Wednesday, June 11, 2025

Simultaneous Localization Mapping (SLAM) #Sciencefather #researchawards #computervision

Introduction

The technology industry is inundated with references to AI (artificial intelligence), ML (machine learning), DNN’s (deep neural networks), CV (computer vision), CNN’s (convolutional neural networks), RNN’s (recurrent neural networks), etc..

What these acronyms represent are some of the components that make up the field of Artificial Intelligence. Imagine an artificial being, and what it needs to successfully interact with the world around it - the ability to sense and perceive its environment (machine perception), the ability to understand speech (natural language processing), the ability to remember information, learn new things, and make inferences (machine learning, knowledge management and reasoning), the ability to plan and execute actions (automated planning), and the ability to interact with its environment (robotics).

Machine perception encompasses the capabilities enabling machines to understand the input from the 5 senses - visual, auditory, tactile, olfactory, and gustatory. (Yes, they do have machines that analyze smell and taste).

Computer Vision and SLAM

Buried among these acronyms, you may have come across references to computer vision and SLAM. Let’s dive into the arena of computer vision and where SLAM fits in. There are a number of different flavors of SLAM, such as topological, semantic and various hybrid approaches, but we’ll start with an illustration of metric SLAM.

As the name suggests (intuitively or not), Simultaneous Localization and Mapping is the capability for a machine agent to sense and create (and constantly update) a representation of its surrounding environment (this is the mapping part), and understand its position and orientation within that environment (this is the localization part). Most humans do this well enough without much effort, but trying to get a computer to do this is another matter.

There are many types of sensors that can detect the surrounding environment, including camera(s), LiDAR, radar, and sonar. As the machine agent with the sensors (such as the ones listed prior) moves through space, a snapshot of the environment is created, while the relative position of the machine agent within that space is tracked. Thus, a picture is formed by features represented as points in space including the relative distance from the observer and with each other.

Over time, this collection of feature points and their registered position in space grow together to form a point cloud, a 3-dimensional representation of the environment. This is the “mapping” part in SLAM.

As the map is being created, the machine agent tracks its relative position and orientation within that point cloud, enabling the “localization” part in SLAM. Once a map is available, then any arbitrary machine agent using the map would be able to “relocalize” within that space - ie. determine its location on the map from what it perceives around it.

International Conference on Computer Vision

The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.

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Nominate now : https://computer-vision-conferences.scifat.com/award-nomination/?ecategory=Awards&rcategory=Awardee

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Tuesday, June 10, 2025

Mastering Deep Learning: Key Concepts and Its Impact on Image Processing #Sciencefather #researchawards #imageprocessing

Introduction

Imagine a world where a computer can identify diseases in medical scans more accurately than human doctors, enhance the quality of old family photos to a stunning degree, or even create entirely new artworks from scratch based on simple text descriptions. This isn’t the distant future — this is the power of deep learning today. One of the most revolutionary developments in artificial intelligence, deep learning has drastically transformed the field of image processing. In recent years, we’ve witnessed deep learning algorithms outperform humans in tasks such as image recognition and classification, leading to groundbreaking advancements across various industries.

Understanding deep learning and its profound impact on image processing is crucial in our increasingly digital world. From improving security through enhanced facial recognition systems to enabling autonomous vehicles to interpret their surroundings, the applications of deep learning in image processing are vast and varied. By mastering these concepts, businesses and individuals can leverage this technology to innovate and stay competitive in a rapidly evolving technological landscape.

In this blog post, we will delve into the key concepts of deep learning and explore how they are applied to image processing. We will cover the fundamentals of neural networks, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and the latest advancements in Large Language Models (LLMs) such as GPT-4. Furthermore, we’ll discuss practical applications and real-world case studies, and highlight leading service providers offering cutting-edge image processing solutions. By the end of this post, you’ll have a comprehensive understanding of how deep learning is reshaping the field of image processing and the potential it holds for the future.

International Conference on Computer Vision

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Thursday, June 5, 2025

Revolutionizing Farming with AI_ Indian #Sciencefather #researchawards #neuralnetwork

Neural Networks

Neural networks are a cornerstone of modern artificial intelligence and machine learning, modeled loosely after the structure and function of the human brain. They consist of layers of interconnected nodes, often referred to as neurons, that process and transmit information. Each neuron receives input, applies a mathematical transformation (usually a weighted sum followed by an activation function), and passes the output to the next layer.

The most basic type of neural network is the feedforward neural network, where information moves in only one direction—from input to output. More advanced architectures include convolutional neural networks (CNNs), which are widely used in image and video recognition, and recurrent neural networks (RNNs), which are effective for sequential data such as language or time series. Neural networks are trained using a process called backpropagation, where the model’s predictions are compared to the actual values using a loss function, and the weights of the connections are adjusted to minimize error. This process typically involves optimization algorithms like stochastic gradient descent.

Over time, the network learns to recognize patterns and make accurate predictions or classifications. The power of neural networks lies in their ability to model complex, non-linear relationships in data, making them especially useful for tasks such as speech recognition, computer vision, natural language processing, and autonomous driving. However, training deep neural networks requires large amounts of data, computational resources, and careful tuning of hyperparameters. Despite these challenges, neural networks have achieved state-of-the-art performance in many fields and continue to drive significant advances in AI research and real-world applications.

International Research Awards on Computer Vision

Join us for The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.

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Computer vision before machine learning #Sciencefather # researchawards #computervision

Computer vision before machine learning

Today’s Internet giants value machine learning so much, of course not for the academic value mainly because it can bring great commercial value. So why did traditional algorithms not achieve the precision of deep learning before?

Before the deep learning algorithm, for the visual algorithm, it can be roughly divided into five steps: Feature Perception, Image Preprocessing, Feature Extraction, Feature Selection, Inference Prediction and Recognition.

Among the early dominant statistical machine learning projects, they were less concerned with the feature parts. So computer vision has to design the first four parts when using these machine learning methods, which is a difficult task for anyone. The traditional computer image recognition method separates the feature extraction and the classifier design, then merges them together in the application. For example, if the input is a motorcycle image, there must first be a feature expression or feature extraction process, and then put the expressed features into the learning algorithm for classification learning.

There have been many excellent feature operators in the past 20 years, such as the most famous Scale Invariant Feature Transform (SIFT) operator, which is based on scale space and maintains invariance to image scaling, rotation, and even affine transformation. SIFT extracts the local features of the image, finds the extreme points in the scale space and extracts the position, scale and direction information. SIFT’s applications include object recognition, robot map perception and navigation, effect stitching, 3D model building, gesture recognition, and effect tracking.

International Research Awards on Computer Vision

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Wednesday, June 4, 2025

What is AI?

Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy.

Applications and devices equipped with AI can see and identify objects. They can understand and respond to human language. They can learn from new information and experience. They can make detailed recommendations to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car).

But in 2024, most AI researchers, practitioners and most AI-related headlines are focused on breakthroughs in generative AI (gen AI), a technology that can create original text, images, video and other content. To fully understand generative AI, it’s important to first understand the technologies on which generative AI tools are built: machine learning (ML) and deep learning.

Deep learning

Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain.

Deep neural networks include an input layer, at least three but usually hundreds of hidden layers, and an output layer, unlike neural networks used in classic machine learning models, which usually have only one or two hidden layers.

These multiple layers enable unsupervised learning: they can automate the extraction of features from large, unlabeled and unstructured data sets, and make their own predictions about what the data represents.

Because deep learning doesn’t require human intervention, it enables machine learning at a tremendous scale. It is well suited to natural language processing (NLP), computer vision, and other tasks that involve the fast, accurate identification complex patterns and relationships in large amounts of data. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.

Generative AI

Generative AI, sometimes called "gen AI", refers to deep learning models that can create complex original content such as long-form text, high-quality images, realistic video or audio and more in response to a user’s prompt or request.

At a high level, generative models encode a simplified representation of their training data, and then draw from that representation to create new work that’s similar, but not identical, to the original data.

Generative models have been used for years in statistics to analyze numerical data. But over the last decade, they evolved to analyze and generate more complex data type.

International Research Awards on Computer Vision

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Global Tech Excellence Awards

Wednesday, June 11, 2025

Simultaneous Localization Mapping (SLAM) #Sciencefather #researchawards #computervision

Introduction

Tuesday, June 10, 2025

Mastering Deep Learning: Key Concepts and Its Impact on Image Processing #Sciencefather #researchawards #imageprocessing

Introduction

Thursday, June 5, 2025

Revolutionizing Farming with AI_ Indian #Sciencefather #researchawards #neuralnetwork

Neural Networks

Computer vision before machine learning #Sciencefather # researchawards #computervision

Computer vision before machine learning

Wednesday, June 4, 2025

What is AI?

Deep learning

Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain.

Generative AI

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