927 resultados para IMAGE PROCESSING COMPUTER-ASSISTED


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Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

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Cellular neural networks (CNNs) have locally connected neurons. This characteristic makes CNNs adequate for hardware implementation and, consequently, for their employment on a variety of applications as real-time image processing and construction of efficient associative memories. Adjustments of CNN parameters is a complex problem involved in the configuration of CNN for associative memories. This paper reviews methods of associative memory design based on CNNs, and provides comparative performance analysis of these approaches.

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This paper presents an automatic method to detect and classify weathered aggregates by assessing changes of colors and textures. The method allows the extraction of aggregate features from images and the automatic classification of them based on surface characteristics. The concept of entropy is used to extract features from digital images. An analysis of the use of this concept is presented and two classification approaches, based on neural networks architectures, are proposed. The classification performance of the proposed approaches is compared to the results obtained by other algorithms (commonly considered for classification purposes). The obtained results confirm that the presented method strongly supports the detection of weathered aggregates.

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This article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension. Leaves were analyzed according to the complexity of their internal and external shapes. A computational program was developed to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification. Results are presented from two experiments, the first to identify plant species from the Brazilian Atlantic forest and Brazilian Cerrado scrublands, using fifty leaf samples from ten different species, and the second to identify four different species from genus Passiflora, using twenty leaf samples for each class. A comparison is made of two methods to estimate fractal dimension (box-counting and multiscale Minkowski). The results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique, when estimating fractal dimension and identifying plants. (C) 2008 Elsevier Inc. All rights reserved.

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The design of binary morphological operators that are translation-invariant and locally defined by a finite neighborhood window corresponds to the problem of designing Boolean functions. As in any supervised classification problem, morphological operators designed from a training sample also suffer from overfitting. Large neighborhood tends to lead to performance degradation of the designed operator. This work proposes a multilevel design approach to deal with the issue of designing large neighborhood-based operators. The main idea is inspired by stacked generalization (a multilevel classifier design approach) and consists of, at each training level, combining the outcomes of the previous level operators. The final operator is a multilevel operator that ultimately depends on a larger neighborhood than of the individual operators that have been combined. Experimental results show that two-level operators obtained by combining operators designed on subwindows of a large window consistently outperform the single-level operators designed on the full window. They also show that iterating two-level operators is an effective multilevel approach to obtain better results.

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Background: Several methods have been utilized to prevent pericardial and retrosternal adhesions, but none of them evaluated the mesothelial regenerative hypothesis. There are evidences that the mesothelial trauma reduces pericardial fibrinolytic capability and induces an adhesion process. Keratinocyte growth factor (KGF) has proven to improve mesothelial cells proliferation. This study investigated the influence of keratinocyte growth factor in reducing post-surgical adhesions. Methods: Twelve pigs were operated and an adhesion protocol was employed. Following a stratified randomization, the animals received a topical application of KGF or saline. At 8 weeks, intrapericardial adhesions were evaluated and a severity score was established. The time spent to dissect the adhesions and the amount of sharp dissection used, were recorded. Histological sections were stained with sirius red and morphometric analyses were assessed with a computer-assisted image analysis system. Results: The severity score was lower in the KGF group than in the control group (11.5 vs 17, p = 0.005). The dissection time was lower in the KGF group (9.2 +/- 1.4 min vs 33.9 +/- 9.2 min, p = 0.004) and presented a significant correlation with the severity score (r = 0.83, p = 0.001). A significantly less sharp dissection was also required in the KGF group. Also, adhesion area and adhesion collagen were significantly tower in the KGF group than in the control group. Conclusion: The simulation of pericardial cells with KGF reduced the intensity of postoperative adhesions and facilitated the re-operation. This study suggests that the mesothelial regeneration is the new horizon in anti-adhesion therapies. (C) 2008 European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved.

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Background. Mesothelial injury is the pivot in the development of adhesions. An increase in the proliferation of mesothelial cells was verified by in vitro studies with the use of keratinocyte growth factor (KGF). This study investigated the influence of KGF associated with thermo-sterilized carboxymethyl chitosan (NOCCts) in the reduction of pericardial adhesions. Methods. An induction model of pericardial adhesion was carried out in 24 pigs. Animals were randomly allocated to receive topical application of KGF, KGF + NOCCts, NOCCts, or saline (control). At 8 weeks, intra-pericardial adhesions were evaluated and a severity score was established. The time spent to dissect the adhesions and the amount of sharp dissection used, were recorded. Histologic sections were stained with sirius red for a morphometric evaluation using a computer-assisted image analysis system. Cytokeratin AE1/AE3 immunostaining were employed to identify mesothelial cells. Results. The severity score expressed in median (minimum to maximum), in relation to the control group (17 [15 to 18]), was lower in the KGF + NOCCts group (7 [6 to 9], p < 0.01) followed by the KGF group (11.5 [9 to 12], 0.01 < p < 0.05) and the NOCCts group (12 [9 to 14], p > 0.05). The dissection time was significantly lower in the KGF + NOCCts group (7.1 +/- 0.6 vs 33.9 +/- 9.2 minutes, p < 0.001). A significantly less sharp dissection was also required in the KGF + NOCCts group. In the adhesion segment, a decreased collagen proportion was found in the KGF + NOCCts group (p < 0.05). Mesothelial cells were present more extensively in groups in which KGF was delivered (p = 0.01). Conclusions. The use of KGF associated with NOCCts resulted in a synergic action that decreases postoperative pericardial adhesions in a highly significant way. (Ann Thorac Surg 2010; 90: 566-72) (C) 2010 by The Society of Thoracic Surgeons

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The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

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Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

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Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.

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In this work, spoke about the importance of image compression for the industry, it is known that processing and image storage is always a challenge in petrobrás to optimize the storage time and store a maximum number of images and data. We present an interactive system for processing and storing images in the wavelet domain and an interface for digital image processing. The proposal is based on the Peano function and wavelet transform in 1D. The storage system aims to optimize the computational space, both for storage and for transmission of images. Being necessary to the application of the Peano function to linearize the images and the 1D wavelet transform to decompose it. These applications allow you to extract relevant information for the storage of an image with a lower computational cost and with a very small margin of error when comparing the images, original and processed, ie, there is little loss of quality when applying the processing system presented . The results obtained from the information extracted from the images are displayed in a graphical interface. It is through the graphical user interface that the user uses the files to view and analyze the results of the programs directly on the computer screen without the worry of dealing with the source code. The graphical user interface, programs for image processing via Peano Function and Wavelet Transform 1D, were developed in Java language, allowing a direct exchange of information between them and the user

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Muita atenção tem sido dada ao desenvolvimento de inseticidas vegetais buscando-se um efetivo controle de ectoparasitas de bovinos, sem prejudicar animais, consumidores e meio ambiente. Este estudo, realizado de abril a julho de 2008, na Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Pecuária Sudeste, em São Carlos, SP, Brasil, avaliou a eficácia de uma torta comercial de nim (Azadirachta indica) no controle da mosca-dos-chifres (Haematobia irritans) em bovinos. A torta de nim, misturada ao sal mineral na concentração de 2%, foi fornecida a 20 vacas Nelore, durante nove semanas, e sua eficácia foi monitorada através de contagens semanais nos grupos tratado e controle. Infestações individuais foram registradas por meio de fotos digitais em todos os animais de ambos os grupos, e o número de moscas foi, posteriormente, quantificado com o auxílio de um sistema de análise de imagem computadorizado. A quantificação dos componentes da torta de nim, por cromatografia líquida, revelou a presença de azadiractina (421 mg.kg-1) e 3-tigloyl-azadirachtol (151 mg.kg-1). A adição da torta de nim a 2% reduziu o consumo de sal mineral em cerca de 22%. O tratamento com torta de nim a 2% não reduziu as infestações por mosca-dos-chifres em bovinos durante as nove semanas do estudo.

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This dissertation presents a cooperative virtual multimedia enviroment for employing on time medical Field, using a TCP/IP computer network. The Virtual Diagnosis Room environment make it possible to perform cooperative tasks using classical image processing. Synchronous and assynchronous text conversation (chat) and content markup, in order to produce remote cooperative diagnosis. The dissertation also describes the tool in detail and its functions, that enables the interaction among users, along with implementation detals, contributions and weakness of this work

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This work proposes the development of a Computer System for Analysis of Mammograms SCAM, that aids the doctor specialist in the identification and analysis of existent lesions in digital mammograms. The computer system for digital mammograms processing will make use of a group of techniques of Digital Image Processing (DIP), with the purpose of aiding the medical professional to extract the information contained in the mammogram. This system possesses an interface of easy use for the user, allowing, starting from the supplied mammogram, a group of processing operations, such as, the enrich of the images through filtering techniques, the segmentation of areas of the mammogram, the calculation the area of the lesions, thresholding the lesion, and other important tools for the medical professional's diagnosis. The Wavelet Transform will used and integrated into the computer system, with the objective of allowing a multiresolution analysis, thus supplying a method for identifying and analyzing microcalcifications

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The vision is one of the five senses of the human body and, in children is responsible for up to 80% of the perception of world around. Studies show that 50% of children with multiple disabilities have some visual impairment, and 4% of all children are diagnosed with strabismus. The strabismus is an eye disability associated with handling capacity of the eye, defined as any deviation from perfect ocular alignment. Besides of aesthetic aspect, the child may report blurred or double vision . Ophthalmological cases not diagnosed correctly are reasons for many school abandonments. The Ministry of Education of Brazil points to the visually impaired as a challenge to the educators of children, particularly in literacy process. The traditional eye examination for diagnosis of strabismus can be accomplished by inducing the eye movements through the doctor s instructions to the patient. This procedure can be played through the computer aided analysis of images captured on video. This paper presents a proposal for distributed system to assist health professionals in remote diagnosis of visual impairment associated with motor abilities of the eye, such as strabismus. It is hoped through this proposal to contribute improving the rates of school learning for children, allowing better diagnosis and, consequently, the student accompaniment