ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization

Clip-art image segmentation is widely used as an essential step to solve many vision problems such as colorization and vectorization. Many of these applications not only demand accurate segmentation results, but also have little tolerance for time cost, which leads to the main challenge of this kind of segmentation. However, most existing segmentation techniques are found not sufficient for this purpose due to either their high computation cost or low accuracy. To address such issues, we propose a novel segmentation approach, ECISER, which is well-suited in this context. The basic idea of ECISER is to take advantage of the particular nature of cartoon images and connect image segmentation with aliased rasterization. Based on such relationship, a clip-art image can be quickly segmented into regions by re-rasterization of the original image and several other computationally efficient techniques developed in this paper. Experimental results show that our method achieves dramatic computational speedups over the current state-of-the-art approaches, while preserving almost the same quality of results.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

Allocentric Versus Egocentric Spatial Memory After Unilateral Temporal Lobectomy in Humans.

Thirty patients who had undergone either a right or left unilateral temporal lobectomy (14 RTL; 16 LTL) and 16 control participants were tested on a computerized human analogue of the Morris Water Maze. The procedure was designed to compare allocentric and egocentric spatial memory. In the allocentric condition, participants searched for a target location on the screen, guided by object cues. Between trials, participants had to walk around the screen, which disrupted egocentric memory representation. In the egocentric condition, participants remained in the same position, but the object cues were shifted between searches to prevent them from using allocentric memory. Only the RTL group was impaired on the allocentric condition, and neither the LTL nor RTL group was impaired on additional tests of spatial working memory or spatial manipulation. The results support the notion that the right anterior temporal lobe stores long-term allocentric spatial memories. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Cluster analysis in industrial market segmentation through artificial neural network

Market segmentation has commonly applied cluster analysis. This study intends to make the comparison of conventional two-stage method with proposed two-stage method through the simulated data. The proposed two-stage method is the combination of self-organizing feature maps and K-means method. The simulation results show that the proposed scheme is better than the conventional two-stage method based on the rate of misclassification.     

A Novel Segmentation and Feedback Model for Resolving Contention in Optical Burst Switching

A novel segmentation and feedback model (SFM) applied to resolve contention is proposed. Simulation and performance analyses show that the SFM effectively avoids contentions in optical burst switching (OBS). The long delay time of deflection routing and the immature technology of wavelength converters and optical buffers are not deployed in the SFM. The SFM does not only realize fast switching but also allows preemption by higher priority bursts.     

Learning-based dynamic coupling of discrete and continuous trackers

We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.     

Pedestrian detection and tracking in infrared imagery using shape and appearance

In this paper, we present an approach toward pedestrian detection and tracking from infrared imagery using joint shape and appearance cues. A layered representation is first introduced and a generalized expectation-maximization (EM) algorithm is developed to separate infrared images into background (still) and foreground (moving) layers regardless of camera panning. In the two-pass scheme of detecting pedestrians from the foreground layer: shape cue is first used to eliminate non-pedestrian moving objects and then appearance cue helps to locate the exact position of pedestrians. Templates with varying sizes are sequentially applied to detect pedestrians at multiple scales to accommodate different camera distances. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm that jointly exploits the shape, appearance and distance information. Experimental results with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithm.     

An improved watershed algorithm based on efficient computation of shortest paths

The present paper describes a new algorithm to calculate the watershed transform through rain simulation of greyscale digital images by means of pixel arrowing. The efficiency of this method is based on limiting the necessary neighbouring operations to compute the transform to the outmost, and in the total number of scannings performed over the whole image. The experiments demonstrate that the proposed algorithm is able to significantly reduce the running time of the fastest known algorithm without involving any loss of efficiency.     

Image segmentation towards new image representation methods

Very low bit-rate video coding has recently become one of the most important areas of image communication and a large variety of applications have already been identified. Since conventional approaches are reaching a saturation point, in terms of coding efficiency, a new generation of video coding techniques, aiming at a deeper “understanding” of the image, is being studied. In this context, image analysis, particularly the identification of objects or regions in images (segmentation), is a very important step. This paper describes a segmentation algorithm based on split and merge. Images are first simplified using mathematical morphology operators, which eliminate perceptually less relevant details. The simplified image is then split according to a quad tree structure and the resulting regions are finally merged in three steps: merge, elimination of small regions and control of the number of regions.     

一种基于数据竞争的高分辨率图像的聚类分割算法

近些年,机器人技术得到了迅猛的发展,应用越来越广泛.随着机器人技术的推广和普及,对机器人使用的要求也越来越高,其中对智能机器人的要求尤显迫切.机器视觉是智能机器人研究领域的一个重要研究方向.在机器人视觉系统中,核心问题是目标提取,对目标实时、准确、快速提取的关键技术是图像分割.由于机器人感知的环境的复杂性及目标的多样性,往往导致机器人感知获得的图像数据量较大且图像本身存在不可预知的复杂性,这就对准确的目标分割和提取处理提出了挑战性问题.本文针对高分辨率图像数据集的分割处理,提出一种新的聚类算法,即根据数据点能量和的大小识别类代表点和类成员点,通过数据点间的竞争识别出最有能力成为簇成员的数据点,并将其与mean shift聚类算法有效地结合应用于彩色图像分割问题中,能够快速高效地实现高分辨率图像的目标分割,并得到较好的图像分割效果.实验结果表明,本文算法在分割效果和分割效率上明显优于传统聚类算法.