Image Processing Techniques to Analyse a 3D Model

2015 ◽  
Vol 787 ◽  
pp. 922-926
Author(s):  
Mirji Sairaj Gururaj ◽  
A Arockia Selvakumar
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Three Dimensional ◽  
Human Perception ◽  
Spur Gear ◽  
Recognition System ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Three Dimensional Model ◽  
Detection Techniques

The Image processing technique incorporates human perception and intelligence which makes this field more interesting to the research community. The edge detection process is the most important step in image recognition system. In this paper a simple three dimensional model is created by taking the best edge detected image followed by comparison with various edge detection techniques using Labview software. HereCatia model of spur gear isdrawn by observing and analysing best suited edge detected image in order to make the design more precise in edges and geometry and also to make object recognition simple and further scope is given to design a Catia model using dimensional parameters with the help of vision assistant tool.

Analysis of Insole Geometry and Deformity by Using a Three-Dimensional Image Processing Technique: A Preliminary Study

2019 ◽  
Vol 109 (2) ◽  
pp. 98-107
Author(s):  
Kit-lun Yick ◽  
Wai-ting Lo ◽  
Sun-pui Ng ◽  
Joanne Yip ◽  
Hung-hei Kwan ◽  
...  
Keyword(s):  
Image Processing ◽  
Cross Sections ◽  
Plantar Pressure ◽  
Three Dimensional ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Diabetic Patients ◽  
Clinical Practices ◽  
Cross Sectional ◽  
Orthotic Treatment

Background: Accurate representation of the insole geometry is crucial for the development and performance evaluation of foot orthoses designed to redistribute plantar pressure, especially for diabetic patients. Methods: Considering the limitations in the type of equipment and space available in clinical practices, this study adopted a simple portable three-dimensional (3-D) desktop scanner to evaluate the 3-D geometry of an orthotic insole and the corresponding deformities after the insole has been worn. The shape of the insole structure along horizontal cross sections is defined with 3-D scanning and image processing. Accompanied by an in-shoe pressure measurement system, plantar pressure distribution in four foot regions (hallux, metatarsal heads, midfoot, and heel) is analyzed and evaluated for insole deformity. Results: Insole deformities are quantified across the four foot regions. The hallux region tends to show the greatest changes in shape geometry (17%–50%) compared with the other foot regions after 2 months of insole wear. As a result of insole deformities, plantar peak pressures change considerably (–4.3% to +69.5%) during the course of treatment. Conclusions: Changes in shape geometry of the insoles could be objectively quantified with 3-D scanning techniques and image processing. This investigation finds that, in general, the design of orthotic insoles may not be adequate for diabetic individuals with similar foot problems. The drastic changes in the insole shape geometry and cross-sectional areas during orthotic treatment may reduce insole fit and conformity. An inadequate insole design may also affect plantar pressure reduction. The approach proposed herein, therefore, allows for objective quantification of insole shape geometry, which results in effective and optimal orthotic treatment.

scholarly journals Segmentation and Classification of Beatboxing Acoustic Voice Tract Variations in MRI through Image Processing Technique

2021 ◽  
Author(s):  
Anurag Sinha ◽  
Harsh soni
Keyword(s):  
Image Processing ◽  
Clustering Algorithms ◽  
Histogram Equalization ◽  
Recognition System ◽  
Processing Technique ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Image Processing Technique ◽  
Sound Recognition ◽  
Vocal Art

Human beatboxing is a vocal art making use of speech organs to produce vocal drum sounds and imitate musical instruments. Beatbox sound classification is a current challenge that can be used for automatic database annotation and music-information retrieval. In this study, a large-vocabulary humanbeatbox sound recognition system was developed with an adaptation of Kaldi toolbox, a widely-used tool for automatic speech recognition. The corpus consisted of eighty boxemes, which were recorded repeatedly by two beatboxers. The sounds were annotated and transcribed to the system by means of a beatbox specific morphographic writing system (Vocal Grammatics). The image processing techniques plays vital role on image Acquisition, image pre-processing, Clustering, Segmentation and Classification techniques with different kind of images such as Fruits, Medical, Vehicle and Digital text images etc. In this study the various images to remove unwanted noise and performs enhancement techniques such as contrast limited adaptive histogram equalization, Laplacian and Harr filtering, unsharp masking, sharpening, high boost filtering and color models then the Clustering algorithms are useful for data logically and extract pattern analysis, grouping, decision-making, and machine-learning techniques and Segment the regions using binary, K-means and OTSU segmentation algorithm. It Classifying the images with the help of SVM and K-Nearest Neighbour(KNN) Classifier to produce good results for those images.

scholarly journals The Implementation of Multilevel Colour Thresholding on a Prototype Coffee Machine

2020 ◽  
Vol 4 (2) ◽  
pp. 121
Author(s):  
Nova Resfita ◽  
Rahmadi Kurnia ◽  
Fitrilina Fitrilina
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Speech Recognition ◽  
Daily Life ◽  
Recognition System ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Speech Recognition System ◽  
Vast Number ◽  
Coffee Machine

The development of computer vision has expanded widely as there is a vast number of its applications in various aspects of daily life. One of its implementations is integrating the image processing technique on a prototype coffee machine based on the speech recognition system. This study aims to detect the requested coffee colour spoken by users which are black, middle and light. The sensor used in this research is a digital PC camera and the applied method is Multilevel Colour Thresholding. Of all experiments conducted, the image processing technique can work perfectly as the camera is able to identify the requested colour of the coffee solution. Furthermore, the system might be developed by improving the multilevel colour thresholding technique as well as advancing the hardware design in order to establish more robust coffee machine based on the requested colour.

scholarly journals Diagnosing Chronic Glaucoma Using Watershed and Convolutional Neural Network

2021 ◽  
Vol 9 (9) ◽  
pp. 272-283
Author(s):  
Naureen Fathima
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Three Dimensional ◽  
Image Plane ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computational Technique ◽  
Fundus Imaging

Abstract: Glaucoma is a disease that relates to the vision of human eye,Glaucoma is a disease that affects the human eye's vision. This sickness is regarded as an irreversible condition that causes eyesight degeneration. One of the most common causes of lifelong blindness is glaucoma in persons over the age of 40. Because of its trade-off between portability, size, and cost, fundus imaging is the most often utilised screening tool for glaucoma detection. Fundus imaging is a two-dimensional (2D) depiction of the three-dimensional (3D), semitransparent retinal tissues projected on to the imaging plane using reflected light. The idea plane that depicts the physical display screen through which a user perceives a virtual 3D scene is referred to as the "image plane”. The bulk of current algorithms for autonomous glaucoma assessment using fundus images rely on handcrafted segmentation-based features, which are influenced by the segmentation method used and the retrieved features. Convolutional neural networks (CNNs) are known for, among other things, their ability to learn highly discriminative features from raw pixel intensities. This work describes a computational technique for detecting glaucoma automatically. The major goal is to use a "image processing technique" to diagnose glaucoma using a fundus image as input. It trains datasets using a convolutional neural network (CNN). The Watershed algorithm is used for segmentation and is the most widely used technique in image processing. The following image processing processes are performed: region of interest, morphological procedures, and segmentation. This technique can be used to determine whether or not a person has Glaucoma. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

Sign in / Sign up

Export Citation Format

Share Document