{"title":"Predicting quality measures in beef cattle using ultrasound imaging","authors":"W. Harron, R. Dony","doi":"10.1109/CIIP.2009.4937887","DOIUrl":"https://doi.org/10.1109/CIIP.2009.4937887","url":null,"abstract":"A method of determining two quality measures of beef cattle using different classification networks is presented. The method involves calculating texture features from ultrasound images of the beef cattle and then predicting the final percentage intramuscular fat (IMF) and marbling grades associated with the beef cattle. This method can be used in the cattle industry to enhance current breeding techniques.","PeriodicalId":349149,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Image Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129763983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A dual belief propagation method for shape recognition","authors":"P. Tipwai, S. Madarasmi","doi":"10.1109/CIIP.2009.4937886","DOIUrl":"https://doi.org/10.1109/CIIP.2009.4937886","url":null,"abstract":"We present a shape recognition framework which includes two steps: shape searching and shape matching by deformation. First, the user can draw a contour shape descriptor as a search template. The first Bayesian belief propagation (BP I) algorithm is used to find possible targets allowing for translation, scale, and rotation transformations to all contours in a cluttered image. The contour segments with common transformation values are grouped and hypothesized as belonging to the contour in the search template. The search template is then transformed for each possible transformation value. A second belief propagation (BP II) is applied to perform a deformable contour matching. The matching score or cost function determines whether there is an actual match. The algorithm overcomes the weaknesses of the other approaches since it does not require any pre-processing to detect feature points, it can match targets at any position, scale, or rotation transformations, and it does not use any accumulation space that my have peak clustering problems such as in the Hough Transform.","PeriodicalId":349149,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Image Processing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116371996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seungdo Jeong, Sang-Wook Kim, Whoiyul Kim, Byung-Uk Choi
{"title":"Effective dimensionality reduction in multimedia applications","authors":"Seungdo Jeong, Sang-Wook Kim, Whoiyul Kim, Byung-Uk Choi","doi":"10.1109/CIIP.2009.4937885","DOIUrl":"https://doi.org/10.1109/CIIP.2009.4937885","url":null,"abstract":"In multimedia information retrieval, multimedia data such as images and videos are represented as vectors in high-dimensional space. To search these vectors efficiently, a variety of indexing methods have been proposed. However, the performance of these indexing methods degrades dramatically with increasing dimensionality, which is known as the dimensionality curse. To resolve the dimensionality curse, dimensionality reduction methods have been proposed. They map feature vectors in high-dimensional space into vectors in low-dimensional space before the data are indexed. This paper proposes an improvement for the previously proposed dimensionality reduction. The previous method uses the norm and the approximated angle for every subvector. However, more storage space and a number of cosine computations are required because of multiple angle components. In this paper, we propose an alternative method employing a single angle component instead of respective angles for all the subvectors. Because only one angle for every subvector is considered, though the loss of information regarding the original data vector increases, which degrades the performance slightly, we can successfully reduce storage space as well as a number of cosine computations. Finally, we verify the superiority of the proposed approach via extensive experiments with synthetic and real-life data sets.","PeriodicalId":349149,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Image Processing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126914156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Felix Bollenbeck, R. Pielot, D. Weier, W. Weschke, U. Seiffert
{"title":"Inter-modality registration of NMRi and histological section images using neural networks regression in Gabor feature space","authors":"Felix Bollenbeck, R. Pielot, D. Weier, W. Weschke, U. Seiffert","doi":"10.1109/CIIP.2009.4937876","DOIUrl":"https://doi.org/10.1109/CIIP.2009.4937876","url":null,"abstract":"Image registration is amongst the most prominent problems in image processing and computer vision. Particularly in biomedical applications, automated alignment of image data from different imaging modalities has received great attention, delivering a high value added for analysis and diagnosis by integrating spatial information of two or more assays. In this context, the use of entropy based mutual information between images has been widely propagated to capture the relation between differential intensity distributions. In this work we address the problem of matching two different intensity distributions in a supervised learning scenario: We approximate a function relating both intensity distributions using a regression neural network predicting intensity values of one modality to the other, thereby allowing direct intensity difference registration. Predictions are based on a Gabor space representation of the input image, in order to capture local image structures. In experiments we show that the approach is i) able to learn a function to predict intensity values and ii) the predictions can be used to correctly register images by direct intensity differences minimization. The latter has the advantage of being computationally appealing and more stable concerning the optimization framework, which we exploit in registering histological section and NMRi data of plant specimen.","PeriodicalId":349149,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Image Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131216989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hybridization of particle swarm optimization with the K-Means algorithm for image classification","authors":"C. Hung, Li-Yong Wan","doi":"10.1109/CIIP.2009.4937881","DOIUrl":"https://doi.org/10.1109/CIIP.2009.4937881","url":null,"abstract":"The K-means algorithm is one of the widely used clustering algorithms in the image classification systems. However, the K-Means algorithm is easily trapped into the local optimal solutions. Several optimization techniques have been proposed to solve this problem such as genetic algorithms, simulated annealing and swarm intelligence. In this paper, we develop hybrid techniques using different particle swarm optimization (PSO) heuristics to optimize the K-Means algorithm and examine the reliability of parametric values for different variants of PSO and K-means algorithms. These PSO heuristics include linear inertia reduction, constriction factor, and dynamic inertia and maximum velocity reduction. The performance of these hybridization of PSO and the K-means algorithms was tested on the image segmentation. These PSO heuristics can make the K-means algorithm more stable for finding better solutions and less dependent on the initial cluster centers based on the preliminary experimental results.","PeriodicalId":349149,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Image Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121816169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Contextual classification of high-resolution satellite images","authors":"Olfa Besbes, N. Boujemaa, Z. Belhadj","doi":"10.1109/CIIP.2009.4937878","DOIUrl":"https://doi.org/10.1109/CIIP.2009.4937878","url":null,"abstract":"We propose a non-homogeneous Conditional Random Field built over an adjacency graph of superpixels for contextual classification of high-resolution satellite images. By introducing the contextual histogram descriptor, our model includes spatially dependent unary and pairwise potentials that capture contextual interactions of the data as well as the labels. This results the non-homogeneity of the fields which improves the accuracy of the classification. Furthermore, our discriminative model performs a multi-cue combination by incorporating efficiently color, texture, edge, curvilinear continuity and familiar configuration cues. As for potentials, both local and global feature functions are learned using joint boosting whereas a likelihood ratio is learned to derive the pairwise edge potential. In this model, the optimal scene interpretation is inferred using a cluster sampling method, the Swendsen-Wang Cut algorithm. Promising results are shown on SPOT-5 satellite images.","PeriodicalId":349149,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Image Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131203670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Khayat, J. Razjouyan, Mina Aghvami, Hamid Reza Shahdoosti, B. Loni
{"title":"An automated GA-based fuzzy image enhancement method","authors":"O. Khayat, J. Razjouyan, Mina Aghvami, Hamid Reza Shahdoosti, B. Loni","doi":"10.1109/CIIP.2009.4937874","DOIUrl":"https://doi.org/10.1109/CIIP.2009.4937874","url":null,"abstract":"This paper presents an automated algorithm for image enhancement. A novel parametric indices of fuzziness (PIF) is introduced, which serves as the optimization criterion of the contrast enhancement procedure. The proposed PIF comprises the Sugeno class of involutive fuzzy complements and the first order fuzzy moment of the image. The PIF as the measure of fuzziness should be maximized, and the maximum of PIF is tuned based on the first-order fuzzy moment of the image. The parameters of the transformation function are found by the genetic algorithm aiming to maximize the PIF. Finally, several experiments are made to demonstrate the efficiency of the proposed method.","PeriodicalId":349149,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Image Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133942036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A video watermarking algorithm based on pseudo 3D DCT","authors":"Hui-Yu Huang, Cheng-Han Yang, W. Hsu","doi":"10.1109/CIIP.2009.4937884","DOIUrl":"https://doi.org/10.1109/CIIP.2009.4937884","url":null,"abstract":"In this paper, we propose an adaptive video watermarking algorithm based on a pseudo 3D DCT to insert the high transparency and slight distortion messages to resist the attacks. The watermark is mainly inserted into the uncompressed domain by adjusting the correlation between DCT coefficients of the selected blocks, and the watermark extraction is blind, i.e., no original unwatermarked video is needed for watermark extraction. The system consists of pseudo 3D DCT technique, watermark embedding, and extraction. A pseudo 3D DCT technique will utilize to calculate the embedding factor and the advantageous messages. In the embedding process, using the quantization index modulation (QIM), we embed the watermark into the quantization regions from the successive raw frames in the uncompressed domain and record the relative information to create a secret embedding key. This secret embedding key will further apply to watermark extraction. Experimental results show that the proposed method can obtain the good performance in transparency and robustness against various attacks such as filtering, compression, and addition of noise.","PeriodicalId":349149,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Image Processing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133316313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Gabor wavelet based vessel segmentation in retinal images","authors":"M. Akram, A. Tariq, S. Nasir, S. Khan","doi":"10.1109/CIIP.2009.4937890","DOIUrl":"https://doi.org/10.1109/CIIP.2009.4937890","url":null,"abstract":"Retinal image vessel segmentation and their branching pattern are used for automated screening and diagnosis of diabetic retinopathy. Vascular pattern is normally not visible in retinal images. We present a method that uses 2-D Gabor wavelet and sharpening filter to enhance and sharpen the vascular pattern respectively. Our technique extracts the vessels from sharpened retinal image using edge detection algorithm and applies morphological operation for their refinement. This technique is tested on publicly available DRIVE database of manually labeled images. The validation of our retinal image vessel segmentation technique is supported by experimental results.","PeriodicalId":349149,"journal":{"name":"2009 IEEE Symposium on Computational Intelligence for Image Processing","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128613262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}