Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel generic ak classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other ak classifiers shows improvement. Each ak classifier is based on the difference beten two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the ak classifiers computation. The computation requires fer arithmetic operations and fer accesses to the main memory in comparison to the ll known Viola-Jones Haar-like classifier. In addition to the faster computation, the ak classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature classifier is as accurate as the Viola-Jones Haar-like classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster.
['Yasir Mohd Mustafah', 'Abbas Bigdeli', 'Amelia Wong Azman', 'Farhad Dadgostar', 'Brian C. Lovell']
I am writing a research paper on a idea Square Patch Feature: Faster weak-classifier for robust object detection
[{"from": "human", "value": "I am writing a research paper on a idea Square Patch Feature: Faster weak-classifier for robust object detection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel generic ak classifier for object detection called \"Square Patch Feature\". The speed and overall performance of a detector utilising Square Patch features in comparison to other ak classifiers shows improvement. Each ak classifier is based on the difference beten two or four fixed size square patches in an image. A pre-calculated representation of the image called \"patch image\" is required to accelerate the ak classifiers computation. The computation requires fer arithmetic operations and fer accesses to the main memory in comparison to the ll known Viola-Jones Haar-like classifier. In addition to the faster computation, the ak classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature classifier is as accurate as the Viola-Jones Haar-like classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster."}]