Cross-correlation-based image matching of coins

Rheumatoid arthritis (RA) is a long lasting autoimmune disorder that affects the multiple joints of human body. The aim and objective of the study was i) to implement the automated segmentation of knee x-ray image and thermal image using fuzzy c means and canny edge detection algorithm. ii) To compare both the imaging modalities by means of feature extraction and correlate with the biochemical method as standard. Fifteen subjects with RA in knee region and 15 healthy controls were included in this study. The segmentation of thermal images was performed using fuzzy c-means algorithm and x-ray segmentation was implemented using canny edge detection algorithm. The skin surface temperature weremeasured in the thermal image of knee regionin both RA and control subjects. The features wereextracted from the segmented region of the knee x-ray image. The automated segmentation implemented in thermal imaging provided better results compared to x-ray image segmentation process. The thermal imaging feature and x-ray imaging features correlated significantly with the standard parameters.

This study proposed a model for highway accident detection that combines the You Only Look Once v3 (YOLOv3) object detection algorithm and Canny edge detection algorithm. It not only detects whether an accident has occurred in front of a vehicle, but further performs a preliminary classification of the accident to determine its severity. First, this study established a dataset consisting of around 4500 images mainly taken from the angle of view of dashcams from an open-source online platform. The dataset was named the Highway Dashcam Car Accident for Classification System (HDCA-CS) and was developed with the aim of conforming to the setting of this study. The HDCA-CS not only considers weather conditions (rainy days, foggy days, nighttime settings, and other low-visibility conditions), but also various types of accidents, thus increasing the diversity of the dataset. In addition, we proposed two types of accidents—accidents involving damaged cars and accidents involving overturned cars—and developed three different design methods for comparing vehicles involved in accidents involving damaged cars. Canny edge detection algorithm processed single high-resolution images of accidents were also added to compensate for the low volume of accident data, thereby addressing the problem of data imbalance for training purposes. Lastly, the results showed that the proposed model achieved a mean average precision (mAP) of 62.60% when applied to the HDCA-CS testing dataset. When comparing the proposed model with a benchmark model, two abovementioned accident types were combined to allow the proposed model to produce binary classification outputs (i.e., non-occurrence and occurrence of an accident). The HDCA-CS was then applied to the two models, and testing was conducted using single high-resolution images. At 76.42%, the mAP of the proposed model outperformed the benchmark model’s 75.18%; and if we were to apply the proposed model to only test scenarios in which an accident has occurred, its performance would be even better relative to the benchmark. Therefore, our findings demonstrate that our proposed model is superior to other existing models.

Image edge detection significantly preserves the important structural properties in an image, while reducing the amount of data and filters out useless information. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. This paper introduces the Canny edge detection algorithm and the implementation of the Canny edge-detection algorithm on an FPGA. Custom implementation in FPGAs, using a combination of hardware and software co-design, allows real-time processing, providing a good trade-off between performance and flexibility. The results show that FPGAs are good alternatives, which can be used to off-load these computationally-intensive and repetitive functions as co-processors and the capability of supporting canny edge detection for real-time image processing.

Since edges contain symbolically important image information, their detection can be exploited for the development of an efficient image compression algorithm. This paper proposes an edge based image compression algorithm in fuzzy transform (F-transform) domain. Input image blocks are classified either as low intensity blocks, medium intensity blocks or a high intensity blocks depending on the edge image obtained using the Canny edge detection algorithm. Based on the intensity values, these blocks are compressed using F-transform. Huffman coding is then performed on compressed blocks to achieve reduced bit rate. Subjective and objective evaluations of the proposed algorithm have been made in comparisons with RFVQ, FTR, FEQ and JPEG. Results show that the proposed algorithm is an efficient image compression algorithm and also possesses low time complexity.

In the area of computer vision, pattern recognition and image processing, image match is a research hotspot with important theoretical significance and practical value. Recently, the image matching algorithm based on SIFT has drown wide attention for its outstanding local feature matching performance. In view of high computation complexity, poor anti-noise ability, and difficulty for practical use of the original SIFT method, a more brief and efficient SIFT image matching algorithm: BE-SIFT is proposed in this paper for computer vision. Through collaborative improvement and optimization on detection of SIFT scale space feature points, allocation of key point principal direction, and calculation of feature descriptors, the proposed method has achieved more brief dimension expression and more efficient local feature match than other existing methods. Experiments based on the Affine Covariant Features dataset provided by Oxford VGG Group demonstrate that BE-SIFT has stronger robustness for image match under the condition of viewpoint changes, rotation + scale changes, blur changes and noise changes. What's more, the computational overhead is sharply reduced and the real-time performance is greatly improved while ensuring the uniqueness of local feature and satisfactory accuracy.

QoS is a very important issue in cognitive radio systems. We present and discuss spectrum matching algorithms to support QoS for cognitive radio users. This work is carried out in the context of a dynamic changing spectrum. In this paper, we propose efficient spectrum matching algorithms, which have low-complexity and quite good performance, based on statistical characteristics of spectrum bands. Those are classified random matching algorithm and greedy algorithm. The classified random matching algorithms would match the most suitable spectrum class to a proper user and randomly choose a spectrum within the class. And, using greedy algorithm, the best spectrum out of available spectrum bands will be chosen whenever a new user joins. The proposed algorithms provide QoS for cognitive radio systems through the reduction of spectrum handoff probability and improvement of fairness. Performance analysis and simulation results validate the efficiency of the proposed algorithms.

The Canny edge detection algorithm contains a number of adjustable parameters, which can affect the computation time and effectiveness of the algorithm. To overcome the shortages, this paper proposes a new way to determine the adjustable parameters and constructs a modified Canny edge detection algorithm. In the algorithm, an image is firstly smoothed by an adaptive filter that is selected based on the properties of the image, instead of a fixed sized Gaussian filter, and then, the high and low thresholds for the gradient magnitude image are determined based on maximum cross-entropy between inter-classes and Bayesian judgment theory, without any manual operation; finally, if it needs, the object closing procedure is carried out. To test and evaluate the algorithm, a number of different images are tested and analysed, and the test results are discussed. The experiments show that the studied algorithm can achieve the better edge detection results in most of the cases, and it is also useful for object boundary closing as a pre-segmentation step.

In this paper, image smoothening, gradient magnitude, hystersis used in Canny edge detection algorithm is presented. The new algorithm used has a low-complexity 8-bin non-uniform gradient magnitude histogram to compute block-based hysteresis thresholds that are used by the Canny edge detector. This also achieves less memory requirements, decreased latency and increased throughput with no loss in edge detection. Furthermore, the hardware architecture of the proposed algorithm is presented in this paper and the architecture is synthesized on the Xilinx Virtex 5 FPGA. The design development is done in VHDL and simulated results are obtained using modelsim 6.3 with Xilinx 12.2.

Novel watermarking model is developed on this paper using laplacian filter from two previous watermarking models that use CRT algorithms. The first model is embedding the watermark on the random pixel on each single block which can degrade the homogeneous areas. The second model use the canny edge detection to determine the edges as embedding locations within binary representation without considering the gradient magnitude that causes less optimal result. The proposed model is developed from the laplacian filter to generate the gradient magnitude of the image regions as embedding locations. The higher gradient magnitude represents the better location for embedding. Bits of watermark are embedding from the location with the highest gradient magnitude to the lower gradient magnitudes according to the number of watermark bits. The proposed model has higher imperceptibility with average SSIM of 0.9996 than the conventional CRT with average SSIM of 0.9985. In compression test, the proposed model has higher robustness with average NC value of 0.6746 than the previous models that have NC average values of 0.6618 and 0.6616 correspondingly. Temporarily, in noise test, all of the models have the similar average NC values. The model has better visual quality of the watermarked image than the previous models that use block division and canny edge detection algorithm. It also has better robustness against compression.

As an important part of the automation industrial system, mechanical arm robot plays an important role in service, exploration, automation engineering and other fields, and has a broad application prospect. The traditional mechanical arm and Sobel edge detection methods have some shortcomings: 1. The PID method is separated from the ontology model and only carries out simple and multiple calculations according to the code of the steering engine, which is not stable enough and will cause more time consumption; 2. In some complex environments, while Sobel edge detection is detecting the shape of objects, the separation between subject and background is not clear, which makes it impossible to obtain accurate object information. Therefore, this paper proposes a new PID method and Canny edge detection algorithm: 1. PID method combined with three-dimensional coordinate system modeling, to achieve a more stable, faster and more robust calculation formula, to complete the mechanical arm grasping function; 2. Using Canny edge detection and three-dimensional coordinate system to analyze the object image to obtain the accurate information of the target object.

The iris, an annulus region where located in the area between the pupil and sclera, its unique texture almost never changes in whole life. Iris location is to locate the effective region and prevent interference with the invalid region. Iris localization is an important part in the Iris recognition system, accuracy and speed of the positioning directly affects the performance of iris recognition system. On the basis of existing analysis on iris recognition algorithms, dividing the positioning process into two steps: the rough positioning and the precise positioning. In the first step, based on gray window, which can reduce the specifically searching range and can avoid the eyelash, eyelids and the disturbance of external spotlight source effectively, etc., we introduce a new method that estimates coarse positioning of pupil center. In the step of fine positioning, based on Canny edge detection algorithm and the Hough transform, we introduce an improved algorithm. In the process of Edge extraction, utilizing the feature between internal and outer edges and the Hough transform, we adjust the size of extracted image block, a small image would be fixed the size due to the prior experimental results. Experimental results show that the localization algorithm ensure the accuracy of iris localization while enhancing the localization speed. Effectively overcome the serious influence of eyelids and eyelashes. In conclusion, The Improved Iris Location Algorithm is feasible.

We examine the use of matching theory for user association in millimeter wave (mmWave)-enabled cellular heterogeneous networks. In a mmWave system, the channel variations can be fast and unpredictable, rendering centralized user association potentially inefficient. We propose an efficient distributed matching algorithm, called early acceptance (EA), tailored for user association in 5G HetNets. The effectiveness of the proposed algorithm is assessed by comparing with the well-known deferred acceptance (DA) matching algorithm, in which user association is delayed until the algorithm terminates. Numerical results show that the proposed distributed EA matching algorithm reaches a near-optimal solution compared to a centralized algorithm, and leads to a more power-efficient and much faster user association process compared to the distributed DA algorithm.

Volumetric matching is an important tool in volume image analysis. In this paper, we propose an efficient volumetric matching framework to extract coarse correspondences between given volume images. The framework includes two steps. First, we extract features based on a Maximally Stable Volumes (MSVs) algorithm then build feature descriptors using a scale-related 3D SURF algorithm reliable to scale changes. Then, we match features using a Spectral Matching (SM) algorithm. We show the effectiveness and robustness of our algorithm on both synthetic and clinical medical images.

Automatic classification of apples is an important application of machine vision technology in agricultural product detection. In this paper, we study apple grading method based on features of color and defect. First, the captured RGB-based apple images are transformed into HIS model. Then, the segmentation between fruit and background is realized via the Oust algorithm for the H values of the image, and the color feature is extracted by the appropriate range of H values. In addition, the Canny edge detection algorithm with hysteresis threshold is employed to extract the edge information of defect area from I values of fruit image, then the defect area is segmented by morphological operation. Finally, serving as input, the characteristic quantities of apple color and surface defect are sent into the SVM model based on particle swarm optimization. The experiment results indicate that accuracy rate can reach to 91%.

Learned active contours via transformer-based deep convolutional neural network using canny edge detection algorithm