What is Visual Analytics Summary?
Computer vision / Visual Analytics describes the process when a computer using artificial intelligence algorithms can identify and process images (photos, videos, etc.) and then create an appropriate output from the analysis because the computer can actually “understand” the content. Specifically, computer vision/ visual analytics can classify, identify, verify, and detect objects. The developments with computer vision / visual analytics in recent years were facilitated by machine learning technology — in particular, the iterative learning process of neural networks — and significant leaps in computing power, data storage, and high-quality yet inexpensive input devices.
Why is Visual Analytics important?
Discover the unexpected; it’s all about the journey. Sounds like the beginning of a travel brochure, right? Well, it fits because visual analytics helps you navigate a world full of data. When you are trying to make sense out of your data, where do you begin? Two popular approaches to visual data analysis include data visualization and visual analytics. Each plays an important role in data exploration. You don’t have to choose one or the other— they both help you to see and understand your data.
For many people, creating dashboards and reports are the goal and final destination of their data exploration. But what if the data is revealing some type of issue, such as lower profits for a certain region or type of product? To get to the root cause of an issue or problem, you need to be able to explore the dashboard’s data directly, beyond the limits of a canned set of filters and categories. You might need to view the data with new types of visualizations, beyond the constraints of report templates and canned chart types, to answer your own questions.
Why Visual Analytics is effective?
Visual analytics is a method for exploring data visually, in real-time. A productive visual analytics experience has certain characteristics. At any moment, you can:
- • Instantly change what data you are looking at (with one click). This is important because different questions require different data.
- • Instantly change the way you are looking at it (with one click). This is important because different views of data answer different questions
With each incremental change, the view of the data updates immediately to help you intuitively explore different visualization types to find the right one. You can focus on exploring, instead of the mechanics of how to build a specific chart or being locked into a canned template. The meaning in your data unfolds as you create different views to answer different questions—so your exploration leads to better analysis, not dead ends.
Your questions and incremental changes don’t touch the underlying data; they only change how the data is visually represented. Because the changes are made incrementally, it is always possible to undo, redo or return to a previous state. Every action is safe, because it can be instantly undone. Best of all, the visual analytics process can lead to visualizations that show you the unexpected. Imagine how surprise findings can stimulate your thought process, and encourage deeper analysis or a different path of exploration
Computer vision – Vision Analytics one in the same.
, visual recognition, and facial recognition. Models can be trained to see almost anything humans can see in real-time.
What is Image recognition?
Image recognition refers to computer vision’s (CV) ability to identify the dominant subject in an image, and apply the relevant “concept” or “tag.” This could be objects, places, people, words and even actions.
Simple Definition: Where an image contains multiple objects, image recognition picks out the central object or what the camera was actually focusing on.
What is Visual recognition?
While image recognition can only recognize the dominant object in an image, visual recognition can do this for both image and video and apply the relevant concepts.
Simple Definitions: Visual recognition is like image recognition but for images and videos.
In this video, the dominant image changes. For instance, in the beginning, the scene takes place in a room with a carpet and the puppy is playing with a human and a piece of tissue. Despite all these objects and subjects, however, the technology recognizes that the puppy is the main focus. This is reflected in the predicted tag.
What is Object Detection?
Object detection is a computer vision technique for detecting many different objects in images or videos, instead of just a single dominant object, using bounding boxes or a simple rectangle or square around an image.
Simple Definition: Object detection uses bounding boxes to identify several different objects are in an image.
What is Object Tracking?
Object tracking refers to the process of following a specific object of interest among multiple objects in a given video. It traditionally has applications in video and real-world interactions where observations are made following an initial object detection.
Simple Definition: Object tracking uses bounding boxes to identify several objects in a video and follows them.
Example of Object Tracking Real Time.
Example Use Cases for computer vision – vision analytics
- Retail: Customer Behavior Tracking
- Agriculture: Detection of Wheat Rust
- Public Health: Image Segmentation of Scans
- Automotive Industry: Object Recognition and Classification in Traffic
- Fitness: Human Pose Estimation