Scale-invariant Image Features Research Articles

Motor Fault Diagnosis Based on Scale Invariant Image Features

Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence.

Feature Keypoint-Based Image Compression Technique Using a Well-Posed Nonlinear Fourth-Order PDE-Based Model

A digital image compression framework based on nonlinear partial differential equations (PDEs) is proposed in this research article. First, a feature keypoint-based sparsification algorithm is proposed for the image coding stage. The interest keypoints corresponding to various scale-invariant image feature descriptors, such as SIFT, SURF, MSER, ORB, and BRIEF, are extracted, and the points from their neighborhoods are then used as sparse pixels and coded using a lossless encoding scheme. An effective nonlinear fourth-order PDE-based scattered data interpolation is proposed for solving the decompression task. A rigorous mathematical investigation of the considered PDE model is also performed, with the well-posedness of this model being demonstrated. It is then solved numerically by applying a consistent finite difference method-based numerical approximation algorithm that is next successfully applied in the image compression and decompression experiments, which are also discussed in this work.

Digital Library ImageRetrieval usingScale Invariant Feature and Relevance Vector Machine

With the advance of digital library, the digital content develops with rich information connotation. Traditional information retrieval methods based on external characteristic and text description are unable to sufficientlyreveal and express the substance and semantic relation of multimedia information, and unable to fully reveal and describe the representative characteristics of information. Because of the abundant connotation of image content and the people’s abstract subjectivity in studying image content, the visual feature of the image is difficult to be described by key words. Therefore, this method not always can meet people’s needs, and the study of digital library image retrieval technique based on content is important to both academic research and application. At present, image retrieval methods are mainly based on the text and content, etc. But these existing algorithms have shortages, such as large errors and slow speeds. Motivated by the above fact, we in this paper propose a new approach based on relevance vector machine (RVM). The proposed approach first extracts the patch-level scale invariant image feature (SIFT), and then constructs the global features for images. The image feature is then delivered into RVM for retrieval. We evaluate the proposed approach on Corel dataset. The experimental result shows that the proposed method in this text has high accuracy when retrieves images.

Derivative-based scale invariant image feature detector with error resilience.

We present a novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups.

Predicting fetal neurodevelopmental age from ultrasound images.

We propose an automated framework for predicting age and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. A topology-preserving manifold representation of the fetal skull enabled design of bespoke scale-invariant image features. Our regression forest model used these features to learn a mapping from age-related sonographic image patterns to fetal age and development. The Sylvian Fissure was identified as a critical region for accurate age estimation, and restricting the search space to this anatomy improved prediction accuracy on a set of 130 healthy fetuses (error ± 3.8 days; r = 0.98 performing the best current clinical method. Our framework remained robust when applied to a routine clinical population.

Texture Classification Using Rotation- and Scale-Invariant Gabor Texture Features

This letter introduces a novel approach to rotation and scale invariant texture classification. The proposed approach is based on Gabor filters that have the capability to collapse the filter responses according to the scale and orientation of the textures. These characteristics are exploited to first calculate the homogeneous texture of images followed by the rearrangement of features as a two-dimensional matrix (scale and orientation), where scaling and rotation of images correspond to shifting in this matrix. The shift invariance property of discrete fourier transform is used to propose rotation and scale invariant image features. The performance of the proposed feature set is evaluated on Brodatz texture album. Experimental results demonstrate the superiority of the proposed descriptor as compared to other methods considered in this letter.

Efficient and robust model-to-image alignment using 3D scale-invariant features.

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down.

A feature-based developmental model of the infant brain in structural MRI.

In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days.

Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks

A key component of a mobile robot system is the ability to localize itself accurately and, simultaneously, to build a map of the environment. Most of the existing algorithms are based on laser range finders, sonar sensors or artificial landmarks. In this paper, we describe a vision-based mobile robot localization and mapping algorithm, which uses scale-invariant image features as natural landmarks in unmodified environments. The invariance of these features to image translation, scaling and rotation makes them suitable landmarks for mobile robot localization and map building. With our Triclops stereo vision system, these landmarks are localized and robot ego-motion is estimated by least-squares minimization of the matched landmarks. Feature viewpoint variation and occlusion are taken into account by maintaining a view direction for each landmark. Experiments show that these visual landmarks are robustly matched, robot pose is estimated and a consistent three-dimensional map is built. As image features are not noise-free, we carry out error analysis for the landmark positions and the robot pose. We use Kalman filters to track these landmarks in a dynamic environment, resulting in a database map with landmark positional uncertainty.