scholarly journals Image Detection System Using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 5356-5362
Author(s):  
Mohini Gawande
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Estimation Method ◽  
Scale Invariant ◽  
Matrix Estimation ◽  
Speeded Up Robust Features ◽  
Invariant Features ◽  
Marketing Analysis ◽  
New Strategies

The increasing popularity of Social Networks makes change the way people interact. These interactions produce a huge amount of data and it opens the door to new strategies and marketing analysis. According to Instagram and Tumblr, an average of 80 and 59 million photos respectively are published every day, and those pictures contain several implicit or explicit brand logos. Image recognition is one of the most important fields of image processing and computer vision. The CNNs are a very effective class of neural networks that is highly effective at the task of image classifying, object detection and other computer vision problems.in recent years, several scale- invariant features have been proposed in literature, this paper analyzes the usage of Speeded Up Robust Features (SURF) as local descriptors, and as we will see, they are not only scale-invariant features, but they also offer the advantage of being computed very efficiently. Furthermore, a fundamental matrix estimation method based on the RANSAC is applied.

Design of Computer Vision and Image Processing Technology Based Car Keys Tooth Identification Detection System

2014 ◽  
Vol 1079-1080 ◽  
pp. 1061-1063 ◽  
Author(s):  
Hong Ying Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Processing Technology ◽  
Direct Impact ◽  
Assembly Process ◽  
Accurate Identification ◽  
Detection Technology ◽  
Key Aspects ◽  
The Cost

This paper can be used as acar key toothed recognition and detection technology and computer vision, imageprocessing technology combined with interdisciplinary applications. Car lockassembly complicated procedures, identification and car keys tooth detection isone of the key aspects of automotive lock assembly, lock a direct impact on theefficiency of the assembly process. The system can effectively improve theexisting car key tooth detection technology to reduce the cost of car keystooth detection recognition, while also rapid and accurate identification, sothat the entire lock assembly process much more efficient.

Reverse Design and Application of Irregular Planar Part Based on Image Processing

2012 ◽  
Vol 229-231 ◽  
pp. 1706-1709
Author(s):  
Jian Jun Yin ◽  
Jia Qing Lin ◽  
S.Mittal Gauri ◽  
Shuang Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Computer Aided Design ◽  
Detection System ◽  
Design Method ◽  
Three Dimensional ◽  
Digital Camera ◽  
Solid Modeling ◽  
Vector Model ◽  
Planar Part

By using a computer vision detection system to obtain high resolution images of a machine part, a kind of reverse design method of solid modeling of irregular planar part with aided implementation of computer vision was proposed in this paper, which integrates image processing function of Matlab software with solid modeling function of computer aided design (CAD) software. The method used a calibrated digital camera to get the image of the tested part, a three-dimensional entity vector model may be built up after image inversion, edge detection, vectorization process of binary image and size matching were operated sequentially. The results of image reverse design showed that it is an easy and convenient way to reverse irregular planar parts based on image processing. One of its remarkable advantages is the saving of design period and the reduction of design cost. Its measurement error can be controlled within 0.1 mm, and can meet general precision requirement of application occasions. Reversed parts may provide a model basis for further analysis on mechanism assembling and motion simulation.

scholarly journals A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1337
Author(s):  
Kai Yit Kok ◽  
Parvathy Rajendran
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Performance Evaluation ◽  
Visual Tracking ◽  
Computational Cost ◽  
Performance Comparison ◽  
Feature Detector ◽  
Minimum Eigenvalue ◽  
Speeded Up Robust Features ◽  
A Performance

Despite years of work, a robust, widely applicable generic “symmetry detector” that can paral-lel other kinds of computer vision/image processing tools for the more basic structural charac-teristics, such as a “edge” or “corner” detector, remains a computational challenge. A new symmetry feature detector with a descriptor is proposed in this paper, namely the Simple Robust Features (SRF) algorithm. A performance comparison is made among SRF with SRF, Speeded-up Robust Features (SURF) with SURF, Maximally Stable Extremal Regions (MSER) with SURF, Harris with Fast Retina Keypoint (FREAK), Minimum Eigenvalue with FREAK, Features from Accelerated Segment Test (FAST) with FREAK, and Binary Robust Invariant Scalable Keypoints (BRISK) with FREAK. A visual tracking dataset is used in this performance evaluation in terms of accuracy and computational cost. The results have shown that combining the SRF detector with the SRF descriptor is preferable, as it has on average the highest accuracy. Additionally, the computational cost of SRF with SRF is much lower than the others.

The Real Time System of Hair Detection in Mushroom Based on Computer Vision

2012 ◽  
Vol 588-589 ◽  
pp. 1199-1203
Author(s):  
Tong Qiang Li ◽  
Cai Feng Zheng ◽  
Jian Peng Gan
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Real Time ◽  
Detection System ◽  
Contour Detection ◽  
Time System ◽  
Real Time System ◽  
Extraction Algorithm ◽  
Structure Extraction ◽  
Line Direction

By analysing the Mushroom image, the paper puts forward a kind of line-structure extraction algorithm combination of local gray value and continuity of line direction .After the operations in many aspects of basis image processing, such as gray-scale, denoising , segmentation, contour detection and morphological, this article has developed a set of hair detection system based on computer vision for the Mushroom. The experimental results show this system could well meet the actual needs, and has a broad market prospect.

scholarly journals Fruit Quality Detection and Gradation

2021 ◽  
pp. 383-388
Author(s):  
Harshal S. Deshmukh ◽  
Dr. S. W. Mohod ◽  
Dr. N. N. Khalsa
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Support Vector ◽  
Color Classification ◽  
Image Processing Techniques ◽  
Background Removal ◽  
Quality Detection ◽  
Analysis Methods ◽  
Processing Techniques

Grading and classification of fruits is based on observations and through experiences. The system exerts image- processing techniques for classification and grading the quality of fruits. Two-dimensional fruit images are classified on shape and color-based analysis methods. However, different fruit images have different or same color and shape values. Hence, using color or shape analysis methods are still not that much effective enough to identify and distinguish fruits images. Therefore, computer vision and image processing techniques have been found increasingly useful in the food industry, especially for applications in quality detection. Research in this area indicates the feasibility of using computer vision systems to improve product quality, the use of computer vision for the inspection of food has increased during recent years. This proposed work presents food quality detection system. The system design considers some feature that includes fruit colors and size, which increases accuracy for detection of roots pixels. Histogram of oriented gradients is used for background removal, for color classification, support vector machine is used.

scholarly journals Driver Drowsiness Detection using MATLAB

2019 ◽  
Vol 8 (2) ◽  
pp. 843-844
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Binary Image ◽  
Detection Function ◽  
Image Processing Techniques ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Face Features ◽  
Processing Techniques

Drowsiness is major cause of accidents. So, this drowsiness detection system alerts the drowsy drivers in order to reduce the risk of potential accidents. The proposed system uses computer vision and image processing technology of MATLAB for detecting the drowsiness. MATLAB detects if eyes are closed or open using various image processing techniques performed using Viola-Jones face features detecting algorithm and skin y,cb,cr values detection function ,converting image into a binary image which was further employed to extract eye characteristics, and its closing frequency, determining drowsiness.

scholarly journals Digital Image Falsification Detection System for Effective Data Communication

2019 ◽  
Vol 9 (2) ◽  
pp. 4081-4086
Keyword(s):  
Digital Image ◽  
Detection System ◽  
Detection Algorithm ◽  
Feature Points ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Extraction Algorithm ◽  
Region Extraction ◽  
Block Based ◽  
Scale Invariant Feature

In this proposed system a digital imagefalsification can be identified using the combination of both adaptive over block based segmentation, feature keypointbased feature extraction algorithms(Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF)) and forgery region extraction algorithm. The proposed falsification detection algorithm comprises both block based falsification detection algorithm (adaptive over block based segmentation and block feature matching algorithm) and the keypoint based falsification detection algorithm(forgery region extraction algorithm). Adaptive over block based Segmentation algorithm adaptively segments the input digital image into separate(non overlapped) blocks in irregular manner. Scale Invariant Feature Transform (SIFT) algorithm and Speeded Up Robust Features (SURF) algorithms are used to draw out features from the segmentedblocks as a block features. Then the extracted features are matched with the feature points of other segmented block. If the feature key points are matched with any other feature point presents in the segmented blocks, then the matched feature points are marked as Labeled key Points (LKP), which can be doubted as a forged regions. Finally, the Forgery Region Extraction algorithm can be used to detect the forged region from the input digital image based on the extracted labeled feature points. The experimental outcomesdisplay that the novelfalsification detection system can accomplished the requirements compared with the existing digital imagefalsification detection methods

scholarly journals An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates

2019 ◽  
Vol 9 (20) ◽  
pp. 4222 ◽  
Author(s):  
Yang Liu ◽  
Ke Xu ◽  
Jinwu Xu
Keyword(s):  
Real Time ◽  
Surface Defects ◽  
Recognition Accuracy ◽  
Detection System ◽  
Detection Algorithm ◽  
Steel Plates ◽  
Detection Accuracy ◽  
Scale Invariant ◽  
Speeded Up Robust Features ◽  
Real Time Detection

The detection of surface defects is very important for the quality improvement of steel plates. In actual production, as the steel plate production line runs faster, the steel surface defect detection algorithm is required to meet the requirements of real-time detection (less than 100 ms/image), and the detection accuracy is improved (at least 90%). In this paper, an improved multi-block local binary pattern (LBP) algorithm is proposed. This algorithm not only has the simplicity and efficiency of the LBP algorithm, but also finds a suitable scale to describe the defect features by changing the block sizes, thus ensuring high recognition accuracy. The experiment proves that the method satisfies the requirements of online real-time detection in terms of speed (63 ms/image), and surpasses the widely-used scale invariant feature transform (SIFT), speeded up robust features (SURF), gray-level co-occurrence matrix (GLCM), and LBP algorithms in recognition accuracy (94.30%), which prove that the MB-LBP has practical application value in an online real-time detection system.

Key Algorithms of Video Target Detection and Recognition in Intelligent Transportation Systems

2019 ◽  
Vol 34 (09) ◽  
pp. 2055016
Author(s):  
Qingchao Pan ◽  
Haohua Zhang
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Intelligent Transportation Systems ◽  
Detection System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Video Image ◽  
Practical Significance ◽  
Vehicle Classification ◽  
Detection And Recognition

With the popularization of video detection and recognition systems and the advancement of video image processing technology, the application research of intelligent transportation systems based on computer vision technology has received more and more attention. It comprehensively utilizes image processing, pattern recognition, artificial intelligence and other technologies. It also involves processing and analyzing the video image sequence collected by the detection system, intelligently understanding the video content and making processing, and dealing with various problems such as accident information judgment, pedestrian and vehicle classification, traffic flow parameter detection, and moving target tracking. It promotes intelligent transportation systems to be more intelligent and practical, and provides comprehensive, real-time traffic status information for traffic management and control. Therefore, the research on the method of traffic information detection based on computer vision has important theoretical and practical significance. The detection and recognition of video targets is an important research direction in the field of intelligent transportation and computer vision. However, due to the background complexity, illumination changes, target occlusion and other factors in the detection and recognition environment, the application still faces many difficulties, and the robustness and accuracy of detection and recognition need to be further improved. In this paper, several key problems in video object detection and recognition are studied, including accurate segmentation of target and background, shadow in complex scenes; accurate classification of extracted foreground targets; and target recognition in complex background. In response to these problems, this paper proposes a corresponding solution.

scholarly journals Face Mask Detection System

2021 ◽  
Vol 9 (11) ◽  
pp. 639-642
Author(s):  
Prof. A. T. Sonwane
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Detection System ◽  
Face Mask ◽  
Public Places ◽  
Protection Method ◽  
The World ◽  
Public Areas

Abstract: There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. Coronavirus disease 2019 has affected the world seriously. One major protection method for people is to wear masks in public areas. The risk of transmission is highest in public places. However, there are only a few research studies about face mask detection based on image analysis. This paper aims to present a review of various methods and algorithms used for human recognition with a face mask. The proposed system to classify face mask detection using COVID-19 precaution both in images and videos using convolution neural network, TensorFlow and OpenCV to detect face masks on people. This system has various applications at public places, schools, etc. where people need to be detected with the presence of a face mask and recognize them and help society. Keywords: COVID-19, Tensorflow, OpenCV, Face Mask, Image Processing, Computer Vision

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