scholarly journals Improvement in Purity of Healthy Tomato Seeds Using an Image-Based One-Class Classification Method

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2690 ◽  
Author(s):  
Jannat Yasmin ◽  
Santosh Lohumi ◽  
Mohammed Raju Ahmed ◽  
Lalit Mohan Kandpal ◽  
Mohammad Akbar Faqeerzada ◽  
...  
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Light Emitting Diode ◽  
Germination Rate ◽  
Classification Method ◽  
Tomato Seeds ◽  
Significant Difference ◽  
Foreign Materials ◽  
The One ◽  
One Class Classification

The feasibility of a color machine vision technique with the one-class classification method was investigated for the quality assessment of tomato seeds. The health of seeds is an important quality factor that affects their germination rate, which may be affected by seed contamination. Hence, segregation of healthy seeds from diseased and infected seeds, along with foreign materials and broken seeds, is important to improve the final yield. In this study, a custom-built machine vision system containing a color camera with a white light emitting diode (LED) light source was adopted for image acquisition. The one-class classification method was used to identify healthy seeds after extracting the features of the samples. A significant difference was observed between the features of healthy and infected seeds, and foreign materials, implying a certain threshold. The results indicated that tomato seeds can be classified with an accuracy exceeding 97%. The infected tomato seeds indicated a lower germination rate (<10%) compared to healthy seeds, as confirmed by the organic growing media germination test. Thus, identification through image analysis and rapid measurement were observed as useful in discriminating between the quality of tomato seeds in real time.

scholarly journals Detection of Foreign Materials in Wheat Kernels using Boundary Descriptors

2020 ◽  
Vol 9 (6) ◽  
pp. 1001-1009
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Recognition Task ◽  
Machine Vision System ◽  
On Line ◽  
Neural Classifier ◽  
Foreign Materials ◽  
Impurity Elements ◽  
Wheat Kernels ◽  
Regional Boundary

The present paper reports the development of a machine vision system for quality inspection of wheat using kernel shape attribute. Shape attribute of agricultural products including wheat kernels is extremely difficult to quantify in digital computation. A new method is proposed in the present work to quantify shape attribute of wheat kernels using regional boundary descriptors. Recognition task in the proposed machine vision system is carried out by neural classifier trained with Levenberg-Marquardt (LM) based supervised learning. Proposed neural classifier was executed using feed-forward backpropagation based three layer artificial neural network. Experimental results indicate more than 98.1% overall average classification accuracy for the involved wheat and impurity elements in the present work. The results of present study are quite promising and the proposed machine vision system has potential future for on-line inspection of agriculture produce in real time environment.

scholarly journals Triple-VAE: A Triple Variational Autoencoder to Represent Events in One-Class Event Detection

2021 ◽  
Author(s):  
Marcos P. S. Gôlo ◽  
Rafael G. Rossi ◽  
Ricardo M. Marcacini
Keyword(s):  
Real World ◽  
Event Detection ◽  
Representation Learning ◽  
The Other ◽  
Specific Time ◽  
Variational Autoencoder ◽  
Significant Difference ◽  
The One ◽  
One Class Classification ◽  
Event Representations

Events are phenomena that occur at a specific time and place. Its detection can bring benefits to society since it is possible to extract knowledge from these events. Event detection is a multimodal task since these events have textual, geographical, and temporal components. Most multimodal research in the literature uses the concatenation of the components to represent the events. These approaches use multi-class or binary learning to detect events of interest which intensifies the user's labeling effort, in which the user should label event classes even if there is no interest in detecting them. In this paper, we present the Triple-VAE approach that learns a unified representation from textual, spatial, and density modalities through a variational autoencoder, one of the state-ofthe-art in representation learning. Our proposed Triple-VAE obtains suitable event representations for one-class classification, where users provide labels only for events of interest, thereby reducing the labeling effort. We carried out an experimental evaluation with ten real-world event datasets, four multimodal representation methods, and five evaluation metrics. Triple-VAE outperforms and presents a statistically significant difference considering the other three representation methods in all datasets. Therefore, Triple-VAE proved to be promising to represent the events in the one-class event detection scenario.

scholarly journals Machine vision based in-process light-emitting diode chip mounting system

2018 ◽  
Vol 51 (7-8) ◽  
pp. 293-303 ◽  
Author(s):  
Chao-Ching Ho ◽  
You-Min Chen ◽  
Po-Chieh Li
Keyword(s):  
Machine Vision ◽  
Printed Circuit Board ◽  
Vision System ◽  
Light Emitting Diode ◽  
Circuit Board ◽  
Robot Arm ◽  
Light Emitting ◽  
Printed Circuit ◽  
Delta Robot ◽  
A Charge

Background: In this study, a machine vision–based method was developed for automated in-process light-emitting diode chip mounting lines with position uncertainty. In order to place the tiny light-emitting diode chips on the pattern of a printed circuit board, a highly accurate mounting process is achieved with online feedback of the visual assistance. Methods: The system consists of a charge-coupled device camera, a six-axis robot arm, and a delta robot. The lighting system is a critical point for the in-process machine vision problem. Hence, designing the optimal lighting solution is one of the most difficult parts of a machine vision system, and several lighting techniques and experiments are examined in this study. In order to commence the mounting process, the light-emitting diode chip targets inside the camera field were identified and used to guide the delta robot to the grabbing zone based on the calibrated homography transformation. Efforts have been focused on the field of machine vision–based feature extraction of the chip pins and the holes on the printed circuit board. The correspondence of each other is determined by the position of the chip pins and the printed circuit board circuit pattern. The image acquisition is achieved directly online in real time. The image analysis algorithm must be sufficiently fast to follow the production rate. In order to compensate for the uncertainty of the light-emitting diode chip mounting process, a visual feedback strategy in conjunction with an uncertainty compensation strategy is employed. Results: Finally, the light-emitting diode chip was automatically grabbed and accurately placed at the desired positions. Conclusion: On-line and off-line experiments were conducted to investigate the performance of the vision system with respect to detecting and mounting light-emitting diode chips.

scholarly journals Detection of Foreign Materials in Wheat Kernels Using Regional Colour Descriptors

2021 ◽  
Author(s):  
Neeraj Julka ◽  
◽  
Singh A. P ◽  
Keyword(s):  
Machine Vision ◽  
Learning Algorithm ◽  
Vision System ◽  
Recognition Rate ◽  
Quality Inspection ◽  
Wheat Kernel ◽  
New Type ◽  
Neural Classifier ◽  
Foreign Materials ◽  
Wheat Kernels

Present paper reports the development of an automated machine vision system for detection of foreign materials in wheat kernels using regional color descriptors. The said system was executed in the form of an integrated flowing pipeline after having proper choice of different possible alternatives at different stages of image processing. A new type of surface colour descriptor is also proposed in this work to define wheat kernel uniquely. The fifteen-element colour descriptor is executed after having thorough comparison of six different colour spaces, each having 72 separate quantifiable components. The fifteen elements of the proposed colour-descriptor, extracted from each segmented region of the sample image, are concatenated in the form of an input to the neural classifier. The neural classifier is trained with Levenberg-Marquardt (LM) learning algorithm to achieve extremely fast convergence. The recognition rate of the executed classifier is found to be more than 99.2% for detection of impurity in unconnected wheat kernels. The results of present investigations are quite promising. The proposed pipeline has potential future in the field of machine vision based quality inspection of wheat and other cereal grains.

scholarly journals Detection of Foreign Materials in Wheat Kernels using Regional Texture Descriptors

2019 ◽  
Vol 8 (4) ◽  
pp. 9321-9328
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Detection Task ◽  
Machine Vision System ◽  
Efficient Machine ◽  
Neural Classifier ◽  
Two Phases ◽  
Foreign Materials ◽  
The Given ◽  
Wheat Kernels

The present paper reports the development of an efficient machine vision system for automatic detection of foreign materials in wheat kernels using regional texture descriptors. In this system, the detection task is performed in two phases. These phases include features extraction phase followed by classification phase. New surface texture descriptors of wheat kernels are developed using Non-Shannon entropies in this work. These entropies are defined using intensity histograms of wheat and non-wheat regions in the given image. Such an attempt has not been made earlier. Experimental results on a database of about 2635 wheat and non-wheat components from 63 images confirm the effectiveness of the proposed method. The classification task is performed by the neural classifier in the proposed machine vision system. An accuracy of more than 98.5% is achieved using proposed system. However, the results of present investigations are quite promising.

Design of Improved and Semi-Automated Red Raspberry Cane Bundling and Taping Machine Based on Field Evaluation

2019 ◽  
Vol 62 (3) ◽  
pp. 821-829
Author(s):  
Kapil Khanal ◽  
Santosh Bhusal ◽  
Manoj Karkee ◽  
Patrick Scharf ◽  
Qin Zhang
Keyword(s):  
Tensile Strength ◽  
Machine Vision ◽  
Success Rate ◽  
Vision System ◽  
Adhesive Tape ◽  
Type I ◽  
Total Production ◽  
Type Ii ◽  
Red Raspberry ◽  
Significant Difference

Abstract. Canopy management activities such as bundling, tying, and pruning are labor-intensive operations in red raspberry production. These activities are responsible for about 50% of the total production cost, the majority of which is spent on bundling and tying canes. In a previous study, a cane bundling and tying mechanism was developed to bundle red raspberry primocanes and wrap adhesive tape around them in an experimental red raspberry plot, with an overall success rate of 83%. The prototype was fabricated primarily for functionality evaluation, which was limited by size and precision issues. Based on the findings from that study with the first prototype, an improved mechanism with novel components was designed and fabricated to increase the accuracy and efficiency of operation in the field. The bundling and taping process was automated using a machine vision system for cane detection and localization. The time required for the mechanism to complete the bundling and taping process was decreased from 3 min to 30 s. The improved system achieved an overall success rate of 90% when tested in a red raspberry plot. The durability of two types of adhesive tape (used in the taping mechanism) in holding the bundled canes together was also evaluated. Forty-five red raspberry plants were randomly selected and tied with type I tape (tensile strength of 316 N m-1) and type II tape (tensile strength of 27 N m-1). The bundles were evaluated six months after tying. The failure of the tape to keep the cane bundles intact was significantly lower with type I tape (higher adhesive and tensile strength; two failures out of 39 tapings) compared to type II tape (seven failures out of 39 tapings). For yield comparison, manual tying was performed with baling twine (~5 mm diameter) on another 45 randomly selected plants. The mean fruit yield between machine taped and manually tied raspberry plants showed no significant difference (two-sample t-test, df = 88, p = 0.67) at 5% significance. All these results showed good potential for the development of a high-speed automated red raspberry cane bundling and taping mechanism. Keywords: Agricultural machinery, Automation, Cane bundling, Cane tying, Machine vision.

F.VIII R:Ag in Hepatitis

1979 ◽  
Author(s):  
R. Kotitschke ◽  
J. Scharrer
Keyword(s):  
Chronic Hepatitis ◽  
Blood Donors ◽  
Blood Donation ◽  
Normal Population ◽  
Rabbit Antiserum ◽  
Two Dimensional ◽  
Plastic Plate ◽  
Significant Difference ◽  
Acute Hepatitis B ◽  
The One

F.VIII R:Ag was determined by quantitative immunelectrophoresis (I.E.) with a prefabricated system. The prefabricated system consists of a monospecific f.VIII rabbit antiserum in agarose on a plastic plate for the one and two dimensional immunelectrophoresis. The lognormal distribution of the f.VIII R:Ag concentration in the normal population was confirmed (for n=70 the f.VIII R:Ag in % of normal is = 95.4 ± 31.9). Among the normal population there was no significant difference between blood donors (one blood donation in 8 weeks; for n=43 the f.VIII R:Ag in % of normal is = 95.9 ± 34.0) and non blood donors (n=27;f.VIII R:Ag = 94.6 ± 28.4 %). The f.VIII R:Ag concentration in acute hepatitis B ranged from normal to raised values (for n=10, a factor of 1.8 times of normal was found) and was normal again after health recovery (n=10, the factor was 1.0). in chronic hepatitis the f.VIII R:Ag concentration was raised in the majority of the cases (for n=10, the factor was 3.8). Out of 22 carrier sera 20 showed reduced, 2 elevated levels of the f.VIII R:Ag concentration. in 5 sera no f.VIII R:Ag could be demonstrated. The f.VIII R:Ag concentration was normal for n=10, reduced for n=20 and elevated for n=6 in non A-non B hepatitis (n=36). Contrary to results found in the literature no difference in the electrophoretic mobility of the f.VIII R:Ag was found between hepatitis patients sera and normal sera.

IMMUNOLOGICAL REACTIVITY OF INSULIN IN HUMAN SERUM

1966 ◽  
Vol 53 (4) ◽  
pp. 673-680 ◽  
Author(s):  
Torsten Deckert ◽  
Kai R. Jorgensen
Keyword(s):  
Normal Subjects ◽  
The Other ◽  
Human Pancreas ◽  
Porcine Pancreas ◽  
Crystalline Insulin ◽  
Endogenous Insulin ◽  
Significant Difference ◽  
Human Sera ◽  
Normal Human ◽  
The One

ABSTRACT The purpose of this study was to investigate whether a difference could be demonstrated between crystalline insulin extracted from normal human pancreas, and crystalline insulin extracted from bovine and porcine pancreas. Using Hales & Randle's (1963) immunoassay no immunological differences could be demonstrated between human and pig insulin. On the other hand, a significant difference was found, between pig and ox insulin. An attempt was also made to determine whether an immunological difference could be demonstrated between crystalline pig insulin and crystalline human insulin from non diabetic subjects on the one hand and endogenous, circulating insulin from normal subjects, obese subjects and diabetic subjects on the other. No such difference was found. From these experiments it is concluded that endogenous insulin in normal, obese and diabetic human sera is immunologically identical with human, crystalline insulin from non diabetic subjects and crystalline pig insulin.

Multifunctional stereoscopic machine vision system with multiple 3D outputs

2020 ◽  
Keyword(s):  
Machine Vision ◽  
International Symposium ◽  
Fast Track ◽  
Vision System ◽  
Machine Vision System ◽  
Stereoscopic Displays ◽  
Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.

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