With the development of information technology and the coming period of large data, the image signals play an increasingly more significant role in our life because of the phenomenal development of system correspondence innovation, and the comparing high proficiency image handling strategies are requested earnestly. The Fourier transform is an important image processing tool, which is used in a wide range of applications, such as image filtering, image analysis, image compression and image reconstruction. It 's the simplest among the other transformation method used in mathematics. The real time consumption is lesser due to this method. It has a vast use in image processing, particularly object 2D, 3D and other representation. This paper proposes a new Fourier transform which is called Non Uniform Fourier Transform (NUFT). The proposed descriptor takes into consideration the change of point index. Also, an application is made on 2D set of points and a real image. The main advantages of the proposed transform are invariance under change of index point and robustness to noise. Also, the extraction of invariant under rotation and affinity is immediate because the linearity is assured. The proposed descriptor is tested on MPEG 7 database and compared with the normal Fourier transform to shows its efficiency. The experimental results prove the effectiveness of the proposed descriptor.
In this work, a new method is presented for the representation of 3D objects with binary matrix. This method is based on two stages: normalization and quantization. This representation allows us to compare 3D objects by computing the similarity between them. In fact our algorithm compute binary matrix, frequency matrix and cluster coordinates. So we can identify an object by comparing those representations.
In this paper, a novel method for binary image comparison is presented. We suppose that the image is a set of transactions and items. The proposed method applies along rows and columns of an image; this image is represented by all frequent itemset. Firstly, the rows of the image are considered as transactions and the columns of the image are considered as items. Secondly, we considered rows as items and columns as transactions. Besides, we also apply our technique to color image; firstly we segment the image and each segmented region is considered as a binary image. The proposed method is tested on the MPEG7 database and compared with the moment’s method to show its efficiency.
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