Abstract: The automated treatment of agricultural fields allows precise application of treatment to only areas of the field requiring treatment, allowing reduced use of agricultural products such as pesticides and the related reduction in environmental stress. Such automated treatment requires the monitoring of image information of the field to detect objects of interest such as weeds. Such monitoring of image information of the field suffers from the real world problem of shadows cast from the agricultural equipment and/or adjacent trees or structures that cast shadows across the field. Shadows are extracted from the image data by smoothing the data, and developing a shadow mask by employing an intensity histogram and identifying a local minima in the darker intensities of the histogram to identify a shadow threshold. The image areas within the shadow area detected by said mask are then corrected by multiplication with correction coefficients to extract the shadow.

Abstract: The automated treatment of agricultural fields allows precise application of treatment to only areas of the field requiring treatment, allowing reduced use of agricultural products such as pesticides and the related reduction in environmental stress. Such automated treatment requires the monitoring of image information of the field to detect objects of interest such as weeds. Such monitoring of image information of the field suffers from the real world problem of shadows cast from the agricultural equipment and/or adjacent trees or structures that cast shadows across the field. Shadows are extracted from the image data by smoothing the data, and developing a shadow mask by employing an intensity histogram and identifying a local minima in the darker intensities of the histogram to identify a shadow threshold. The image areas within the shadow area detected by said mask are then corrected by multiplication with correction coefficients to extract the shadow.

Abstract: The automated treatment of agricultural fields allows precise application of treatment to only areas of the field requiring treatment, allowing reduced use of agricultural products such as pesticides and the related reduction in environmental stress. Such automated treatment requires the monitoring of image information of the field to detect objects of interest such as weeds. Such monitoring of image information of the field suffers from the real world problem of shadows cast from the agricultural equipment and/or adjacent trees or structures that cast shadows across the field. Shadows are extracted from the image data by smoothing the data, and developing a shadow mask by employing an intensity histogram and identifying a local minima in the darker intensities of the histogram to identify a shadow threshold. The image areas within the shadow area detected by said mask are then corrected by multiplication with correction coefficients to extract the shadow.

Abstract: A computer-implemented image analysis process, including: accessing image data representing an image including an animal object representing an animal; selecting one more start points on a boundary of the animal object using an intensity gradient of the image; and generating boundary data, representing at least a portion of the boundary, starting at each of the selected one or more start points, using the intensity gradient.

Abstract: A computer-implemented image analysis process, including: accessing image data representing an image including an animal object representing an animal; selecting one more start points on a boundary of the animal object using an intensity gradient of the image; and generating boundary data, representing at least a portion of the boundary, starting at each of the selected one or more start points, using the intensity gradient.