This paper presents the preliminary results, for a technique that help in the analysis of skin cancer using a vision system. The technique, start with the learning system of the ROI in the image, this ROI are analyzed to extract the characteristics of image. Global population is created with the extracted data, for reference and comparison. For the new image, a neural linear system, were used to identify the ROI possible that may be skin cancer. The system is able to estimate and learn for the new ROI to improve the results. The system use only the system vision to acquire the image and illumination control to improve the image capture, the results are compared with other skin cancer image analyzed and other images.
This paper presents the preliminary results for a system in tree dimension that use a system vision to manipulate plants in a tissue culture process. The system is able to estimate the position of the plant in the work area, first calculate the position and send information to the mechanical system, and recalculate the position again, and if it is necessary, repositioning the mechanical system, using an neural system to improve the location of the plant. The system use only the system vision to sense the position and control loop using a neural system to detect the target and positioning the mechanical system, the results are compared with an open loop system.
This paper shows the results obtained in a system vision applied to plant reproduction by tissue culture using adaptive image segmentation and pattern matching algorithms, this analysis improves the number of tissue obtained and minimize errors, the image features of tissue are considered join to statistical analysis to determine the best match and results. Tests make on potato plants are used to present comparative results with original images processed with adaptive segmentation algorithm and non adaptive algorithms and pattern matching.
This paper present the comparation and performance on no adaptive image segmentation techniques using illumination and adaptive image segmentation techniques. Results obtained on indoor plant reproduction by tissue culture, show the improve in time process, simplify the image segmentation process, experimental results are presented using common techniques in image processing and illumination, contrasted with adaptive image segmentation.
This paper presents that experimental results obtained on indoor tissue culture using the adaptive image segmentation system. The performance of the adaptive technique is contrasted with different non-adaptive techniques commonly used in the computer vision field to demonstrate the improvement provided by the adaptive image segmentation system.
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