Partial Pose Estimation Of Rigid Object System Using Cad Database

Partial pose estimation identification is required for inspection in manufacturing industry. By knowing the partial pose estimation before inspection, overall inspection processing time can be reduced. System development for partial pose estimation identification consists of a few main parts which are image acquisition, pre-processing, processing and camera calibration. Image acquisition is divided into CAD image acquisition and Projection Real Image (PRI) acquisition. Image pre-processing consists of image rescaling, image segmentation and image registration. In image segmentation, high level feature of Outer Box object segmentation method was proposed. In pre-processing development section, the development of CAD model database imitates inspection environment was implemented. The object was represented by the area combined with the edge information of the object. Within this shape representation, partial pose estimation was identified by linking the CAD model database to the inspected object. A few techniques were suggested in pre-processing stage which included 1D Fourier Descriptor, Euclidean Distance, 2D Fourier Descriptor Subspace Matrix and template matching. Partial pose estimation identification using template matching method showed a high performance result. The tested objects were automotive component, bottom automotive component, Arduino board, computer mouse, labelled object and USB connector. Study on template matching was preceded for 360 images CAD model for partial pose estimation identification of ±1 degree accuracy. Ten tests of partial pose estimation were carried out. All the testsshowed the right identification with score 10/10. Average processing time consumption in this ±1 degree accuracy was 1032.6s for automotive component, 997.7s for bottom automotive component, 948.5s for Arduino board, 1198.7s for USB connector and 972.1s for labelled automotive component. For automotive component, partial pose estimation identification linked to random CAD model database gave 974.0s average processing time for ten trials. Then, partial pose estimation identification linked to various CAD models data was studied. Three tests for every object were carried out and gained 3/3 score for automotive component, 3/3 score for bottom automotive component, 3/3 score for Arduino board and 3/3 score for USB connector. After that, study on surface inspection was carried out. Stereo image registration through centre hypotenuse length of outer box method was implemented. The similarity of both CAD stereo image registration and real object stereo image registration resulted in the range of 81.9% to 91.8%

A Study of 3D CAD Model and Feature Analysis for Casting Object

When dealing with computer vision inspection testing parts in production line, the appearance of noise such as dust and inconsistent light distribution should be consider for further analysis on the parts image. In this paper, shape representation model using feature vector and Fourier descriptor were presented on the 3D CAD model image with the aim to gain the shape feature analysis for casting object. By adding light and salt & pepper noise on the CAD model image, the predicted database was compared to its original CAD image. In feature vector method, calculation on its Similarity, Correlation, Matching black and white points was carried out. Results observation show similarity of feature vector method performs 68% accuracy for light noise appearance, while correlation method performs 98% accuracy on disturbance of salt & pepper noise. Fourier Descriptor used to present the pose estimation of images on CCW and CW direction. Result shows matching sets similarity is value high since the dissimilarity value keeps below 0.3 and achieve few similar points in certain position. Thus, it is sufficient for casting object by implementing feature vector method which were very useful in analyze the noise on the image while pose estimation position described by Fourier Descriptor function

A systematic literature review on vision-based hand gesture for sign language translation

Deaf and hard of hearing people use sign language to communicate. People around mute and deaf people have difficulty communicating with each other if they do not understand sign language. This problem has prompted many researchers to conduct studies on sign language translation. However, there is a lack of compilation of SLR on this topic. Therefore, this paper aims to provide a thorough literature review of previous studies on sign language to text translation based on the vision method. PRISMA (Preferred Reporting Items to writing a standard Systematic Review and Meta-Analyses) is used in this systematic review. Two primary databases, Web of Science and Scopus, have been used to search for relevant articles and resources included in this systematic literature review. Based on the outcome of the systematic review of the topic, the primary studies on sign language translation systems were conducted using self-generated datasets more than public datasets. More static action sign language was studied compared to dynamic action sign language. For the type of recognition, more alphabet sign language was studied compared to digit, word, or sentence sign language. Other than that, most studies used digital cameras rather than Microsoft Kinect or a webcam. The most used classification method was Convolution Neural Network (CNN). The study is intended to guide readers and researchers for future research and knowledge enhancement in the field of sign language recognition

Comparison of activated carbon and zeolites' filtering efficiency in freshwater

Water quality plays an important role in ensuring the healthy growth of aquatic living. Fish in ponds release nitrogen, phosphorus, ammonia, nitrates and organic waste. The presence of these chemicals in water leads to an increment in the pH level of the pond water, which could affect the growth of the fish. Therefore, it is vital to have an effective filter system for pond water to remove the unwanted waste. At current literature, the filtering effectiveness of activated carbon and zeolite in fresh water pond systems are remained inexplicit. In this work, a lab-scale filtering system was set up to study the ammonia removal efficiency of activated carbon and zeolite. The efficiency of these two types of filtering media was first studied separately and then combined as a hybrid filter with a weight ratio of 1:1. The effectiveness of the single filter media and hybrid media filter were evaluated based on the measurement of the concentration of ammonia, nitrite and nitrate, and the pH level. It was found that the hybrid filter - a combination of activated carbon and zeolite - tended to have higher efficiency in maintaining good water quality compared to the single media system; the ammonia level was reduced from 4mg/L to 1mg/L in 2.5 days. The greater the amount of the hybrid filter media presents in the system, the better the conversion rate of ammonia to nitrate. A combination of activated carbon and zeolite (150g each) was found to be able to maintain suitable water quality (1mg/L ammonia, 0mg/L nitrite, <50mg/L nitrate, 6.8-8.0pH) for fish