PDF superior Procesamiento y clasificación de imágenes citogenéticasCytogenetics image processing and classification

Procesamiento y clasificación de imágenes citogenéticasCytogenetics image processing and  classification

Procesamiento y clasificación de imágenes citogenéticasCytogenetics image processing and classification

Generalmente las transformacioneslineales tienen la forma.. La variable b ajusta el brillo de la imagen, mientras que c ajusta su contraste. La figura 10 presenta varios ejemplos de curv[r]

153 Lee mas

III informe preliminar proyecto BIOMARCC : identificación y clasificación marino – costero en la costa pacífica de Costa Rica a partir del procesamiento de imágenes de los sensores remotos Rapid Eye y Worldview2

III informe preliminar proyecto BIOMARCC : identificación y clasificación marino – costero en la costa pacífica de Costa Rica a partir del procesamiento de imágenes de los sensores remotos Rapid Eye y Worldview2

Previo al curso se realizó una gira de campo para toma de firmas espectrales (ver anexo 1). El curso se llevó a cabo del 7 de Mayo al 1 de Junio del 2012 y se focalizó en el análisis de datos RapidEye y Worldview-2, para ambos sensores se enseñaron temas de pre- procesamiento, clasificación y post-clasificación. Todos los temas fueron cubiertos por la PhD. Margaret Kalacska y el PhD. Pablo Arroyo. La asistente del curso Sienna Svob cubrió el tema de Geobase de datos relacionales.

28 Lee mas

Clasificación de calidad de fresas usando procesamiento de imágenes y máquinas de vectores de soporte

Clasificación de calidad de fresas usando procesamiento de imágenes y máquinas de vectores de soporte

La fresa es reconocida por su sabor, aroma y color llamativo, además al ser una fuente de vitaminas y minerales posee propiedades nutricionales y curativas que la hace apetecida a nivel industrial, medicinal y culinario, debido a estos usos variados, a nivel nacional e internacional se ha incrementado la demanda de la fruta incrementando también su valor comercial [1]. En Colombia se ha tecnificado el cultivo de fresa en los últimos años generando una producción continua, las principales zonas productoras del país son Cundinamarca, Antioquia, Norte de Santander, Cauca, Boyacá y Valle del Cauca. Algunas de las características mínimas para la aceptación de la fresa en Colombia son el aspecto fresco y consistencia firme, sanas y libres de ataques de insectos, libres de magulladuras y humedad exterior, coloración del fruto homogénea acorde al estado de madures, limpias y exentas de colores extraños [2]. De forma general la clasificación de la fruta una vez se recolecta se hace de acuerdo al grado de maduración (color), tamaño y forma, sin embargo esta clasificación se hace de forma manual y es muy propensa a errores debido a que es una labor repetitiva y factores como el cansancio y la subjetividad intervienen. El avance en la tecnología de visión por computador ha hecho posible que se propongan soluciones basadas en la inspección visual automática. En general las técnicas basadas en visión por computador para medir la calidad en las frutas se dividen en dos
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7 Lee mas

Aplicación del procesamiento de imágenes en la caracterización y clasificación de áreas agrícolas

Aplicación del procesamiento de imágenes en la caracterización y clasificación de áreas agrícolas

En el presente trabajo se aborda el desarrollo e implementaci ´on de un sistema de acon- dicionamiento y posterior procesamiento de im ´agenes para la estimaci ´on de la cantidad y calidad de vegetaci ´on, as´ı como la cuantificaci ´on de superficies inundadas. Para ello, se emple ´o el c ´omputo del ´ındice de vegetaci ´on de diferencia normalizada y el ´ındice de agua de diferencia normalizada, utilizando como elemento sensor una c ´amara web gen ´erica, mo- dificada para captar las porciones necesarias del espectro electromagn ´etico.

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Clasificación de granos de lentejas emplenado procesamiento digital de imágenes

Clasificación de granos de lentejas emplenado procesamiento digital de imágenes

Las diferencias observadas entre el método de clasificación manual y el de imágenes se debe a que este último evita las subjetividades del operador para los casos de granos cuyas características se encuentran en las proximidades de los límites entre las clases categorias. El operador reconoce como normales a 120 granos, mientras que el método propuesto considera solo 110, reclasificando 10 granos como oscuros. La diferencia entre decolorados y partidos es 1.

7 Lee mas

Sistema de visión para la clasificación de rambután

Sistema de visión para la clasificación de rambután

En cuanto a los elementos mecánicos, la banda transportadora presentaba problemas de fricción con los tambores de accionamiento, debido al desbalance de tensión de la banda, lo cual generó problemas de atascamiento en diversas ocasiones. Tampoco se logró hacer que la banda se desplazara uniformemente, ya que presentó pequeñas variaciones en cuanto a su ciclo de rotación, este problema afectó en pequeña porción todas las mediciones. Un motor de mayor potencia podría solucionar este problema. La caja de iluminación requiere mejoras sustanciosas en su diseño, lo cual mejorará en gran medida la medición correcta de todas las variables, ya que se eliminarían los problemas de sombras que alteran las mediciones y la percepción del color. La webcam es muy inestable para realizar las mediciones, aunque es aceptable su comportamiento, una cámara industrial lograría grandes mejoras en cuanto a la estimación del color y el procesamiento de la línea de luz.
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38 Lee mas

Retinal Image Analysis: Image Processing and Feature Extraction Oriented to the Clinical Task

Retinal Image Analysis: Image Processing and Feature Extraction Oriented to the Clinical Task

To test the robustness of the proposed FM, a set of 140 artificially degraded images were produced. The dataset consisted in blurring the images and then adding noise. We tested for Gaussian noise, Speckle noise, and Impulse noise. From these tests we concluded that S1, S4, and Sa performed the best. We then tested on real images. One of the experiments we carried out was to analyze the performance of the FMs in subjects of different ages. In Fig. 4 we show the focusing curves obtained from subjects with the ages: 27, 40, 68, and 70. In general, from the comparison against S1 and S4 it is clear that the proposed FM Sa outperforms them in the considered cases. From the four cases shown only in one (Fig. 4(c)) the Sa measure peak did not coincide precisely with the optimal focus position. However, the error is no more than a single position. The FMs curves of S1 and S4 are generally flatter than those of Sa which in a focus search strategy is not wanted because of the difficulty to properly distinguish the optimum position in a coarse or initial search. In the other experiments the proposed measure outperformed the considered measures in robustness and accuracy. The code is available in [14].
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Histopathology Image Classification using Bag of Features and Kernel Functions

Histopathology Image Classification using Bag of Features and Kernel Functions

In histopathology images that tendency can also be observed. Most of the previous works in the context of histology, pathology and tissue image classifi- cation have approached the problem using segmentation techniques [9,10]. They first define the target object to be segmented (e.g. cells, nuclei, tissues) and then apply a computational strategy to identify it. Global features have also been used to retrieve and classify histology images [11,12]. Those two global approaches are in one extreme of the balance between explicit semantics and practical gener- alization or adaptation. Other kind of works have oriented the image content analysis by window-based features, under the observation that histology images are “usually composed of different kinds of texture components” [13]. In [14] those sub-images are classified individually and then a semantic analyzer is used to de- cide the final image classification on the complete image. This approach is close to the bag of features one since the unit of analysis is a small sub-image and a learning algorithm is applied to evaluate the categorization of small sub-images. However, the approach requires the annotation of example sub-images to train first-stage classifiers, a process that is performed in an unsupervised fashion under the bag of features framework.
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10 Lee mas

Digital image processing techniques as a tool for evaluating and mapping patinas on granite monuments

Digital image processing techniques as a tool for evaluating and mapping patinas on granite monuments

Digital image processing techniques as a tool for evaluating and mapping patinas on granite monumentsR. Bustamante.[r]

7 Lee mas

Software for calibrating a digital image processing

Software for calibrating a digital image processing

Pontificia Universidad Católica del Perú Escuela de Posgrado. Tesis de Maestría[r]

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Comprehensive retinal image analysis: image processing and feature extraction techniques oriented to the clinical task

Comprehensive retinal image analysis: image processing and feature extraction techniques oriented to the clinical task

The local PSF estimation procedure not always succeeds. For instance, if we use the estimated local PSFs shown in Fig. 6.5 for deconvolution, the reconstruction develops ringing artifacts (see Fig. 6.9(b)). Consequently, such non-valid PSFs must be identified, removed and replaced. In our case we replace them by an average of adjacent valid kernels. The main rea- son why the kernel estimation fails is due to the existence of textureless or nearly homogenous regions bereft of structures with edges (e.g. blood vessels) to provide sufficient information (Tall´on et al., 2012). To identify them we devised an eye-domain knowledge strategy. The incorporation of proper a priori assumptions and domain knowledge about the blur into the method provides an effective mechanism for a successful identification of non-valid PSFs. First, because the patient’s eye is part of the imaging system, it is logical to consider that the PSF’s shape is partly determined by the eye’s PSF. Moreover, experimental measurements of the eye’s PSF (Navarro, 2009) have shown it to be characterized by a ring or star shape. Great deviations from this pattern are unlikely and have no physical basis supporting them; despite the fact that it could well satisfy a numerical so- lution. Along the same line of thought, Meitav & Ribak (2012) proposed a method for enhancing the contrast of high-resolution retinal images. For the reconstruction process they avoided distant lobes of the estimated PSF and used only the PSF area that would be under the central lobe and the first ring of the Airy pattern.
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159 Lee mas

Image processing and computing for digital holography with ImageJ

Image processing and computing for digital holography with ImageJ

Existen  diferentes  formas  de  escritura  de  plugins  en  ImageJ  de  acuerdo  a  las  necesidades  del  usuario  [16].   Si  éste  desea  generar  una  aplicación  que  no  necesite  trabajar  directamente  sobre  una  imagen  abierta,  se   recomienda   utilizar   el   tipo   plugin.   Por   el   contrario   si   se   requiere   un   algoritmo   que   realice   operaciones   sobre  imágenes  que  han  sido  previamente  abiertas  lo  más  recomendable  es  que  se  utilice  el  tipo  plugin   filter.   Por   último   si   se   necesita   una   interfaz   gráfica,   sobre   la   cual   el   usuario   ingresa   o   selecciona   parámetros   necesarios   para   llevar   a   cabo   un   procesamiento,   se   recomienda   el   tipo   plugin   frame.   Los   detalles  necesarios  para  la  escritura  de  un  plugin  de  cualquiera  de  los  tipos  se  pueden  leer  en  [16].  
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Hyperspectral image representation and processing with binary partition trees

Hyperspectral image representation and processing with binary partition trees

defining a spectral similarity measure is that most of the spectral signatures cannot be discriminated broadly along all the wavebands. The reason of this difficulty is the redundancy of the spectral information or equivalently the correlation between consecutive values of the spectral signature curve. It should be remembered that being very small the difference between two consecutive wavelengths, the radiance values has not suffered an important change in consecutive positions. Imaging that we want to discriminate the three different spectral signatures plotted in Fig.1.6. These three spectral signatures belong to three different classes: tree, bare soil and meadows. In it, the red curve can be strongly discriminated between the blue one across all the wavelength domain. Contrarily, this discrimination difference between the red and the green spectral signatures is only found in the last 30 bands. This fact explains why the most effective similarity measure between spectral signatures are characterized by taking into account the overall shape of them instead of local radiance differences. These last characteristics of the spectral signatures makes the definition of a region model and a similarity metrics, defining a good merging order for the BPT construction, an opened research problem.
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171 Lee mas

Application of image processing methodologies for fruit detection and analysis

Application of image processing methodologies for fruit detection and analysis

them, which is a time- and cost-consuming operation since it must be performed periodically through the field and so, its automation implies benefits in all aspects. In [42] an autonomous system based on a low-cost image sensor was responsible of monitoring pests by capturing and sending images of trap contents, which were distributed through the field, to a control station. The images were processed in the control station in order to calculate the number of insects. In [43] a development of an immunocapture real-time reverse transcription-polymerase chain reaction (RT-PCR) assay to detect the tobacco mosaic virus in the soil was presented. In [44] a system to detect root colonization by microorganism in potatoes was developed. A technique to excite material and produce fluorescence was applied for this purpose. In [45] an ultrasonic distance sensor in combination with a camera was used to estimate plant height in cereal crops and to determine the weed and crop coverage (see Fig. 1.4). The results showed a success of 92.8% when separating weed infested zones and non-infested zones. The acquisition of sounds through a bio-acoustic sensor was used to detect real palm weevil for pest control [46]. Finally, in [47], a non-destructive method based on the Raman spectroscopy in combination with a laser source in order to detect pesticide residues on apple skin surfaces was developed. The results showed that the system was able to detect pesticide residues up to 6.69 mg/kg in less than 4 s.
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Electromagnetic models for ultrasound image processing

Electromagnetic models for ultrasound image processing

To solve these problems, Frery et al., 1997 deduced a new statistical model, the GA model, based on the product model assuming a Gamma distribution for the speckle component of multi-look SAR images and a generalized inverse Gaussian (GIG) law for the signal component. It was Frery who first proposed to divide a region as ho- mogeneous, commonly heterogeneous or extremely heterogeneous according to its ho- mogeneous degree when deducing the GA model. The K and G0 (also called B distri- bution) distributions are two special forms of the G model. The former is appropriate for the heterogeneous region and the latter is appropriate for the extremely heteroge- neous region. The G0 distribution can be converted into the Beta-Prime distribution under the single-look condition. Although the G0 distribution is a specific example of the G model, it has a more compact form in comparison with the G model and conse- quently has a simple parameter estimation method. The relationship between the G0 distribution and the K distribution cannot be deduced theoretically, but has been eval- uated via Montecarlo Simulation Methods(Mejail et al., 2001). The parameters of the G0 distribution are sensitive to the homogeneous degree of a region, which makes the G0 model appropriate for modeling either heterogeneous or extremely heterogeneous region. Moreover, moments method can be easily and successfully applied to parame- ter estimation of the G0 distribution; and the Log-Compressed G0 distribution, namely HG0 distribution, has an analytical expression. Also, Frery et al., 1997 and Muller and Pac, 1999 carried out experiments on many SAR images of different kinds of terrain with various band, polarization, resolution and look numbers, such as different urban areas, homogeneous and heterogeneous regions, etc. Their results testified the good characteristics of the G0 distribution. In next sections the GA and GA0 models will be presented and adapted to ultrasound B-scan images modeling.
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161 Lee mas

A neural networks benchmark for image classification

A neural networks benchmark for image classification

Being one the greatest contributions of this network to the field development, the Inception module is used as a way to enable the network choose how to combine different filters on type and shape in runtime, as well as to reduce computation overhead in larger convolution filters [6]. The Inception module captures the intuitive idea that different spatial features can be captured at different scales - 5 ∗ 5 and 3 ∗ 3 filters - which can be aggregated later - filter concatenation - so that the next stage can abstract features from different scales simultaneously [6]. To do so it stacks convolutional layers in parallel, splitting the input in various processing pipes and joining them later. To extract different scales, different filters must be used, but it comes at a cost; with large filters computation blows significantly, to over come this issue, various 1 ∗ 1 layers are used, specially before each large convolution filter - 3 ∗ 3 and 5 ∗ 5 -, therefore these 1 ∗ 1 serve mainly for two purposes:
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44 Lee mas

Capsule Networks for Hyperspectral Image Classification

Capsule Networks for Hyperspectral Image Classification

Since Chen et al. adopted in [25] the CNN approach for HSI classification purposes, different CNN-based extensions have been also proposed in the literature to learn enhanced spectral-spatial features. For instance, Li et al. propose in [26] the use of pixel-pair features under a CNN-based classification scheme in order to increase the number of training samples and, hence, the resulting classification performance. Zhao and Du [27] also propose a classification approach which merges CNN-based spatial features and the spectral information uncovered by the balanced local discriminant embedding algorithm. Other important works make use of several independent CNN-based architectures to combine spec- tral and spatial features, such as [28], [29]. Despite the fact that all these methods have shown to obtain certain performance benefits, they still struggle at facing the two aforementioned issues when dealing with remotely sensed HSI data, that is, the high data complexity and the limited availability of training samples, mainly because they fuse different data components using independent CNN-based procedures. In this sense, the work presented in [30] defines a novel CNN architecture which is able to jointly uncover improved spectral-spatial features that are useful to classify HSI data.
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23 Lee mas

Automatic stereoscopic video object-based watermarking using qualified significant wavelet trees

Automatic stereoscopic video object-based watermarking using qualified significant wavelet trees

(d) Section 6 (Experimental Results) is significantly ex- tended by performing several new experiments. More specifi- cally in our initial work we have tested watermark robustness under salt & pepper noise, Gaussian noise, blur, JPEG lossy compression and sharpening. In this paper many new mixed attacks are included, which present significant interest to the research community: combination of sharpening and bluring, combination of sharpening and bluring under JPEG compres- sion and combination of different JPEG compression ratios under various Bit Error Rates, corresponding to typical mobile radio channels. All these new results provide a more integrated aspect of the presented methodology, leading to clearer conclu- sions of the advantages of the proposed watermarking scheme. (e) Finally in terms of bibliography (Section 8), this paper follows the terminology of the updated version of the ISO/IEC 14496-2:2004 standard that focuses on “ coding of audio-visual objects ” . Furthermore regarding state-of-art completeness, this paper contains fourteen new references that extensively cover the topic of video object watermarking.
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Real-time speckle image processing

Real-time speckle image processing

Captured sequences of laser speckle images must be processed offline via a general purpose computer to char- acterize the phenomena, using ad hoc-designed descriptors that analyze the behavior of every pixel across the sequence. A wide set of these descriptors can be found in [18]; many of them carry out processes in the time domain, such as the descriptor of Fujii [10], Generalized Differ- ences [1], Fuzzy Granular Descriptor (FGD) [5], among others. Other descriptors carry out this task using frequency domain tools, like the High to Low Frequency ratio (HLR) [11], and Frequency Band Decomposition [19]. Others deal with the frequency–time domain, such as the Wavelet Entropy Descriptor [16].
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CCD image processing of messier objects caught from astronomical observatory of the UTP

CCD image processing of messier objects caught from astronomical observatory of the UTP

Sin duda alguna, la astronomía es una de las ramas de la física con mayor acogida dentro de las personas del común, dado el gran asombro y admiración que causan los fenómenos celestes. Una posible explicación para ello consiste en que desde su aparición sobre la tierra, el ser humano ha manifestado una insaciable necesidad de conocer el cosmos más allá de las fronteras establecidas por su entorno, lo cual lo ha llevado a construir una visión mucho más general del mundo que lo rodea. Esta curiosidad por el cosmos ha permitido que los avances en la tecnología siempre lleguen a modernizar la instrumentación utilizada para el estudio de los cuerpos celestes. Desde la invención del telescopio a manos de Galileo Galilei en 1609 [1], se ha mejorado sustancialmente el proceso de fabricación de los elementos ópticos que componen este tipo de instrumentos, gracias al perfeccionamiento y automatización de los equipos necesarios para su construcción. Sin embrago, la astronomía óptica recibió su mayor impulso, en el momento en el cual se logró un desarrollo importante de los equipos electrónicos digitales de fotografía. Antiguamente, el astrónomo profesional o aficionado, debía dedicar horas a la adquisición de imágenes de cuerpos celestes, las cuales se imprimían en películas fotográficas tras largos periodos de exposición, con la dificultad de no poder conocer los resultados de su trabajo hasta el momento en
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