Avaliação de imagens através de Similaridade Estrutural e do conceito de Mínima Diferença de Cor Perceptível.

A avaliação objetiva da qualidade de imagens é de especial importância em diversas aplicações, por exemplo na compressão de imagens, onde pode ser utilizada para regular a taxa que deve ser empregada para que haja a máxima compressão (permitindo perda de dados) sem comprometer a qualidade final; outro exemplo é na inserção de marcas dágua, isto é, introdução de informações descritivas utilizadas para atestar a autenticidade de uma imagem, que devem ser invisíveis para o observador. O SSIM (Structural SIMilarity) é uma métrica de avaliação objetiva da qualidade de imagens de referência completa projetada para imagens em tons de cinza. Esta dissertação investiga sua aplicação na avaliação de imagens coloridas. Para tanto, inicialmente é feito um estudo do SSIM utilizando quatro diferentes espaços de cores RGB, YCbCr, Lαβ e CIELAB. O SSIM é primeiramente calculado nos canais individuais desses espaços de cores. Em seguida, com inspiração no trabalho desenvolvido em (1) são testadas formas de se combinar os valores SSIM obtidos para cada canal em um valor único os chamados SSIM Compostos. Finalmente, a fim de buscar melhores correlações entre SSIM e avaliação subjetiva, propomos a utilização da mínima diferença de cor perceptível, calculada utilizando o espaço de cores CIELAB, conjuntamente com o SSIM. Para os testes são utilizados três bancos de dados de imagens coloridas, LIVE, IVC e TID, a fim de se conferir consistência aos resultados. A avaliação dos resultados é feita utilizando as métricas empregadas pelo VQEG (Video Quality Experts Group) para a avaliação da qualidade de vídeos, com uma adaptação. As conclusões do trabalho sugerem que os melhores resultados para avaliação da qualidade de imagens coloridas usando o SSIM são obtidas usando os canais de luminância dos espaços de cores YCbCr, Lαβ e especialmente o CIELAB. Também se concluiu que a utilização da mínima diferença de cor perceptível contribui para o melhoramento dos resultados da avaliação objetiva.

基于光谱分析的主动式距离图像获取方法

Rainbow三维摄像机是一种基于光谱分析的快速三维信息获取方法。该方法利用连续变化的彩色光谱照射景物 ,彩色CCD摄像机摄取的景物图像将呈现有规律的颜色变化 ,而且不同的颜色 (波长 )构成了不同的空间颜色面。通过标定这些颜色面和摄像机成象模型 ,即可计算出图像中各点的三维坐标。

基于光谱分析的主动式距离图象获取方法

Ｒａｉｎｂｏｗ 三维摄像机是一种基于光谱分析的快速三维信息获取方法。该方法利用连续变化的彩色光谱照射景物，彩色ＣＣＤ 摄像机摄取的景物图象将呈现有规律的颜色变化，而且不同的颜色（波长）构成了不同的空间颜色面。通过标定这些颜色面和摄像机成象模型，即可计算出图象中各点的三维坐标。该文重点讨论实现该方法的标定技术和颜色分类技术，最后给出实验结果。

图象噪声的模糊聚类平滑方法

模糊C-means算法在聚类分析中已得到了成功的应用,本文提出一种利用模糊C-means算法消除噪声的新方法。一般来说,图象中的噪声点就是其灰度值与其周围象素的灰度值之差超过某个门限值的点。根据这个事实,首先利用模糊C-means算法分类,再利用标准核函数检测出噪声点,然后将噪声点去掉。由于只修改噪声点处的象素灰度值,而对于其它象素的灰度值不予改变,所以本算法能够很好地保护细节和边缘。本方法每次处理3×3个点,而以往的方法只能每次处理一个点,所以本方法能提高运算速度。文中给出了利用本方法对实际图象处理的结果,并与梯度倒数权值法进行了定量的比较。

Head Tracking via Robust Registration in Texture Map Images

A novel method for 3D head tracking in the presence of large head rotations and facial expression changes is described. Tracking is formulated in terms of color image registration in the texture map of a 3D surface model. Model appearance is recursively updated via image mosaicking in the texture map as the head orientation varies. The resulting dynamic texture map provides a stabilized view of the face that can be used as input to many existing 2D techniques for face recognition, facial expressions analysis, lip reading, and eye tracking. Parameters are estimated via a robust minimization procedure; this provides robustness to occlusions, wrinkles, shadows, and specular highlights. The system was tested on a variety of sequences taken with low quality, uncalibrated video cameras. Experimental results are reported.

A Neuromorphic Model for Achromatic and Chromatic Surface Representation of Natural Images

This study develops a neuromorphic model of human lightness perception that is inspired by how the mammalian visual system is designed for this function. It is known that biological visual representations can adapt to a billion-fold change in luminance. How such a system determines absolute lightness under varying illumination conditions to generate a consistent interpretation of surface lightness remains an unsolved problem. Such a process, called "anchoring" of lightness, has properties including articulation, insulation, configuration, and area effects. The model quantitatively simulates such psychophysical lightness data, as well as other data such as discounting the illuminant, the double brilliant illusion, and lightness constancy and contrast effects. The model retina embodies gain control at retinal photoreceptors, and spatial contrast adaptation at the negative feedback circuit between mechanisms that model the inner segment of photoreceptors and interacting horizontal cells. The model can thereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A new anchoring mechanism, called the Blurred-Highest-Luminance-As-White (BHLAW) rule, helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural color images under variable lighting conditions, and is compared with the popular RETINEX model.

Land-cover classification from airborne LIDAR data fused with aerial optical images

Airborne LIght Detection And Ranging (LIDAR) provides accurate height information for objects on the earth, which makes LIDAR become more and more popular in terrain and land surveying. In particular, LIDAR data offer vital and significant features for land-cover classification which is an important task in many application domains. In this paper, an unsupervised approach based on an improved fuzzy Markov random field (FMRF) model is developed, by which the LIDAR data, its co-registered images acquired by optical sensors, i.e. aerial color image and near infrared image, and other derived features are fused effectively to improve the ability of the LIDAR system for the accurate land-cover classification. In the proposed FMRF model-based approach, the spatial contextual information is applied by modeling the image as a Markov random field (MRF), with which the fuzzy logic is introduced simultaneously to reduce the errors caused by the hard classification. Moreover, a Lagrange-Multiplier (LM) algorithm is employed to calculate a maximum A posteriori (MAP) estimate for the classification. The experimental results have proved that fusing the height data and optical images is particularly suited for the land-cover classification. The proposed approach works very well for the classification from airborne LIDAR data fused with its coregistered optical images and the average accuracy is improved to 88.9%.

Limiarização contextual automática de imagens coloridas: aplicação na extração de sementes de rodovia

In this paper is a totally automatic strategy proposed to reduce the complexity of patterns ( vegetation, building, soils etc.) that interact with the object 'road' in color images, thus reducing the difficulty of the automatic extraction of this object. The proposed methodology consists of three sequential steps. In the first step the punctual operator is applied for artificiality index computation known as NandA ( Natural and Artificial). The result is an image whose the intensity attribute is the NandA response. The second step consists in automatically thresholding the image obtained in the previous step, resulting in a binary image. This image usually allows the separation between artificial and natural objects. The third step consists in applying a preexisting road seed extraction methodology to the previous generated binary image. Several experiments carried out with real images made the verification of the potential of the proposed methodology possible. The comparison of the obtained result to others obtained by a similar methodology for road seed extraction from gray level images, showed that the main benefit was the drastic reduction of the computational effort.

Automated diagnosis of human intestinal parasites using optical microscopy images

Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes. © 2013 IEEE.

Eliminação de ruído impulsivo em imagens coloridas usando um filtro mediano seletivo e retoque digital

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Processamento de fotomicrografias por meio da transformada wavelet starlet

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Computer-assisted arthroplasty using bioengineered autografts

Recent advances in tissue-engineered cartilage open the door to new clinical treatments of joint lesions. Common to all therapies with in-vitro-engineered autografts is the need for optimal fit of the construct to allow screwless implantation and optimal integration into the live joint. Computer-assisted surgery (CAS) techniques are prime candidates to ensure the required accuracy, while at the same time simplifying the procedure. A pilot study has been conducted aiming at assembling a new set of methods to support ankle joint arthroplasty using bioengineered autografts. Computer assistance allows planning of the implant shape on a computed tomography (CT) image, manufacturing the construct according to the plan, and interoperatively navigating the surgical tools for implantation. A rotational symmetric model of the joint surface was used to avoid segmentation of the CT image; new software was developed to determine the joint axis and make the implant shape parameterizable. A complete cycle of treatment from planning to operation was conducted on a human cadaveric foot, thus proving the feasibility of computer-assisted arthroplasty using bioengineered autografts

Effects of camera aperture correction on keying and compositing of broadcast video

This contribution discusses the effects of camera aperture correction in broadcast video on colour-based keying. The aperture correction is used to ’sharpen’ an image and is one element that distinguishes the ’TV-look’ from ’film-look’. ’If a very high level of sharpening is applied, as is the case in many TV productions then this significantly shifts the colours around object boundaries with hight contrast. This paper discusses these effects and their impact on keying and describes a simple low-pass filter to compensate for them. Tests with colour-based segmentation algorithms show that the proposed compensation is an effective way of decreasing the keying artefacts on object boundaries.

Spare Time Activity Sheets from Photo Albums

Given arbitrary pictures, we explore the possibility of using new techniques from computer vision and artificial intelligence to create customized visual games on-the-fly. This includes coloring books, link-the-dot and spot-the-difference popular games. The feasibility of these systems is discussed and we describe prototype implementation that work well in practice in an automatic or semi-automatic way.