Conventional Scale Invariant Feature Transform Research Articles

Image-matching enhancement using a polarized intensity-hue-saturation fusion method.

Atmospheric absorption and scattering (e.g., haze) cause degradation in the image quality of outdoor scenes, which affects the image-matching process. The scale-invariant feature transform (SIFT) algorithm is not effective in haze. Edge information is required to enhance the matching process. Utilizing the polarization information expressed by the Stokes vector component S1 with its edge information can improve the keypoint localization in the matching process. In this paper, a novel, to the best of our knowledge, fusion method called polarized intensity-hue-saturation is proposed that uses polarization and depth information by fusion of a polarized haze-removed image with the estimated depth and by applying S1. The instant dehazing method uses polarized images to obtain a haze-removed image and its estimated depth map. The fused image has high spatial details required for enhancing the matching process. The experimental results show that the proposed method outperforms the existing image-matching schemes and improves the conventional SIFT matching method.

Depth recovery for unstructured farmland road image using an improved SIFT algorithm

Road visual navigation relies on accurate road models. This study was aimed at proposing an improved scale-invariant feature transform (SIFT) algorithm for recovering depth information from farmland road images, which would provide a reliable path for visual navigation. The mean image of pixel value in five channels (R, G, B, S and V) were treated as the inspected image and the feature points of the inspected image were extracted by the Canny algorithm, for achieving precise location of the feature points and ensuring the uniformity and density of the feature points. The mean value of the pixels in 5×5 neighborhood around the feature point at an interval of 45o in eight directions was then treated as the feature vector, and the differences of the feature vectors were calculated for preliminary matching of the left and right image feature points. In order to achieve the depth information of farmland road images, the energy method of feature points was used for eliminating the mismatched points. Experiments with a binocular stereo vision system were conducted and the results showed that the matching accuracy and time consuming for depth recovery when using the improved SIFT algorithm were 96.48% and 5.6 s, respectively, with the accuracy for depth recovery of –7.17%-2.97% in a certain sight distance. The mean uniformity, time consuming and matching accuracy for all the 60 images under various climates and road conditions were 50%-70%, 5.0-6.5 s, and higher than 88%, respectively, indicating that performance for achieving the feature points (e.g., uniformity, matching accuracy, and algorithm real-time) of the improved SIFT algorithm were superior to that of conventional SIFT algorithm. This study provides an important reference for navigation technology of agricultural equipment based on machine vision. Keywords: scale-invariant feature transform (sift), feature matching, canny operator, energy method of feature point, farmland road, depth recovery, visual navigation DOI: 10.25165/j.ijabe.20191204.4821 Citation: Yao L J, Hu D, Yang Z D, Li H B, Qian M B. Depth recovery for unstructured farmland road image using an improved SIFT algorithm. Int J Agric & Biol Eng, 2019; 12(4): 141–147.

GPU-based parallel optimization for real-time scale-invariant feature transform in binocular visual registration

Scale-invariant feature transform (SIFT) is one of the widely used interest point features. It has been successfully applied in various computer vision algorithms like object detection, object tracking, robotic mapping, and large-scale image retrieval. Although SIFT descriptor is highly robust towards scale and rotation variations, the high computational complexity of the SIFT algorithm inhibits its use in applications demanding real-time response and in algorithms dealing with very large-scale databases. In order to be effective for image matching process in near real-time, the Compute Unified Device Architecture (CUDA) application programming interface of a graphics processing unit (GPU) is incorporated to speed up or improve the SIFT method. Experimental results show that the proposed GPU-based SIFT framework is suitable for image application in real time. It can improve the image matching process both in time and accuracy compared with conventional SIFT method.

Self‐geometric relationship filter for efficient SIFT key‐points matching in full and partial palmprint recognition

Recently, palmprints have been broadly reported in the literature as an effective biometric modality. Although scale-invariant feature transform (SIFT)-based features have been proven to be robust against image transformations and deformations, SIFT has not been as successful as other methods in palmprint recognition. In fact, SIFT-based identification has been widely criticised in biometrics due to its high false matching rate. To overcome this weakness, a new filtering method for SIFT-based palmprint matching, called the self-geometric relationship-based filter (SGR-filter) is presented. While existing SIFT matching considers only the relationship between the SIFT points of the query image, on one hand, and their corresponding points in the reference image, on the other hand, SGR-filtering further takes into account the geometric relationship between SIFT points within the query image in comparison with the relationship of the corresponding matched points in the reference image. Assessed with the proposed SGR-filter on various datasets, the SIFT-based palmprint recognition system has been shown to deliver significantly higher performance when compared with the conventional SIFT matching as well as another related key-points filtering technique. Furthermore, experimental results on a number of different full and partial palmprint datasets have shown the superiority of the proposed system over state-of-the-art techniques.

Wavelet Transform based Score Fusion for Face Recognition using SIFT Descriptors

One of the main areas in computer vision is automatic face recognition which deals with detecting human face autonomously. Developments and the progress in the field of face recognition have shown that many face recognition systems and applications the automated methods outperform humans. The conventional Scale-Invariant Feature Transform (SIFT) is used in face recognition where they provide high performances. However, this performance can be improved further by transforming the input into different domains before applying SIFT algorithm. Hence, we apply Discrete Wavelet Transform (DWT) or Gabor Wavelet Transform (GWT) at the input face images, which provides denser and extra information to be used by the conventional SIFT algorithm. Matching scores of SIFT from each subimage is fused before making final decision. Simulations show that the proposed approaches based on wavelet transforms using SIFT provides very high performance compared to the conventional algorithm.

Improving scale invariant feature transform-based descriptors with shape–color alliance robust feature

Constructing appropriate descriptors for interest points in image matching is a critical aspect task in computer vision and pattern recognition. A method as an extension of the scale invariant feature transform (SIFT) descriptor called shape–color alliance robust feature (SCARF) descriptor is presented. To address the problem that SIFT is designed mainly for gray images and lack of global information for feature points, the proposed approach improves the SIFT descriptor by means of a concentric-rings model, as well as integrating the color invariant space and shape context with SIFT to construct the SCARF descriptor. The SCARF method developed is more robust than the conventional SIFT with respect to not only the color and photometrical variations but also the measuring similarity as a global variation between two shapes. A comparative evaluation of different descriptors is carried out showing that the SCARF approach provides better results than the other four state-of-the-art related methods.

Comparative Study of Corner and Feature Extractors for Real-Time Object Recognition in Image Processing

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

Nonrigid Registration of Lung CT Images Based on Tissue Features

Nonrigid image registration is a prerequisite for various medical image process and analysis applications. Much effort has been devoted to thoracic image registration due to breathing motion. Recently, scale-invariant feature transform (SIFT) has been used in medical image registration and obtained promising results. However, SIFT is apt to detect blob features. Blobs key points are generally detected in smooth areas which may contain few diagnostic points. In general, diagnostic points used in medical image are often vessel crossing points, vascular endpoints, and tissue boundary points, which provide abundant information about vessels and can reflect the motion of lungs accurately. These points generally have high gradients as opposed to blob key points and can be detected by Harris. In this work, we proposed a hybrid feature detection method which can detect tissue features of lungs effectively based on Harris and SIFT. In addition, a novel method which can remove mismatched landmarks is also proposed. A series of thoracic CT images are tested by using the proposed algorithm, and the quantitative and qualitative evaluations show that our method is statistically significantly better than conventional SIFT method especially in the case of large deformation of lungs during respiration.

Wide-angle Visual Feature Matching for Outdoor Localization

Wide-angle images exhibit significant distortion for which existing scale-space detectors such as the scale-invariant feature transform (SIFT) are inappropriate. The required scale-space images for feature detection are correctly obtained through the convolution of the image, mapped to the sphere, with the spherical Gaussian. A new visual key-point detector, based on this principle, is developed and several computational approaches to the convolution are investigated in both the spatial and frequency domain. In particular, a close approximation is developed that has comparable computation time to conventional SIFT but with improved matching performance. Results are presented for monocular wide-angle outdoor image sequences obtained using fisheye and equiangular catadioptric cameras. We evaluate the overall matching performance (recall versus 1-precision) of these methods compared to conventional SIFT. We also demonstrate the use of the technique for variable frame-rate visual odometry and its application to place recognition.