3-D Mesh geometry compression with set partitioning in the spectral domain

This paper explains the development of a highly efficient progressive 3-D mesh geometry coder based on the region adaptive transform in the spectral mesh compression method. A hierarchical set partitioning technique, originally proposed for the efficient compression of wavelet transform coefficients in high-performance wavelet-based image coding methods, is proposed for the efficient compression of the coefficients of this transform. Experiments confirm that the proposed coder employing such a region adaptive transform has a high compression performance rarely achieved by other state of the art 3-D mesh geometry compression algorithms. A new, high-performance fixed spectral basis method is also proposed for reducing the computational complexity of the transform. Many-to-one mappings are employed to relate the coded irregular mesh region to a regular mesh whose basis is used. To prevent loss of compression performance due to the low-pass nature of such mappings, transitions are made from transform-based coding to spatial coding on a per region basis at high coding rates. Experimental results show the performance advantage of the newly proposed fixed spectral basis method over the original fixed spectral basis method in the literature that employs one-to-one mappings.This work was supported in part by the Scientific and Technological Research Council of Turkey, and conducted under Project 106E064Publisher's Versio

Computerized detection of spina bifida using SVM with Zernike moments of fetal skulls in ultrasound screening

A computer aided detection scheme for the neural tube defect of spina bifida is proposed. Features from Zernike moments of fetal skull regions viewed by ultrasound are utilized in SVM classification. Rotational invariance of magnitudes of Zernike moments and their easy normalization with respect to translation and scale make them attractive for image and shape description. In particular, they are perfect candidates for classifying shapes of fetal skulls that possess markers of spina bifida. The automated detection system may act in decision support to help specialists avoid false negatives. Problems of rarity are handled with combinations of oversampling and undersampling. A variant of the synthetic minority oversampling technique (SMOTE) and random undersampling (RU) have been applied on training data. Experiments show the trade-off in various performance indicators depending on different sampling choices. The average values of 0.6276 F-measure and 0.6306 GMRP are achieved on non-sampled (original) test sets when training is performed using sampled data after 400% borderline-SMOTE followed by 50% RU with respective accuracy and specificity realizations of 94% and 98%. © 2018 Elsevier Ltd2007K120610 TÜTF-GOKAEK 2014/85, 09/07 Firat University Scientific Research Projects Management Unit: BAP 10A01D5, BAP 14A01P2My thanks are for Füsun Varol from Trakya University and İbrahim Kalelioğlu from Istanbul University, especially for providing ultrasound data. Ethical approval was provided by the Scientific Research Ethical Committee of the Medical Faculty of Trakya University on 30/04/2014 with protocol code TÜTF-GOKAEK 2014/85 and decision reference 09/07. I also thank Fikret Gürgen and Lale Akarun from Boğaziçi University and the Computer Engineering Department of the Faculty of Engineering of Bülent Ecevit University. The funding resources are the Scientific Research Projects fund ( BAP 10A01D5 , BAP 14A01P2 ) of Boğaziçi University and the Turkish Ministry of Development under the TAM Project, number 2007K120610

Compressing mesh geometry using spectral methods and a set partitioning approach

Orcay, Özgür (Dogus Author) -- Conference full title: 14th Signal Processing and Communications Applications, IEEE 2006; Antalya; Turkey; 17 April 2006 through 19 April 2006.We propose a mesh geometry coder that utilizes spectral methods and a set partitioning approach for coding the spectral coefficients. The spectral method of [1] not only achieved high rate-distortion performance on irregular meshes, but also allowed progressive transmission of meshes by truncating the coefficient vector and performing reconstruction with a small subset of coefficients that contain most of the total energy. In this paper, mesh geometry is projected onto an orthonormal basis that is derived from the mesh topology as in [1], and the spectral coefficients are coded with the set partitioning sorting algorithm of [2]. Since the method achieves implicit bit allocation to the spectral coefficients of the three coordinates and efficiently codes the significant coefficient location information by jointly coding the zeroes in the bit planes of these coefficients the rate- distortion performance of the proposed method is superior to that of [1] as demonstrated by our experiments on common irregular meshes. The generated bit stream is also truly embedded.Spektral dönüşümle elde edilen katsayıları küme bölüntüleme yaklaşımlarıyla işleyerek 3B nesne geometrilerini kodlayan bir yöntem öneriyoruz. [1]' de anlatılan spektral yöntem düzensiz tel filelerde yüksek hız-bozunum başarımı sağlamakla kalmayıp, geriçatımı, katsayı vektörünü kırparak elde edilen ve toplam enerjisinin büyük bir bölümünü taşıyan alt vektörüyle gerçekleştirdiği için aşamalı aşamalı iletim de sağlayabilmektedir. Önerilen spektral yöntemde, nesne geometrisinin [1]'de olduğu gibi topolojiden türetilen birimdik bir taban üzerine izdüşümü alınmakta ve elde edilen katsayılar [2]'nin küme bölüntüleme algoritmasıyla kodlanmaktadır. Yöntem üç koordinata ait spektral katsayılara dolaylı bit ataması başardığı ve önemli katsayılara ait konum bilgisini bu katsayıların bit düzlemlerindeki sıfırlarını birleşik kodlayarak verimli kodlama sağladığı için, yaygın düzensiz tel fileler üzerinde yaptığımız deneylerde [1]'e göre daha iyi hız-bozunum başarımı vermektedir. Üretilen bit katarı da tamamen gönüllüdür

Predictive vector quantization of 3-D polygonal mesh geometry by representation of vertices in local coordinate systems

A large family of lossy 3-D mesh geometry compression schemes operate by predicting the position of each vertex from the coded neighboring vertices and encoding the prediction error vectors. In this work, we first employ entropy constrained extensions of the predictive vector quantization and asymptotically closed loop predictive vector quantization techniques that have been suggested in [3] for coding these prediction error vectors. Then we propose the representation of the prediction error vectors in a local coordinate system with an axis coinciding with the surface normal vector in order to cluster the prediction error vectors around a 2-D subspace. We adopt a least squares approach to estimate the surface normal vector from the non-coplanar, previously coded neighboring vertices. Our simulation results demonstrate that the prediction error vectors can be more efficiently vector quantized by representation in local coordinate systems than in global coordinate systems.Publisher's Versio

3-D object mesh geometry compression with vector quantization

Bu çalışmanın amacı, bağlanırlık kodlaması için gerçeklenmiş [I]'deki algoritmayı bazı yenilikler ile uygulamak ve koordinat sıkıştırması için 3-B (3 Boyutlu) nesne gösterimleri alanında kullanımı kısıtlı olan kayıplı bir göz sıkıştırma algoritması geliştirmektir. İlk kısımda, bağlanırlık kodlamasını gerçeklemek için kullanılan algoritma ([I]) üzerinde yapılan ufak değişiklikler ile amaca uygun iyileştirmeler sağlanmıştır. İkinci kısımda ise bu alanda kullanımı kısıtlı olan Vektor Nicemleme yöntemleri gerçekleştirilmiştir. Çalışmanın diğer önerilen algoritmalardan farkı, Entropi kısıtlı Vektör Nicemleme (ECVQ) yönteminin geometri sıkıştırma algoritması olarak kullanılmasıdır. Bu sayede gerek bağlanırlık gerekse geometri sıkıştırması algoritmaları için tatmin edici hata düşürme oranlarına ulaşılmıştır.In this study, the objective is to develop a new combined method for efficient compression of classical 3-D object mesh representation. This can be realized in two primary steps: Mesh connectivity coding and data (geometry) compression. For realizing the first step, the algorithm of Isenburg [1] has been employed. For the second step, vector quantization methods have been used to compress the vertex coordinate. The difference between our study and the others is that our study uses ECVQ method for vertex coordinate compression to improve the results.Publisher's Versio

Predictive vector quantization of 3-D polygonal mesh geometry by representation of vertices in local coordinate systems

Konur, Umut (Dogus Author)A large family of lossy 3-D mesh geometry compression schemes operate by predicting the position of each vertex from the coded neighboring vertices and encoding the prediction error vectors. In this work, we first employ entropy constrained extensions of the predictive vector quantization and asymptotically closed loop predictive vector quantization techniques that have been suggested in [3] for coding these prediction error vectors. Then we propose the representation of the prediction error vectors in a local coordinate system with an axis coinciding with the surface normal vector in order to cluster the prediction error vectors around a 2-D subspace. We adopt a least squares approach to estimate the surface normal vector from the non-coplanar, previously coded neighboring vertices. Our simulation results demonstrate that the prediction error vectors can be more efficiently vector quantized by representation in local coordinate systems than in global coordinate systems

Predictive vector quantization of 3-D mesh geometry by representation of vertices in local coordinate systems

In predictive 3-D mesh geometry coding, the position of each vertex is predicted from the previously coded neighboring vertices and the resultant prediction error vectors are coded. In this work, the prediction error vectors are represented in a local coordinate system in order to cluster them around a subset of a 2-D planar subspace and thereby increase block coding efficiency. Alphabet entropy constrained vector quantization (AECVQ) of Rao and Pearlman is preferred to the previously employed minimum distortion vector quatitization (MDVQ) for block coding the prediction error vectors with high coding efficiency and low implementation complexity. Estimation and compensation of the bias in the parallelogram prediction rule and partial adaptation of the AECVQ codebook to the encoded vector source by normalization using source statistics, are the other salient features of the proposed coding system. Experimental results verify the advantage of the use of the local coordinate system over the global one. The visual error of the proposed coding system is lower than the predictive coding method of Touma and Gotsman especially at low rates, and lower than the spectral coding method of Karni and Gotsman at medium-to-high rates.Publisher's Versio

Spectral coding of mesh geometry with a hierarchical set partitioning algorithm

We propose a progressive mesh geometry coder, which expresses geometry information in terms of spectral coefficients obtained through a transformation and codes these coefficients using a hierarchical set partitioning algorithm. The spectral transformation used is the one proposed in [10] where the spectral coefficients are obtained by projecting the mesh geometry onto an orthonormal basis determined by mesh topology. The set partitioning method that jointly codes the zeroes of these coefficients, treats the spectral coefficients for each of the three spatial coordinates with the right priority at all bit planes and realizes a truly embedded bitstream by implicit bit allocation. The experiments on common irregular meshes reveal that the distortion-rate performance of our coder is significantly superior to that of the spectral coder of [10]

Predictive vector quantization of 3-D polygonal mesh geometry by representation of vertices in local coordinate systems

Orcay, Özgür (Dogus Author)A large family of lossy 3-D mesh geometry compression schemes operate by predicting the position of each vertex from the coded neighboring vertices and encoding the prediction error vectors. In this work, we first employ entropy constrained extensions of the predictive vector quantization and asymptotically closed loop predictive vector quantization techniques that have been suggested in [3] for coding these prediction error vectors. Then we propose the representation of the prediction error vectors in a local coordinate system with an axis coinciding with the surface normal vector in order to cluster the prediction error vectors around a 2-D subspace. We adopt a least squares approach to estimate the surface normal vector from the non-coplanar, previously coded neighboring vertices. Our simulation results demonstrate that the prediction error vectors can be more efficiently vector quantized by representation in local coordinate systems than in global coordinate systems.European Association for Signal Processing (EURASIP), KAREL, TUBITAK, NORTEL Networks - Business Without Boundaries, ARGELA Technologies