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It is indisputable that the public's sentiment toward corporations determines their brand image and enterprise value. Nonetheless, the majority of studies have merely focused on financial indexes and polarity-based sentiment analysis. The absence of an effective sentiment analysis method has limited corporations' opportunity to grasp and manage the public's sentiment systematically. In this research, we propose a practical multidimensional sentiment analysis methodology, which consists of constructing a multi-categorical lexicon process using morphological analysis, detecting corporate events with social network analysis, and classifying detected events by stakeholder-based event classification. We tested the proposed framework with data collected from Twitter. Examining the analysis, we concluded that the suggested approach provides more worthwhile information about how the public's sentiment is composed and what the causes are of each sentiment, which could not be identified from previous studies. We are also convinced about the feasibility of the proposed methodology for business purposes and the possibility of extended usage regardless of discipline.

Although the perceptual image hashing is one of the promising techniques for image authentication, most existing methods cannot well distinguish content changing manipulations from acceptable content preserving modifications, especially when the size of the manipulated area is relatively small. In this regard, a new image hash algorithm is proposed to enhance the tamper detection capability by employing one of the most well-known local feature descriptors, Histogram of Oriented Gradients (HOG), for the feature extraction method. In this paper, image intensity transform using a random number generator, HOG feature computation, Successive Mean Quantization Transform (SMQT), and bit-level permutation are utilized to obtain a secure and robust hash value. Additionally, the performance of the proposed method is measured, and compared with existing algorithms by the Receiver Operating Characteristics (ROC) analysis.

We describe a robot system that can navigate in indoor environments, such as office buildings and laboratories, without having a detailed map of its environment and which can accept symbolic commands such as “go through the door on the left of the first desk on your right” (expressed in a formal language). Such a system can operate in different instances of similar environments and does not require the effort of constructing a detailed map of the environment. It is also not sensitive to changes in the environment such as those caused by moving furniture. It uses generic representations of the objects in the environment such as walls, desks and doors to recognize them for the purposes of landmark detection and avoids obstacles which may not be modeled explicitly.

Autonomous indoor navigation requires a robot to have abilities to recognize landmarks and to avoid obstacles. Our goal is to provide these abilities in a generic way so that the robot need not have an accurate and complete geometric model of all the objects in its environment. Rather, we wish to provide abilities to recognize common objects such as desks and doors which can be used as landmarks. We use a functionality-based representation for objects. Objects consist of functional parts, which are characterized by their significant surfaces, and by the accompanying functional evidence for them. Our system works with planar surfaces only and assumes that the objects are in a ‘standard’ pose. The localization and orientation of an object are represented with the most significant surface in an ‘s-map’. Our system has been tested on a number of real scenes where it performs robustly and efficiently; some results are shown in the paper.

Medical image segmentation is the crucial process for three-dimensional (3D) modeling. Many studies have claimed to suggest more accurate ways of segmentation. However, such research seems to have the drawback that it works against the parsimony principle. These methods have to reesegment the regions of interest (ROls) for the already segmented data, without taking advantage of the vast amount of past segmentation results. The present report proposes a “seqrnentatlon by evolution” method. It is an advanced segmentation paradigm that incorporates the existing segmentation results. The “seqrnentation by evolution” paradigm was applied to the color images of the Visible Human data.

In this paper, we propose a fully automatic method for vertebra segmentation in the CT volume data. The method constructs 3D fences that separate adjacent vertebrae from valley-emphasized Gaussian images. Initial curves for the 3D fences are extracted from intervertebral discs, detected with anatomical characteristics, then optimized using a deformable model. A minimum cost path finding method corrects any erroneous curves trapped into a local minimum. Final volume is labeled with help of the 3D fences by a fence-limited region growing method. This method has been applied to 50-patient data sets and has proved to be very successful.

The use of virtualization is rapidly expanding from server consolidation to various computing systems including PC, multimedia set-top box and gaming console. However, different from the server environment, timeliness response for the external input is an essential property for the user-interactive applications. To provide timeliness scheduling of virtual machine this paper presents a priority-based scheduling scheme for virtual machine monitors. The suggested scheduling scheme selects the next task to be scheduled based on the task priorities and the I/O usage stats of the virtual machines. The suggested algorithm was implemented and evaluated on Xen virtual machine monitor. The results showed that the average response time to I/O events is improved by 5~22% for highly consolidated environment.

The popularity of mobile and multimedia applications made real-time support a mandatory feature for embedded operating systems. However, the current situation is that the overall performance is significantly degraded due to the real-time support. This paper suggests a novel scheme to minimize the performance degradation in embedded operating systems with real-time support. Especially, we propose transparent and selective real-time interrupt services which transparently monitor the system and postpone interrupt handling that are not relevant to real-time tasks. The proposed scheme was implemented on the Linux 2.6 kernel and the experimental results show that our scheme improves the throughput by up to 86% for Hackbench benchmark while providing almost the same scheduling latency compared to the previous work.

We introduce an efficient method for recognition and localization of generic objects for robot navigation, which works on real scenes. The generic objects used in our experiments are desks and doors as they are suitable landmarks for navigation. The recognition method uses significant surfaces and accompanying functional evidence for recognition of such objects. Currently, our system works with planar surfaces only and assumes that the objects are in a "standard" pose. The localization and orientation of an object are represented with the most significant surface in an "s-map". Some results for laboratory scenes are given. >

RATIONALE AND OBJECTIVES. To document a simple algorithm that quantifies power Doppler signals by computer-based statistical image analysis. METHODS. Personal computer-based software was developed. Color pixels representing power Doppler signals were separated from the background gray pixels in power Doppler images converted in BMP format. To avoid misregistration during this segmentation, a threshold value is required to adequately distinguish between gray and color pixels. Equating the intensity of each color pixel to a Power Value (PV), a histogram of Power Values in power Doppler images was obtained. RESULTS. The software was designed to allow users to adjust two thresholds (Difference Threshold and Intensity Threshold) by observing the segmentation results in real time. The software calculates the following indexes within any region of interest as defined by the operator: mean PV, peak PV, standard deviation of PV, percentage power Doppler area, and integrated PV per unit area. CONCLUSIONS. Power Doppler signals can be objectively quantified using this straightforward algorithm. This simple and practical method can be applied to comparative or longitudinal studies upon vascularity or blood flow.

This paper proposes a method that segments an object in 3D image slices by tracking not only the target objects to segment but also adjacent non-target objects. The tracking methodology enables the proposed method to detect and prevent a leakage, which is one of the most challenging problems in medical image segmentation. The method also provides an efficient way of modification of segmentation results by informing only suspicious image slices because of a leakage. The proposed method is applied to segment vertebrae in angio image slices in which blood vessels are enhanced with contrast media, and produced promising results in terms of both accuracy and efficiency.

Achieving accurate segmentation results is essential for quantitative diagnosis of disease using medical image. This paper proposes a searching-and-bridging method for robust and accurate segmentation of the proximal femur, which is challenging because it has complex shape, ambiguous boundary surfaces, and interference boundary of adjacent acetabulum.

This paper proposes an approach integrating multiple segmentation methods in a systematic way, which can improve overall accuracy without deteriorating accuracy of highly confident segments of a boundary. A segmentation method produces boundary segments, which are then evaluated with an evaluation function considering pros/cons of the current and next methods to apply. Boundary segments with low confidence are replaced by next method while the other segments are kept. These steps are repeated until all segmentation methods are applied. Coarser and more robust method is applied earlier than the others. The proposed approach is implemented for the segmentation of muscles in the Visible Human color images. A balloon method, a minimum cost path finding method, and a Seeded Region Growing method are integrated. The final segmentation results showed improvements in both overall evaluation and segment-based evaluation.

We introduce a spatial representation, s-map, for an indoor navigation robot. The s-map represents the locations of obstacles in a planar domain, where obstacles are defined as any objects that can block movement of the robot. In building the s-map, the viewing triangle constraint and the stability constraint are introduced for efficient verification of vertical surfaces. These verified vertical surfaces and 3-D segments of obstacles smaller than a robot, are mapped to the s-map by simply dropping height information. Thus, the s-map is made directly from 3-D segments with simple verification, and represents obstacles in a planar domain so that it becomes a navigable map for the robot without further processing. In addition to efficient map building, the s-map represents the environment more realistically and completely. Furthermore, the s-map converts many navigation problems in 3-D, such as map fusion and path planning, into 2-D ones. We present the analysis of the s-map in terms of complexity and reliability, and discuss its pros and cons. Moreover, we show the results of the s-maps for indoor environments. >

We demonstrate a novel method that employs a frame-accumulation and shaped-function technique to improve an image sensor's detection sensitivity using probability statistics. It can obtain the unbiased estimate for the low-light-level image signal, thus upgrading the signal-to-noise ratio to a high degree. It was verified by an experiment in a sealed box. By the help of a variable light beam-shaped function saw-tooth signal in front of a camera, the low-light-level shadow image that was invisible to the camera could be revealed clearly from the frame accumulation data. The method can surpass an image sensor's detection limitation.

To reduce the redundancy within samples that result from a uniform sampling method like PCM, nonuniform sampling or nonredundant-sample coding methods can be considered. However, it is well known that when conventional nonuniform sampling methods are applied directly to the speech signal, the required amount of data is comparable to, or more than that of the uniform sampling method. To overcome this problem, a new nonuniform sampling method is proposed, in which nonuniform sampling is applied to the low-pass filtered speech signal and the remaining signals are compensated by 16 colored Gaussian random noise with various noise levels. By this method, the speech signal waveform can be encoded by a 1.8 times larger compression ratio than that of the conventional nonuniform sampling method.