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A novel set of color image morphological operators is proposed and is based on using a modified vector distance measure with an efficient preprocessing color pixel classification procedure.
The components include hop vector distance based filtering, Bloom filters and signature hashing and are designed to work with different combinations of network and neighbor set sizes.
After creating each model a new set of measurements is input to the system, and the current state of the process is determined using vector distance calculation.
In this research, we present a preliminary design and experimental results of object recognition from a mobile device that utilizes the texture and the color features by image pre-processing with a simple vector distance matching classifier to train and extract the characteristics.
The distance is measured by vector distance following expression (6).
Speaker clustering based on feature vector distance employs the distance of samples to measure the similarity of two speech segments.
Similar(32)
The measure is referred to as cosine similarity or vector space distance.
Most of the time is spent on computing vector distances.
We first evaluate the resultant vector-space semantics through its correlation with WordNet distances, and find vector distances to be strongly correlated with linguistic semantics.
The weights are based on the patch intensity vector distances.
We then calculate all vector distances across the two clusters.
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