Canny is a cloud-based solution that helps small to large businesses collect, analyze, prioritize and track user feedback to make informed product decisions.
Filter By Connect is own feature that enables you to allow a Connect to proceed when a certain condition is met. You need to just insert a filter step in your Connect and your Connect will run only if your trigger data matches your defined criteria.Filter By Connect Integrations
Canny + Filter By ConnectOnly continue if in Filter By Connect when New Post is created in Canny Read More...
Canny + Filter By ConnectOnly continue if in Filter By Connect when Post Status Change is added to Canny Read More...
Canny + Filter By ConnectOnly continue if in Filter By Connect when New Vote is created in Canny Read More...
Canny + Filter By ConnectOnly continue if in Filter By Connect when New Comment is created in Canny Read More...
It's easy to connect Canny + Filter By Connect without coding knowledge. Start creating your own business flow.
Triggers when a new comment is created.
Triggers when a new post is created.
Triggers when a new vote is created.
Triggers when a post's status is changed.
Changes a post's status.
Set up rules to specify when this Connect can continue running
Introduction of the article
In this assignment, I am going to show you the integration of Canny and Filter By Connect.
Canny is an image processing algorithm that is used for detecting edges in digital images. It was designed by John F. Canny and the original paper was published in 1986.
In general, we can say that Canny is a detection algorithm that is used for locating edges in images. These edges are represented by intensity discontinuities between adjacent image pixels. Edge detection algorithms have many applications in computer vision (e.g., target recognition, image registration, image segmentation, etc..
Canny edge detector operates on gray-level images. It detects edges using a multi-stage algorithm. The first stage is called the "Laplacian filter", which produces a version of the image with enhanced contrast near edges; the second stage uses non-maximum suppression to eliminate insignificant edge fragments; and the third stage uses a series of subsequent convpution steps to produce an output image that exhibits only significant edges (edges of high intensity.
Many people use Canny for edge detection because it is very simple, fast and efficient. It has a good detection precision, which means that it can detect many edges of the same intensity in the image. In addition, it is an optimal method for edge detection because it works very well on both low contrast images and high contrast images.
However, Canny's detection precision can be improved using the Filter By Connect algorithm. Therefore, we can integrate these two algorithms in order to achieve better results for detecting edges in images.
First part of the body (Integration)
There are two main reasons why we want to combine Canny and Filter By Connect into one algorithm. The first reason is due to the fact that both algorithms can work independently in order to detect edges in images. However, Canny does not do a great job for detecting edges that have low contrast or high contrast. On the other hand, Filter By Connect can detect edges with low contrast but it cannot detect edges with high contrast. Therefore, combining these two algorithms will produce better results for detecting edges in images.
The second reason is due to the fact that both algorithms can work simultaneously in order to detect edges in images. When working together, they can complement each other by detecting different types of edges in the image. For example, Canny will detect the low contrast edges while Filter By Connect will detect the high contrast edges. This way, we can improve the detection precision and obtain better results for detecting edges in images.
Second part of the body (Benefits)
By integrating Canny and Filter By Connect into one algorithm, we can get better results for detecting edges in images. First of all, we have improved performance for both low and high contrast edges because we have combined two algorithms that are capable of detecting both types of edges separately. Second of all, we have improved detection precision because we have combined two algorithms that can detect different types of edges simultaneously.
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