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Constructive understanding of computational principles of multimedia, signal and visual information processing, perception and cognition is one of the most fundamental challenges of contemporary science. Deeper insight into such computational intelligence helps to advance intelligent systems research to achieve robust performance. Implementing integrated principles in artificial systems may help us...
Contextual understanding, which consists of memory, reasoning and recall, is a key process of human-computer interactions and interfaces. To determine the possibility of predicting the recall accuracy of reading and memorizing tasks using features of eye movements for a targeted text, a contextual understanding task experiment was conducted. The relationship between eye movement during memorization...
In order to create reading support systems, the feasibility of using viewed size predictions was evaluated using features of EEG waveforms. Viewer's occipital single channel potentials were measured while six sizes of Landolt circles were shown. Features such as series of potentials, latencies of P1 and N1 peaks, and 5 vectors of Fourier descriptors (FD) were extracted from the EEG waveforms. The...
In this paper, a lossless data-hiding approach is presented based on quantized coefficients of discrete wavelet transform (DWT) in the frequency domain to embed secret message. Using the quantized coefficients for 9/7 wavelet filter in DWT, we embed secret data into the successive zero coefficients of the medium-high frequency components in each reconstructed block for 3-level 2-D DWT of a cover-image...
This paper describes parameter setting of self-quotient ε-filter (SQEF) using Histograms of Oriented Gradients (HOG) feature distance. Parameter setting problem is generally solved by maximization or minimization of some objective evaluation functions such as correlation and statistical independence. However, it is not always easy to set such objective evaluation functions when we handle feature extracted...
In this work, we investigate the use of a multimodal search framework to deal with a representative formulation of the Sparse Component Analysis (SCA) problem. The proposed method, which employs an artificial immune network in the role of multimodal optimization tool, is explained and tested in different scenarios. The results are promising and indicate the relevance of using global search tool in...
Frame rate up-conversion (FRUC) increases the quality of a video by increasing its temporal frequency. Motion compensated and non-motion compensated frame rate up conversion techniques make up the two main classes of techniques used in this area. Halo artifacts and jaggy edges cause the quality of video to be reduced both subjectively and objectively in these techniques. In this paper, we introduce...
representing an image as a set of its key and interesting lines facilitates the image understanding and classification. In this paper, we propose a method to extract the significant and interesting lines of the scene, which probably are useful in image classification. The proposed method is inspired from the Visual Attention, which is a perceptual mechanism in human and other primates that direct...
This paper proposes a method to recognize bare hand gestures using a dynamic vision sensor (DVS) camera. Different from conventional cameras, DVS cameras only respond to pixels with temporal luminance differences, which can greatly reduce the computational cost of comparing consecutive frames to track moving objects. Due to differences in available information, conventional vision techniques for gesture...
Recently non-local means (NLM) has been known to be one of the most attractive denoising algorithms. It alters each pixel by a weighted average of pixels in the image. The weights express the level of similarity between two small patches defined for two involved pixels. There are many propositions to ameliorate the performance of this method. One of branches is to seek the whole image the most similar...
Image encryption is somehow different from text encryption due to some inherent features of image such as bulk data capacity and high correlation among pixels, which are generally difficult to handle by traditional methods. The exceptionally desirable properties of the chaotic maps such as sensitivity to initial conditions and random-like behavior have attracted the attention of cryptographers to...
In this paper, we combine image segmentation techniques and face detection methods to extract the human from scenes. Firstly, skin regions are detected and an ellipse fitting method is employed to detect the face region and consequently locate the human position. Then we propose an improved automatic seeded region growing algorithm to segment the image. The initial seeds are generated automatically,...
Attention plays an important role in natural vision. Implementing realistic attentional processes in artificial vision systems could greatly improve their performance. However, existing models of attention do not adequately capture all of its complex effects on neural activity. In particular, existing models cannot reproduce recently reported effects such as shifting and scaling of receptive fields...
In this paper, a fast motion estimation algorithm combined hybrid predictor and search pattern (called HPS) is proposed to find the best motion vector (MV). Block-based motion estimation (ME) and pattern searching strategy are used to detect the fast vector motion. The advantages of the approach can improve the performance of existing fast ME algorithm and can efficiently use to video compression...
This work is concerned with the empirical evaluation of a set of local and global features under the context of frontal (including semi-profile) and full profile face classification. Integral LBP, Integral Histograms, PCA and Optimized Face Ratios features have been evaluated using SVM classifiers. A data set of about 14,000 face and 300,000 non face images has been used in the experiments. Face images...
This paper presents a target-surround feature attention (TSFA) model for constructing attention-based visual tracking algorithm. This model extracts attentive region by distinguishing the color contrast between the interested target and its surround. A preference generator provides online feature transformation to update the target/surround biasing masks that describes the color composition associated...
Computational visual attention (CVA) model is one of the methods which focus on finding region of interesting (ROI) in an image or in a scene. Similarity attention is one important task in CVA. If there are many objects in a scene, people will pick up the most abnormal one, which perhaps the similar one or dissimilar one, according to the composition objects of the scene. Capability of similarity...
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