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The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
The cosine similarity measure is widely used in big data analysis to compare vectors. In this article a new set of vector similarity measures are proposed. New vector similarity measures are based on a multiplication-free operator which requires only additions and sign operations. A vector ‘product’ using the multiplication-free operator is also defined. The new vector product induces the ℓ1-norm...
Indirect immunofluorescence (IIF) with HEp-2 cells is considered as a powerful, sensitive and comprehensive technique for analyzing antinuclear autoantibodies (ANAs). Fractal dimension can be used on the analysis of image representing and also on the property quantification like texture complexity and spatial occupation. In this study, we apply the fractal theory in the application of HEp-2 cell staining...
Scene classification is a hot problem in the computer vision. In this paper, a novel rapid scene classification method is proposed based on the discrete cosine transform (DCT) domain. Firstly, we divided the whole image into several areas of the same size without repetition, do DCT transform with the size of the В ∗ В in each separate sub image area divided above. Secondly, scanning AC coefficients...
In this work, we discuss the benefits of image compression on FITS image files to perform image retrieval tasks on the enormous NASA Solar Dynamics Observatory (SDO) image repository. With the objective of making solar image files more portable and easy to distribute and archive, we test several lossless compression algorithms as well as lossy compression algorithms in order to determine the rate...
A texture descriptor based on a set of indices of degrees of local approximating polynomials is proposed in this paper. An image is split into non-overlapping patches, reshaped into one-dimensional source vectors and convolved with the polynomial approximation kernels of various degrees p. As a result, a set of approximations is obtained. For each element of the source vector, these approximations...
Sentiment classification is the main and popular task in the field of sentiment analysis. Most of the existing researches focus on how to extract the effective features, such as lexical features and syntactic features, while limited work has been done on the extraction of semantic features, which can make more contributions to sentiment classification. This paper presents a method for sentiment classification...
This paper examines the problem of transfer learning in the context of object recognition in a heterogeneous robot team. We specifically look at the case where robots individually learn object classifiers and must then transfer the resulting learned knowledge to another robot. Recent trends in computer vision and robotics have moved towards feature representation learning, where the underlying feature...
Glaucoma is the most common cause of blindness and it affects most of the ageing society and this occurs due to pressure increases in the optic nerve which damages the optic nerve. This paper is an attempt to study and analyze the texture features of the Fundus image and its variations when the Fundus image is infected with glaucoma. The texture features extracted are localized around the optic cup...
Determining the original file type of data fragments helps data recovery, spam detection, virus scanning, and network monitoring operations. In many cases, only unordered fragments of the original file are available for investigation. Therefore, we can only base on the content of a fragment to identify its file type. However, data fragments come with different sizes, as they may be the residual data...
In biomedical image analysis, object description and classification tasks are very common. Our work relates to the problem of classification of Human Epithelial (HEp-2) cells. Since the crucial part of each classification process is the feature extraction and selection, much attention should be concentrated to the development of proper image descriptors. In this article, we introduce a new efficient...
Currently, the evaluation of placental maturity has mainly focused on subjective measure, which highly depends on the observation and experiences of the clinicians and not reliable. This paper proposes a new method for grading placenta maturity in B-mod ultrasound (US) images automatically based on local intensity order pattern (LIOP) and fisher vector (FV). After extracting invariant LIOP feature...
This paper presents an audio feature extraction scheme based on spectral decomposition where the decomposition is performed iteratively by matching-pursuit in frequency domain. Motivated by psychoacoustic studies, a set of spectral basis vectors are constructed to extract pitch, timbre and residual inharmonic components from spectrum. The audio feature is represented by the scales of basis vectors...
This work expounds a target classification method in the resonance scattering region having reduced target's distance, aspect angle and noise dependencies. In the given method, crucial optimum late-time intervals of the scattered signals are determined by using time-frequency representations. The time instants belonging to maximum and mean power values in time-frequency distributions are used, which...
Modern and future many-core systems represent complex architectures. The communication fabrics of these large systems heavily influence their performance and power consumption. Current simulation methodologies for evaluating networks-on-chip (NoCs) are not keeping pace with the increased complexity of our systems; architects often want to explore many different design knobs quickly. Methodologies...
Classification of malicious code by machine learning gives more flexible and adaptable prediction result than by existing approaches [1]. But the approach just can identify looks-like malicious code instead of real malicious one. In this research, a novel method to reduce the vagueness in the classification by machine learning to consider code sequence.
This paper presents categorization of Croatian texts using Non-Standard Words (NSW) as features. NonStandard Words are: numbers, dates, acronyms, abbreviations, currency, etc. NSWs in Croatian language are determined according to Croatian NSW taxonomy. For the purpose of this research, 390 text documents were collected and formed the SKIPEZ collection with 6 classes: official, literary, informative,...
This paper presents a simple and efficient design of a face recognition system, where feature extraction algorithm is employed based on the principle of spatial cross-correlation. In the feature extraction process, instead of processing the entire image at a time, only a pair of rows or columns of an image is considered which makes the algorithm very efficient and low-cost. Considering the cross-correlations...
Effective motor imagery (MI) classification based on electroencephalogram (EEG) signals for Brain Computer Interface (BCI) is an active area of research. Classification is largely dependent on the feature vector and the type of classifier. This paper reports a study on the use of bispectrum for classifying left and right hand MI based on surface EEG from electrode positions C3 and C4. EEG signals...
The work presents an effective approach for subpixel motion estimation for Super-resolution (SR). The objective is to improve the quality of the estimated SR image by increasing the accuracy of the motion vectors used in the SR procedure. The correction of the motion vectors is based on appearance of error artifacts in the SR image, introduced due to registration errors. First, SR is performed using...
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