66,600 research outputs found
Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters
Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. This research demonstrates the combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection of the dataset is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The detection is established by extracting the variant distinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identify the plant pests to obtain correlation peak values for different datasets. This work further establishes that the recognition probability from the pest image is directly proportional to the height of the output signal and inversely proportional to the viewing angles, which further confirmed that the recognition of plant pests is a function of their position and viewing angle. It is encouraging to note that the correspondence filter can achieve rotational invariance of pests up to angles of 360 degrees, which proves the effectiveness of the algorithm for the detection and recognition of plant pests
Adaptive Target Recognition: A Case Study Involving Airport Baggage Screening
This work addresses the question whether it is possible to design a
computer-vision based automatic threat recognition (ATR) system so that it can
adapt to changing specifications of a threat without having to create a new ATR
each time. The changes in threat specifications, which may be warranted by
intelligence reports and world events, are typically regarding the physical
characteristics of what constitutes a threat: its material composition, its
shape, its method of concealment, etc. Here we present our design of an AATR
system (Adaptive ATR) that can adapt to changing specifications in materials
characterization (meaning density, as measured by its x-ray attenuation
coefficient), its mass, and its thickness. Our design uses a two-stage cascaded
approach, in which the first stage is characterized by a high recall rate over
the entire range of possibilities for the threat parameters that are allowed to
change. The purpose of the second stage is to then fine-tune the performance of
the overall system for the current threat specifications. The computational
effort for this fine-tuning for achieving a desired PD/PFA rate is far less
than what it would take to create a new classifier with the same overall
performance for the new set of threat specifications
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