We have designed a graphical interface that enables 3D visual artists or developers of interactive 3D virtual environments to efficiently define sophisticated camera compositions by creating storyboard flames, indicating how a desired shot should appear. These storyboard frames are then automatically encoded into an extensive set of virtual camera constraints that capture the key visual composition elements of the storyboard frame. Visual composition elements include the size and position of a subject in a camera shot. A recursive heuristic constraint solver then searches the space of a given 3D virtual environment to determine camera parameter values which produce a shot closely matching the one in the given storyboard frame. The search method uses given ranges of allowable parameter values expressed by each constraint to reduce the size of the 7 Degree of Freedom search space of possible camera positions, aim direction vectors, and field of view angles. In contrast, some existing methods of automatically positioning cameras in 3D virtual environments rely on pre-defined camera placements that cannot account for unanticipated configurations and movement of objects or use program-like scripts to define constraint-based camera shots. For example, it is more intuitive to directly manipulate an object's size in the frame rather than editing a constraint script to specify that the object should cover 10% of the frame's area.
Problem statement: Uniformly herbicide rate is used as a conventional practice in Thailand for controlling weeds in sugarcane fields. Since weeds usually grow in certain areas with nonuniformly distribution, uniform herbicide rate approach is not suitable and non-sustainable agricultural technique both in terms of economic an environmental aspect. To address these issues, Variable Herbicide Rate (VHR) was introduced. The VHR composes of two main components, which are weed monitoring and real-time spraying. Approach: This study investigated with a development of a fast and robust weed monitoring system for VHR using over between-row of sugarcane fields. The proposed method was designed to work under natural illumination condition. The near-ground images were captured using a typical web camera without any assistant light diffuser. The proposed weed monitoring is a machine vision based approach. The Non Green Subtraction (NGS) technique was proposed for soil background segmentation. Results: The proposed technique exploited variations among three triplets, which are red, green and blue under bright and dull lighting condition to achieve better background segmentation results. The non-background pixels were then classified into weeds and non-weeds using the Offset Excessive Green (OEG) technique. Conclusion: From our experimental results, the proposed method is robust under illumination variations such as in sunny and after raining day conditions. Weeds under different lighting conditions are reliably detects. The approach is less sensitive to chosen threshold value comparing to the OEG technique. The proposed method is very effective especially in spare weeds condition. It is fast, suitable for using in real-time application.
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