Search results for "Pattern recognition"
showing 10 items of 2301 documents
Modelling the Effects of Internal Textures on Symmetry Detection Using Fuzzy Operators
2009
Symmetry is a crucial dimension which aids the visual system, human as well as artificial, to organize its environment and to recognize forms and objects. In humans, detection of symmetry, especially bilateral and rotational, is considered to be a primary factor for discovering and interacting with the surrounding environment. Rotational symmetry detecting can be affected by less-known factors, such as the stimulus internal texture. This paper explores how fuzzy operators can be usefully employed in modeling the effects of the internal texture on symmetry detection. To this aim, we selected two symmetry detection algorithms, based on different computational models, and compared their output…
BELM: Bayesian Extreme Learning Machine
2011
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap…
A Battery-Free Smart Sensor Powered with RF Energy
2018
The development of Internet of Things (IoT) infrastructure and applications is stimulating advanced and innovative ideas and solutions, some of which are pushing the limits of state-of-the-art technology. The increasing demand for Wireless Sensor Network (WSN) that must be capable of collecting and sharing data wirelessly while often positioned in places hard to reach and service, motivates engineers to look for innovative energy harvesting and wireless power transfer solutions to implement battery-free sensor nodes. Due to the pervasiveness of RF (Radio Frequency) energy, RF harvesting that can reach out-of-sight places could be a key technology to wirelessly power IoT sensor devices, whic…
Analysis of HMAX Algorithm on Black Bar Image Dataset
2020
An accurate detection and classification of scenes and objects is essential for interacting with the world, both for living beings and for artificial systems. To reproduce this ability, which is so effective in the animal world, numerous computational models have been proposed, frequently based on bioinspired, computational structures. Among these, Hierarchical Max-pooling (HMAX) is probably one of the most important models. HMAX is a recognition model, mimicking the structures and functions of the primate visual cortex. HMAX has already proven its effectiveness and versatility. Nevertheless, its computational structure presents some criticalities, whose impact on the results has never been…
Perceptual adaptive insensitivity for support vector machine image coding.
2005
Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant epsilon-insensitivity zone by Robinson and Kecman. However, according to the statistical properties of natural images and the properties of human perception, a constant insensitivity makes sense in the spatial domain but it is certainly not a good option in a frequency domain. In fact, in their approach, they made a fixed low-pass assumption as the number of discrete cosine transform (DCT) coefficients to be used in the training was limited. This paper extends the work of Robinson and Kecman by proposing the use of adaptive insensitivity SVMs [2] for image coding u…
SDN@home: A Method for Controlling Future Wireless Home Networks
2016
Recent advances in wireless networking technologies are leading toward the proliferation of novel home network applications. However, the landscape of emerging scenarios is fragmented due to their varying technological requirements and the heterogeneity of current wireless technologies. We argue that the development of flexible software-defined wireless architectures, including such efforts as the wireless MAC processor, coupled with SDN concepts, will enable the support of both emerging and future home applications. In this article, we first identify problems with managing current home networks composed of separate network segments governed by different technologies. Second, we point out t…
Spectral clustering to model deformations for fast multimodal prostate registration
2012
International audience; This paper proposes a method to learn deformation parameters off-line for fast multimodal registration of ultrasound and magnetic resonance prostate images during ultrasound guided needle biopsy. The method is based on a learning phase where deformation models are built from the deformation parameters of a splinebased non-linear diffeomorphism between training ultrasound and magnetic resonance prostate images using spectral clustering. Deformation models comprising of the eigen-modes of each cluster in a Gaussian space are applied on a test magnetic resonance image to register with the test ultrasound prostate image. The deformation model with the least registration …
SVM approximation for real-time image segmentation by using an improved hyperrectangles-based method
2003
A real-time implementation of an approximation of the support vector machine (SVM) decision rule is proposed. This method is based on an improvement of a supervised classification method using hyperrectangles, which is useful for real-time image segmentation. The final decision combines the accuracy of the SVM learning algorithm and the speed of a hyperrectangles-based method. We review the principles of the classification methods and we evaluate the hardware implementation cost of each method. We present the combination algorithm, which consists of rejecting ambiguities in the learning set using SVM decision, before using the learning step of the hyperrectangles-based method. We present re…
Learning spatial filters for multispectral image segmentation.
2010
International audience; We present a novel filtering method for multispectral satel- lite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments car- ried out on multiclass one-against-all classification and tar- get detection show the capabilities of the learned spatial fil- ters.
Support Vector Machine and Kernel Classification Algorithms
2018
This chapter introduces the basics of support vector machine (SVM) and other kernel classifiers for pattern recognition and detection. It also introduces the main elements and concept underlying the successful binary SVM. The chapter starts by introducing the main elements and concept underlying the successful binary SVM. Next, it introduces more advanced topics in SVM for classification, including large margin filtering (LMF), SSL, active learning, and large‐scale classification using SVMs. The LMF method performs both signal filtering and classification simultaneously by learning the most appropriate filters. SSL with SVMs exploits the information contained in both labeled and unlabeled e…