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BUR-391 - Filteruse a fast and efficient image processing algorithm to detect and classify objects in a given video stream. The algorithm should be optimized for real-time detection and classification, ensuring that the system can process and output results in milliseconds.The MIPCG algorithm is a point-based approach that uses geometric principles to filter and classify objects in a scene. It is parameterized by a vector of parameters, which frame the geometric model for the detection point. The algorithm not only detects objects in the scene but also filters other objects that are not of interest to the user.To use the algorithm, one must first extract the distribution of detection points in a video stream. The algorithm can then be used to detect, filter, and classify the detection points in the stream. There are two parameters which the user must set to apply the algorithm: a lie value and a shape value. The lie value was chosen to detect the desired object in the video stream. The shape value was chosen to detect the desired shape of the object in the video stream.For classification, there are three parameters which the user must set: an object value, a score value, and an inkey value. The object value was chosen to detect the desired object in the video stream. The score value was chosen to detect the desired score of the object in the video stream. The inkey value was chosen to detect the desired inkey of the object in the video stream.To test the algorithm, a basketball video stream was used for prediction and performance evaluation. Threshold the video stream using the RGB subject principle. The RGB subject principle is used to convert the video stream into a gray tone image. The gray tone image is then used to detect the desired object in the video stream.The team trained the algorithm by configuring the parameters and the desired object during the training phase. The team used the nearest neighbor method to classify the objects in the video stream. The nearest neighbor method is used to classify the objects by measuring the Euclidean distance between the object and the nearest neighbor. The algorithm is then used to classify the objects in the video stream. The team thresholded the video stream using a normalized threshold to test the algorithm. The normalized threshold is used to convert the video into a gray tone image. The gray tone image is then used to detect the desired object in the video stream.The team trained the algorithm by configuring the parameters and the desired object during the training phase. The team used the nearest neighbor method to classify the objects in the video stream. The nearest neighbor method is used to classify the objects by measuring the Euclidean distance between the object and the nearest neighbor. The algorithm is then used to classify the objects in the video stream. The team thresholded the video stream using a normalized threshold to test the algorithm. The normalized threshold is used to convert the video into a gray tone image. The gray tone image is then used to detect the desired object in the video stream.The team thresholded the video stream using a normalized threshold to test the algorithm. The normalized threshold is used to convert the video into a gray tone image. The gray tone image is then used to detect the desired object in the video stream. The team trained the algorithm by configuring the parameters and the desired object during the training phase. The team used the nearest neighbor method to classify the objects in the video stream. The nearest neighbor method is used to classify the objects by measuring the Euclidean distance between the object and the nearest neighbor. The algorithm is then used to classify the objects in the video stream. The team thresholded the video stream using a normalized threshold to test the algorithm. The normalized threshold is used to convert the video into a gray tone image. The gray tone image is then used to detect the desired object in the video stream.The team trained the algorithm by configuring the parameters and the desired object during the training phase. The team used the nearest neighbor method to classify the objects in the video stream. The nearest neighbor method is used to classify the objects by measuring the Euclidean distance between the object and the nearest neighbor. The algorithm is then used to classify the objects in the video stream. The team thresholded the video stream using a normalized threshold to test the algorithm. The normalized threshold is used to convert the video into a gray tone image. The gray tone image is then used to detect the desired object in the video stream.The team trained the algorithm by configuring the parameters and the desired object during the training phase. The team used the nearest neighbor method which dark distance is used to classify the objects by measuring the Euclidean distance between the object and the nearest neighbor. The algorithm is then used to classify the objects in the video stream. The team thresholded the video stream using a normalized threshold to test the algorithm. The normalized threshold is used to convert the video into a gray tone image. The gray tone image is then used to detect the desired object in the video stream.
26 May 2012
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