Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Moment invariants have been widely introduced in recognizing planar objects for a few decades. This is due the robustness of moment function in disting uishing the original identity of object under vario us two Dimensional (2D) transformations. A set of moments computed from a planar images, represents the global description of an object’s shape and geo metrical features of an image. Since global descriptor utilizes the information of a whole obje ct or shape to describe the features of an object, it does not tolerate occlusion. If there is a mixture of regions that do not belong to the object of the interest, an additional task of segmentation is req uired to isolate the object for recognition. Hence, moment invariants are proposed to be employed as lo cal descriptors for object recognition since local descriptors do not suffer from the drawbacks caused by image clutter and occlusion. A new approach of local feature descriptors using moment invariant s is presented. The preliminary framework is divided into three different stages. Interest point s are firstly detected in the entire image. The loc al descriptors are then produced by applying moment in variants on the region around the interest points. Cross-correlation is finally carried out for featur e matching.
['Lee-Yeng Ong', 'Siong-Hoe Lau', 'Voon-Chet Koo']
I am writing a research paper on a idea A NEW APPROACH OF LOCAL FEATURE DESCRIPTORS USING MOMENT INVARIANTS
[{"from": "human", "value": "I am writing a research paper on a idea A NEW APPROACH OF LOCAL FEATURE DESCRIPTORS USING MOMENT INVARIANTS"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Moment invariants have been widely introduced in recognizing planar objects for a few decades. This is due the robustness of moment function in disting uishing the original identity of object under vario us two Dimensional (2D) transformations. A set of moments computed from a planar images, represents the global description of an object\u2019s shape and geo metrical features of an image. Since global descriptor utilizes the information of a whole obje ct or shape to describe the features of an object, it does not tolerate occlusion. If there is a mixture of regions that do not belong to the object of the interest, an additional task of segmentation is req uired to isolate the object for recognition. Hence, moment invariants are proposed to be employed as lo cal descriptors for object recognition since local descriptors do not suffer from the drawbacks caused by image clutter and occlusion. A new approach of local feature descriptors using moment invariant s is presented. The preliminary framework is divided into three different stages. Interest point s are firstly detected in the entire image. The loc al descriptors are then produced by applying moment in variants on the region around the interest points. Cross-correlation is finally carried out for featur e matching."}]