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Using color histograms in automatic emotion recognition systems faces different issues. One of the important challenges is to determine the appropriate number of bins in the color histogram to achieve the highest recognition performance possible with minimal computations. This research focuses on emotion recognition induced by visual contents of images, or REVC for short, using ARTphoto dataset. Twenty-two...
This paper presents a novel local posture orientation-context descriptor, and proposes a FDDL(Fisher discriminant dictionary learning) method based on local orientation-preserving(LOP-FDDL) for sparse coding in action recognition task. To take full use of the information about the position of the local body-part related to the center of the torso, ant the spatial-temporal shape changes of the human...
In this paper, we proposed a dorsal hand vein recognition method based on Convolutional Neural Network (CNN), compared the recognition rate of different depth CNN models and analyzed the influence of dataset size on dorsal hand vein recognition rate. Firstly, the region of interest (ROI) of dorsal hand vein images was extracted, and contrast limited adaptive histogram equalization (CLAHE) and Gaussian...
Gender is one of the most useful facial attributes which are detected from human face images. In this work, we introduce a new gender classification system based on features extracted by Local Phase Quantization (LPQ) operators from intensity and Monogenic images. More detailed, the LPQ features are obtained from the input image (the intensity one) and from three other Monogenic components in the...
Autoimmune diseases are the third cause of mortality in the world. The identification of anti-nuclear antibody (ANA) via Immunofluorescence (IIF) test in human epithelial type-2 cells (HEp-2) is a conventional method to support the diagnosis of such diseases. In the present work, three popular Convolutional Neural Networks (CNNs) are evaluated for this task: LeNet-5, AlexNet, and GoogLeNet. We also...
Video surveillance systems have enabled the monitoring of complex events in several places, such as airports, banks, streets, schools, industries, among others. Due to the massive amount of multimedia data acquired by video cameras, traditional visual inspection by human operators is a very tedious and time consuming task, whose performance is affected by fatigue and stress. A challenge is to develop...
The current focus of our research is to detect and classify the plant disease in agricultural domain, by implementing image processing techniques. We aim to propose an innovative set of statistical texture features for classification of plant diseases images of leaves. The input images are taken by various mobile cameras. The Scale-invariant feature transform (SIFT) features used as texture feature...
This paper introduces a new system to identify handwritten signatures. For feature generation, we propose the Histogram of templates, while the Artificial Immune recognition System (AIRS) is used to achieve the identification task. A writer-independent strategy is proposed to train the AIRS to get an open system that can identify any new writer. Experiments are conducted on a benchmark dataset composed...
This paper presents a multiple classifier system (MCS) to identify plants species based on the texture and shape features extracted from leaf images. A diverse pool of SVM and Neural Network classifiers is trained on four different feature sets, namely, Local Binary Pattern (LBP), Histogram of Gradients (HOG), Speed of Robust Features (SURF) and Zernike Moments (ZM). Then, a static classifier selection...
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement...
Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings. In this work, we show how to improve the robustness of embeddings by exploiting independence in ensembles. We divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each...
In this paper an advanced iris-biometric comparator is presented. In the proposed scheme an analysis of bit-error patterns produced by Hamming distance-based iris-code comparisons is performed. The lengths of sequences of horizontal consecutive mis-matching bits are measured and a frequency distribution is estimated. The difference of the extracted frequency distribution to that of an average genuine...
Insulator is an important component in the power grid. Therefore, faulty insulator can cause a great damage to the power grid that would lead to leakage currents flowing through line supports. This leads to increase in electrical loses, voltage drop and put human safety to risk. Hence, it is very important to monitor the condition of an insulator before resulting to a great damage in the power grid...
Despite of the ultra-wideband (UWB) system's robustness against multipath in cluttered environments, a number of challenges remain before UWB localization can be implemented. In particular, non-line-of-sight (NLOS) propagation is especially critical for high-resolution localization systems because non-negligibly positive biases will be introduced in distance measurements, thus degrading the localization...
In recent years, Deep Neural Networks (DNNs) have been of special interest in the area of image processing and scene perception. Albeit being effective and accurate, DNNs demand challenging computational resources. Fortunately, dedicated low bitwidth accelerators enable efficient, real-time inference of DNNs. We present an approximate evaluation method and a specialized multiplierless accelerator...
Facial recognition applications present a great interest in the area of computer vision, with various methods and approaches that provide impressive performance. However, not all studies investigate the possibilities of using proper feature extraction methods with efficient classifiers, for applications that facial expression is not required for detection. In this sense, we propose another facial...
Histopathological analysis of tissues has been gaining a lot of interests recently, from developing computer algorithms to assist pathologists from cell detection and counting, to tissue classification and cancer grading. With the advent of whole slide imaging, the field of digital pathology has gained enormous popularity, and is currently regarded as one of the most promising avenues of diagnostic...
Identification of spoken word(s) can be used to control external device. This research was result word identification in speech using Mel-Frequency Cepstrum Coefficients (MFCC) and Learning Vector Quantization (LVQ). The output of system operated the computer in certain genre song appropriate with the identified word. Identification was divided into three classes contain words such as "Klasik",...
Most activity-based person identity recognition methods operate on video data. Moreover, the vast majority of these methods focus on gait recognition. Obviously, recognition of a subject's identity using only gait imposes limitations to the applicability of the corresponding methods whereas a method capable of recognizing the subject's identity from various activities would be much more widely applicable...
Detecting potential aerial threats like drones with computer vision is at the paramount of interest for the protection of critical locations. This type of a system should prevent efficiently the false alarms caused by non-malign objects such as birds, which intrude the image plane. In this paper, we propose an improved version of a previously presented Speeded-up Robust Feature Transform (SURF) based...
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