Kernel Principal Component Analysis feature detection and classification for underwater images

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.

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.

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.

Kernel principal component analysis (KPCA) has gained much attention for capturing nonlinear image features which is particularly important for clustering high-dimensional multi-class features. We introduce KPCA in this paper for detecting and classifying moving vehicles from its viewpoint images. The KPCA extracts non-linear features of multi-class moving vehicles by mapping input space to a higher dimensional feature space through a nonlinear map. The results provide us a clustered feature space of the car and non-car for classifying them by separating the dimensional space or eigenvector. The experimental results show the robustness of the KPCA's feature separation in our car database that lead to the cars' classification. Extended experiments on various vehicles detection have also shown the remarkable performance of the proposed method

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.

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.

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.

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%.

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.

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

A hardware-software system implemented for visual servoing of prosthetic arms. The prosthetic system comprises glass worn eye-tracker and video camera and a prosthesis - mounted stereo camera system. The proposed light-weight architecture has to be implemented as a wearable device. We propose a hybrid solution which contains object selection and recognition, in glass camera view, object matching to the prosthesis-mounted camera to compute depth map for object reaching by the prosthetic arm. The object selection and object matching both use the Scale Invariant Feature Transform (SIFT) algorithm. The SIFT algorithm is time-consuming, so the FPGA acceleration is proposed for the light-weight device. The proposed implementation is compatible with real-time servoing of the prosthetic arm.

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

Fully pipelined FPGA-based architecture for real-time SIFT extraction