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A neural networks benchmark for image classification

A neural networks benchmark for image classification

In this report we state a clue on which method to choose for the task of image classification. In chapter 2 we expose the state of the art. In chapters 3 and 4 we briefly explore the history of image classification tasks and some of the most used methods, reviewing both classic, raw statistical methods and more modern artificial-network-oriented methods, going through it's contributions, constraints and pitfalls. Two experiments are proposed to test each method in chapter 5. In chapter 6 we explain both methods used and it's peculiarities, we continue explaining how the tests were conducted and the results obtained in chapter 7 to end up with the obtained conclusions and further lines of work in the last chapter. Appendixes 1 and 2 contains the gory details about software, hardware and source code.

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Exponential family Fisher vector for image classification

Exponential family Fisher vector for image classification

In this work we focus on the problem of image classification, i.e. the task of assigning labels to images based on its content. Motivated by the tremendous growth on the volume and complexity of the image-related data, the problem has attracted great interest. Currently, not only the number of images has grown but also the nature of the visual information is changing towards more complex modalities, e.g. the use of deep information with the advent of RGBD cameras (Wang et al., 2014; Gupta et al., 2014) or the recent interest on hyperspectral imaging (Salamati et al., 2014) for solving di ff erent perception problems. Devising methods that allow us to capture the semantically rich information encoded in the images remains a major concern.

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Fisher vectors for leaf image classification: an experimental evaluation

Fisher vectors for leaf image classification: an experimental evaluation

The FV [18] representation is one of the most robust for image classification [14] and fine-grained classification [8]. This representation encodes an image as a gradient vector that characterizes the distribution of a set of low-level descriptors with respect to the parameters of a probabilistic generative model which in case of the traditional FV, corresponds to a mixture of multivariate Gaussian pdfs with diagonal covariances. The eFV generalizes the FV by considering mixtures of a more general class of distributions known as the exponential family. This allow the model to deal with input spaces other than R D in a principled manner.

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Histopathology Image Classification using Bag of Features and Kernel Functions

Histopathology Image Classification using Bag of Features and Kernel Functions

The visual dictionary or codebook is built using a clustering or vector quantiza- tion algorithm. In the previous stage of the bag of features framework, a set of local features has been extracted. All local features, over a training image set, are brougth together independently of the source image and are clustered to learn a set of representative visual words from the whole collection. The k-means algo- rithm is used in this work to find a set of centroids in the local features dataset. An important decision in the construction of the codebook is the selection of its size, that is, how many codeblocks are needed to represent image contents. According to different works on natural image classification, the larger the code- book size the better [16,2]. However, Tomassi et. al [4] found that the size of the codebook is not a significant aspect in a medical image classification task. We evaluated different codebook sizes, to analyze the impact of this parameter in the classification of histopathology images.

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Deep Pyramidal Residual Networks for Spectral Spatial Hyperspectral Image Classification

Deep Pyramidal Residual Networks for Spectral Spatial Hyperspectral Image Classification

In this paper, we propose a new residual network model based on pyramidal bottleneck residual units to achieve fast and accurate HSI analysis and classification, using both spectral and spatial information. This new deep model is composed by several blocks of stacked convolutional layers, which have a diabolo (bottleneck) architecture in which the output layer is larger than the input layer. In this way, the number of spectral channels in the original HSI cube is increased step by step on each block, creating the illusion of a pyramid where, as the residual units are deeper, more feature maps can be extracted, allowing to learn more robust spectral- spatial representations from HSI cubes. However, these HSI pyramidal bottleneck residual units are still compu- tationally expensive, which forces to adopt acceleration techniques to reduce execution time. In this sense, the proposed network has been accelerated using graphics processing units (GPUs). The obtained results (using four well-known hyperspectral datasets) show that the proposed model can outperform not only the spectral- spatial CNN, but also the baseline HSI-ResNet classi- fication results, extracting more discriminative spectral- spatial features without the need to use large amounts of training data, which may have great uncertainty.

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Capsule Networks for Hyperspectral Image Classification

Capsule Networks for Hyperspectral Image Classification

HSI classification due to the fact that convolutional filters provide an excellent tool to detect relevant spectral- spatial features present in the data. That is, initial con- volutional layers are able to learn simple HSI features, while deeper layers combine these low-level characteris- tics to obtain higher-level data representations. However, under this straightforward CNN-based scheme, the ca- pability of exploiting the relationships between features detected at different positions within the image is rather limited. Although the insertion of pooling layers and the gradual reduction of the filters’ spatial size allow detecting higher order features in a larger region of the HSI input image (by achieving translation invariance), the internal data representation of a regular CNN does not take into account the existing hierarchies between simple and complex features. Note that the pooling operation is based on downsampling the feature space size to a manageable level and, logically, this introduces an unavoidable loss of information; specifically, pool- ing methods are unable to capture information about the positional data, which may be a key factor when classifying HSI data. As a result, CNNs may exhibit poor performance if the input data presents rotations, tilts or any other orientation changes, being incapable of identifying the position of one object relative to another in the scene because they cannot model properly and accurately such spatial relationships. Several methods have been implemented in order to encode the invari- ances and symmetries that exist in the data, including the transformation of the original input samples during the training phase via data augmenting [25], [31]. However this method fails to capture local equivariances in the data, and does not ensure equivariance at every layer within the CNN [32].

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TítuloAn Approach for the Customized High Dimensional Segmentation of Remote Sensing Hyperspectral Images

TítuloAn Approach for the Customized High Dimensional Segmentation of Remote Sensing Hyperspectral Images

The earliest hyperspectral image classification methods address the problem from a pixel-wise perspective. This means that each pixel is processed independently without considering contextual (spatial) information. Among these traditional classification methods, which have been used since the 1990s, we may find maximum-likelihood (ML) based methods [2], multinomial logistic regression based techniques (MLR) [3], Fisher’s linear discriminant analysis (LDA) [4], Linearly Constrained Discriminant Analysis (LCDA) [5], shape filtering [6], Artificial Neural Networks (ANN) [7] and fuzzy methods [8]. In [9], a Support Vector Machine (SVM) technique was applied to hyperspectral data for the first time. The SVM algorithm seeks the optimal separation surface between classes. It does this by identifying the most representative training sample of each class, called support vector. SVM has shown very good performances for classifying high-dimensional data in situations with small training sets. Furthermore, a feature selection step to reduce the dimensionality of the image cube is not required [10–13]. In [14,15], a K-nearest neighbor method is applied. These well-known types of methods seek a predefined number of training samples that are close to the test sample and assign a label to the test sample that reflects the majority category label of these k-nearest training samples.

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Improved estimates of glacier change rates at Nevado Coropuna Ice Cap, Peru

Improved estimates of glacier change rates at Nevado Coropuna Ice Cap, Peru

Although nine studies have previously attempted to quantify the glacierized area and rates of ice loss for Coropuna (Ames and others, 1988; de Silva and Francis, 1990; Nunez-Juarez and Valenzuela-Ortiz, 2001; Racoviteanu and others, 2007; Forget and others, 2008; Peduzzi and others, 2010; Úbeda, 2011; Silverio and Jaquet, 2012; Veettil and others, 2016), the results of these studies are inconsistent with each other (Fig. 2) and with what has been found at other tropical gla- ciers. Inspection of our NDSI-based snow and ice extent esti- mates for the observation dates included in these previous studies (Fig. 2) reveals that de Silva and Francis (1990), Ames and others (1988), Nunez-Juarez and Valenzuela- Ortiz (2001), Racoviteanu and others (2007), Forget and others (2008), Peduzzi and others (2010) and Veettil and others (2016) overestimated at least one glacierized area measurement in their time series because of the presence of seasonal snow. Silverio and Jaquet (2012) are the most fre- quently cited source on Coropuna’s glaciers by government agencies, yet our snow and ice covered area time series shows that they overestimate rates of glacier recession by using images blanketed by snow in early years and images with less snow in years closer to the present. For example, comparison of an image from 1 August 1985 used by Silverio and Jaquet (2012) to an image from 5 December 1987 shows a 45% decrease in the estimation of glacier area (Fig. 9). This is physically untenable. Our analysis shows that the image from 5 December 1987 more accur- ately represents glacierized area because the glacial margins are not obscured by snow. Úbeda (2011) provides two new area estimates from a 1986 orthophotograph and a 2007 ASTER scene that are consistent with our findings and support our inference that the Silverio and Jaquet (2012) results overestimate ice area in the 1980s, and thus rates of ice cap recession.

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Wetland Resources and Priorities for Wetland Inventory (GRoWI), from which it was indicated to

Wetland Resources and Priorities for Wetland Inventory (GRoWI), from which it was indicated to

35. The Ramsar Classification System for Wetland Type (Resolution VI.5) is increasingly being used as a classification basis for national wetland inventories. However, when it was first developed it was not anticipated that the Ramsar classification would be used for this inventory purpose, so its usefulness as a habitat classification for any specific wetland inventory should be carefully assessed. Whilst the Ramsar Classification System has value as a basic habitat description for sites designated for the Ramsar List of Wetlands of International Importance, it does not readily accommodate description of all wetland habitats in the form and level of description that are now commonly included in many wetland inventories.

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A Framework for Wetland Inventory

A Framework for Wetland Inventory

35. The Ramsar Classification System for Wetland Type (Resolution VI.5) is increasingly being used as a classification basis for national wetland inventories. However, when it was first developed it was not anticipated that the Ramsar classification would be used for this inventory purpose, so its usefulness as a habitat classification for any specific wetland inventory should be carefully assessed. Whilst the Ramsar Classification System has value as a basic habitat description for sites designated for the Ramsar List of Wetlands of International Importance, it does not readily accommodate description of all wetland habitats in the form and level of description that are now commonly included in many wetland inventories.

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El sentido, el paisaje y la imagen. Cómo se replica el destino turístico en tiempos posmodernos

El sentido, el paisaje y la imagen. Cómo se replica el destino turístico en tiempos posmodernos

The French legacy has been critique respec- ting to the obsession by aesthetics that charac- terizes the modern world. P. Bourdieu alerts that our love for photography fulfils certain psychological needs such as protection, fear of time passing, communication, evasion, and status. Photography would serve as a mediator to reduce the sentiment of anxiety that genera- tes the passing of time, this means the fear of death. Nevertheless, Bourdieu adds, if one pays attention to the connection between photogra- phy and classes, it is not surprising to see that image (picture) replaced the place of social bond. His main thesis is that those professional skilled classes that show serious emotional problems in their relatives or friends take more pictures than other blue-collar worker classes. The level of richness disarticulates the social bond to the extent this gap should be filled by secondary mediators, photography, alcohol and drugs are part of a large list. Once the modernity has radi- cally altered the tradition and styles of life, the obsession for image and aesthetics has surfaced (Bourdieu, 2003).

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Image et hallucination

Image et hallucination

Il y a toujours cette curieuse tendance à considérer que l’image n’est pas la réalité bien qu’elle le soit tout de même. Par exemple, quand je parle, je vois un être humain en trois dimensions, mais si en même temps je fais un mouvement, je m’aperçois que c’est une image. Si on prend une photo on s’aperçoit que c’est la réalité. Il y a une certaine tendance de l’esprit à considérer que les images finalement sont fluentes: elles ne correspondent pas à la réalité, et on peut d’ailleurs tout faire avec des images.

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Complex Modulation Code for Low  Resolution Modulation Devices

Complex Modulation Code for Low Resolution Modulation Devices

Digital information technology is constantly developed using electronic devices. The three dimensional (3D) image processing is also supported by electronic devices to record and display signals. Computer generated holograms (CGH) and integral imaging (II) use liquid-crystal spatial light modulator (SLM). This doctoral dissertation studies and develops the application of a commercial twisted nematic liquid crystal display (TNLCD) in computer generated holography and integral imaging. The goal is to encode and reconstruct complex wave fronts with computer generated holograms, and 3D images using Integral Imaging systems. Light modulation curves are presented: amplitude and phase-mostly modulation. Holographic codes are designed and implemented experimentally with optimum reconstruction efficiency, maximum signal bandwidth, and high signal to noise ratio (SNR). The study of TNLCD into II is presented as a review of the basics techniques of display. A digital magnification of 3D images is proposed and implemented. 3D digital magnified images have the same quality of optical magnified images, but the magnified system is less complex. Recognition system for partially occluded object is solved using a 3D II volumetric reconstruction. 3D Recognition solution presents better performance than the conventional 2D image systems. The importance in holography and 3D II is supported by the applications as: optical tweezers, as dynamic trapping light configurations, invariant beams, and 3D medical images.

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Non-negative matrix factorization for medical imaging

Non-negative matrix factorization for medical imaging

Abstract. A non-negative matrix factorization approach to dimension- ality reduction is proposed to aid classification of images. The original images can be stored as lower-dimensional columns of a matrix that hold degrees of belonging to feature components, so they can be used in the training phase of the classification at lower runtime and without loss in ac- curacy. The extracted features can be visually examined and images recon- structed with limited error. The proof of concept is performed on a bench- mark of handwritten digits, followed by the application to histopathologi- cal colorectal cancer slides. Results are encouraging, though dealing with real-world medical data raises a number of issues.

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TítuloFully automatic multi temporal land cover classification using Sentinel 2 image data

TítuloFully automatic multi temporal land cover classification using Sentinel 2 image data

The Sentinel-2 satellite constellation is an Earth observation mission developed by the European Spatial Agency (ESA) under the Copernicus programme with the aim of providing accurate and easily accessible information about many characteristics of the earth’s surface. The higher resolution that is achieved for the visible light spectrum products and the free, full and open data policy of the Copernicus programme place nowadays the Sentinel-2 image products among the most used RS solutions worldwide. Two satellites (A and B, in orbit since 2015 and 2017, respectively) provide high-resolution multi-spectral images across 13 di ff erent spectral bands (413–2190 nm) with spatial resolu- tions of 10m (visible light spectrum and near-infrared bands), 20m (6 red-edge and shortwave-infrared bands) and 60m (3 atmospheric correction bands) with a high revisit frequency of 5 days at the Equator. Given the significant potential of the availability of all this information, the Sentinel-2 products have been exploited in a wide array of applications, such as aboveground biomass estimation [4], fire severity estimation [17], vegetation phenology [22], carbon prediction [2] or land cover mapping [7].

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Analysis of urban land use and land cover changes: a case study in Bahir Dar, Ethiopia

Analysis of urban land use and land cover changes: a case study in Bahir Dar, Ethiopia

Results of user's accuracy in this study showed that in 1986 the maximum class accuracy was 98%, which was water bodies where correctly classified and the minimum was agricultural areas class with an accuracy of 80.6% as presented in table 4.2 below. In 2001, the class accuracies range from 62% to 100% where as in the period 2010, it ranges from 75% to 97.3% as indicated in tables 4.3 and 4.4 respectively. The lowest values of class accuracies were misclassified due to spectral property similarities among other land cover classes. As shown from tables 4.2, 4.3 and 4.4, the user's accuracy was lowest for agricultural areas as some of the agricultural areas were largely misclassified as built up, forest and semi-natural and open areas. Moreover, the time of image acquisition has a great role for such misclassification problems. Since the images obtained during the season where most agricultural activities were carried out in Ethiopia, other land cover classes appear as agriculture and vice versa. According to Václavík and Rogan (2009), the category of agriculture was the most problematic because it represented a mixture of various crops in different phenological stages as well as bare soil (plowed fields). In addition to this, the spatial resolution of Landsat data could have an influence on the image classification. According to Zhou et al (2009) for detailed urban land cover mapping at very fine scales, high spatial resolution imagery from satellite sensors such as IKONOS and QuickBird become more accurate.

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Automatic classification of oranges using image processing and data mining techniques

Automatic classification of oranges using image processing and data mining techniques

During the last years, there has been an increase in the need to measure the quality of several products, in order to satisfy customers needs in the industry and services levels. In fruits and vegetables production lines, the quality assurance is the only step which is not done automat- ically. For oranges, quality assurance is done by trained people who inspect the fruits while they move in a conveyor belt, and classify them in several categories based on visual features. In the industry, there are very few automatic classification machines principally because of the need of advanced image processing, and the price of the hardware needed to satisfy the speed requirements of the production lines [15].

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Método automático para el reconocimiento de gestos de manos para la categorización de vocales y números en lenguaje de señas colombiano

Método automático para el reconocimiento de gestos de manos para la categorización de vocales y números en lenguaje de señas colombiano

• It is recommended to perform the experiment using other classification techniques such as neural networks convolutional, Naive Bayes, Logistic regression, among others. In order to buy performance and accuracy of predictions, as you can get better results with other classification methods.

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Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks

Semiautomated segmentation of bone marrow biopsies images based on texture features and Generalized Regression Neural Networks

One of the most interesting fields in Digital Image Processing is the segmentation of an image into its different objects (Gonzalez and Woods, 1993). Segmentation plays a vital role in numerous biomedical imaging applications, such as the quantification of tissue volumes, diagnosis, localization of pathologies, study of anatomical structures and others (Glasbey 1995). The segmentation techniques can be divided into two groups: techniques based on contour detection which search for local grey level discontinuity in the image and those involving region growing which seek homogeneous image parts according to statistical measurements such as grey level and texture. The segmentation process of medical images is a difficult task to be accomplished in digital image processing (Chalama 1997).

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From the projected to the transmitted image: the 2.0 construction of tourist destination image and identity in Catalonia

From the projected to the transmitted image: the 2.0 construction of tourist destination image and identity in Catalonia

This transformation of place identity attached to image representations takes place when tourists act and behave in relation to their mental image constructs. “Place and space are fundamental constructs in tourism studies” (McCabe & Stokoe, 2004). As Lefebvre (1996) proposed, “space is produced and consumed by collective social practice, involving social relationships and presenting an arena of social struggle” (as cited in Almeida & Buzinde, 2007). As Kim and Richardson (2003) explain, this is related to Hall’s (1997) “circuit of culture”, where visual language provides representations that produce meanings. “These meanings regulate social practices, influence people’s conduct, and consequently have real practical effects” (Kim & Richardson, 2003). As noted by Anton (2010), the semiological spaces mentioned by Urry would embody tourism spaces being transformed by their own representations. They are “spaces able to be identified at different territorial levels tending to become reproductions of what they pretend to represent as they become part of tourist circuits, trying to adapt permanently to the idealised picture tourists have” (Anton, 2010). Several studies have been conducted in relation to this place identity transformation, influenced by tourist image representations. In their study, Kim and Richardson (2003) contended that “the popular view of a place offered by media may prompt that place to recreate its own identity in this image”. Similarly, Morgan and Pritchard (1998) explained that televised images of a certain place may alter the reality of that place and may become the reference model followed to recreate “places as living environments and tourism sites”. Almeida and Buzinde (2007) demonstrate that tourist image representations go beyond the world of ideas into the physical spatial world and influence it in several aspects in the case of a contested space, when a community transforms its landscape, habits and habitat to struggle against oppression and affirm and maintain its identity. Hence, tourism studies should examine representations within socio-political frameworks, because such frameworks have implications for understanding cultural identity in relation to tourism (Almeida & Buzinde, 2007).

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