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To successfully move a robot into the building, the elevator button and elevator floor number detection and recognition can play an important role. It can help a robot move in the building, just as it also can help a visually impaired person who wants to move another floor in the building. Due to vision-based approach, the difference in lighting condition and the complex background are the main obstacles...
Every organism emits energy around it which comprises UV-radiation, EM-radiation, infrared and thermal radiation. This energy around human body represents health condition of the subject under study. These energy fields are called as aura of the body under consideration. Several types of equipments are there to capture such energy. Kirlian camera captures the distribution of energy radiation around...
Features derived from Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run-Length (GLRL) matrix are widely used for image characterization based on texture analysis. In this paper, we propose the application of suitably selected texture discriminating features for classification of oral cancer lesions in digital camera images into six groups. Backpropagation based Artificial Neural Network (BPANN)...
Texture classification is an important and challenging factor in image processing system which refers to the process of partitioning a digital image into multiple constituent segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Artificial Neural Network (ANN) Based texture classification or Segmentation...
The feasibility of automating the evaluation of stroke chronic patients' motor functions has been explored while analyzing their corresponding fMRI studies with statistical parametric analysis, statistical inference analysis and a nonlinear multivoxel pattern-analysis classifier based on a feed-forward backward-propagation neural network. After doing principal component analysis and independent component...
This paper proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing unconstrained offline handwritten texts. The structural part of the optical models has been modeled with Markov chains, and a Multilayer Perceptron is used to estimate the emission probabilities. This paper also presents new techniques to remove slope and slant from handwritten text...
In this work, a combination of artificial neural network (ANN), Fourier descriptors (FD) and spatial domain analysis (SDA) has been proposed for the development of an automatic fruits identification and sorting system. Fruits images are captured using digital camera inclined at different angles to the horizontal. Segmentation is used for the classification of the preprocessed images into two non-overlapping...
Accurate land use/cover (LUC) classification data derived from remotely sensed data are very important for land use planning and environment sustainable development. Traditionally, statistical classifiers are often used to generate these data, but these classifiers rely on assumptions that may limit their utilities for many datasets. Conversely, artificial neural network (ANN) and decision tree (DT)...
In this project, the main focus is to apply image processing techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems,...
Image classification is an important task for many aspects of global change studies and environmental applications. This paper emphasizes on the analysis and usage of different advanced image classification techniques like Cloud Basis Functions (CBFs) Neural Networks, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for object based classification to get better accuracy. For comparison,...
Generally, IQMs are not able to well predict the image quality for all degradations. Indeed, well performance could be obtained for a given degradation and poor results for others. This is essentially due to the fact that the efficiency of IQMs depends highly on the degradation specificity. To overcome this limitation, we propose to first identify the type of degradation before measuring the quality...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques and allows computer to learn from past examples and detect patterns from large data sets, which is particularly well-suited to assist medical practitioners in diagnosis of disease based on a variety of test results. Therefore, in this research, we deemed further...
Tumour classification and quantification in positron emission tomography (PET) imaging at early stage of illness are important for radiotherapy planning, tumour diagnosis, and fast recovery. Analysing large medical volumes using traditional techniques requires a decent amount of time, and in some approaches poor accuracy is achieved. Artificial intelligence (AI) technologies can provide better accuracy...
A Brain Cancer Detection and Classification System has been designed and developed. The system uses computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network in MRI images of different patients with Astrocytoma type of brain tumors. The image processing techniques such as histogram equalization, image segmentation, image enhancement,...
Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach...
This study focuses on segmentation and validation of brain MR images. Artificial neural network (ANN) has been applied to obtain the targeted segments from these images. In preprocessing step for avoiding the chances of misclassification during training of ANN, the unwanted skull tissues were removed by employing active contour modeling (ACM). The removal of these tissues leaves an image containing...
The main objective of this project is to develop a technique to classify the ripeness of bananas into 3 categories, which is unripe, ripe and overripe systematically based on their histogram RGB value components. This system involved the process of collecting samples with different level of ripeness, image processing and image classification by using artificial neural network. Collecting bananas sample...
Pearl's color is an important feature to assess its value, including the hue and its color depth. A method for pearl color classification was investigated in this paper. Computer Vision is used to process the pearl image after transforming it from RGB to HSV color model, which can show the hue and color depth information of pearl. According to the histogram of V (Value) weight, the bright area is...
This paper presents a method for automatic temporal location and recognition of human actions. The data are obtained from a motion capture system. They are then animated and optical flow vectors are subsequently calculated. The system performs in two phases. The first phase employs nearest neighbor search to locate an action along the temporal axis taking into account both the angle and length of...
The purpose of this research is to develop a system that used to recognize image of vehicle and classified it into their classes using image processing method and artificial neural network. In the research, all the selected images are required to go through image processing technique to obtained desired data. Images are converted into data using singular value decomposition extraction method and the...
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