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10 Applications of Deep Learning for Computer Vision

Deep learning models have the ability to learn patterns and to derive meaning from images. Thus, they reduce the need for methods based on hand-crafted features. Deep learning methods are used in a wide range of different computer vision applications such as motion detection, face recognition, and image synthesis. Let’s take a look at some of the most popular computer vision applications that are powered by deep learning.

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5 Reasons to Outsource Your Data Annotation Projects

When training an image classifier, it is important to use data that contains high-quality annotations. Even the most sophisticated computer vision algorithms will be rendered useless when they are trained on data that is of poor quality. Outsourcing the data annotation tasks helps to ensure that your datasets are of the highest quality.

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Top 5 Myths About Outsourcing Image Annotation Services

More and more companies are choosing to outsource the annotation of their images as they realize the complexity of doing in-house image annotation. However, many companies that could benefit from outsourcing still choose not to outsource. This could be due to some persistent myths about the nature of outsourcing image annotation.

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Selecting The Right Image Labeling Tool For Your Company

If you’re hoping to streamline your company’s image annotation process, there are a variety of image labeling tools you can use to make creating annotations quicker and easier. Considering the plethora of image annotation tools out there, how do you choose the tools that satisfy your needs? Choosing the best image labeling tool involves understanding the different image labeling techniques and their use cases

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Automatic Image Annotation VS. Manual Image Annotation

Image annotation companies are constantly investing in new ways to reduce the amount of time needed to annotate a database full of images. One of the most powerful image annotation techniques is automatic image annotation, which can drastically speed up the annotation process. However, there are drawbacks to the use of automatic image annotation techniques, and human annotators still have their roles to play.

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Understanding Automatic Image Annotation: Uses and Limitations

Image annotation is a complicated and time-consuming task. For this reason, automatic image annotation techniques have been created with the goal of reducing the amount of time needed to annotate images. Automatic image annotation techniques have their uses, but they also have their limitations, such as typically being less accurate than human made annotations. A closer examination of automatic image annotation techniques, as well

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Making Annotations With The Bounding Box Tool

Using a bounding box creation tool can be overwhelming the first few times you do it. When you make annotations with the bounding box creation tool you’ll want to familiarize yourself with the tool and pay attention to certain considerations. Let’s go over considerations for using the bounding box creation tool, so that you can better understand how to make the best bounding boxes.
In order for computer vision systems and applications to be able to recognize objects, they must be trained on a large amount

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Artificial Intelligence Image Recognition

Have you wondered how artificial intelligence driven image recognition works? There are two ingredients to image recognition with artificial intelligence: neural networks and image data. Let’s take a close look at both of these concepts individually, and then see how they come together to accomplish image recognition.

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Major Challenges In Image/Video Annotation

Image and video annotation techniques and computer vision, in general, have rapidly progressed over the past five years, and they can be used in many different fields. However, there are still many challenges that must be dealt with when annotating for images and videos. What are the major challenges when it comes to image and video annotation?

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Deep Learning For Single Image Super Resolution

Single image super-resolution is one application of deep learning that can help overcome the problems created by a lack of resolution in images when creating a computer vision based application.The goal of a single image super-resolution is to scale up small images while keeping a drop in image quality to a minimum. The predominant deep learning architecture used to accomplish this is called a Generative Adversarial Network, or GAN.

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Image Annotation For Deep Learning And Computer Vision

Before we cover image annotation for deep learning, let’s take a moment to make sure we’re clear on what deep learning is. Deep learning is a subdiscipline of machine learning, and machine learning can be described as techniques that enable computers to carry out tasks without being explicitly programmed to do so. Machine learning systems often utilize neural networks, which analyze data and extract relevant patterns of information from them.

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5 Main Applications Of Image Annotation

Image annotation is the process of adding new metadata to existing data.  In the case of computer vision this typically means surrounding important objects with structures like bounding boxes, which helps the computer recognize similar objects in the future. Image annotation speeds up the process of pattern recognition when a computer vision system is presented with new data, and this useful technique has many different applications. 
Image annotation is typically done by humans, and it can be very time intensive...

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Object Tracking In Videos

Object tracking in videos, or video object tracking, is the process of detecting an object as it moves through space in a video. Video object tracking is used for a variety of applications like traffic control, video editing, medical imaging, tracking faces and eyes for human-computer interaction, and tracking objects or people for surveillance and security.
How exactly is object tracking in videos carried out by a neural network? What strategies and algorithms can be applied to create robust object tracking for videos?

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Small Object Detection: Face Detection Using Machine Learning

If your goal is to create a facial recognition system for use in an app of some sort, whether the app is intended for social networking or security, you’ll need to decide on one of the various ways to detect faces in images and video. Over the years many different facial detection algorithms have been designed, and both regular machine learning techniques, as well as deep learning techniques,  can be used to carry out face detection.
If you are trying to detect small images of faces, there are a few additional considerations...

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What Is Object Detection In Deep Learning

So what is deep learning exactly? It is an extension of machine learning, and machine learning is the study of techniques that let machines carry out various tasks without being explicitly programmed to do so.
Machine learning systems have three principal components: an input, a node/neuron, and an output. The input is the data that is being fed into the machine learning system, and the node/neuron represents one type of mathematical algorithm that will manipulate this information.

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Importance Of Semantic Segmentation Annotation Tool

Semantic segmentation annotation tools are excellent investments, as they can drastically reduce the amount of time spent preparing images for input into a deep neural network.
Semantic segmentation is the process of associating every pixel, within a region of an image that has semantic value, with a specific label. Semantic segmentation is a powerful tool for deep learning, as it makes images easier to analyze by assigning parts...

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AI In Fashion: Image Annotation and Tagging in the Fashion Industry

When it comes to the use of artificial intelligence in the fashion industry, one of the most common implementations of AI is for image classification/recognition. AI systems can be trained to recognize specific items of clothing. In order for an AI to classify clothing items, the images of the clothes must first be annotated and tagged. Image annotation is the process of adding metadata to images that can enhance the accuracy of an image...

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What Is Computer Vision And Object Detection?

Object detection algorithms for computer vision tasks are some of the most powerful tools in all of machine learning and artificial intelligence. These are decision algorithms that enable computer systems to make inferences about the real world around them, as filtered through a camera. Without object recognition, robots that manipulate objects, autonomous vehicles, and image classification software would be almost impossible to create

Understanding computer vision and object detection will allow you to consider...

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How AgTech Is Growing Up With Machine Learning Techniques

The AgTech industry seems to have grown substantially over the past few years, and seems poised to continue its substantial growth rate for the foreseeable future. In 2018, AgTech as an industry reached almost $17 billion in funding stretching across 1450 different investment projects. As this industry continues to grow and mature, many ...

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Image Annotation Services

Image annotation is a complex task, with many different variables that must be taken into account. Creating bounding boxes requires considering things like edge cases, where the image was taken, what kinds of features apply to different objects, etc. Because of the complexity of image annotation, many companies choose to outsource the task.

We’ve previously discussed why you would want to outsource image annotation, but we only discussed the complexity of creating bounding boxes. Beyond bounding boxes...

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Image Annotation Companies

Are you looking for a company to handle your image annotation needs? If you have images you need annotated for the purposes of training a neural network, there are many different companies that can provide image annotation services to you.
These image annotation companies include: Flatworld Solutions, Cogito, CloudFactory, Playment, Taskware, Gengo, and Microwork.

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Image Annotation Outsourcing

In order for an image classifier to work, the image dataset that is fed to the classifier must be annotated. Many companies consider annotating their own images, under the impression that it will save them time and resources. However, while image annotation is something that seems simple in concept, in reality it’s quite difficult.
Image annotation has many different, complicated, steps/phases to it and unless these steps are carried out correctly the resulting image classifier will perform ...

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