Rotation Invariant Method Research Articles

Rotation-invariant method for texture matching using model-based histograms and GLCM

Nowadays, researchers are interested in informatics systems that process automatically the information. Image is an interesting research area due to the growth of the technologies. In this paper, we have proposed a new texture analysis method. One of the key problems in image processing is the rotation. Therefore, the proposed method is robust against rotation. The goal of this study is to construct a model from each texture. After that, the system classifies the query texture based on the extracted texture models. In this work, we applied a recent and efficient feature extraction method called rotation invariant neighbourhood-based binary pattern (RINBP). The proposed system combines between two parts. First, extract the RINBP model from the texture. Second, we apply the GLCM method in order to extract statistical measures. In the experiments, we have used the Brodats album database. Experimental parts illustrate the efficiency and the robustness of the proposed system against rotation.

Rotation-invariant method for texture matching using model-based histograms and GLCM

Rotation-invariant method for texture matching using model-based histograms and GLCM

Rotation Invariant Automatic Infrared Target Recognition using G-Radon

In recent research, automatic target recognition (ATR) of infrared targets has been taking a lot of interest to the researchers. A rotation invariant method is useful in target recognition, classification and image analysis to reduce the number of training data. In particular, rotation invariant method, Radon transform, is an effective technique that is used for medical care such as computerized tomography (CT) image. This paper proposes a new rotation invariant algorithm for target recognition. The proposed method combines the gradient information and radon transform. The propose method, called gradient Radon (G-Radon), is applied to synthesized infrared images and compared with traditional radon transform and Zernike moments for validation.

Hardware implementation of a scale and rotation invariant object detection algorithm on FPGA for real-time applications

A hardware implementation of a computationally light, scale, and rotation invariant method for shape detection on FPGA is devised. The method is based on histogram of oriented gradients (HOG) and average magnitude difference function (AMDF). AMDF is used as a decision module that measures the similarity/dissi between HOG vectors of an image in order to classify the object. In addition, a simulation environment implemented on MATLAB is developed in order to overcome the time-consuming and tedious process of hardware verication on the FPGA platform. The simulation environment provides specic tools to quickly implement the proposed methods. It is shown that the simulator is able to produce exactly the same results as those obtained from FPGA implementation. The results indicate that the proposed approach leads to a shape detection method that is computationally light, scale, and rotation invariant, and, therefore, suitable for real-time industrial and robotic vision applications.

Rotation-Invariant Object Detection in High-Resolution Satellite Imagery Using Superpixel-Based Deep Hough Forests

This letter presents a rotation-invariant method for detecting geospatial objects from high-resolution satellite images. First, a superpixel segmentation strategy is proposed to generate meaningful and nonredundant patches. Second, a multilayer deep feature generation model is developed to generate high-level feature representations of patches using deep learning techniques. Third, a set of multiscale Hough forests with embedded patch orientations is constructed to cast rotation-invariant votes for estimating object centroids. Quantitative evaluations on the images collected from Google Earth service show that an average completeness, correctness, quality, and $F_1$ - measure values of 0.958, 0.969, 0.929, and 0.963, respectively, are obtained. Comparative studies with three existing methods demonstrate the superior performance of the proposed method in accurately and correctly detecting objects that are arbitrarily oriented and of varying sizes.

RST Invariant Fragile Watermarking for 2D Vector Map Authentication

D vector maps are very important data in geographical information systems (GISs). It is considered to be content for which verification of integrity and authentication are urgently required. Using a marking technique and a rotation, scaling and translation (RST) invariant watermark generation method, we propose a scheme that detects and locates malicious attacks with high accuracy while providing the RST invariance property. In particular, we propose setting a unique mark on each feature, calculating the authentication watermark for each spatial feature group by folding the hash results of the differences of the log-radiuses, and embedding the watermark using a RST invariant watermarking method. While the watermark ensures both the sensitivity to malicious manipulations and the RST invariance property, the mark of each feature provides superior accuracy of tamper localization. Moreover, this algorithm is immune to feature rearrangement and vertex reversing operations. Theoretical analysis and experimental results are provided to demonstrate the effectiveness of our method.

Rotation invariant texture retrieval considering the scale dependence of Gabor wavelet.

Obtaining robust and efficient rotation-invariant texture features in content-based image retrieval field is a challenging work. We propose three efficient rotation-invariant methods for texture image retrieval using copula model based in the domains of Gabor wavelet (GW) and circularly symmetric GW (CSGW). The proposed copula models use copula function to capture the scale dependence of GW/CSGW for improving the retrieval performance. It is well known that the Kullback-Leibler distance (KLD) is the commonly used similarity measurement between probability models. However, it is difficult to deduce the closed-form of KLD between two copula models due to the complexity of the copula model. We also put forward a kind of retrieval scheme using the KLDs of marginal distributions and the KLD of copula function to calculate the KLD of copula model. The proposed texture retrieval method has low computational complexity and high retrieval precision. The experimental results on VisTex and Brodatz data sets show that the proposed retrieval method is more effective compared with the state-of-the-art methods.

Object Detection in Terrestrial Laser Scanning Point Clouds Based on Hough Forest

This letter presents a novel rotation-invariant method for object detection from terrestrial 3-D laser scanning point clouds acquired in complex urban environments. We utilize the Implicit Shape Model to describe object categories, and extend the Hough Forest framework for object detection in 3-D point clouds. A 3-D local patch is described by structure and reflectance features and then mapped to the probabilistic vote about the possible location of the object center. Objects are detected at the peak points in the 3-D Hough voting space. To deal with the arbitrary azimuths of objects in real world, circular voting strategy is introduced by rotating the offset vector. To deal with the interference of adjacent objects, distance weighted voting is proposed. Large-scale real-world point cloud data collected by terrestrial mobile laser scanning systems are used to evaluate the performance. Experimental results demonstrate that the proposed method outperforms the state-of-the-art 3-D object detection methods.

Description of Rotation-Invariant Textures using Local Binary Pattern Features

classification is one of the most interesting research topics in the field of computer vision. This paper aims at classifying static as well as dynamic textures (DT). Uniform Local binary pattern (LBP) is a combination of structural and statistical analysis model for classification of both static and dynamic textures. The LBP Histogram Fourier (LBP-HF) uses Fast Fourier Transform (FFT) for calculating global rotation invariant of LBP histogram for static texture classification. For dynamic texture classification, LBP-TOP method is used, which computes LBP from three orthogonal planes. It combines both motion and appearance together for classification. The results show that, LBP-HF outperforms uniform LBP as well as basic rotation invariant LBP method for static-textures and LBP-TOP gives higher accuracy for dynamic-textures.

A shape based rotation invariant method for ultrasound-MR image registration: A phantom study.

In this work, a new shape based method to improve the accuracy of Brain Ultrasound-MRI image registration is proposed. The method is based on modified Shape Context (SC) descriptor in combination with CPD algorithm. An extensive experiment was carried out to evaluate the robustness of this method against different initialization conditions. As the results prove, the overall performance of the proposed algorithm outperforms both SC and CPD methods. In order to have control over the registration procedure, we simulated the deformations, missing points and outliers according to our Phantom MRI images.

Rotation Invariant Local Frequency Descriptors for Texture Classification

This paper presents a novel rotation invariant method for texture classification based on local frequency components. The local frequency components are computed by applying 1-D Fourier transform on a neighboring function defined on a circle of radius R at each pixel. We observed that the low frequency components are the major constituents of the circular functions and can effectively represent textures. Three sets of features are extracted from the low frequency components, two based on the phase and one based on the magnitude. The proposed features are invariant to rotation and linear changes of illumination. Moreover, by using low frequency components, the proposed features are very robust to noise. While the proposed method uses a relatively small number of features, it outperforms state-of-the-art methods in three well-known datasets: Brodatz, Outex, and CUReT. In addition, the proposed method is very robust to noise and can remarkably improve the classification accuracy especially in the presence of high levels of noise.

In-Plane Rotation and Scale Invariant Clustering Using Dictionaries

In this paper, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries and clusters images in the radon transform domain. The main feature of the proposed approach is that it provides both in-plane rotation and scale invariant clustering, which is useful in numerous applications, including content-based image retrieval (CBIR). We demonstrate the effectiveness of our rotation and scale invariant clustering method on a series of CBIR experiments. Experiments are performed on the Smithsonian isolated leaf, Kimia shape, and Brodatz texture datasets. Our method provides both good retrieval performance and greater robustness compared to standard Gabor-based and three state-of-the-art shape-based methods that have similar objectives.

카메라 영상 위에서의 문자 영역 추출 및 OCR

기존의 OCR 엔진은 보정된 환경에서 읽혀진 서류 영상에 맞게 설계되어있다. 스마트 폰을 비롯한 검정 화면 거리가 보정되지 않은 기기에서 읽혀진 영상에서는 삼차원 원근 투시에 의한 찌그러짐 또는 곡면상에서의 찌그러짐 등이 핵심적인 문제점들로 여겨진다. 휴대용 단말기에서 읽혀진 영상들에서의 OCR 기능에 대한 요구가 증가일로에 있는 시점에서, 본 논문에서는 문제점들을 세 가지로 구분하고 - 회전에 무관한 문자 영역 추출, 폰트 등의 크기에 무관한 문자 선 영역 추출, 3차원 매핑 이론 - 이를 해결하기위한 방법을 제시하였다. 이러한 방법론을 통합하여 카메라 영상 위에서의 OCR을 개발하였다. Traditional OCR engines are designed to the scanned documents in calibrated environment. Three dimensional perspective distortion and smooth distortion in images are critical problems caused by un-calibrated devices, e.g. image from smart phones. To meet the growing demand of character recognition of texts embedded in the photos acquired from the non-calibrated hand-held devices, we address the problem in three categorical aspects: rotational invariant method of text region extraction, scale invariant method of text line segmentation, and three dimensional perspective mapping. With the integration of the methods, we developed an OCR for camera-captured images.

Rotation invariant texture classification using LBP variance (LBPV) with global matching

Local or global rotation invariant feature extraction has been widely used in texture classification. Local invariant features, e.g. local binary pattern (LBP), have the drawback of losing global spatial information, while global features preserve little local texture information. This paper proposes an alternative hybrid scheme, globally rotation invariant matching with locally variant LBP texture features. Using LBP distribution, we first estimate the principal orientations of the texture image and then use them to align LBP histograms. The aligned histograms are then in turn used to measure the dissimilarity between images. A new texture descriptor, LBP variance (LBPV), is proposed to characterize the local contrast information into the one-dimensional LBP histogram. LBPV does not need any quantization and it is totally training-free. To further speed up the proposed matching scheme, we propose a method to reduce feature dimensions using distance measurement. The experimental results on representative databases show that the proposed LBPV operator and global matching scheme can achieve significant improvement, sometimes more than 10% in terms of classification accuracy, over traditional locally rotation invariant LBP method.

Persian/arabic handwritten word recognition using M-band packet wavelet transform

The extraction of rotation and scale invariant features is an essential problem in document image analysis. This paper proposes an effective rotation and scale invariant holistic handwritten word recognition scheme. This approach utilizes M-band packet wavelet transform to extract feature vector of Farsi word image. The global and local features extracted are exploited in recognition of limited-size lexicon of handwritten words. The rotation and scale invariant feature of a word image involves applying a polar transform to eliminate rotation and scale effects, but this produces M-row shifted polar image, which is passed to a row shift invariant M-band wavelet packet transform to eliminate the row shift effects. The output wavelet coefficients are rotation and scale invariant. For each subband of these wavelet coefficients a set of local energy features are computed and we extract feature vectors from the subbands of wavelet coefficients. The proposed polar M-band wavelet features have been tested by employing Mahalanobis algorithm to classify a set of distinct natural handwriting Farsi words. We compared the proposed scheme with two well-known rotation invariant methods; Fourier-wavelet and Zernike moments. The experimental results show that the proposed algorithm improves the recognition rate about 12 percents.

Scaling and rotation invariant analysis approach to object recognition based on Radon and Fourier–Mellin transforms

Various types of orthogonal moments have been widely used for object recognition and classification. However, these moments do not natively possess scaling invariance, essential image normalization and binarization prior to moments extraction will lead to error of resampling and requantifying. This paper describes a new scaling and rotation invariant analysis method for object recognition. In the proposed method, the Radon transform is utilized to project the image onto projection space to convert the rotation of the original image to a translation of the projection in the angle variable and the scaling of the original image to a scaling of the projection in the spatial variable together with an amplitude scaling of the projection, and then the Fourier–Mellin transform is applied to the result to convert the translation in the angle variable and the scaling in the spatial variable as well as the amplitude scaling of the projection to a phase shift and an amplitude scaling, respectively. In order to achieve a set of completely invariant descriptors, a rotation and scaling invariant function is constructed. A k -nearest neighbors’ classifier is employed to implement classification. Theoretical and experimental results show the high classification accuracy of this approach as a result of using the rotation and scaling invariant function instead of image binarization and normalization, it is also shown that this method is relatively robust in the presence of white noise.