scholarly journals Design and sorting of an object identification on machine vision by using line scan camera

2021 ◽  
Vol 3 (3) ◽  
pp. 100-112
Author(s):  
Zeravan M. Mosa ◽  
Erhan Akin
Keyword(s):  
Machine Vision ◽  
Moving Objects ◽  
Conveyor Belt ◽  
Object Identification ◽  
Line Scan ◽  
Geometric Patterns ◽  
Object Based ◽  
Physical Attributes ◽  
Pass Through ◽  
Good Object

This paper illustrates the design of a system to identify objects on a conveyor belt using machine vision. In the present study, a machine vision based on one line scan sorting was developed, the purpose being to sort objects based on various stages of maturity. Many different methods are available for object identification. But we made design a system that separates and counting them. Different objects placed on the conveyor belt moves along, a camera placed above the belt takes real-time video and feeds it to the MATLAB software for processing the object to compare with the basic template object. The vision camera understands an object based on its physical attributes, such as shape and size for effectively controlling the hardware, which will use in this work. Besides, the number of objects of a particular section that cross the conveyor to demonstrate the identification of moving objects is counted and displayed. A low-speed conveyor belt is manufactured with various test objects that pass through it. For identifying a good object, the wavelength data is used, determining the way to match the geometric patterns and to identify the dimensions, and edge detection is applied. The ability to count specific attributes objects is testing different test paths. The sorting of objects using machine vision was performed using an algorithm of pattern matching of machine vision. A pattern image template was built and stored in a computer's memory. When the object is sorting the application run, the camera receives the image of the object into MATLAB. The vision application investigates the image and transfers it to the classifier if the received image matches the model image or not matches.

scholarly journals Object-Based Approach for Adaptive Source Coding of Surveillance Video

2019 ◽  
Vol 9 (10) ◽  
pp. 2003 ◽  
Author(s):  
Tung-Ming Pan ◽  
Kuo-Chin Fan ◽  
Yuan-Kai Wang
Keyword(s):  
Linear Model ◽  
Video Transmission ◽  
Moving Objects ◽  
Source Coding ◽  
Video Quality ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Object Based ◽  
High Recognition Accuracy ◽  
Object Dynamics

Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects.

High-speed high-resolution color line scan camera for machine vision

1993 ◽  
Author(s):  
David J. Litwiller ◽  
Mike Miethig ◽  
Brian C. Doody ◽  
P. Tom Jenkins
Keyword(s):  
High Resolution ◽  
Machine Vision ◽  
High Speed ◽  
Color Line ◽  
Line Scan

scholarly journals Segmentation of Database Images Using Gradient Based Multiscale Morphological Reconstruction

2015 ◽  
pp. 271-278
Author(s):  
A. Nithya ◽  
R. Kayalvizhi
Keyword(s):  
Large Scale ◽  
Object Segmentation ◽  
Object Identification ◽  
Segmentation Algorithm ◽  
Phase Object ◽  
Morphological Reconstruction ◽  
Object Based ◽  
Gradient Based ◽  
Object Region ◽  
Identification Phase

The main purpose of this research is to improve the accuracy of object segmentation in database images by constructing an object segmentation algorithm. Image segmentation is a crucial step in the field of image processing and pattern recognition. Segmentation allows the identification of structures in an image which can be utilized for further processing. Both region-based and object-based segmentation are utilized for large-scale database images in a robust and principled manner. Gradient based MultiScalE Graylevel mOrphological recoNstructions (G-SEGON) is used for segmenting an image. SEGON roughly identifies the background and object regions in the image. This proposed method comprises of four phases namely pre-processing phase, object identification phase, object region segmentation phase, majority selection and refinement phase. After developing the grey level mesh the resultant image is converted into gradient and K-means clustering segmentation algorithm is used to segment the object from the gradient image. After implementation the accuracy of the proposed G-SEGON technique is compared with the existing method to prove its efficiency.

Inhibitory Tagging on Randomly Moving Objects

2002 ◽  
Vol 13 (2) ◽  
pp. 125-129 ◽  
Author(s):  
Hirokazu Ogawa ◽  
Yuji Takeda ◽  
Akihiro Yagi
Keyword(s):  
Moving Objects ◽  
Inhibitory Effect ◽  
Detection Task ◽  
Moving Target ◽  
Search Performance ◽  
Probe Detection ◽  
Focal Attention ◽  
Object Based ◽  
Tagging Effect

Inhibitory tagging is a process that prevents focal attention from revisiting previously checked items in inefficient searches, facilitating search performance. Recent studies suggested that inhibitory tagging is object rather than location based, but it was unclear whether inhibitory tagging operates on moving objects. The present study investigated the tagging effect on moving objects. Participants were asked to search for a moving target among randomly and independently moving distractors. After either efficient or inefficient search, participants performed a probe detection task that measured the inhibitory effect on search items. The inhibitory effect on distractors was observed only after inefficient searches. The present results support the concept of object-based inhibitory tagging.

Object-Based Surveillance Video Synopsis Using Genetic Algorithm

2017 ◽  
pp. 193-219
Author(s):  
Shefali Gandhi ◽  
Tushar V. Ratanpara
Keyword(s):  
Genetic Algorithm ◽  
Background Subtraction ◽  
Moving Objects ◽  
Time Lapse ◽  
Original Video ◽  
Surveillance Video ◽  
Time Activity ◽  
Video Synopsis ◽  
Object Based ◽  
Poisson Image Editing

Video synopsis provides representation of the long surveillance video, while preserving the essential activities of the original video. The activity in the original video is covered into a shorter period by simultaneously displaying multiple activities, which originally occurred at different time segments. As activities are to be displayed in different time segments than original video, the process begins with extracting moving objects. Temporal median algorithm is used to model background and foreground objects are detected using background subtraction method. Each moving object is represented as a space-time activity tube in the video. The concept of genetic algorithm is used for optimized temporal shifting of activity tubes. The temporal arrangement of tubes which results in minimum collision and maintains chronological order of events is considered as the best solution. The time-lapse background video is generated next, which is used as background for the synopsis video. Finally, the activity tubes are stitched on the time-lapse background video using Poisson image editing.

Moving Object Classification in a Video Sequence

2017 ◽  
pp. 70-101
Author(s):  
S. Vasavi ◽  
T. Naga Jyothi ◽  
V. Srinivasa Rao
Keyword(s):  
Wavelet Transforms ◽  
Moving Objects ◽  
Performance Test ◽  
Texture Features ◽  
Classification Performance ◽  
Object Identification ◽  
Svm Classifier ◽  
Classification Methods ◽  
Military Installations ◽  
Outdoor Environments

Now-a-day's monitoring objects in a video is a major issue in areas such as airports, banks, military installations. Object identification and recognition are the two important tasks in such areas. These require scanning the entire video which is a time consuming process and hence requires a Robust method to detect and classify the objects. Outdoor environments are more challenging because of occlusion and large distance between camera and moving objects. Existing classification methods have proven to have set of limitations under different conditions. In the proposed system, video is divided into frames and Color features using RGB, HSV histograms, Structure features using HoG, DHoG, Harris, Prewitt, LoG operators and Texture features using LBP, Fourier and Wavelet transforms are extracted. Additionally BoV is used for improving the classification performance. Test results proved that SVM classifier works better compared to Bagging, Boosting, J48 classifiers and works well in outdoor environments.

Image Identification of a Moving Object Based on an Improved Canny Edge Detection Algorithm

2018 ◽  
Author(s):  
Yang Liu ◽  
Lingyu Sun ◽  
Lijun Li ◽  
Yiben Zhang ◽  
Zongmiao Dai ◽  
...  
Keyword(s):  
Edge Detection ◽  
Moving Objects ◽  
Median Filter ◽  
Critical Role ◽  
Detection Algorithm ◽  
Object Identification ◽  
Moving Object ◽  
Detection Accuracy ◽  
Image Process ◽  
Canny Operator

Edge detection plays an increasingly critical role in image process community, especially for moving object identification problems. For this case, the target object can be captured straightly via the edges beside which there is an obvious jump of grey value or texture. Nowadays, Canny operator has gained great popularity as it shows higher anti-noise performance and presents better detection accuracy in comparison with other edge detection operators like Robert’s, Sobel’s, Prewitt’s etc. However, the Gaussian filter associated with the classic Canny operator is sometimes too simple to decrease the all-type-noise. Additionally, in order to enhance the detection accuracy and lower the pseudo-edges detection ratio, two thresholds, high and low, are chosen artificially which have actually limited the adaptability of the algorithm. In this work, a compound filter, Gaussian-Median filter, is proposed to improve the smoothing effect. The self-adaptive multi-threshold Otsu algorithm is realized to determine the high/low threshold automatically according to the grey value statistic. Image moment method is conducted on basis of the detected moving object edges to locate the centroid and to compute the principal orientation. The experimental results based upon locating the edges of both static and moving objects proved the good robustness and the excellent accuracy of the proposed method.

scholarly journals Compressive Imaging of Moving Object Based on Linear Array Sensor

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Changjun Zha ◽  
Yao Li ◽  
Jinyao Gui ◽  
Huimin Duan ◽  
Tailong Xu
Keyword(s):  
Moving Objects ◽  
Linear Array ◽  
Matching Pursuit ◽  
Moving Object ◽  
Imaging Method ◽  
Compressive Imaging ◽  
Array Sensor ◽  
Object Based ◽  
Direct Separation ◽  
Matching Pursuit Algorithm

Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.

Sign in / Sign up

Export Citation Format

Share Document