Euresys EasyImage

Highlights

The Euresys EasyImage includes operations usually performed as pre-processing steps to improve the image quality and obtain a good contrast between the background and the objects to be inspected. EasyImage supports gray-level and color images. Selected morphology functions are also optimized for binary (1-bit per pixel) and bi-level images. The Euresys EasyImage includes numerous image processing functions, such as enhancement and restoration by linear or non-linear filtering, arithmetic and logic operations, geometric transformations for image registration, histogram analysis for thresholding, projection.

Refactoring improving the execution time due to SSE2 technology. Flexible Masks for selected image analysis functions. They provide a powerful way of restricting the processing to freely parts of the image. The Canny detector is known as the optimal edge detector. It operates on a gray-scale BW8 image and delivers a black-and-white BW8 image where pixels have only 2 possible values, 0 and 255. Pixels corresponding to edges in the source image are set to value 255 while other pixels are set to value 0. The Canny edge detector offers three optimal characteristics for image processing:

• A good detection: find as many edges in the image as possible.
• A good localization: the found edges are as close as possible to the “real” edges in the image.
• A minimal response: a single edge response is accepted for each position, i.e. avoiding multiple close or intersecting edge responses.

The Harris corner detector is popular due to its strong invariance to rotation, illumination variation, and image noise. It operates only on a grayscale BW8 image. The Harris Corner Detector delivers a vector of points of interest. The following characteristics are available for every point of interest: the corner position (pixel coordinates with sub-pixel accuracy if enabled), the corners measure, the magnitude of the gradient w.r.t. the differentiation scale ?d, the value of the gradient along the X-axis w.r.t. the differentiation scale ?d, the value of the gradient along the Y-axis w.r.t. the differentiation scale.

Features

• Convolution and morphology.
• Geometric transformations.
• Image statistics.
• 16-bit accuracy processing.

Applications

• Typical applications include image enhancement, image restoration, and presence/absence check.