George  Koelpin

George Koelpin

1604142780

Roadmap to Computer Vision

Introduction

Computer Vision (CV) is nowadays one of the main application of Artificial Intelligence (eg. Image Recognition, Object Tracking, Multilabel Classification). In this article, I will walk you through some of the main steps which compose a Computer Vision System.

A standard representation of the workflow of a Computer Vision system is:

  1. A set of images enters the system.
  2. A Feature Extractor is used in order to pre-process and extract features from these images.
  3. A Machine Learning system makes use of the feature extracted in order to train a model and make predictions.

We will now briefly walk through some of the main processes our data might go through each of these three different steps.

Images Enter the System

When trying to implement a CV system, we need to take into consideration two main components: the image acquisition hardware and the image processing software. One of the main requirements to meet in order to deploy a CV system is to test its robustness. Our system should, in fact, be able to be invariant to environmental changes (such as changes in illumination, orientation, scaling) and able to perform it’s designed task repeatably. In order to satisfy these requirements, it might be necessary to apply some form of constraints to either the hardware or software of our system (eg. remotely control the lighting environment).

Once an image is acquired from a hardware device, there are many possible ways to numerically represents colours (Colour Spaces) within a software system. Two of the most famous colour spaces are RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value). One of the main advantages of using an HSV colour space is that by taking just the HS components we can make our system illumination invariant (Figure 1).

#data-science

What is GEEK

Buddha Community

Roadmap to Computer Vision

Computer Vision using Mediapipe

Computer vision can be defined as a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects and then react to what they “see.”

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.

In today’s world computer vision is very useful in many fields such as — :

*_ Inventory management — : _**In the case of inventory management, the applications can be in the field of security camera image analysis where a computer vision algorithm can generate a very accurate estimate of the items available in the store. Another field can be Analyzing the use of shelf space to identify suboptimal configurations.

* **Manufacturing — : **In the Field of manufacturing Computer vision can help in **predictive maintenance **of the machines.

*** Healthcare — : In the field of healthcare computer Vision can be used in medical image analysis.** Images from CT scans and X-rays are analyzed to find anomalies such as tumors or search for signs of neurological illnesses.

* **Autonomous vehicles — : **The field of computer vision plays a central role in the domain of autonomous vehicles since it allows them to perceive and understand the environment around them in order to operate correctly. One of the most exciting challenges in computer vision is object detection in images and videos. This involves locating a varying number of objects and the ability to classify them, in order to distinguish if an object is a traffic light, a car, or a person, as in the video below.

#computer-vision #opencv #mediapipe #anaconda-navigator #python #computer vision using mediapipe

Macey  Kling

Macey Kling

1597499940

How To Deter Adversarial Attacks In Computer Vision Models

While computer vision has become one of the most used technologies across the globe, computer vision models are not immune to threats. One of the reasons for this threat is the underlying lack of robustness of the models. Indrajit Kar, who is the Principal Solution Architect at Accenture, took through a talk at CVDC 2020 on how to make AI more resilient to attack.

As Kar shared, AI has become the new target for attackers, and the instances of manipulation and adversaries have increased dramatically over the last few years. From companies such as Google and Tesla to startups are affected by adversarial attacks.

“While we celebrate advancements in AI, deep neural networks (DNNs)—the algorithms intrinsic to much of AI—have recently been proven to be at risk from attack through seemingly benign inputs. It is possible to fool DNNs by making subtle alterations to input data that often either remain undetected or are overlooked if presented to a human,” he said.

Type Of Adversarial Attacks

Alterations to images that are so small as to remain unnoticed by humans can cause DNNs to misinterpret the image content. As many AI systems take their input from external sources—voice recognition devices or social media upload, for example—this ability to be tricked by adversarial input opens a new, often intriguing, security threat. This has called for an increase in cybersecurity which is coming together to address the crevices in computer vision and machine learning.

#developers corner #adversarial attacks #computer vision #computer vision adversarial attack

Osiki  Douglas

Osiki Douglas

1624803840

The Best Project to Start in Computer Vision with Python

GrabCut — A Google Colab NoteBook implementation for Image Matting (background removal)

Follow the article along with the complete code implementation on GitHub. Open the notebook in Google Colab, import your image(s), and run the cells!Originally published on louisbouchard.ai, read it 2 days before on my blog!

Image matting is an extremely interesting task where the goal is to find any object of interest, or human, in a picture and remove its background. This task is hard to achieve due to its complexity, finding the person, people, or objects with the perfect contour. This post reviews an exciting technique using basic computer vision algorithms to achieve this task. The GrabCut algorithm. It is swift but not very precise for complex objects like humans or animals. Nonetheless, it can be handy in specific contexts and is a perfect applied first project to start in computer vision and python! As mentioned above, the implementation uses Google Colab, thus having no requirements or setup needed, making it an exciting project to duplicate for learning.

#computer-vision #python #ai #machine-learning #artificial-intelligence #the best project to start in computer vision with python

Dominic  Feeney

Dominic Feeney

1620458262

Computer Vision Using TensorFlow Keras - Analytics India Magazine

Computer Vision attempts to perform the tasks that a human brain does with the aid of human eyes. Computer Vision is a branch of Deep Learning that deals with images and videos. Computer Vision tasks can be roughly classified into two categories:

  1. Discriminative tasks
  2. Generative tasks

Discriminative tasks, in general, are about predicting the probability of occurrence (e.g. class of an image) given probability distribution (e.g. features of an image). Generative tasks, in general, are about generating the probability distribution (e.g. generating an image) given the probability of occurrence (e.g. class of an image) and/or other conditions.

Discriminative Computer Vision finds applications in image classificationobject detectionobject recognitionshape detectionpose estimationimage segmentation, etc. Generative Computer Vision finds applications in photo enhancementimage synthesisaugmentationdeepfake videos, etc.

This article aims to give a strong foundation to Computer Vision by exploring image classification tasks using Convolutional Neural Networks built with TensorFlow Keras. More importance has been given to both the coding part and the key concepts of theory and math behind each operation. Let’s start our Computer Vision journey!

Readers are expected to have a basic understanding of deep learning. This article, “Getting Started With Deep Learning Using TensorFlow Keras”, helps one grasp the fundamentals of deep learning.

#developers corner #computer vision #fashion mnist #image #image classification #keras #tensorflow #vision

Thurman  Mills

Thurman Mills

1620874140

Cloud Computing Vs Grid Computing

The similarity between cloud computing and grid computing is uncanny. The underlying concepts that make these two inherently different are actually so similar to one and another, which is responsible for creating a lot of confusion. Both cloud and grid computing aims to provide a similar kind of services to a large user base by sharing assets among an enormous pool of clients.

Both of these technologies are obviously network-based and are capable enough to sport multitasking. The availability of multitasking allows the users of either of the two services to use multiple applications at the same time. You are also not limited to the kind of applications that you can use. You are free to choose any number of applications that can accomplish any tasks that you want. Learn more about cloud computing applications.

#cloud computing #cloud computing vs grid computing #grid computing #cloud