Publication

Abstract : Objective: This research aims at formulating a method to categorise a given class of objects by obtaining a weighted matrix computed as explained below. Methods/Analysis: The method deployed can be branched into two phases: Training and Testing. In the first phase, a set of images of the concerned objects are taken. By set of images, one can refer to images of different objects, or different positions of the same object. The features are then, extracted for these input images and stored in the database as vectors. Any computation hence forth, is performed using these vectors. In testing stage, the algorithm uses its knowledge to identify the input image to a specified class. Findings: Our method is computationally inexpensive since all the calculations are performed on the basic grounds of matrix operations. This method is not just limited to the domain of object recognition alone. Any real-time entity that can be statistically represented in a vector form can be deployed. All that is required of the application is that the range of vectors is defined so as to obtain the minimum components and maximum components, individually. Once this is obtained, the algorithm will be sufficient to identify any input and will accordingly determine the category to which it belongs. The only challenge identified is that the range of vectors obtained from the input data for various categories must not overlap. That being the case will result in multiple hits or in simpler words, will give an incorrect result. Further work can be implemented on how to make the algorithm independent of this dependency. Also, the algorithm improves the results through various illumination and scaling conditions and this has been discussed in results and analysis section. Even with the existing methods to recognize an object, this algorithm can be combined to categorize or classify objects. Conclusion/Application: The proposed algorithm successfully classifies the input image into one of the trained categories by identifying the features followed by computing these obtained features as prescribed the given algorithm.