Shearing invariant texture descriptor from a local binary pattern and its application in paper fingerprinting
نویسندگان
چکیده
In this paper, a Shearing Invariant Texture Descriptor (SITD) is proposed, which is a theoretically and computationally simple method based on the Rotation invariant Local Binary Pattern (Rot-LBP) descriptor. In real-world applications using flatbed scanners, such as paper texture fingerprinting, it’s common for a sheet of paper to rotate during the image acquisition process. Because the rotation is usually not based on the paper’s geometrical centre pivot, the produced image is deformed with irregular rotation resulting in shearing transforms. To tackle the shearing problem, the proposed SITD selects a few patterns from the conventional Rot-LBP to achieve either horizontal or vertical invariance. This paper presents the construction of the SITD operators and their performance in recognizing self-developed and standard image datasets, including real paper texture and Outex images, as well as those with distinctive shapes. The images were distorted with only a shearing transform. The self-developed images were distorted manually, while the standard images were distorted by software. The proposed description method achieved up to 100% correctly recognition rate in all the tested datasets based on the horizontal shear invariant operator. In addition to the accurate performance in all the conducted experiments, the operator significantly outperformed the Rot-LBP and another benchmark method, the Shearing Moment Invariant (SMI). The superiority of the descriptor in recognizing different types of patterns demonstrate its ability to be used in applications where the shearing transform is present.
منابع مشابه
Rotated Local Binary Pattern (RLBP) - Rotation Invariant Texture Descriptor
In this paper we propose two novel rotation invariant local texture descriptors. They are based on Local Binary Pattern (LBP), which is one of the most effective and frequently used texture descriptor. Although LBP efficiently captures the local structure, it is not rotation invariant. In the proposed methods, a dominant direction is evaluated in a circular neighbourhood and the descriptor is c...
متن کاملRotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
In this paper, we propose Local Binary Pattern Histogram Fourier features (LBP-HF), a novel rotation invariant image descriptor computed from discrete Fourier transforms of local binary pattern (LBP) histograms. Unlike most other histogram based invariant texture descriptors which normalize rotation locally, the proposed invariants are constructed globally for the whole region to be described. ...
متن کاملFWLBP: A Scale Invariant Descriptor for Texture Classification
In this paper we propose a novel texture recognition feature called Fractal Weighted Local Binary Pattern (FWLBP). It has been observed that fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human perception of surface roughness. We have utilized this property to construct a scale-invariant descriptor. We have sampled the input image u...
متن کاملRotationally Invariant Hashing of Median Binary Patterns for Texture Classification
We present a novel image feature descriptor for rotationally invariant 2D texture classification. This extends our previous work on noise-resistant and intensity-shift invariant median binary patterns (MBPs), which use binary pattern vectors based on adaptive median thresholding. In this paper the MBPs are hashed to a binary chain or equivalence class using a circular bit-shift operator. One bi...
متن کاملDescriptor Learning Based on Fisher Separation Criterion for Texture Classification
This paper proposes a novel method to deal with the representation issue in texture classification. A learning framework of image descriptor is designed based on the Fisher separation criteria (FSC) to learn most reliable and robust dominant pattern types considering intraclass similarity and inter-class distance. Image structures are thus be described by a new FSC-based learning (FBL) encoding...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Int. Arab J. Inf. Technol.
دوره 14 شماره
صفحات -
تاریخ انتشار 2017