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Virtual humans are often presented as mixed reality characters projected onto screens that are blended into a physical setting. Stereo loudspeakers to the left and right of the screen are typically used for virtual human audio. Unfortunately, stereo pairs can produce an effect known as precedence, which causes users standing close to a particular loudspeaker to perceive a collapse of the stereo sound...
A new block-based multi-metric fusion (BMMF) approach is proposed for perceptual image quality assessment. The proposed BMMF scheme automatically detects image content and distortion types in a block via machine learning, which is motivated by the observation that the performance of an image quality metric is highly influenced by these factors. Locally, image block content is classified into three...
An innovative way of object shape representation using Density Histogram of Feature Points (DHFP) is introduced and used in this paper. We have named this method Enhanced Density Histogram of Feature Points (EDHFP). We use silhouette images where the image region ξ consists of only those pixels that correspond to points on the object and have a value one (1) indicating “on” pixels. We count the number...
The block-based multi-metric fusion (BMMF) is one of the state-of-the-art perceptual image quality assessment (IQA) schemes. With this scheme, image quality is analyzed in a block-by-block fashion according to the block content type (i.e. smooth, edge and texture blocks) and the distortion type. Then, a suitable IQA metric is adopted to evaluate the quality of each block. Various fusion strategies...
A number of bottom-up saliency detection algorithms have been proposed in the literature. Since these have been developed from intuition and principles inspired by psychophysical studies of human vision, the theoretical relations among them are unclear. In this paper, we present a unifying perspective. Saliency of an image area is defined in terms of divergence between certain feature distributions...
Visual object categorisation problem has attracted significant attention during the last ten years, and the two main hypotheses adopted by virtually all methods are i) detection of visual saliency and ii) bag-of-visual-words based categorisation. It is, however, difficult to verify the hypotheses with humans since many recordings, such as gaze fixation locations, represent processing after the recognition...
This paper proposes a method for estimating the human performance of pedestrian detectability from in-vehicle camera images in order to warn a driver of the positions of pedestrians in an appropriate timing. By introducing features related to visual search and motion of the target, the proposed method estimates the detectability of pedestrians accurately. Support Vector Regression (SVR) is used to...
Analysis of composite shapes recently receives increasing amount of research attention. Different from a silhouette, a composite shape rarely contains a complete envelope. In the paper, we propose a novel envelope extraction algorithm based on the Delaunay triangulation for composite shapes. By analyzing the spatial relationship among individual components of contours and their concavities, we establish...
In this paper, we propose a new region-based saliency model to simulate the human visual attention. First, we construct a pixel-level fully-connected graph representation for an image, and perform normalized cut to segment the image based on the proximity and similarity principles. After obtaining image regions, we reconstruct a region-based fully-connected graph. Based on the saliency principle “center-surround...
The detection of salient regions from mesh surfaces is an important preprocessing step for many 3D applications, such as mesh simplification, registration, segmentation and compression, etc. The detected salient regions can facilitate the understanding of the structure and finding the regions/components that are important on 3D surfaces. This paper proposes a novel method for saliency detection by...
This paper presents a simple and effective method to compute the pixel saliency with full resolution in an image. First, the proposed method creates an image representation of four color channels through the modified computation on the basis of Itti et al.[5]. Then the most informative channel is automatically identified from the derived four color channels. Finally, the pixel saliency is computed...
We may represent human actions as a bag of spatiotemporal visual words extracted from input video sequences. For human action categorization, labeled LDA (L-LDA) is an extension of latent Dirichlet allocation (LDA) by providing action class labels to each video. To handle parameter uncertainty in L-LDA, this paper further extends L-LDA within the type-2 fuzzy set (T2 FS) framework, referred to as...
Human action recognition is an active area with applications in several domains such as visual surveillance, video retrieval and human-computer interaction. Current approaches assign action labels to video streams considering the whole video as a single image sequence. Such approaches, albeit very refined, may fail on some samples due to large variability between frames, suggesting that features extracted...
The paper presents our recent results obtained with a new auditory spatial localization based BCI paradigm in which the ERP shape differences at early latencies are employed to enhance the traditional P300 responses in an oddball experimental setting. The concept relies on the recent results in auditory neuroscience showing a possibility to differentiate early anterior contralateral responses to attended...
Saliency in 2D imagery has been receiving increasing attention over the last few years owing to the need to minimize computation requirements through visual search space reduction, especially in the field of domestic robotics. Saliency and pre-attention mechanisms such as the Itti-Koch model have largely been focused on multi-scale local features mimicking low level attention processes in visual system,...
In this paper, a new dual dictionaries learning (DDL) method is proposed for robust 3D human pose estimation. The performance and applicability of traditional methods are limited by a lack of robustness to corrupted observations caused by occlusions or poor background subtraction. Our DDL approach aims at simultaneously constructing two overcomplete dictionaries, called the visual observation dictionary...
Interest point detectors (e.g. SIFT, SURF, and MSER) have been successfully applied to numerous applications in high level computer vision tasks such as object detection, and image classification. Despite their popularity, the perceptual relevance of these detectors has not been thoroughly studied. Here, perceptual relevance is meant to define the correlation between these point detectors and free-viewing...
Despite their high stability and compactness, affine moment invariants have received a relatively little attention in action recognition literature. In this paper, we introduce an approach for activity recognition, based on affine moment invariants. In the proposed approach, a compact computationally-efficient shape descriptor is developed by using affine moment invariants. Affine moment invariants...
In this paper, we address an interesting application of computer vision technique, namely classification of Indian Classical Dance (ICD). With the best of our knowledge, the problem has not been addressed so far in computer vision domain. To deal with this problem, we use a sparse representation based dictionary learning technique. First, we represent each frame of a dance video by a pose descriptor...
We review the literatures on human evolution, organizational communication, and CMC, focusing on research addressing CMC support for the transmission of socio-emotional signals, Theory of Mind (ToM), and social capital. We develop a social capital theory of communication in organizations, linking the use of CMC for the transmission of socio-emotional signals with one's ability to develop social capital,...
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