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.     

高分辨率合成孔径雷达图像舰船检测方法

寻找针对高分辨率SAR图像的舰船目标检测算法。利用KSW双阈值分割技术，其效果比传统检测方法好，有利于进一步的目标分类和识别。且必须根据SAR图像分辨率来选择舰船检测算法。     

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.     

XIII. Yeast sequencing reports. Cloning and sequencing of the NES24 gene of Saccharomyces cerevisiae

We have cloned NES24 using a temperature-sensitive nes24-1 mutant as a host and sequenced a 3162 bp XhoI-EcoRI DNA fragment containing the NES24 gene. Computer analysis revealed that this segment contains a 1806 bp open reading frame which is needed for complementation of the nes24-1 mutation. We found SUP8 in the region upstream of the NES24 gene, placing the NES24 gene on chromosome XIII. A protein homology search indicated that NES24 encodes a new protein. The disruption of the NES24 gene resulted in temperature-sensitive growth. The sequence has been deposited in DDBJ/EmBL/GenBank data bases under Accession Number D15052.     

XV. Yeast sequencing reports. Isolation and DNA sequence of the STE13 gene encoding dipeptidyl aminopeptidase

We have isolated a mutant which exhibits partial constitutivity for a -specific gene expression in α cells. The wild-type gene was cloned and demonstrated to be allelic to the STE13 gene, which encodes the dipeptidyl aminopeptidase involved in processing of the α-factor prepropheromone. Thus, the mating defect of the ste13 mutations in α cells may result both from the production of incompletely processed α-factor and from the increased expression of a -specific genes. The STE13 open reading frame of 931 amino acids contains a putative membrane-spanning segment near its amino terminus and is 31% identical to a second yeast dipeptidyl aminopeptidase (DAP2). A null mutant of STE13 has been constructed: it is viable and sporulation-proficient. The sequence has been deposited in the GenBank data library under Accession Number L21944.     

II. Yeast sequencing reports. Sequence of a segment of yeast chromosome II shows two novel genes,one almost entirely hydrophobic and the other extremely asparagine-serine rich

A 3·2 kb EcoRI fragment of yeast Saccharomyces cerevisiae was entirely sequenced. Two new open reading frames were identified. The first is extremely hydrophobic, and would likely be an integral membrane protein. It has significant similarity to only one reported gene, a gene of unknown function from Drosophila melanogaster. The second ORF is asparagine-rich and very serine-rich, with a remarkable stretch of nearly 26 consecutive asparagine residues comprised of the same codon. It has no significant similarity to any reported gene. The fragment maps to chromosome II on the left arm between the CDC27 and ILS1 loci. The nucleotide sequence reported in this paper has been deposited in the GenBank database with the Accession Number M89908.     

The complete sequence of a 10.8 kb segment distal of SUF2 on the right arm of chromosome III from Saccharomyces cerevisiae reveals seven open reading frames including the RVS161, ADP1 and PGK genes.

We have entirely sequenced a 10,835 bp segment of the right arm from chromosome III contained in the J11D and J11D-K3B GF clones. The segment contains seven open reading frames longer then 100 amino acids. Three of them, RVS161 (Urdaci et al., 1990; Crouzet et al., 1991), ADP1 (Purnelle et al., 1991) and PGK1 (Hitzeman et al., 1982) have been described previously. YCR10C encodes a putative membrane protein. YCR8W (encoding a putative protein kinase) and YCR14C extend inside the D10H (Skala et al., 1991) and 62B5-2D clones respectively. Four ARS elements previously reported by Palzkill et al. (1986) are located between RVS161 and YCR10C.