Image Recognition

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The document discusses an image recognition plugin that allows macros to search for and interact with images on websites. It provides two commands: IMAGESEARCH to find an image on a page, and IMAGECLICK to simulate clicking on an image. These commands use image recognition technology to locate images based on their visual characteristics rather than coordinates. The document also provides examples of using these commands to automate interactions with flash-based chat applications.

Business Technology

Image recognition Plugin ,[object Object],[object Object],[object Object],The Image Recognition feature provides two new commands that use state-of-the-art image recognition technology:

IMAGESEARCH command ,[object Object],[object Object],[object Object],[object Object]

IMAGECLICK ,[object Object],[object Object],[object Object],[object Object],[object Object],The coordinates are in the {{!IMAGEX}} and {{!IMAGEY}} variables

Test Case ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Create Input Images “ inputbox.bmp” “ sendbutton.bmp” “ text.bmp”

Image Recognition Tips ,[object Object],[object Object],[object Object],[object Object],[object Object]

Demo ,[object Object],[object Object],[object Object]

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Image recognition of line drawing involves feature extraction and feature comparison. Works on the extraction required the representation of the image to be compared. Combining these two requirements, a framework that develops a new extraction algorithm of a chain code representation is presented. In addition, new corner detection is presented as pre-processing to the line drawing input in order to derive the chain code. This paper presents a new framework that consist of five steps namely pre-processing and image processing, new corner detection algorithm, chain code generator, feature extraction algorithm, and recognition process. Heuristic approach that is applied in the corner detection algorithm accepts input of thinned binary image and produce a modified thinned binary image consisted of J character to represent corners in the image. Using the modified thinned binary image, a new chain code scheme that is based on Freeman chain code is proposed and an algorithm is developed to generate a single chain code series that is representing the line drawing input. The feature extraction algorithm is then extracting the three pre-defined features of the chain code for recognition purpose. The features are corner properties, distance between corners, and angle from a corner to the connected corner. The explanation of steps in the framework is supported with two line drawings. The results show that the framework successfully recognizes line drawing into five categories namely not similar line drawing, and four other categories that are similar but with attributes of rotation angle and scaling ratio.

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