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This research describes skin disease recognition by using neural network which based on the texture analysis. There are many skin diseases which have a lot of similarities in their symptoms, such as Measles (rubeola), German measles (rubella), and Chickenpox etc. In general, these diseases have similarities in pattern of infection and symptoms such as redness and rash. Diagnosis and recognition of...
Detecting traffic signal lights (e.g., red) is an important subject of intersection safety since many accidents are the result of road users' non-conforming behavior to traffic signals. This work shows that traffic signal phases can be inferred through traffic cameras in order to detect temporal violations of road users. The idea is to understand the traffic phase by learning the moving features of...
Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While...
Road detection is a key component of Advanced Driving Assistance Systems, which provides valid space and candidate regions of objects for vehicles. Mainstream road detection methods have focused on extracting discriminative features. In this paper, we propose a robust feature fusion framework, called “Feature++”, which is combined with superpixel feature and 3D feature extracted from stereo images...
Grape constitutes one of the most widely grown fruit crop in the India. Manual observation of experts is used in practice for detection of leaf diseases, which takes more time for further control action. Without accurate disease diagnosis, proper control actions cannot be taken at appropriate time. This is where modern agriculture technique is required to detect and prevent the leaf from different...
Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. This paper represents a new approach for TSR system using hybrid features formed by two robust features descriptors, named Histogram Oriented Gradient(HOG) features and Speeded Up Robust Features(SURF) and artificial neural...
A neural network based clothing style analysis method is proposed via deep filter bank in this paper. Clothing styles are complicated and high-level concept. We propose to construct the deep filter bank by combining Convolution Neural Network(CNN) with Fisher Vector(FV). Then, the extracted features from the body part are used to train the Part-CNN (p-CNN) model. Multiple p-CNNs are integrated along...
Traffic Sign Recognition (TSR) system is a vital component of intelligent transport system. It plays an important role by enhancing the safety of the drivers, pedestrians and vehicles as traffic signs provide important information of the traffic environment of the road and assist the drivers to drive more safely and easily by guiding and warning. This paper represents road sign detection and recognition...
In this paper Content Base Image Retrieval (CBIR) system with relevance feedback is presented, where image database search is performed using singularity strength (Holder exponent). Images in database are described with low-level features for color and texture, which are concatenated in feature vectors (FV). Relevance feedback is implemented in CBIR system employing the artificial intelligence based...
This paper introduces a novel method, based on Gaussian Markov Random Field Model with back-propagation learning algorithm to retrieve multi-spectral satellite color imagery. The proposed method segregates the texture part and structure part of the imagery, and extracts features in the texture and structure parts separately. The extracted features are formed as a feature vector. The feature vector...
Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines Computer Vision and Machine Learning on clinically evaluated histopathological...
Is it possible that paintings created by an artificial agent touch a human? We investigate how an artificial agent “creates” paintings and what elements of paintings influence the human feeling. This paper presents the challenge of creating paintings by an artificial agent: a painting creating system. We propose the impression feedback so that an artificial agent can create impressive paintings, not...
Roadside vegetation classification has recently attracted increasing attention, due to its significance in applications such as vegetation growth management and fire hazard identification. Existing studies primarily focus on learning visible feature based classifiers or invisible feature based thresholds, which often suffer from a generalization problem to new data. This paper proposes an approach...
This study proposes and evaluates the application of two classifiers: decision tree (DT) and neural network (NN) to discriminate three region types: cancer (CC), lymphocyte (LC), and stromal (SC) in the breast cancer cell images. The feature extraction from area based texture information of BCCI is studied to compare results from the segmented cells. A combination between texture features based on...
Diabetic Retinopathy is a progressive eye diseases that causes changes in the blood vessels of the retina which may cause blindness if not prevented and treated at early stage. Diabetic retinopathy is one of the complications caused by diabetes and it appears in the retina, which is the tissue responsible for the vision in the eye. The early detection and diagnosis is essential to save the vision...
This paper presents an accurate and automatic algorithm to recognize and count fish in the video footages of fishery operations. The unique character of the approach is that it combines machine learning techniques with statistical methods to fully make use the benefits of these algorithms. The approach consists of three major stages including video data preparation such as noise deduction, preliminary...
Today shopping markets pay attention towards customer needs and services. Unfortunately the blind and vision impaired person are still incapable to access these environments without reliance. Assistive technology is trying to sway the living style of the blind by introducing support systems for routinely actions like reading, writing, walking, Web surfing, and shopping. However, still the blind have...
Now-a-days as there is prohibitive demand for agricultural industry, effective growth and improved yield of fruit is necessary and important. For this purpose farmers need manual monitoring of fruits from harvest till its progress period. But manual monitoring will not give satisfactory result all the times and they always need satisfactory advice from expert. So it requires proposing an efficient...
As far as out-door activities are concerned the blind face difficulties in safe and independent mobility depriving them of normal professional and social life. Also there are issues of communication and access to information. There are software applications for computers and touch screen devices equipped with speech synthesizers. This project is for the visually impaired people and is based on the...
In this paper, artificial neural network (ANN) and improved binary gravitational search algorithm (IBGSA) are utilized to detect objects in images. Watershed algorithm is used to segment images and extract the objects. Color, texture and geometric features are extracted from each object. IBGSA is used as a feature selection method to find the best subset of features for classifying the desired objects...
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