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Classification of soil is the dissolution to soil sets to particular group having a like characteristics and similar manners. Almost all countries do product exporting, in which those countries exporting higher agricultural product are very much depend on the soil characteristics. Thus, soil characteristics identification and classification is very much important. Identification of the soil type helps...
In this research, a new technique For Content Based Image Retrieval (CBIR) with contrast enhancement using multi-feature and multi kernel Support Vector Machine (SVM) method. Color moment (CM), Auto Correlogram (AC), Discrete Wavelet Transform (DWT), Gabor Filter (GF) features are proposed. We extended the previous work which used binary SVM classifier and color features. First of all take a query...
Agriculture is the mother of all culture. Economy and prosperity of a country depends on agriculture production. Agriculture provides food as well as raw material for industry. Agriculture production is inversely affected by pest infestation and plant diseases. Early pest identification and disease detection will help to minimize the loss of production. Naked eye observation is a common using method...
Object semantic reduces the semantic gap in Content Based Image Retrieval (CBIR). In recent years, numerous methods for object semantic categorization have been proposed. Semantic segmentation is a key factor affecting the accuracy of object semantic categorization. The existing semantic segmentation methods usually chose pixel or super-pixel as the processing input. But the information contained...
Traffic light detection is an important system because it can alert driver on upcoming traffic light so that he/she can anticipate a head of time. In this paper we described our work on detecting traffic light color using machine learning approach. Using HSV color representation, our approach is to extract features based on an area of X×X pixels. Traffic light color model is then created by applying...
There have been some traffic light detection systems developed by researchers and most of them use camera to detect traffic light. Sometimes, those systems still detect many non-traffic light objects as traffic lights. This paper presents methods for eliminating traffic light candidate based on the traffic light candidate's position in video's frame by using machine learning algorithm. The traffic...
This research aims to improve the capability of semantic segmentation through data perspective. This research proposed a parameterized Conditional Random Fields model and learns the model by using Structured Support Vector Machine (SSVM). The SSVM utilizes Hamming loss function for optimizing 1-slack Margin Rescaling formulation. The joint feature vector is derived from energy potentials. Variation...
Road sign recognition plays an exigent role for easy, immune and suitable driving. In this paper, a road sign detection system is developed to automatically recognize Bengali road signs. The proposed method is based on HSV transformation along with a template matching technique to detect and recognize circular, triangular, rectangular and octagonal signs and it covers all existing Bengali road signs...
In order to identify a large number of very similar objects, a novel recognition approach is proposed by mean of combination of two dynamic grouping algorithms, the visual processing mechanism, PCA and multi-pathway SVM. The samples have been segmented to appropriate groups by grouping features, and then features with rotation invariance and translation invariance of each group are extracted. Finally,...
Previous work on fire detection has focused on hand-designed features or carefully designed detectors. However, there are no universal hand-designed features or detectors that work well for various classification tasks or even for various fire detection scenarios. In this paper we propose a new method of video-based fire detection by learning multi-layer ICA spatiotemporal features. This method can...
In this paper, a single trial classification is introduced for the Electroencephalography (EEG) signals evoked by RGB colors. The effectiveness of a single trial classification is an important step towards online classification of EEG signals. Signals are analyzed by Empirical Mode Decomposition (EMD) technique, and the last decomposition is used in the feature extraction stage. We investigate different...
Tongue diagnosis is one of the main components of traditional Chinese medicine (TCM). Developing an objective and quantitative recognition model is very importantly and useful in the modernization of TCM. Currently, major problems in digital diagnoses of tongue images are extracting suitable features and building a high-performance classifier. To address these two issues, we present a robust approach...
This paper presents novel hardware architecture with low-complexity color conversion scheme and parallel processing of red region detection for the applications of automatic traffic sign detection system. By the inherent parallelism of the various red region detections, we designed a fully pipelined architecture implemented on the FPGA platform. The proposed architecture enables a real-time traffic...
A hyperspectral image classification method based on probabilistic weighted fusion of multiple spectral–spatial features is proposed in this letter. First, dimensionality reduction and feature extraction of hyperspectral images are conducted by minimum noise fraction. Then, two spectral–spatial features are composed through a combination of texture features and multiscale morphological features with...
The aim of this research is to develop a method for prediction of left ventricular recovery one year after myocardial infarction using texture parameters estimated for static ultrasound images. The study is performed for the monochrome and color (contrast based) echocardiograms that allow advanced evaluation of myocardial function. The analysis includes investigation of different texture feature selection...
In areas of ecological interest, the detection and control of seaweed such as Posidonia Oceanica is usually performed by divers. Due to the limited capacity of the scuba tanks and the human security protocols, this task involves several short immersions leading to poor temporal and spatial data resolution. Thus, it is desirable to automate this task by means of underwater robots. This paper describes...
Diabetes Mellitus (DM) is becoming one of the fastest growing and most common non-infectious diseases in the world today. This has set a huge measure of burden on governments and medical service authorities. As of now, researchers have identified that DM can be recognized in a non-invasive approach through the analysis of human facial blocks. In this paper, a novel way has been proposed to detect...
Automatic detection of human in a video sequence is a canonical instance of object detection. It's considered as a nonrigid object; it has many appearances at different perspectives. Different approaches are used by several methods to combine what is specific to the pedestrian detection, and what is common to the object recognition. A robust solution to this problem would have numerous applications...
Image retrieval is an active research area for the last two decades. This area is gaining more importance as the multimedia content over the internet is increasing. Color Texture and shape are the low level image descriptor in Content Based Image Retrieval. These low level image descriptors are used for image representation and retrieval in CBIR. This paper presents a Content Base Image Retrieval...
In this paper, we analyze the effect of boosting in image quality assessment through multi-method fusion. On the contrary of existing studies that propose a single quality estimator, we investigate the generalizability of multi-method fusion as a framework. In addition to support vector machines that are commonly used in the multi-method fusion studies, we propose using neural networks in the boosting...
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