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

Extracting well-distributed, reliable, and precisely aligned point pairs for accurate image registration is a difficult task, particularly for multisource remote sensing images that have significant illumination, rotation, and scene differences. The scale-invariant feature transform (SIFT) approach, as a well-known feature-based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, the SIFT algorithm suffers from some problems in the quality, quantity, and distribution of extracted features particularly in multisource remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images, even with those that are five times the difference in scale. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then, the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluation of efficiency, distribution quality, and positional accuracy of the extracted point pairs proves the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.

Exploration on Data Collection and Analysis System Based on Integrated SIFT Algorithm

There remain many difficult problems in computer vision research such as object recognition, three dimensional reconstruction, object tracking, etc. And the basis of solving these problems relies on image matching. The scale invariant feature transform (SIFT) algorithm has been widely used for image matching application. The SIFT algorithm can successfully extract the most descriptive feature points in given input images taken under different viewpoints. However, the performance of the original SIFT algorithm degrades under the influence of noise. We propose to modify the SIFT algorithm to produce better invariant feature points for image matching under noise. We also propose to employ the Earth mover's distance (EMD) as the measurement of similarity between two descriptors. We present extensive experiment results to demonstrate the performance of the proposed methods in image matching under noise.

Extracting image feature points and classification methods is the key of content-based image classification. In this paper, SIFT(Scale-invariant feature transform) algorithm is used to extract feature points, all feature points extracted are clustered by K-means clustering algorithm, and then BOW(bag of word) of each image is constructed. Finally, SVM(Support Vector Machine) is used to train a multi-class classifier to classify images. The SIFT algorithm has a strong tolerance for scaling, rotation, brightness changes, and noise. The k-means algorithm is simple in structure and fast in convergence. SVM can get better results in small sample training set and has excellent generalization ability. Experimental results show that the accuracy of image classification in the above methods is about 90%.

A novel digital image stabilization approach using Harris and Scale Invariant Feature Transform (SIFT) was presented in this article. Using SIFT in digital image stabilization, too many feature points and matches were extracted, but some of them were not so stable. Using these feature points and matches can not only increase the computational effort, but also enhance the wrong matching probability. We proposed to use SIFT to detect feature points and incorporate the Harris criterion to select the most stable feature points in the video sequence where image motion was happened due to vehicle or platform vibration. With these feature points, we use general feature descriptor and matching algorithm to achieve the image stabilization. Experimental results show that the proposed algorithm can not only bring down the probability of wrong matching and get more accurate matches, but also reduce the computation burden than SIFT effectively.

In this paper, an affine invariance feature detection method based on Scale Invariant Feature Transform (SIFT) and Maximally Stable Extremal Regions (MSER) is proposed. Classical SIFT algorithm is not robust to affine deformations, because it is based on DOG detector which extracts circle regions for keypoint location. In order to overcome this disadvantage, DOG detector in conventional SIFT algorithm is replaced by MSER detector which is robust to affine deformation. Then these regions are normalized and extracted using SIFT. Simulation studies are carried out to show the effectiveness of the proposed method to affine transform in comparison to traditional SIFT algorithm.

The traditional rearview mirror method cannot fully guarantee safety when driving trucks. RGB and infrared images collected by cameras are used for registration and recognition, so as to achieve the perception of surroundings and ensure safe driving. The traditional scale-invariant feature transform (SIFT) algorithm has a mismatching rate, and the YOLO algorithm has an optimization space in feature extraction. To address these issues, this paper proposes a truck surround sensing technique based on multi-features and an improved YOLOv5 algorithm. Firstly, the edge corner points and infrared features of the preset target region are extracted, and then a feature point set containing the improved SIFT algorithm is generated for registration. Finally, the YOLOv5 algorithm is improved by fusing infrared features and introducing a composite prediction mechanism at the prediction end. The simulation results show that, on average, the image stitching accuracy is improved by 17%, the time is reduced by 89%, and the target recognition accuracy is improved by 2.86%. The experimental results show that this method can effectively perceive the surroundings of trucks, accurately identify targets, and reduce the missed alarm rate and false alarm rate.

Human vision can do amazing things such as recognizing people or objects, navigating through obstacles, recognizing the mood in a scene, and imagining stories. To do mimicry of the human vision, the computer requires a sensor that functions like the human eye and a computer program that serves as a data processor from the sensor. Computer vision is the science that uses image processing to make decisions based on images obtained from sensors. In other words, computer vision aims to build an intelligent machine that can see. Computer vision can be used to detect skin diseases, for example, to detect disease Shingles (Herpes Zoster), Hives (Urticaria), Psoriasis, Eczema, Rosacea, Cold Sores (Fever Blisters), Rash, Razor Bumps, Skin Tags, Acne, Athlete's Foot, moles, Age or Liver Spots, Pityriasis Rosea, Melasma (Pregnancy Mask), Warts, and Seborrheic keratoses. Prewitt, Sobel, Roberts, and Canny operator are used to detect the edges of one or more objects. Then the results will be match with the results of edge detection image data base to determine the type of disease using Scale invariant Feature Transform (SIFT) algorithm. Skin Disease Detection Expert System will be implemented with C++ programming language, IDE MS Visual Studio 2010 and OpenCV 2.4 library. Keywords— computer vision, edge detection, SIFT algorithm, skin disease

Scale Invariant Feature Transform (SIFT) algorithm is a widely used computer vision algorithm that detects and extracts local feature descriptors from images. SIFT is computationally intensive, making it infeasible for single threaded im-plementation to extract local feature descriptors for high-resolution images in real time. In this paper, an approach to parallelization of the SIFT algorithm is demonstrated using NVIDIA’s Graphics Processing Unit (GPU). The parallel-ization design for SIFT on GPUs is divided into two stages, a) Algorithm de-sign-generic design strategies which focuses on data and b) Implementation de-sign-architecture specific design strategies which focuses on optimally using GPU resources for maximum occupancy. Increasing memory latency hiding, eliminating branches and data blocking achieve a significant decrease in aver-age computational time. Furthermore, it is observed via Paraver tools that our approach to parallelization while optimizing for maximum occupancy allows GPU to execute memory bound SIFT algorithm at optimal levels.

 Untitled Page In this thesis we study the running of the Scale Invariant Feature Transform (SIFT) algorithm on a pipelined computational platform. The SIFT algorithm is one of the most widely used methods for image feature extraction. We develop a tile based template for running SIFT that facilitates the analysis while abstracting away lower-level details. We formalize the computational pipeline and the time to execute any algorithm on it based on the relative times taken by the pipeline stages. In the context of the SIFT algorithm, this reduces the time to that of running the entire image through a bottlenecked stage and the time to run either the first or last tile through the remaining stages. Through an experimental study of the SIFT algorithm on a broad collection of test images, we determined image feature fraction values, that relate the sizes of the image extracts as it the computation proceeds through the stages of the SIFT algorithm. We show that for a single chip uniprocessor pipeline, the computational stage is the bottleneck. Specifically we show that for an N x N image with n x n tiles the overall time complexity is θ ( (n+x)2 pi Г0+αβN2x2 Г1+ (αβ+γ)n2logx P0 Г2 ) ; here x is the neigborhood of the tile, pi , po are the number of input, output pins of the chip, α,β,γ are the feature fractions, and Г0, Г1, Г2 are the input, compute, output clocks. The three terms in the expression represents the time complexities of input, compute and output stages. The input and output stages can be slowed down substantially without appreciate degradation of the overall performance. This slowdown can be traded off for lower power and higher signal quantity. For multicore chips, we show that for an N x N image on a P-core chip, the overall time complexity to process the image is θ ( N2 pi Г0+ (n2w2 + αβn2x2) P Г1+ (αβ+γ)n2logx P0 Г2 ) ; in addition to the quantities described earlier w is the window size used for the Gaussian blurring. Overall we establish that without improvements in the input bandwidth, the power of multicore processing cannot be used efficiently for SIFT.

Multimedia information processing is a new technology developing rapidly recent years, among which the Image Matching Technology has wide range of applications in various fields, such as face recognition and building recognition, image database retrieval, etc. This paper focus on the accuracy of image matching using SIFT (Scale Invariant Feature Transform) algorithm to extract feature points from images. And then the feature points are used to find out the matching points between two images by calculating the correlation degree based on the Analysis of Grey Correlation Degree. Compared with the original algorithm, in the case that the matching time is almost the same, the algorithm in this paper has higher matching accuracy and robustness, proved by experiments, can adapt to the requirements of image recognition in various conditions and can eliminate the influence of stretching, rotation and illumination change.

In this paper a novel method is proposed to improve the performance of the SIFT (Scale Invariant Feature Transformation) algorithm in adverse illumination conditions (in an outdoor environment at night), for this research it is proposed to work with CLAHE (Contrast Limited Adaptive Histogram Equalization), adding a preprocessing stage to the traditional methodology of the SIFT algorithm, this will be applied to the building to be found in the scene, i.e., the image pattern.A comparison with different illumination conditions (day, evening and night) will be held to know the response that will have the SIFT algorithm and to identify which moment the algorithm has a better performance.

In the process of water quality monitoring, qualitative evaluation can be carried out according to the changes in the number of underwater animals and the state of motion and other parameters, because of the underwater image resolution is low, mistracking and loss can occur when tracking underwater targets, this paper proposes a matching algorithm based on scale invariant feature transform (SIFT) and nuclear correlation filter (KCF) of underwater target tracking algorithm. SIFT algorithm was used for feature extraction to obtain the detection frame, and the improved HOG feature was used for KCF training and tracking. According to the experimental results, it can effectively detect and track underwater fish under complicated underwater conditions.

SIFT (scale invariant feature transform) is one of the most robust and widely used image matching algorithms based on local features. However, its computational complexity is high. In order to reduce the matching time, an improved feature matching algorithm is proposed in this paper under the premise of stable registration accuracy. This paper proposed a normalized cross-correlation with SIFT combination of remote sensing image matching algorithm. The basic idea of the algorithm is performing the space geometry transformation of the input image with reference to the base image. Then the normalized cross-correlation captures the relevant part of the remote sensing images. By this way, we can reduce the matching range. So some unnecessary calculations are properly omitted. By utilizing the SIFT algorithm, we match the preprocessed remote sensing images, and get the registration points. This can shorten the matching time and improve the matching accuracy. Its robustness is increased correspondingly. The experimental results show that the proposed Normalized cross-correlation plus SIFT algorithm is more rapid than the standard SIFT algorithm while the performance is favorably compared to the standard SIFT algorithm when matching among structured scene images. The experiment results confirm the feasibility of our methods.

To explore the impact of different image registration algorithms on the diagnosis of visual path damage in patients with primary open angle glaucoma (POAG), 60 cases of suspected POAG patients were selected as the research objects. Shape-preserving scale invariant feature transform (SP-SIFT) algorithm, scale invariant feature transform (SIFT) algorithm, and Kanade-Lucas-Tomasi (KLT) algorithm were compared and applied to MRI images of 60 POAG patients. It was found that the SP-SIFT algorithm converged after 33 iterations, which had a higher registration speed than the SIFT algorithm and the KLT algorithm. The mean errors of the SP-SIFT algorithm in the rotation angle, X-direction translation, and Y-direction translation were 2.11%, 4.56%, and 4.31%, respectively. Those of the SIFT algorithm were 5.55%, 9.98%, and 7.01%, respectively. Those of the KLT algorithm were 7.45%, 11.31%, and 8.56%, respectively, and the difference among algorithms was significant ( P < 0.05 ). The diagnostic sensitivity and accuracy of the SP-SIFT algorithm for POAG were 96.15% and 94.34%, respectively. Those of the SIFT algorithm were 94.68% and 90.74%, respectively. Those of the KLT algorithm were 94.21% and 90.57%, respectively, and the three algorithms had significant differences ( P < 0.05 ). The results of MRI images based on the SP-SIFT algorithm showed that the average thickness of the cortex of the patient’s left talar sulcus, right talar sulcus, left middle temporal gyrus, and left fusiform gyrus were 2.49 ± 0.15 mm, 2.62 ± 0.13 mm, 3.00 ± 0.10 mm, and 2.99 ± 0.17 mm, respectively. Those of the SIFT algorithm were 2.51 ± 0.17 mm, 2.69 ± 0.12 mm, 3.11 ± 0.13 mm, and 3.09 ± 0.14 mm, respectively. Those of the KLT algorithm were 2.35 ± 0.12 mm, 2.52 ± 0.16 mm, 2.77 ± 0.11 mm, and 2.87 ± 0.17 mm, respectively, and the three algorithms had significant differences ( P < 0.05 ). In summary, the SP-SIFT algorithm was ideal for POAG visual pathway diagnosis and was of great adoption potential in clinical diagnosis.