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Image saliency attempts to describe the most conspicuous part in an input image by mimicking human visual selective attention mechanism. Naturally, it could be adopted for improving object recognition. To demonstrate the effectiveness of saliency in object recognition, this paper proposes a salient hierarchical model. First, the traditional saliency model is modified for more robust saliency estimation...
When user uploads a video clip to the video sharing websites, a video thumbnail needs to be generated as the cover to represent the video content. In this paper, a novel video thumbnail generation framework is presented. For generating a good thumbnail, three criteria are considered: (1) the thumbnail should be distinct, in order to make user feel more pleasant; (2) the thumbnail should be easily...
The main objective of this work is to study the degree of engagement levels of human subjects in completing a series of tasks based on their physiological signals. The tasks are in the form of tracking a set of trajectories on the computer screen by using a mouse… The subjects are chosen randomly based on both sexes, aged from 20 to 40 years old. After completing the required tasks in a series of...
Human action classification is an important task in computer vision. The Bag-of-Words model uses spatio-temporal features assigned to visual words of a vocabulary and some classification algorithm to attain this goal. In this work we have studied the effect of reducing the vocabulary size using a video word ranking method. We have applied this method to the KTH dataset to obtain a vocabulary with...
Enterprise architecture (EA) frameworks have been successfully used to support enterprise engineering activities by providing graphical and machine-processable constructs to represent enterprise systems. However, existing EA frameworks lack modeling approaches to represent the increasing complexity and coupling of modern enterprises, as these frameworks do not distinguish architectural blocks supporting...
Selective visual attention is a kind of mechanism of the primate visual system for rapidly focusing on attractive objects or regions in visual environment. Numerous visual attention models have been developed and optimized over the past decades. Most of the existing models concentrate on static monocular image, but little attention has been devoted to stereo depth information which is an important...
Image interpolation, or to obtain a high-resolution image from a corresponding low-resolution image, is still a hard question. In order to better solve this question, we demonstrate a partitioned image interpolation model and propose a perception-motivated image interpolation algorithm according to human eye visual mechanism (PMIA). The PMIA main implementation includes two processes. Firstly the...
The question of scene information whether can help realistic action recognition has been investigated in this paper. The salience region of each frame in video was acquired by using Itti-Koch algorithm. The information outside the salience region represented scene information. Two action recognition methods were tested on the YouTube action dataset. One method got rid of partial scene information,...
The “Nyquist plot” is a visual analytical method we previously developed for identifying various pathological glottal conditions when applied to high-speed digital imaging (HSDI) derived glottal signals and acoustic signals. In this study, we applied the Nyquist plot to acoustic voice output aiming to: 1) identify differences between the properties of five vowel sounds commonly used in human language,...
Visual attention is useful for computer vision and it has been applied in image compression and object recognition. In existing methods on saliency detection, most of them are unrelated to the depth feature. So we propose a bottom-up saliency detection model that combines the depth feature with region contrast based saliency model and the precision and recall rate of our algorithm is higher than those...
A picture is worth a thousand words. To take advantage of powerful human vision, we generate visualizations for people to view and to understand the underlying data. However, these a thousand words do not necessarily tell the truth about the data. A good visualization can make the data understanding process effective, while a bad visualization may hinder the process, even convey misleading information...
In this paper, we apply Web images to the problem of automatically extracting video shots corresponding to specific actions from Web videos. Our framework modifies the unsupervised method on automatic collecting of Web video shots corresponding to the given actions which we proposed last year [9]. For each action, following that work, we first exploit tag relevance to gather 200 most relevant videos...
This paper proposes a novel system to assist human image analysts to effectively browse and search for objects in a camera network. In contrast to the existing approaches that focus on finding global trajectories across cameras, the proposed approach directly models the relationship among raw camera observations. A graph model is proposed to represent detected/tracked objects, their appearance and...
Falls are a major threat to the independence and quality of life of elderly people. As the worldwide population of elderly increases each year, responding to falls is essential. Computer vision systems provide a new promising solution in responding falls through detecting fall events. This paper presents a new technique in detecting falls based on human shape variation. The proposed visual based fall...
The design of automated online security and intrusion tolerance solutions for computer systems and networks is a theoretically achievable objective. However, after two decades of research, this objective has still not yet successfully passed the practicality test to become widely adopted. We believe that the main reason behind this gap lies in the misleading definition of autonomous security that...
In this paper, we present a unified statistical framework for modeling both saccadic eye movements and visual saliency. By analyzing the statistical properties of human eye fixations on natural images, we found that human attention is sparsely distributed and usually deployed to locations with abundant structural information. This new observations inspired us to model saccadic behavior and visual...
Video saliency mechanism is crucial in the human visual system and helpful to object detection and recognition. In this paper we propose a novel video saliency model that video saliency should be both consistently salient among consecutive frames and temporally novel due to motion or appearance changes. Based on the model, temporal coherence, in addition to spatial saliency, is fully considered by...
This paper explores the idea of applying evolutionary algorithms to those search spaces that are defined extensionally, i.e. by listing every item in the space. When these spaces are with a function that returns similar elements given a key element, analogies of mutation and crossover can be defined. This idea is discussed in general, and specific examples are given where the search is for images,...
We describe a novel learning scheme for hidden dependencies in video streams. The proposed scheme aims to transform a given sequential stream into a dependency structure of particle populations. Each particle population summarizes an associated segment. The novel point of the proposed scheme is that both of dependency learning and segment summarization are performed in an unsupervised online manner...
In the past decade, the bag-of-feature model has established itself as the state-of-the-art method in various visual classification tasks. Despite its simplicity and high performance, it normally works as a black box and the classification rule is not transparent to users. However, to better understand the classification process, it is favorable to look into the black box to see how an image is recognized...
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