistics, COLING is expected to be held on October 12-17, 2022, in Gyeongju, South Korea.

The conference has been held every year since 1965. Participants from both top-ranked research centers and emerging countries attend this conference as it provides equal opportunities to researchers from educational institutes and academia, as well as from the corporate sector.

COLING focuses on all aspects of natural language processing and computation.

Not only is this one of the most prestigious conferences on NLP and computational linguistics, but it is also heavily sponsored by names such as LG Electronics, Hyundai, Google, and Apple, among many others.

Further details regarding the conference can be found here.

4. IROS 2022: International Conference on Intelligent Robots and Systems – Kyoto, Japan

One of the flagship conferences of the robotics community, IROS is one of the world’s oldest forums for the global robotics community to explore intelligent robots and systems. Held every year in Kyoto, Japan since 1987, the conference will be held on 23-27 October.

Not only does the conference feature numerous research works from various international authors, but the conference also includes workshops and training, as well as multiple guest lectures by professionals in academia and industry.

Further details regarding the conference can be found here.

5. ACCV 2022: The 16th Asian Conference on Computer Vision

The Asian Conference on Computer Vision (AACV) 2022 focuses on computer vision and pattern recognition and will be held on 4-8 December in Macau, China.

The biennial international conference is sponsored by the Asian Federation of Computer Vision and provides like-minded individuals an opportunity to discuss the latest problems, solutions, and technologies in the field of computer vision and other similar areas.

The conference proceedings are published by Springer as Lecture Notes. Moreover, the award-winning papers are invited for publication in a special issue of the International Journal of Computer Vision (IJCV).

More details on the conference can be found here.

6. The 29th International Conference on Neural Information Processing (ICONIP 2022), New Delhi, India

One of the leading international conferences in the fields of pattern recognition, neuroscience, intelligent control, information security, and brain-machine interface, the ICONIP will be held in New Delhi, India on 22nd -26th November 2022.

It is the annual flagship conference organized by the Asia Pacific Neural Network Society (APNNS), which strives towards bridging the gap between educational institutions and industry.

The conference provides an international forum for anyone working in neuroscience, neural networks, deep learning, and other similar fields.

The conference is divided into four categories: Theory and Algorithms, Computational and Cognitive Neurosciences, Human-Centered Computing, and other machine learning applications.

Further details on the conference can be found here.

7. The 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI) – Shanghai, China

A biennial international conference, the PRICAI focuses on AI theories, technologies, and their applications in areas of social and economic importance, specifically focusing on countries in the Pacific Rim. Held since 1990, PRICAI will take place on 10-13th November, in the financial hub of China – Shanghai.

The conference focuses on all things related to AI, machine learning, data mining, robotics, computer vision, and much more.

Further information regarding the conference can be found here.

8. The 4th International Conference on Data-driven Optimization of Complex Systems (DOCS2022) – Chengdu, China

Focused on data-driven optimization, learning and control, and their applications to complex systems, DOCS 2022 will be held 23-25th September, Chengdu, Sichuan, China.

The conference focuses on topics ranging from data-driven machine learning, optimization, decision-making, analysis, and application.

Further details on the conference can be found here.

9. The 9th IEEE International Conference on Data Science and Advanced Analytics (DSAA) – Shenzhen, China

Widely recognized as a dedicated flagship annual conference, the International Conference on Data Science and Advanced Analytics (DSAA) will be held in Shenzhen, China on the 13th –16th of October 2022.

The conference not only focuses on computing and information/intelligence sciences but also considers their relationship with statistics, and the crossover of data science and analytics.

An interesting aspect of this conference is that it is a dual-track conference with both a research track and an application track. Further details regarding these different tracks can be found here.

While more details on the conference can be found here.

10. The 5th International Conference on Intelligent Autonomous Systems (ICoIAS 2022) – Dalian, China

The ICoIAS conference focuses on intelligent autonomous systems that play a significant role in multiple control and engineering applications.

The conference will be held on 23-25 September at the Dalian Maritime University, Dalian, China, in collaboration with Tianjin University, the IEEE Computational Intelligence Society, and The Institution of Engineers, Singapore.

The conference focuses on distinct aspects of intelligent autonomous systems. Moreover, IEEE fellows from all over the world are expected to attend the conference as guest speakers.

For further information regarding the conference, click here.

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Data Science Dojo has launched  Jupyter Hub for Computer Vision using Python offering to the Azure Marketplace with pre-installed libraries and pre-cloned GitHub repositories of famous Computer Vision books and courses which enables the learner to run the example codes provided.

 

Challenges for individuals

Individuals who want to understand digital images and want to start with it usually lack the resources to gain hands-on experience with Computer Vision. A beginner in Computer Vision also faces compatibility issues while installing libraries along with the following:

1. Image noise and variability: Images can be noisy or low quality, which can make it difficult for algorithms to accurately interpret them.
2. Scale and resolution: Objects in an image can be at different scales and resolutions, which can make it difficult for algorithms to recognize them.
3. Occlusion and clutter: Objects in an image can be occluded or cluttered, which can make it difficult for algorithms to distinguish them.
4. Illumination and lighting: Changes in lighting conditions can significantly affect the appearance of objects in an image, making it difficult for algorithms to recognize them.
5. Viewpoint and pose: The orientation of objects in an image can vary, which can make it difficult for algorithms to recognize them.
6. Occlusion and clutter: Objects in an image can be occluded or cluttered, which can make it difficult for algorithms to distinguish them.
7. Background distractions: Background distractions can make it difficult for algorithms to focus on the relevant objects in an image.
8. Real-time performance: Many applications require real-time performance, which can be a challenge for algorithms to achieve.

 

What we provide

Jupyter Hub for Computer Vision using the language solves all the challenges by providing you an effortless coding environment in the cloud with pre-installed computer vision python libraries which reduces the burden of installation and maintenance of tasks hence solving the compatibility issues for an individual.

Moreover, this offer provides the learner with repositories of famous books and courses on the subject which contain helpful notebooks which serve as a learning resource for a learner in gaining hands-on experience with it.

The heavy computations required for its applications are not performed on the learner’s local machine. Instead, they are performed in the Azure cloud, which increases responsiveness and processing speed.

Listed below are the pre-installed python libraries and the sources of repositories of Computer Vision books provided by this offer:

Python libraries

• Numpy
• Matplotlib
• Pandas
• Seaborn
• OpenCV
• Scikit Image
• Simple CV
• PyTorch
• Torchvision
• Pillow
• Tesseract
• Pytorchcv
• Fastai
• Keras
• TensorFlow
• Imutils
• Albumentations

 Repositories

• GitHub repository of book Modern Computer Vision with PyTorch, by author V Kishore Ayyadevara and Yeshwanth Reddy.
• GitHub repository of Computer Vision Nanodegree Program, by Udacity.
• GitHub repository of book OpenCV 3 Computer Vision with Python Cookbook, by author Aleksandr Rybnikov.
• GitHub repository of book Hands-On Computer Vision with TensorFlow 2, by authors Benjamin Planche and Eliot Andres.

Conclusion

Jupyter Hub for Computer Vision using Python provides an in-browser coding environment with just a single click, hence providing ease of installation. Through this offer, a learner can dive into the world of this industry to work with its various applications including automotive safety, self-driving cars, medical imaging, fraud detection, surveillance, intelligent video analytics, image segmentation, and code and character reader (or OCR).

Jupyter Hub for Computer Vision using Python offered by Data Science Dojo is ideal to learn more about the subject without the need to worry about configurations and computing resources. The heavy resource requirement to deal with large Images, and process and analyzes those images with its techniques is no more an issue as data-intensive computations are now performed on Microsoft Azure which increases processing speed.

At Data Science Dojo, we deliver data science education, consulting, and technical services to increase the power of data. We are therefore adding a free Jupyter Notebook Environment dedicated specifically for it using Python. Install the Jupyter Hub offer now from the Azure Marketplace, your ideal companion in your journey to learn data science!