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Studying fish recognition has important realistic and theoretical significance to aquaculture and marine biology. Fish recognition is challenging problem because of distortion, overlap and occlusion of digital images. Previous researchers have done a lot of work on fish recognition, but the classification accuracy may be not high enough. Classification and recognition methods based on convolutional...
Fine-grained vehicle classiflcation is a challenging task due to the subtle differences between vehicle classes. Several successful approaches to fine-grained image classification rely on part-based models, where the image is classified according to discriminative object parts. Such approaches require however that parts in the training images be manually annotated, a laborintensive process. We propose...
Fruit flies are of huge biological and economic importance for the farming of different countries in the World, especially for Brazil. Brazil is the third largest fruit producer in the world with 44 million tons in 2016. The direct and indirect losses caused by fruit flies can exceed USD 2 billion, putting these pests as one of the biggest problems of the world agriculture. In Brazil, it is estimated...
Image classification is a method that distinguishes the different categories of targets based on the different features of image. The current problem usually is that the feature modeling of target has a great influence on recognition robustness. In order to solve this problem, a correlation-based method is presented to optimize the bag-of-visual-word (BOVW) model by reducing the dictionary size. The...
Deep learning has brought a series of breakthroughs in image processing. Specifically, there are significant improvements in the application of food image classification using deep learning techniques. However, very little work has been studied for the classification of food ingredients. Therefore, this paper proposes a new framework, called DeepFood which not only extracts rich and effective features...
Specific phobia which is a state of excessive and constant fear although individuals know it is illogical. During phobia in individuals there is several changes. This study based on recorded pupil video during phobia and analysis of changes in pupil size using image processing and signal processing algorithms on this video. Feature extraction are made from the signals obtained from the pupil dimensions...
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...
In view of textual remote sensing image classification, a classification approach based on Extreme Learning Machine (ELM) in introduced. As the performance of ELM is mainly affected by the value of input weights and hidden biases genetic algorithm (GA) and particle swarm optimization algorithm (PSO) have been used to learn these parameters for ELM in order to improve the stability of extreme learning...
Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts the researchers to develop many algorithms for this application domain. The Multi-Instance Multi-Label Learning (MIML) is an important type of machine learning framework proposed recently for IMC. In this framework, an image is described with...
Inspired by recent successful deep learning methods, this paper presents a new approach for polarimetric synthetic aperture radar (PolSAR) image classification. It combines both advantages of pixel-based and object-based methods. An improved simple linear iterative clustering (SLIC) superpixel segmentation algorithm is used to obtain spatial information in the PolSAR image. Then, a Deep Belief Network...
Machine learning has been increasingly used in current days. Great improvements, especially in deep neural networks, helped to boost the achievable performance in computer vision and signal processing applications. Although different techniques were applied for deep architectures, the frequency domain has not been thoroughly explored in this field. In this context, this paper presents a new method...
Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected...
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...
Brain tumor is the abnormal growth of cells or tissues within the brain or surroundings of the brain. MRI is the commonly used modality for the diagnosis of brain tumor. Brain tumor image classification is one of the fundamental problem faced by the clinicians and doctors who worked in this field. In this paper, we proposed a robust method for MR brain image classification based on fractal dimensions...
Image classification algorithms using state-of-the art methods have grabbed much attention in computer vision area. In-domain classification assumes the testing data to be in the same domain as of the training data. Cross-Domain classification is a paradigm where testing data is from a different but related domain to the training data. We use Speeded-Up Robust Features (SURF) for feature extraction,...
Previous studies showed that it was possible to have a prediction or an early detection of osteoporosis by measuring the thickness of the cortex of the clavicle of thorax x-ray image. The drawback of this system was that it was still dependent on the operator of subjective vision applications in the measurement. In addition, the accuracy of the system very much relied on the x-ray image quality. Therefore,...
In recent years, intelligent mathematics problem solving has aroused the interest of researchers. In the intelligent mathematics problem solving system related to high school, the classification of statistical graph is a key step. Consequently, the classification of statistical graphs has become an urgent problem to be solved. In this paper, a new method is proposed for statistical graphs classification...
Herlev dataset consists of 7 cervical cell classes, i.e. superficial squamous, intermediate squamous, columnar, mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ is considered. The dataset will be tested to classify two classes, consisting of normal and abnormal cells. Seven different cell types will be classified to separate the cells into 7 classes which are 3 normal cell...
In the big data era, machine learning has become an increasingly popular approach for data processing. Data could be in various forms, such as text, images, audios, videos and signals. The essence of machine learning is to learn any patterns from features of data. In the above types of data, the number of features is massively high, which could result in the presence of a large number of irrelevant...
Different information visualization techniques can be found in the literature due to the quantity and variety of data stored in computational systems. In this context, the classification of chart images becomes important because it allows various types of graphs to be detected automatically in different contexts, allowing a more specific processing for each type of visualization, for example, data...
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