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Plants are fundamental for human beings, so it's very important to catalog and preserve all the plants species. Identifying an unknown plant species is not a simple task. Automatic image processing techniques based on leaves recognition can help to find the best features useful for plant representation and classification. Many methods present in literature use only a small and complex set of features,...
Mars rover is a robot which explores the Mars surface, is equipped to front-line Panoramic Camera (Pancam). Automatic processing and segmentation of images taken by Pancam is one of the most important and most significant tasks of Mars rover since the transformation cost of images from Mars to earth is extremely high. In this paper, a new feature vector for image pixels will be proposed as well as...
Millions of people die from Diabetes Mellitus every year. Recently, researchers have discovered that Diabetes Mellitus can be detected in a non-invasive manner through the analysis of human facial blocks. Although algorithms have been developed to detect Diabetes Mellitus using facial block color features, use of its texture features to detect this disease has not been fully investigated. In this...
In this paper, we propose a system of automatically classifying different types of dates from their images. Different dates have various distinguished features that can be useful to recognize a particular date. These features include color, texture, and shape. In the proposed system, a color image of a date is decomposed into its color components. Then, local texture descriptor in the form of local...
Digital visual media is one of the most commonly used means of communication. But, with the use of low-cost editing tools, tampering and counterfeiting visual contents are increasing enormously. In almost all the Image forensic application areas, the device used for capturing the image is of utmost importance as the origin of the particular image can act as a key evidence to substantiate the legitimacy...
In this paper, we developed the system for recognizing the orchid species by using the images of flower. We used MSRM (Maximal Similarity based on Region Merging) method for segmenting the flower object from the background and extracting the shape feature such as the distance from the edge to the centroid point of the flower, aspect ratio, roundness, moment invariant, fractal dimension and also extract...
Due to the semantic gap, we can only extract the image feature to identify indirectly the image emotional semantic. In view of the feature extraction problem of image emotional semantic identification, the image feature fusion algorithm with weights is proposed and applied to the identification of image emotional semantic in our paper. According to the effects of the extracted color, texture and shape...
The human face is an important human body part which plays an extraordinary role in the human to human or human to machine communications. As such, it is important to design robust emotion detection system for real world applications like human decision making and effective human computer interaction. Facial expression provides non-verbal communication for human computer interactions. This study identifies...
In this article, a real-time, accurate and objective identification of different varieties of corn seeds is proposed, which is a large number of original features, contained color, texture and shape features, were extracted from corn seed images. Then, genetic algorithm and support vector machine (SVM) were used to select important ones and determine species. The proposed methods have optimized varieties...
In order to eliminate the shortcomings in apple color grading, such as the slow speed, the large error, a novel fast intelligent grading method is presented, which is based on the improved particle swarm optimization (PSO) algorithm and Support Vector Machine. The main process is to acquire the colority of apple surface by the computer vision technology and extract its features which used as the samples...
We propose a novel approach for content based color image classification using Support Vector Machine (SVM). Traditional classification approaches deal poorly on content based image classification tasks being one of the reasons of high dimensionality of the feature space. In this paper, color image classification is done on features extracted from histograms of color components. The benefit of using...
There is an increasing need for automatically segmenting the regions of different landforms from a multispectral satellite image. The problem of Landform classification using data only from a 3-band optical sensor (IRS-series), in the absence of DEM (Digital Elevation Model) data, is complex due to overlapping and confusing spectral reflectance from several different landform classes. We propose a...
This paper presents a method to classify food images by updating the model of Bayesian network incrementally. We have been investigating a “food log” system which makes use of image analysis, and it can automatically detect food images and estimate the food balance (using a simple nutrition model). It also enables users to easily modify the results of the analysis when they contain errors. So far,...
In this paper, we propose a new scheme aimed for gastrointestinal (GI) tumor capsule endoscopy (CE) images classification, which utilizes sequential forward floating selection (SFFS) together with support vector machine (SVM). To achieve this goal, candidate features related to texture characteristics of CE images are extracted. With these candidate features, SFFS based on SVM is applied to select...
A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is...
This paper presents a vision-based continuous sign language recognition system to interpret the Taiwanese Sign Language (TSL). The continuous sign language, which consists of a sequence of hold and movement segments, can be decomposed into non-signs and signs. The signs can be either static signs or dynamic signs. The former can be found in the hold segment, whereas the latter can be identified in...
Support Vector Machines (SVM) are very powerful classifiers in theory but their efficiency in practice rely on an optimal selection of hyper-parameters. This paper proposes an image classifier based on Support Vector Machine which related parameters are optimized by an improved Particle Swarm Optimization (PSO) algorithm. Because control parameters selection of PSO have no corresponding theoretical...
Color variation in medical images degrades the classification performance of computer aided diagnosis systems. Traditionally, color segmentation algorithms mitigate this variability and improve performance. However, consistent and robust segmentation remains an open research problem. In this study, we avoid the tenuous phase of color segmentation by adapting a bag-of-features approach using scale...
In a content-based image retrieval (CBIR) system, rational and effective organization of the image database plays an important role in improving the performance of the system. In this paper, we propose a new method to classify the images database of CBIR system. Using SVM we attempt to construct a mapping between the low-level features and the semantically level in order to determine which category...
A color image splicing detection method based on gray level co-occurrence matrix (GLCM) of thresholded edge image of image chroma is proposed in this paper. Edge images are generated by subtracting horizontal, vertical, main and minor diagonal pixel values from current pixel values respectively and then thresholded with a predefined threshold T. The GLCMs of edge images along the four directions serve...
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