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.     

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.     

五号桩—长堤地区沙河街组砂体储集性及控制因素研究

沙河街组是沾化凹陷东部五号桩-长堤地区重要的储油层系，主要目的层段为沙三段和沙二段。沙三段和沙二段主要发育有浊积扇、水下冲积扇和滩坝沉积体系，在浊积扇、水下冲积扇中发育辫状水道、扇中前缘、辫状水道间以及扇根、扇缘等沉积微相类型，其中辫状水道、扇中前缘砂体物性好，原生孔隙及次生孔隙均较发育，辫状水道间以及扇根砂体物性较差。其储集特征主要受岩性、沉积微相、成岩作用等因素控制，结合该地区实际情况，将沙二段、沙三段储集层划分为好、中、差3种类型。图4表1参7     

复杂连通区域扫描线填充算法的问题及改进

针对复杂连通区域扫描线填充算法可能出现的重复填充错误，分析了产生错误的原因，并给出了相应的改进办法．从而实现了复杂连通区域的快速正确填充。     

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.     

Convex relaxation for a 3D spatiotemporal segmentation model using the primal-dual method

A method based on 3D videos is proposed for multi-target segmentation and tracking with a moving viewing system. A spatiotemporal energy functional is built up to perform motion segmentation and estimation simultaneously. To overcome the limitation of the local minimum problem with the level set method, a convex relaxation method is applied to the 3D spatiotemporal segmentation model. The relaxed convex model is independent of the initial condition. A primal-dual algorithm is used to improve computational efficiency. Several indoor experiments show the validity of the proposed method.