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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...
In this paper, we propose a two-step textural feature extraction method, which utilizes the feature learning ability of Convolutional Neural Networks (CNN) to extract a set of low level primitive filter kernels, and then generalizes the discriminative power by forming a histogram based descriptor. The proposed method is applied to a practical medical diagnosis problem of classifying different stages...
A high recurrence rate, and progression to higher stages are observed for patients diagnosed with urothelial car-cinoma (previously known as transitional cell carcinoma). Low prognostic value of the current grading systems result in extensive follow-up of patients for multiple years after first diagnosis. Although, the aid of computer systems for prognosis prediction of superficial urothelial carcinomas...
This paper proposes an insulator defect detection algorithm based on computer vision for helicopter aerial insulator imaging in complex backgrounds. The algorithm runs fast with high detection accuracy, which meets the requirements for detecting missing insulators. However, because the background of the insulator image acquired by aerial photography is complicated and there is more than one insulator...
The segmentation of some scenes can be better for its next step in the processing and analysis. In this paper, the Gaussian mixture model clustering is used to detect and segment the scenes in the sports competition. Firstly, the main color of the scene is extracted by the method of color space histogram, and it is used as a sample for local training. Then we use the expectation maximization algorithm...
Convolutional neural network (CNN) based face detectors are inefficient in handling faces of diverse scales. They rely on either fitting a large single model to faces across a large scale range or multi-scale testing. Both are computationally expensive. We propose Scale-aware Face Detection (SAFD) to handle scale explicitly using CNN, and achieve better performance with less computation cost. Prior...
We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN). Inspired by the deep residual network (ResNet) that simplifies the learning process by changing the mapping form, we propose a deep detail network to directly reduce the mapping range from input to output, which makes the learning process easier. To further...
Deep learning methods achieve great success recently on many computer vision problems. In spite of these practical successes, optimization of deep networks remains an active topic in deep learning research. In this work, we focus on investigation of the network solution properties that can potentially lead to good performance. Our research is inspired by theoretical and empirical results that use...
Vision-based traffic light detection has been widely studied over the past decade. However, it is still a challenging task to build a real-time and robust classifier-based detector without a high dependency on prior knowledge. In this paper, we have a deep look at the design of features and detection mechanism in the domain of traffic light detection; propose a multi-scale and multi-phase detector...
The present paper introduces an extension of attribute profiles (APs) by extracting their local features. The so-called local feature-based attribute profiles (LFAPs) are expected to provide a better characterization of each APs' filtered pixel (i.e. APs' sample) within its neighborhood, hence better deal with local texture information from the image's content. In this work, LFAP is constructed by...
In this work, we consider the problem of detecting target objects in remote sensing imagery; such as detecting rooftops, trees, or cars in color/hyperspectral imagery. Many detection algorithms for this problem work by assigning a decision statistic (or “confidence”) to all, or a subset, of spatial locations in the data. A threshold is then applied to the statistics to identify detections. The detection...
This paper presents an implementation of a stereo system using image processing techniques in order to determine the distance to an object from a pair of images. Starting from this, the algorithm calculates the disparity image. The distance to an object is determined by using filters, making a histogram of the disparity image and interpolating. The entire algorithm has been implemented on the BeagleBoneBlack...
Visual words of Bag-of-Visual-Words (BoVW) framework are independent each other, which results in not only discarding spatial orders between visual words but also lacking semantic information. This study is inspired by word embeddings that a similar embedding procedure is applied to a large number of visual words. By this way, the corresponding embedding vectors of the visual words can be formulated...
This paper presents 2D image processing approach to playback detection in automatic speaker verification (ASV) systems using spectrograms as speech signal representation. Three feature extraction and classification methods: histograms of oriented gradients (HOG) with support vector machines (SVM), HAAR wavelets with AdaBoost classifier and deep convolutional neural networks (CNN) were compared on...
Frame dropping is a type of video manipulation where consecutive frames are deleted to omit content from the original video. Automatically detecting dropped frames across a large archive of videos while maintaining a low false alarm rate is a challenging task in digital video forensics. We propose a new approach for forensic analysis by exploiting the local spatio-temporal relationships within a portion...
This paper describes a component of an Augmented Reality (AR) based system focused on supporting workers in manufacturing and maintenance industry. Particularly, it describes a component responsible for verification of performed steps. Correct handling is crucial in both manufacturing and maintenance industries and deviations may cause problems in later stages of the production and assembly. The primary...
Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches typically consider training models on large datasets. However, there might only be a small training dataset to start with and more images can be obtained during the real-time applications. This paper proposes an online image recognition...
This paper proposes an image analysis method for separating abnormal regions caused by nutrient deficiencies on plants' leaves. The proposed method analyzes a histogram of normal leaves' colors to identify abnormalities on leaves. It can be divided into three main steps. Firstly, color features of leaf region in an input image are computed. Secondly, for each pixel, its color features are compared...
When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG recompression. Different techniques have been developed based on diverse theoretical assumptions but very effective solutions have not been developed yet. Recently, machine learning based approaches have been started to appear in the field of image forensics to solve diverse tasks such as acquisition source identification...
In today world the necessity for the autonomous mobile robots and vehicles is increasing. The safety autonomous moving demands the reliable and fast detection algorithms. The Histogram of Oriented Gradients (HOG) descriptors show significantly outperforms the existing feature sets for a human detection. Though the given method has a lot of type I errors. The amount of these errors can be decreased...
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