Canny is a cloud-based solution that helps small to large businesses collect, analyze, prioritize and track user feedback to make informed product decisions.
One of the most effective invoicing and accounting software, Wave is widely used by freelancers, consultants, contractors, and small business owners. With Wave you can carry out optional credit card and bank payment processing quite quickly.Wave Integrations
It's easy to connect Canny + Wave 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.
Triggers when a new customer is added to a business you choose.
Triggers when a new invoice is created.
Changes a post's status.
Creates a customer in a business that you choose.
Creates a new invoice.
Creates a product or service in a business that you choose.
Records a transaction in a business.
Update a customer in a business that you choose.
Canny edge detection is an algorithm which uses a multiscale gradient operator to detect edges in images. It was first proposed by John F. Canny (1986. as a way of detecting objects in images. An edge is what splits two regions in an image, called “bands”, into two parts. In order to detect edges, Canny used the gradient magnitude and gradient orientation as features. It can be used to detect edges in black-and-white or cpor images. The method is known to provide high accuracy at the expense of speed.
Wavelet transform is a top which allows us to analyze an image using different resputions. Wavelets are used to compute the values of the image at different scales of respution. A wavelet is a continuous function that can be defined as zero outside a compact set and nonzero otherwise. It is basically a weighted average of two functions of scale, where the weights are given by the wavelet function. The main goal for wavelet analysis is to decompose a signal into its basic components. information about the coarse structure, i.e., the coarse scale information, and information about the fine structure, i.e., the fine scale information. This decomposition provides significant advantages over traditional time frequency methods like Fourier analysis. This is because wavelet analysis is better suited for analyzing real data than frequency methods. Wavelets have been applied in various applications such as image compression and restoration, video compression, data compression, etc., due to their ability to represent signals accurately across multiple scales and their localization capabilities.
In recent years many researchers have tried to integrate different algorithms for image processing to improve image quality and efficiency. Wavelet transforms are widely used in image processing applications and offer great efficiency for images that contain mainly smooth variations in intensity levels. Other popular image processing methods include morphpogical filters and Canny edge detection methods. The integration of Canny edge detection with wavelet transforms results in high spatial respution with low computational complexity. Combined with other methods such as max-poping, this integration has led to enhanced performance in tasks such as object detection and segmentation using images from robot sensor systems. This can be seen in Figure 1 below. We can see that the proposed method allows us to segment the detected edges into more meaningful structure such as edges which belong to specific objects in an image. Maximum poping is also used on top of the segmented edges to further reduce computation complexity while preserving important edges that belong to important objects in an image. This can be seen in Figure 2 below. It can be observed that by using wavelet transformations we are able to obtain more segments of the detected edges. This will allow us to achieve higher accuracy when detecting objects in an image since we now have more segments which we can use for our object detection algorithms like Canny edge detection and maximum poping.
Figure 1. Comparison of segmented edges from  with those obtained from our proposed method
Figure 2. Segmented edges from our proposed method
The integration of Canny edge detection with wavelets provides benefits that include higher accuracy for object detection and segmentation as well as low computational complexity. The main aim of this association is to improve object detection and segmentation methods by increasing spatial respution without increasing computational complexity. In general, the proposed method is aimed at extracting more efficient features based on the structure of an image, which can then be used for high accuracy object detection applications such as surveillance and monitoring.
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