Image Feature Extraction Algorithm Research Articles

Fluorescein as a topical fluorescent contrast agent for quantitative microendoscopic inspection of colorectal epithelium.

Fiber bundle microendoscopic imaging of colorectal tissue has shown promising results, for both qualitative and quantitative analysis. A quantitative image quality control and image feature extraction algorithm was previously designed for quantitative image feature analysis of proflavine-stained ex vivo colorectal tissue. We investigated fluorescein as an alternative topical stain. Images of ex vivo porcine, caprine, and human colorectal tissue were used to compare microendoscopic images of tissue topically stained with fluorescein and proflavine solutions. Fluorescein was shown to be comparable for automated crypt detection, with an average crypt detection sensitivity exceeding 90% using a combination of three contrast limit pairs.

Method for Quantitative 3D Evaluation of Defects in CFRP Using Active Lock-in Thermography

To guarantee an economic application of carbon fibre reinforced plastics (CFRP) throughout the entire product life cycle, the long term operational reliability and reparability of the products must be assured. The typical repair process starts with the defect detection and the defect evaluation. Based on the size and position of the defects the mechanical properties are evaluated and the most efficient repair process is selected. Therefore, the accuracy of the defect detection is essential for an economic repair process.One approach to detect defects in CFRP is the active lock-in thermography, which is an imaging non-destructive testing method. The application of image processing algorithms on phase images enables automated defect detection in CFRP components. The determination of the depth position of defects is still a challenge since the phase images contain a superposition of different depth layers.This paper introduces a method to quantitatively evaluate the 3D defect geometry from a stack of thermography images captured with different excitation frequencies. The phase images of blind bore holes are analysed. Digital evaluation criteria, e.g. area, enveloping circle and position, are defined. Algorithms for image correction, feature extraction and image analysis are implemented for automated defect detection.The stack of thermography images captured with different excitation frequencies is used to estimate the defect's depth range. With the given thermal diffusivity and the excitation frequency the depth information is calculated. The results are visualized three-dimensionally. The method is validated by the comparison of the experimental results with calibrated coordinate measuring machine data.

The Application of One-Class Classifier Based on CNN in Image Defect Detection

In the field of defect detection, image processing algorithms and feature extraction algorithms have some limitations, owing to their necessity for extracting a large number of different features of diverse products images. Meanwhile, the images of defective products are less and various. Aiming at these problems, we presented a One-Class classifier based on deep convolution neural network to detect the defect images in this paper. We design a loss function with the penalty term based on Euclidean distance to train the deep convolution neural network model. A hypersphere is used as classification decision surface after setting an appropriate hypersphere radius according to the inspection accuracy. It maps the non-defective products into a hypersphere in a high dimensional feature space, while the defect images are mapped somewhere far from the center of hypersphere. Thus, a One-Class classifier based on convolutional neural network(CNN) model is proposed to detect the defects. Experiments show that the proposed method, with less number of iteration, help build the classifier for image defect detection with high generalization ability and high detection precision.

A robust butt welding seam finding technique for intelligent robotic welding system using active laser vision

Intelligent robotic welding requires automatic finding of the seam geometrical features in order for an efficient intelligent control. Performance of the system, therefore, heavily depends on the success of the seam finding stage. Among various seam finding techniques, active laser vision is the most effective approach. It typically requires high-quality lasers, camera and optical filters. The success of the algorithm is highly sensitive to the image processing and feature extraction algorithms. In this work, sequential image processing and feature extraction algorithms are proposed to effectively extract the seam geometrical properties from a low-quality laser image captured without the conventional narrow band filter. A novel method of laser segmentation and detection is proposed. The segmentation method involves averaging, colour processing and blob analysis. The detection method is based on a novel median filtering technique that involves enhancing of the image object based on its underlying structure and orientation in the image. The method when applied enhances the vertically oriented laser stripe in the image which improves the laser peak detection. The image processing steps are performed to make sure that the laser profile is accurately extracted within the region of interest (ROI). Feature extraction algorithm based on pixels’ intensity distribution and neighbourhood search is also proposed that can effectively extract the seam feature points. The proposed algorithms have been implemented and evaluated on various background complexities, seam sizes, material type and laser types before and during the welding operation.

Sports Video Structure Analysis and Feature Extraction in Long Jump Video

How to help people to find their favorite sports in the massive video, in order to achieve this goal in the finite state machine (FSM) theory based on in-depth research. Combined with the actual situation of this paper, first, we determined the reasonable FSM model. Then, a fast robust global motion estimation algorithm is used to estimate the global motion of the video sequences, and the foreground is separated from the background by motion compensation. After the foreground of the video, a series of image feature extraction algorithm, which is based on the main color histogram and histogram, is used to extract the low level feature extraction. Finally, the results of the experimental operation, the performance of the algorithm, and the results of the system are presented.

Quantitative analysis of ex vivo colorectal epithelium using an automated feature extraction algorithm for microendoscopy image data.

Qualitative screening for colorectal polyps via fiber bundle microendoscopy imaging has shown promising results, with studies reporting high rates of sensitivity and specificity, as well as low interobserver variability with trained clinicians. A quantitative image quality control and image feature extraction algorithm (QFEA) was designed to lessen the burden of training and provide objective data for improved clinical efficacy of this method. After a quantitative image quality control step, QFEA extracts field-of-view area, crypt area, crypt circularity, and crypt number per image. To develop and validate this QFEA, a training set of microendoscopy images was collected from freshly resected porcine colon epithelium. The algorithm was then further validated on ex vivo image data collected from eight human subjects, selected from clinically normal appearing regions distant from grossly visible tumor in surgically resected colorectal tissue. QFEA has proven flexible in application to both mosaics and individual images, and its automated crypt detection sensitivity ranges from 71 to 94% despite intensity and contrast variation within the field of view. It also demonstrates the ability to detect and quantify differences in grossly normal regions among different subjects, suggesting the potential efficacy of this approach in detecting occult regions of dysplasia.

Projective robust nonnegative factorization

Nonnegative matrix factorization (NMF) has been successfully used in many fields as a low-dimensional representation method. Projective nonnegative matrix factorization (PNMF) is a variant of NMF that was proposed to learn a subspace for feature extraction. However, both original NMF and PNMF are sensitive to noise and are unsuitable for feature extraction if data is grossly corrupted. In order to improve the robustness of NMF, a framework named Projective Robust Nonnegative Factorization (PRNF) is proposed in this paper for robust image feature extraction and classification. Since learned projections can weaken noise disturbances, PRNF is more suitable for classification and feature extraction. In order to preserve the geometrical structure of original data, PRNF introduces a graph regularization term which encodes geometrical structure. In the PRNF framework, three algorithms are proposed that add a sparsity constraint on the noise matrix based on L1/2 norm, L1 norm, and L2, 1 norm, respectively. Robustness and classification performance of the three proposed algorithms are verified with experiments on four face image databases and results are compared with state-of-the-art robust NMF-based algorithms. Experimental results demonstrate the robustness and effectiveness of the algorithms for image classification and feature extraction.

Parallelizing image feature extraction algorithms on multi-core platforms

Currently, multimedia data has become one of the most important data types processed and transferred over the Internet. To extract useful information from a huge amount of such data, SIFT and SURF, as two most popular image feature extraction algorithms, have been widely used in many applications running on multi-core platforms. However, limited parallelism in existing designs makes it hard or impossible to apply them in many applications with real-time requirements. Therefore, it has become one of the major challenges to improve the processing speed of image feature extraction algorithms.In this paper, we first analyze the parallelism constraints in the algorithms, such as imbalanced workloads and indeterminate time distributions. Based on such analyses, we present an adaptive pipeline parallel scheme (AD-PIPE) to adjust the thread number in different stages according to their workloads dynamically, which achieves a balanced partition for constant input workloads. Furthermore, we also implement a power efficient version (AE-PIPE) for AD-PIPE through scheduling threads based on variable input workloads. Experimental results show that AD-PIPE achieves a speedup of 16.88X and 20.33X respectively over SIFT and SURF on a 16-core machine. Moreover, AE-PIPE achieves up to 52.94% and 58.82% power saving with only 3% performance loss.

Computer Image Feature Extraction Algorithm for Robot Visual Equipment

Computer Image Feature Extraction Algorithm for Robot Visual Equipment

A modified welding image feature extraction algorithm for rotating arc narrow gap MAG welding

Purpose The purpose of this paper is to provide a modified welding image feature extraction algorithm for rotating arc narrow gap metal active-gas welding (MAG) welding, which is significant for improving the accuracy and reliability of the welding process. Design/methodology/approach An infrared charge-coupled device (CCD) camera was utilized to obtain the welding image by passive vision. The left/right arc position was used as a triggering signal to capture the image when the arc is approaching left/right sidewall. Comparing with the conventional method, the authors’ sidewall detection method reduces the interference from arc; the median filter removes the welding spatter; and the size of the arc area was verified to reduce the reflection from welding pool. In addition, the frame loss was also considered in the authors’ method. Findings The modified welding image feature extraction method improves the accuracy and reliability of sidewall edge and arc position detection. Practical implications The algorithm can be applied to welding seam tracking and penetration control in rotating or swing arc narrow gap welding. Originality/value The modified welding image feature extraction method is robust to typical interference and, thus, can improve the accuracy and reliability of the detection of sidewall edge and arc position.

The Image Feature Extraction Algorithm Based on the DWT and the Improved 2DPCA

The technology of 2DPCA is the feature extraction method proposed aiming at two-dimension image based on the traditional PCA algorithm. The paper proposed a improved weighting 2DPCA algorithm, combined with the two-dimension discrete DWT to handle the image, posing the new feature abstraction method, experiment improved that the new feature abstraction method can improve the target recognition efficiently compared with the original 2DPCA algorithm.

Towards Affordable Computing: SiftCU a Simple but Elegant GPU-based Implementation of SIFT

This article presents a fully functional GPU-based implementation of Scale Invariant Feature Transform (SIFT) algorithm. SIFT is a popular image feature extraction algorithm. Although it is a powerful algorithm for image matching but it is also computationally very expensive. This makes it difficult to use especially in real time applications. We purpose to expedite SIFT through GPU-based implementation. There has been some related works on this issue since SIFT was introduced. Our focus is solely on describing GPU-based implementation. We will discuss our implementation in detail. Our implementation is simpler and more efficient than previous works. Part of this paper‟s purpose is to discuss challenges and strategies related to implementing SIFT like image processing algorithms on GPU. In addition, we are going to present a full comparison between serial implementations of SIFT and our GPU-based implementation, namely siftCU, both in accuracy and time consumption.

Study of Fingerprint Image Feature Extraction Algorithm

Fingerprint image feature extraction is a critical step to fingerprint recognition system, which studies topological structure, mathematical model and extraction algorithm of fingerprint feature. This paper presents system design and realization of feature extraction algorithm for fingerprint image. On the basis of fingerprint skeleton image, feature points including ending points, bifurcation points and singular points are extracted at first. Then false feature points are detected and eliminated by the violent changes of ambient orientation field. True feature points are marked at last. Test result shows that the method presented has good accuracy, quick speed and strong robustness for realtime application.

Study of Fingerprint Image Feature Extraction Algorithm

Fingerprint image feature extraction is a critical step to fingerprint recognition system, which studies topological structure, mathematical model and extraction algorithm of fingerprint feature. This paper presents system design and realization of feature extraction algorithm for fingerprint image. On the basis of fingerprint skeleton image, feature points including ending points, bifurcation points and singular points are extracted at first. Then false feature points are detected and eliminated by the violent changes of ambient orientation field. True feature points are marked at last. Test result shows that the method presented has good accuracy, quick speed and strong robustness for realtime application.

Robust Cephalometric Landmark Identification on Cephalometric Downs Analysis

Cephalometric landmark identification on cephalometric analysis Downs has been developed. Here we identify a 10 point landmarks used in cephalometric Downs analysis: Gnation, Gonion, Menton, Nation, Orbital, Porion, Point A, Point B, Pogonion, and Sella. The research was conducted in three phases: preprocessing, feature extraction and identification. In the preprocessing phase, the things we do are: image size normalization, Enhance contrast, and took the ROI for each landmark. We use of PPED algorithms for image feature extraction and to measure the similarity between the template vector and vector test, euclidean distance gives good results, despite the large number of image samples do not always give the results was always good. To improve systems performance, we use a multithreading technique. The experimental results showed that the methods used can work well although still found deficiencies and inaccuracies. Accuracy of projections mainly on bilateral landmarks greatly affect the outcome of the identification.

Realization of an Automated Microassembly Task Involving Micro Adhesive Bonding

An automated approach is proposed for a microassembly task, which is to insert a 10 µm diameter glass tube into a 12 µm diameter hole in a silicon substrate, and bond them together with UV curable adhesive. Two three-degree-of-freedom micromanipulators are used to move the glass tube and the dispensing needle, respectively. Visual feedback is provided by an optical microscope. The angle of the microscope axis is precisely calibrated using an autofocus strategy. Robust image segmentation method and feature extraction algorithm are developed to obtain the features of the hole, the glass tube and the dispensing needle. Visual servo control is employed to achieve accurate aligning for the tube and the hole. Automated adhesive dispensing is used to bond the glass tube and the silicon substrate together after the insertion. Online monitoring ensures that the diameter of the adhesive spot is within a desired range. Experimental results demonstrate the effectiveness of the proposed strategy.

An Improved Face Recognition Algorithm Based on SIFT and LBP

A new face image feature extraction and recognition algorithm based on Scale Invariant Feature Transform (SIFT) and Local Linary Patterns (LBP) is proposed in this paper. Firstly, a set of keypoints are extracted from images by using the SIFT algorithm; Secondly, each keypoint is described by LBP patterns; Finally, a combination of the global and local similarity is adopted to calculate the matching results for face images. Calculation results show that the algorithm can reduce the matching dimension of feature points, improve the recognition rate and perspective; it has nice robustness against the interferences such as rotation, lighting and expression.

Local comprehensive patterns: A novel face feature descriptor

In this letter, we propose a novel face image feature extraction algorithm using local comprehensive patterns (LCP) for face feature descriptor. The traditional local binary patterns (LBP) and local ternary pattern (LTP) compute the relationship between the referenced pixel and its surrounding neighbor pixels by encoding gray-level difference. The proposed method computes the relationship between the referenced pixel and its neighbors by encoding gray-level difference based on 0°, 45°, 90°, 135°, 180°, 225°, 270°, 315° high orders direction derivatives patterns (DDP) and the direction magnitude patterns (DMP), which can extract more detailed discriminating information. Finally, both the direction derivatives patterns and the direction tendency patterns are respectively exploited to handle the feature fusion. Simulated experiments and comparisons on subsets of ORL and Yale B face databases under ideal condition, different illumination condition, different facial expression and partial occlusion show that the proposed algorithm is an outstanding method better than the LBP, the local derivative patterns, and the LTP.

Ant algorithms for image feature extraction

This paper extends on previous work in applying an ant algorithm to image feature extraction, focusing on edge pattern extraction, as well as the broader study of self-organisation mechanisms in digital image environments. A novel method of distributed adaptive thresholding is introduced to the ant algorithm, which enables automated distributed adaptive thresholding across the swarm. This technique is shown to increase performance of the algorithm, and furthermore, eliminates the requirement for a user set threshold, allowing the algorithm to autonomously adapt an appropriate threshold for a given image, or data set. Additionally this approach is extended to allow for simultaneous multiple-swarm multiple-feature extraction, as well as dynamic adaptation to changing imagery.

Standardization of islet isolation outcome – A new automatic system to determine pancreatic islet viability

Pancreatic islet transplantation is emerging as a therapeutic approach for patients affected by diabetes. This approach consists of a minimally invasive procedure replacing insulin-producing cells (pancreatic islets). The technique has been proven successful, but limitations have been identified. One of the major challenges of the procedure is the counting of the isolated pancreatic islets, which is currently jeopardized by subjectivity and inaccuracy. Determination of the accurate islet number is a crucial factor in determining the correlation between the isolation product and clinical outcome. In the proposed study, we have developed software capable of objectively evaluating islet numbers and other viability variables by image analysis. This software is based on image processing and feature extraction algorithms for recognition of the area of interest. This is the first step toward standardization of the isolation outcome and potential clinical success predictability.