Scale Invariant Feature Extraction Research Articles

A novel normal to tangent line (NTL) algorithm for scale invariant feature extraction for Urdu OCR

A novel normal to tangent line (NTL) algorithm for scale invariant feature extraction for Urdu OCR

R-K algorithm: A novel Dynamic Feature Matching Method of flotation froth

Froth flotation is a common process used for extracting a desired mineral from its ore. The dynamic characteristics extraction towards froth flotation can better identify the collapse and fragmentation. According to the characteristics of continuous movement of flotation froth, a new flotation froth feature matching method is proposed, which based on Kalman filtering and RANSAC algorithm, called R-K matching algorithm. And then, combining the R-K matching algorithm and SURF (fast robust scale invariant feature extraction algorithm) to extract the dynamic characteristics of flotation froth. Finally, the velocity and the entropy of the flotation froth are calculated to confirm the effectiveness and the practicability. The simulation results from the industrial experimental data show that the method can extract more feature matching pairs than the existing methods. So, it can obtain more accurate dynamic information on flotation froth in the changed state.

An Integrated Methods for Refined Facial-Image Retrieval using Sift and Click-Through Data

In the modern era, the emergence of social networking sites paved the way to the people to upload lot of images online. Social sites like Instagram and Flickr allow users to add semantic information to the images in the form of tags. Often these tags are the firsthand semantic data for retrieving the images from the Internet. When a user searches for images in the web, the images with tags relevant to user query are retrieved. Most of the time, these semantic data are not relevant to the content of the image and hence the user gets irrelevant images in contrast to their intended search. This is more common in facial image search. In this paper, we propose an integrated approach to refine the relevance of retrieved facial images. Using Scale Invariant Feature Extraction (SIFT) the facial features of semantically most relevant image and click-through data of all the retrieved images are used to rank and present a meaningful search result. Along with facial features and click through information, the co-occurrence of related tags is also considered. Also we propose the construction of inverted index structure to improve the search performance.

A scale-invariant change detection method for land use/cover change research

Land Use/Cover Change (LUCC) detection relies increasingly on comparing remote sensing images with different spatial and spectral scales. Based on scale-invariant image analysis algorithms in computer vision, we propose a scale-invariant LUCC detection method to identify changes from scale heterogeneous images. This method is composed of an entropy-based spatial decomposition, two scale-invariant feature extraction methods, Maximally Stable Extremal Region (MSER) and Scale-Invariant Feature Transformation (SIFT) algorithms, a spatial regression voting method to integrate MSER and SIFT results, a Markov Random Field-based smoothing method, and a support vector machine classification method to assign LUCC labels. We test the scale invariance of our new method with a LUCC case study in Montreal, Canada, 2005–2012. We found that the scale-invariant LUCC detection method provides similar accuracy compared with the resampling-based approach but this method avoids the LUCC distortion incurred by resampling.

Scale-invariant feature extraction of neural network and renormalization group flow.

Theoretical understanding of how a deep neural network (DNN) extracts features from input images is still unclear, but it is widely believed that the extraction is performed hierarchically through a process of coarse graining. It reminds us of the basic renormalization group (RG) concept in statistical physics. In order to explore possible relations between DNN and RG, we use the restricted Boltzmann machine (RBM) applied to an Ising model and construct a flow of model parameters (in particular, temperature) generated by the RBM. We show that the unsupervised RBM trained by spin configurations at various temperatures from T=0 to T=6 generates a flow along which the temperature approaches the critical value T_{c}=2.27. This behavior is the opposite of the typical RG flow of the Ising model. By analyzing various properties of the weight matrices of the trained RBM, we discuss why it flows towards T_{c} and how the RBM learns to extract features of spin configurations.

Automated Method to Determine Two Critical Growth Stages of Wheat: Heading and Flowering.

Recording growth stage information is an important aspect of precision agriculture, crop breeding and phenotyping. In practice, crop growth stage is still primarily monitored by-eye, which is not only laborious and time-consuming, but also subjective and error-prone. The application of computer vision on digital images offers a high-throughput and non-invasive alternative to manual observations and its use in agriculture and high-throughput phenotyping is increasing. This paper presents an automated method to detect wheat heading and flowering stages, which uses the application of computer vision on digital images. The bag-of-visual-word technique is used to identify the growth stage during heading and flowering within digital images. Scale invariant feature transformation feature extraction technique is used for lower level feature extraction; subsequently, local linear constraint coding and spatial pyramid matching are developed in the mid-level representation stage. At the end, support vector machine classification is used to train and test the data samples. The method outperformed existing algorithms, having yielded 95.24, 97.79, 99.59% at early, medium and late stages of heading, respectively and 85.45% accuracy for flowering detection. The results also illustrate that the proposed method is robust enough to handle complex environmental changes (illumination, occlusion). Although the proposed method is applied only on identifying growth stage in wheat, there is potential for application to other crops and categorization concepts, such as disease classification.

Scale invariant and fixed-length feature extraction by integrating discrete cosine transform and autoregressive signal modeling for palmprint identification

Recently, the need for automatic identification has caused researchers to focus on biometric identification methods. Palmprint-based biometric identification has several advantages such as user friendliness, low-cost capturing devices, and robustness. In this paper, a method that integrates the discrete cosine transform (DCT) and an autoregressive (AR) signal modeling is proposed for biometric identification. The method provides scale invariance and produces a fixed-length feature vector. In particular, the Burg algorithm is used for the determination of the AR parameters used as a feature vector. Experimental results demonstrate that a small number of the AR parameters that are modeling the DCT coefficients of a palmprint are sufficient to constitute a practically applicable identification system achieving a correct recognition rate of 99.79%. The accuracy of the proposed approach is not overly dependent on the number of training samples, another advantage of the method.

Detection and localization of specular surfaces using image motion cues

Successful identification of specularities in an image can be crucial for an artificial vision system when extracting the semantic content of an image or while interacting with the environment. We developed an algorithm that relies on scale and rotation invariant feature extraction techniques and uses motion cues to detect and localize specular surfaces. Appearance change in feature vectors is used to quantify the appearance distortion on specular surfaces, which has previously been shown to be a powerful indicator for specularity (Doerschner et al. in Curr Biol, 2011). The algorithm combines epipolar deviations (Swaminathan et al. in Lect Notes Comput Sci 2350:508–523, 2002) and appearance distortion, and succeeds in localizing specular objects in computer-rendered and real scenes, across a wide range of camera motions and speeds, object sizes and shapes, and performs well under image noise and blur conditions.

Scale invariant texture classification via sparse representation

Scale change exists very commonly in real-world textural images which remains one of the biggest challenges in texture classification due to the tremendous changes involved in texture appearance. While most research efforts have been devoted to extracting various scale invariant features, these methods are either unsuitable to describe a texture or unable to handle the situations where a large amount of scale change exists. Other works attempt to avoid scale invariant feature extraction by generating a set of multi-scale representations from training images for classification, but they are not only computation intensive but also limited to dealing with small scale changes between training images and test images. In this paper we investigate the scaling properties of textures and introduce a low dimensional linear subspace for the multi-scale representations of a texture, in which the collaboration between the multi-scale representations is beneficial for the scale invariant texture classification. We therefore propose a new scale invariant texture classification framework without extracting scale invariant features, by using a sparse representation technique to model the multi-scale representations of a texture and taking the advantages of collaboration between them for classification. Specifically, a multi-scale dictionary is constructed from the Gaussian-pyramid-generated scale space of a small set of training images at one scale, and then the test images at arbitrary scales are classified via a modified sparse representation based classification method. Experiments on two benchmark texture databases show that the proposed method is able to deal with large scale changes between the training images and the test images and achieve comparative results to the state-of-the-art approaches for the classification of textures with various variations, especially scale.

Abnormal human activity recognition system based on R-transform and independent component features for elderly healthcare

This article presents a vision-based human activity recognition (HAR) system to recognize abnormal activities among a set of daily-life activities of elderly people. A novel method is proposed for feature extraction by the combination of R-transform, principal component analysis (PCA) and independent component analysis (ICA). R-transform is used for periodic, scale, and translation invariant feature extraction. PCA is used for dimensions reduction and ICA is used for independent features extraction. The features are processed by k-means to get observation sequences which are used by hidden Markov model for HAR. The dataset consisting of six abnormal activities (backward fall, forward fall, chest pain, headache, vomit, and faint) and three normal activities (walk, raise one hand, and raise both hands) from the view angles (90°, −90°, 45°, −45°, and 0°) is produced. Experimental results show average recognition rate of 86.86% for abnormal and normal activities from different view angles by our proposed system using (R-transform + PCA + ICA) as compared to R-transform, PCA, and ICA methods.

Scale Invariant Feature Extraction Algorithm for the Automatic Diagnosis of Rotor Asymmetries in Induction Motors

The development of portable devices that make the reliable diagnosis of faults in electric motors possible has become a challenge for many researchers and maintenance enterprises. These machines intervene in a huge amount of processes and applications and their eventual failure may imply important costs in terms of time and money. However, the aforementioned issue remains unsolved because most of the developed fault diagnosis techniques rely on the user expertise, since they are based on a qualitative interpretation of the results. This complicates the implementation of these methodologies in condition monitoring systems or devices. The objective of this paper is to propose an integral methodology that is able to diagnose the presence of rotor bar failures in an automatic way. The proposed algorithm combines the Discrete Wavelet Transform with the scale transform for feature extraction and correlation coefficient for pattern recognition. The algorithm is applied to both small and large motors operating in a wide range of conditions. The results illustrate the validity and generality of the approach for automatic condition monitoring of electric motors.

Robust localized image watermarking based on invariant regions

The robustness of the localized watermarking methods mainly depends on the robustness of the feature locating the watermark. Based on the mean luminance of the disk, a rotation and scale invariant feature extraction algorithm is proposed. A theoretical verification of the rotation and scale invariance of extracted feature points in the continuous image is further performed. The extracted feature points are used to construct rotation and scale invariant circular regions, where the watermark is embedded after affine normalization. Experimental results show that the constructed regions fit the watermarking applications much better than those in previous feature-based watermarking schemes from the aspect of robustness against common attacks including filtering, JPEG compression, cropping, rotation and scaling, and the proposed localized image watermarking scheme has better robustness than previous feature-based watermarking schemes against common signal process and geometrical attacks while maintaining imperceptibility.

An expert system based on wavelet transform and radon neural network for pavement distress classification

Nowadays, pavement distresses classification becomes more important, as the computational power increases. Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification. In this paper an expert system is proposed for pavement distress classification. A radon neural network, based on wavelet transform expert system is used for increasing the effectiveness of the scale invariant feature extraction algorithm. Wavelet modulus is calculated and Radon transform is then applied to the wavelet modulus. The features and parameters of the peaks are finally used for training and testing the neural network. Experimental results demonstrate that the proposed expert system is an effective method for pavement distress classification. The test performances of this study show the advantages of proposed expert system: it is rapid, easy to operate, and have simple structure.

3D object recognition from range images using transform invariant object representation

3D object recognition is performed using a scale and orientation invariant feature extraction method and a scale and orientation invariant topological representation. 3D surfaces are represented by sparse, repeatable, informative and semantically meaningful 3D surface structures, which are called multiscale features. These features are extracted with their scale (metric size and resolution) using the classified scale-space of 3D surface curvatures. Triplets of these features are used to represent the surface topologies invariant to transformation (scaling, rotation, translation) and geometric hashing is used for object recognition. Scaled and both scaled and occluded versions of range images from a 3D object database are tested and the experimental results are compared with other methods in the literature.

An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification

Nowadays, texture classification becomes more important, as the computational power increases. The most important hardness of texture image analysis in the past was the deficiency of enough tools to characterize variety scales of texture images effectively. Recently, multi-resolution analysis such as Gabor filters, wavelet decompositions provide very good multi-resolution analytical tools for different scales of texture analysis and classification. In this paper, a Wavelet Neural Network based on Adaptive Norm Entropy (WNN-ANE) expert system is used for increasing the effectiveness of the scale invariant feature extraction algorithm (Best Wavelet Statistical Features (WSF)-Wavelet Co-occurrence Features (WCF)). Efficiently of proposed method was proved using exhaustive experiments conducted with Brodatz texture images.

Log-polar wavelet energy signatures for rotation and scale invariant texture classification

Classification of texture images is important in image analysis and classification. This paper proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures. The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O(n /spl middot/ log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a Mahalanobis classifier to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the proposed classification scheme using log-polar wavelet signatures outperforms two other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 90.8 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features. Concerning its robustness to noise, the classification scheme also performs better than the other methods.

New approach for scale, rotation, and translation invariant pattern recognition

A new approach for scaling, rotation, and translation invariant object recognition is proposed. The geometric discrepancy between the test pattern and the related reference pattern can be reduced by applying affine transform to the test pattern with the parameters estimated from the affine relation between the control points of both parties. The automatically paired control points are the centroids of patterns’ desired regions with specific gray level distributions. By means of the analysis of intrinsic invariance properties, a small number of reference templates are required to deal with the residual geometric variation of the transformed test pattern. Consequently, a simple similarity measure can be employed directly without any scale and rotation invariant feature extraction.

Rotation- and scale-invariant feature extraction by a diffractive optical inner-product transform

A diffractive optical element that is capable of extracting rotation- and scale-invariant features from input images is presented. The diffractive optical element performs an inner product between the input image and a set of basis functions. This set of basis functions is derived from that used in the Fourier-Mellin transform. Simulated results from scaled and rotated versions of two input images are presented.

Circular-Mellin features for texture segmentation

Texture is an important cue in region-based segmentation of images. We provide an insight into the development of a new set of distortion-invariant texture operators. These "circular-Mellin" operators are invariant to both scale and orientation of the target and represent the spectral decomposition of the image scene in the polar-log coordinate system. Coupled with the unique shift invariance property of the correlator architecture, we show that these circular-Mellin operators can be used for rotation-and scale-invariant feature extraction. We note that while these feature extractors have a functional form that is similar to the Gabor operators, they have distortion-invariant characteristics unlike the Gabor functions that make them more suitable for texture segmentation. A detailed analytical description of these operators and segmentation results to highlight their salient properties are presented.

Circular-Mellin features for texture segmentation

Texture is an important cue in region-based segmentation of images. We provide an insight into the development of a new set of distortion-invariant texture operators. These "circular-Mellin" operators are invariant to both scale and orientation of the target and represent the spectral decomposition of the image scene in the polar-log coordinate system. Coupled with the unique shift invariance property of the correlator architecture, we show that these circular-Mellin operators can be used for rotation-and scale-invariant feature extraction. We note that while these feature extractors have a functional form that is similar to the Gabor operators, they have distortion-invariant characteristics unlike the Gabor functions that make them more suitable for texture segmentation. A detailed analytical description of these operators and segmentation results to highlight their salient properties are presented.