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This paper deals with classification algorithms as one of the basic principles of pattern recognition. We analyze their effect to a feature space and compare the type and the shape of the separating and decision surface, respectively. We proposed a novel classification approach based on Cumulative Fuzzy Membership Function that creates a decision surface in a different way as an MF ARTMAP neural network...
Pollen granules are one of the most stable microstructures of herbal flowers, and their ektexines possess strong anti-acid, anti-base and anti-biolysis properties. Therefore, the microstructures of pollen granules are not destroyed during storage, manufacturing and the production of different preparations. The shapes, sizes and colors of pollen granules are different in different families, genera...
Nowadays, image processing is getting more popular due to the daily increase of diverse data acquisition methods such as digital scanners and cameras. Due to the high volume of archived documents, automatic document classification methods can help to save the time and space in digital document organization. Logos in official and business documents are used to identify document identities. Different...
Archival of images in databases, enabling further study with respect to their contents, is at our focus of attention. The major difficulties are i) the processing of a large number of images, ii) that the steadily growing number of images increase the complexity of the pattern recognition problems to be solved. We propose orientation radiograms, to be used as image signatures for shape based queries...
Biomedical research in last decade or so has seen the development of highly accurate algorithms focused on the detection and classification of the brain tumor into malignant or benign. As a result of these advancements a new research direction has emerged which focuses on categorizing the brain tumors based on their types, such as Glioma, Metastases, and Meningioma etc. In this paper, we present a...
We proposed a framework for human action recognition by learning pose dictionary as the human appearance representation. At first, the shape based pose feature is constructed based on the contour points of the human silhouette and invariant to translation and scaling. After the local pose features are extracted from the original videos, the class-specific dictionaries are learned individually on the...
Based on analysis of plate shape defect pattern in cold rolling, a defect recognition method using RBF-BP combinational neural network model optimized by genetic algorithm is proposed in this paper. The method makes use of genetic algorithm to optimize the weights and thresholds of the input layer, hidden layer and output layer in the RBF-BP network, and a GA-RBF-BP network model is formed. It can...
Invariant descriptor for shape and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from shape images via human knowledge and works. The descriptors need to have strong Local Binary Pattern (LBP) in order to encode the information distinguishing them. Local Binary Pattern (LBP) ensures encoding global and...
The research presented in this paper refers to classification of geometric shapes (cubes, pyramids and cylinders) using multilayer neural network. The input data of the algorithm are the images of shapes placed in different positions and distances from the camera. The classification is based on feature vectors that are obtained using methods of digital image processing. Feature vectors are inputs...
Pedestrian detection is of much importance for its practical applications. This paper develops a novel pedestrian detection system which consists of three stages: motion region detection based on background modeling, feature extraction in the guidance of prior information, and map-based classification applying support vector machine (SVM) and Adaboost. First of all, an adaptive Gaussian Mixture Model...
Scale invariance is a desirable property for many vision tasks such as image segmentation and classification. One way to achieve such invariance is to collect images containing objects of all scales and then train a classifie r. In practice, however, only a finite number of images at a finite number of scales can be collected, and this poses the problem of scale sampling. In this paper, we focus on...
Vein image recognition based on modeling shape or geometrical layout of feature points is generative approach, and the performance is usually limited by segmentation error due to poor vein image quality. This paper instead proposes to model the discriminative appearance of local image patch using the vocabulary tree model. The discriminative approach is further extended to consider the geometrical...
Breast cancer grading of histological tissue samples by visual inspection is the standard clinical practice for the diagnosis and prognosis of cancer development. An important parameter for tumor prognosis is the number of mitotic cells present in histologically stained breast cancer tissue sections. We propose a hierarchical learning workflow for automated mitosis detection in breast cancer. From...
This paper addresses the problem of shape classification and proposes a method able to exploit peculiarities of both, local and global shape descriptors. In the proposed shape classification framework, the silhouettes of symbols are firstly described through Bags of Shape Contexts. This shape signature is used to solve correspondence problem between points of two shapes. The obtained correspondences...
Multiple Instance Learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In MIL it is often assumed that positive bags contain at least one instance from a so-called concept in instance space. However, there are many MIL problems that do not fit this formulation well, and hence cause traditional MIL algorithms, which focus...
The fingerspelling recognition by hand shape is an important step for developing a human-computer interaction system. A method of fingerspelling recognition by hand shape using higher-order local auto-correlation(HLAC) features is proposed. From the experimental results, the proposed method is promising. And to redeuce image resolution and to thresholding an image are shown to be effective. In this...
When studying mobile robot to recognize shape of object in dynamic surroundings, we proposed a hybrid recognition algorithm based on the combination of rough set theory and BP neural network. RS has the capability for intelligent data analysis, and BP network can approach most problems accurately and exactly, the algorithm put respective advantages of two theories to use. Firstly, information table...
Computer vision is a growing field of computer science that intends to extract some useful information from images, usually taken from cameras or scanners. The ability to recognize shapes in images is often necessary in computer vision programs. This article describes how to make a program able to recognize basic geometrical figures by using machine learning. This article shows the image processing...
This paper presents a contactless hand based biometric identification system using geometric and palm features. Hand images are acquired using two commercial webcams with 1200×1600 pixel resolution which are refered to as the “IR” and “visible” webcams. The IR webcam has been modified by exchanging the IR filter with a visible filter lens and reducing the gain and exposure time to improve the hand...
The design and use of statistical pattern recognition models can be regarded as one of the core research topics in the segmentation of the left ventricle of the heart from ultrasound data. These models trade a strong prior model of the shape and appearance of the left ventricle for a statistical model whose parameters can be learned using a manually segmented data set (this set is commonly known as...
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