STUDIJA DOHVATA SLIKA POMOĆU POJAČANE TRANSFORMACIJE RADONA I PCS I LDA TEHNIKA

Image Retrieval is very one of the biggest task in the recent years. It is widely used in many real time databases to retrieve related images in various fields like medicine, military, online shopping etc. This paper offers with using radon transform followed by PCA and LDA techniques for image retrieval is called as Combined Radon Space Features Set (CRSFS). Caltech 101 database image sets used in this paper. The correct direction is select means the computation time and complexity of operation is less to achieve good retrieval rate.Obrada slika je jedan od najvećih zadataka u posljednjih nekoliko godina. Naširoko se koristi u mnogim bazama podataka kad se u realnom vremenu koriste povezane slike u različitim područjima kao što su medicina, vojska, online trgovina, itd. Ovaj rad nudi pomoć radon pretvorbe i zatim PCA i LDA tehnika za popravljanje slike (CRSFS). Korištena je Caltech 101 baza slika. Ispravan smjer je odabrati način računanja vremena i složenosti rada da bi se postigla manja cijena preuzimanja

Adaptive Redundant Lifting Wavelet Transform Based on Fitting for Fault Feature Extraction of Roller Bearings

A least square method based on data fitting is proposed to construct a new lifting wavelet, together with the nonlinear idea and redundant algorithm, the adaptive redundant lifting transform based on fitting is firstly stated in this paper. By variable combination selections of basis function, sample number and dimension of basis function, a total of nine wavelets with different characteristics are constructed, which are respectively adopted to perform redundant lifting wavelet transforms on low-frequency approximate signals at each layer. Then the normalized lP norms of the new node-signal obtained through decomposition are calculated to adaptively determine the optimal wavelet for the decomposed approximate signal. Next, the original signal is taken for subsection power spectrum analysis to choose the node-signal for single branch reconstruction and demodulation. Experiment signals and engineering signals are respectively used to verify the above method and the results show that bearing faults can be diagnosed more effectively by the method presented here than by both spectrum analysis and demodulation analysis. Meanwhile, compared with the symmetrical wavelets constructed with Lagrange interpolation algorithm, the asymmetrical wavelets constructed based on data fitting are more suitable in feature extraction of fault signal of roller bearings

A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity

The richness of natural images makes the quest for optimal representations in image processing and computer vision challenging. The latter observation has not prevented the design of image representations, which trade off between efficiency and complexity, while achieving accurate rendering of smooth regions as well as reproducing faithful contours and textures. The most recent ones, proposed in the past decade, share an hybrid heritage highlighting the multiscale and oriented nature of edges and patterns in images. This paper presents a panorama of the aforementioned literature on decompositions in multiscale, multi-orientation bases or dictionaries. They typically exhibit redundancy to improve sparsity in the transformed domain and sometimes its invariance with respect to simple geometric deformations (translation, rotation). Oriented multiscale dictionaries extend traditional wavelet processing and may offer rotation invariance. Highly redundant dictionaries require specific algorithms to simplify the search for an efficient (sparse) representation. We also discuss the extension of multiscale geometric decompositions to non-Euclidean domains such as the sphere or arbitrary meshed surfaces. The etymology of panorama suggests an overview, based on a choice of partially overlapping "pictures". We hope that this paper will contribute to the appreciation and apprehension of a stream of current research directions in image understanding.Comment: 65 pages, 33 figures, 303 reference

ROBUSNA AUTOMATSKA VIZUALNA METODA ZA ODREĐIVANJE KUTOVA LICA IZ SLIKA

A analysis of human facial images has become increasingly important due to its numerous applications. In this regards, extracting facial parameter is vital and various studies have been done in this field. Hence in our proposed work, first time up to our knowledge, a robust automatic method is introduced for determining facial angles from profile view images using radon transform. Radon transform is a kind of linear integration along a specific direction and angles play an important role to do this transform. The global features were rather considered by constructing a linear discriminant analysis (LDA) and also local features were rather considered by locality preserving projection (LPP). Our proposed combined algorithm has not only good precision, but also efficient performance and robust with noisy, scale and rotated image environments. In this work, several experiments have been conducted to analyze the robustness of our proposed Radon Combined Global and Local Preserving Features (RCGLPF) algorithm along with other existing conventional algorithms.Analiza ljudskih slika lica postaje sve važnija zbog brojnih primjena. U tom smislu, ekstrakcija parametra lica je od vitalnog značaja i na tom su području učinjene različite studije. Stoga se u našem predloženom radu, prvi put po našem saznanju, uvodi robusna automatska metoda za određivanje kutova lica iz slika profila pomoću radonske transformacije. Transformacija radona je vrsta linearne integracije duž određenog smjera, a kutovi igraju važnu ulogu u toj transformaciji. Globalna obilježja razmotrena su konstrukcijom linearne diskriminacijske analize (LDA), a lokalna obilježja razmatrana su pomoću projekcije očuvanja lokaliteta (LPP). Naš predloženi kombinirani algoritam ima ne samo dobru preciznost, nego i učinkovite performanse i robustnost s bučnim, skaliranim i rotiranim okruženjima slike. U ovom radu provedeno je nekoliko eksperimenata za analizu robustnosti našeg predloženog algoritma globalnog i lokalnog očuvanja radona (RCGLPF) zajedno s drugim postojećim konvencionalnim algoritmima

Multimedia data mining for automatic diabetic retinopathy screening

International audience— This paper presents TeleOphta, an automatic sys-tem for screening diabetic retinopathy in teleophthalmology networks. Its goal is to reduce the burden on ophthalmologists by automatically detecting non referable examination records, i.e. examination records presenting no image quality problems and no pathological signs related to diabetic retinopathy or any other retinal pathology. TeleOphta is an attempt to put into practice years of algorithmic developments from our groups. It combines image quality metrics, specific lesion detectors and a generic pathological pattern miner to process the visual content of eye fundus photographs. This visual information is further combined with contextual data in order to compute an abnormality risk for each examination record. The TeleOphta system was trained and tested on a large dataset of 25,702 examination records from the OPHDIAT screening network in Paris. It was able to automatically detect 68% of the non referable examination records while achieving the same sensitivity as a second ophthalmologist. This suggests that it could safely reduce the burden on ophthalmologists by 56%

Algebraic Approaches for Constructing Multi-D Wavelets

Wavelets have been a powerful tool in data representation and had a growing impact on various signal processing applications. As multi-dimensional (multi-D) wavelets are needed in multi-D data representation, the construction methods of multi-D wavelets are of great interest. Tensor product has been the most prevailing method in multi-D wavelet construction, however, there are many limitations of tensor product that make it insufficient in some cases. In this dissertation, we provide three non-tensor-based methods to construct multi-D wavelets. The first method is an alternative to tensor product, called coset sum, to construct multi-D wavelets from a pair of $1$-D biorthogonal refinement masks. Coset sum shares many important features of tensor product. It is associated with fast algorithms, which in certain cases, are faster than the tensor product fast algorithms. Moreover, it shows great potentials in image processing applications. The second method is a generalization of coset sum to non-dyadic dilation cases. In particular, we deal with the situations when the dilation matrix is \dil=p{\tt I}_\dm, where $p$ is a prime number and {\tt I}_\dm is the \dm-D identity matrix, thus we call it the prime coset sum method. Prime coset sum inherits many advantages from coset sum including that it is also associated with fast algorithms. The third method is a relatively more general recipe to construct multi-D wavelets. Different from the first two methods, we attempt to solve the wavelet construction problem as a matrix equation problem. By employing the Quillen-Suslin Theorem in Algebraic Geometry, we are able to build \dm-D wavelets from a single \dm-D refinement mask. This method is more general in the sense that it works for any dilation matrix and does not assume additional constraints on the refinement masks. This dissertation also includes one appendix on the topic of constructing directional wavelet filter banks

Multimedia Applications of the Wavelet Transform

This dissertation investigates novel applications of the wavelet transform in the analysis and compression of audio, still images, and video. Most recently, some surveys have been published on the restoration of noisy audio signals. Based on these, we have developed a wavelet-based denoising program for audio signals that allows flexible parameter settings. The multiscale property of the wavelet transform can successfully be exploited for the detection of semantic structures in images: A comparison of the coefficients allows the extraction of a predominant structure. This idea forms the basis of our semiautomatic edge detection algorithm. Empirical evaluations and the resulting recommendations follow. In the context of the teleteaching project Virtual University of the Upper Rhine Valley (VIROR), many lectures were transmitted between remote locations. We thus encountered the problem of scalability of a video stream for different access bandwidths in the Internet. A substantial contribution of this dissertation is the introduction of the wavelet transform into hierarchical video coding and the recommendation of parameter settings based on empirical surveys. Furthermore, a prototype implementation proves the principal feasibility of a wavelet-based, nearly arbitrarily scalable application. Mathematical transformations constitute a commonly underestimated problem for students in their first semesters of study. Motivated by the VIROR project, we spent a considerable amount of time and effort on the exploration of approaches to enhance mathematical topics with multimedia; both the technical design and the didactic integration into the curriculum are discussed. In a large field trial on "traditional teaching versus multimedia-enhanced teaching", the objective knowledge gained by the students was measured. This allows us to objectively rate positive the efficiency of our teaching modules

Efficient compression of motion compensated residuals

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