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Visual texture fidelity evaluation is important but still unsolved problem. Evaluation of how well various texture models conform with human visual perception of their original measured pattern is required not only for assessing the visual dissimilarities between a model output and the original measured texture, but also for optimal settings of model parameters, for fair comparison of distinct models,...
Moiré patterns, an artifact of aliasing interference between details in the subject matter and the grid of the sensor, heavily disturb the qualitative and quantitative analysis of images. It is hard to effectively remove moiré patterns since they are similar to image textures. We propose a novel low-rank and sparse matrix decomposition model for moiré pattern removal. This method is grounded on the...
One of the most important tasks in building environment maps with partial information is to find a good alignment between pairs of point clouds representing consecutive frames. RANSAC and ICP are widely used algorithms to align pairs of frames: the former finds an initial transformation which is refined by the latter. Decreasing the alignment error in the first step can reduce the computational cost...
Growth of the image mining arena calls for the need of quality image retrieval techniques in par with the human perception which are invariant to scale and rotation. An optimized content based image retrieval system based on local visual attention features to bridge the semantic gap problem is proposed. The approach involves the salient point detection using Scale Up Robust Features (SURF) detector...
This paper presents a novel region merging segmentation method for color image based on color and texture distribution features. The segmentation strategy includes two phases. In the first phase, we select initial seed points for super pixels extraction in the texture energy image at average intervals. Then we implement pixels clustering to extract over segmentation regions at local areas using color...
Many chronic diseases, such as heart diseases, diabetes, and obesity, can be related to diet. Hence, the need to accurately measure diet becomes imperative. We are developing methods to use image analysis tools for the identification and quantification of food consumed at a meal. In this paper we describe a new approach to food identification using several features based on local and global measures...
With the rapid development of the information age and the wide range of use of multimedia technology, the problem how to solve a large number of efficient management of multimedia information has become an urgent need. In this paper, improvement ideas of the algorithm by analyzing the principle of traditional texture roughness retrieval algorithm is summarized and realized, and improved texture roughness...
For natural image segmentation, due to features from a single image are hard to describe the complex scene information, this paper presents a new method based on the fusion model evaluation index PRI to fuse color histogram features in 3 color spaces, RGB, XYZ, LUV, and texture features. We experiment on images from Berkeley segmentation databases and compare the quantitative and qualitative experimental...
We compare the scene classification performance of 13 features, including structure, texture and color features. First, image classification are performed using a single feature and the performance of different features are compared. Both the k-nearest-neighbor (KNN) classifier and the support vector machine classifier (SVM) are employed. And for the KNN classifier, we use four different distance...
A rapid visualisation of change in urban crisis areas is an important condition for planning and coordination of help. For automated change detection, a large number of algorithms has been proposed and developed. This paper describes the results of a colour and texture based change detection approach that was applied to satellite and aircraft images of the earthquake region in Haiti. In our integrated...
An algorithm was proposed to remove the moving shadow in video surveillance. This method gets the moving objects from motion detection in first, in which removal shadow on invariable features of colors and local texture, then gets rid of the change suddenly pixels in edge of shadow. Experiments show that having a strong ability to adapt to different scenes, the algorithm retains the integrity of the...
Scene classification from images is a challenging problem in computer vision due to its significant variability of scale, illumination, and view. Recently, Latent Dirichlet Allocation (LDA) model has grown popular in computer vision field, especially in scene labeling and classification. However, the effectiveness of the LDA model for the scene classification has not yet been addressed thoroughly...
Image identification of plant leaves based on human vision is difficult task as well as plant identification based on keywords retrieval. It requires the domain knowledge in the botanist field. This work proposes the image texture analysis using Discrete Wavelet Transformation (DWT) and combined with an entropy measurement to identify a query image to one of seven classes that consists of 280 plant...
In this paper, we proposed a novel algorithm of automatic image structure completion. Different from traditional image completion algorithms directly copying patches from the unknown region to the damaged part, our completion approach first reconstructs the geometry structures in the damaged region with edges inferred by Constrained Delaunay Triangulation (CDT), and select correct edges through a...
Facial rejuvenation has driven a lot of research in the field of dermatology and plastic surgery, leading to many medical procedures. This paper proposes an age prediction method that could be used to better understand the ageing process and to evaluate the benefits of a rejuvenating treatment, for example. A supervised Facial Model (SFM) is built using Partial Least Squares regression (PLSR) to capture...
This paper presents an algorithm to classify pixels in uterine cervix images into two classes, namely normal and abnormal tissues, and simultaneously select relevant features, using group sparsity. Because of the large variations in image appearance due to changes of illumination, specular reflections and other visual noise, the two classes have a strong overlap in feature space, whether features...
Automatic classification of cancer lesions for gastroenterology imaging scenarios poses novel challenges to computer assisted decision systems, owing to their distinct visual characteristics such as reduced color spaces or natural organic textures. In this paper, we explore the prospects of using Gabor filters in a texton framework for the classification of images from two distinct imaging modalities...
In this paper we compare different approaches to combine color and statistical texture descriptors. Previous studies on this topic were conducted on natural images only. We focus on the particular case of histological datasets where color plays an important role due to the staining process of the biological samples. We also introduce two new variants of the well-known Local Binary Patterns (LBP) operator...
In this paper, we propose a new approach for color textured image segmentation. It is a two stage technique, where in the first stage, textural features using gray level co-occurrence matrix (GLCM) are computed for regions of interest (ROI)considered for each class. ROI act as ground truths for the classes. Ohta model (I1, I2, I3) is the colour model used for segmentation. Mean at inter pixel distance...
This paper proposes an interactive texture design method to obtain adequate texture patterns for 3D computer graphics shapes. Texture mapping is widely used to improve the visual richness of surface appearances. Designing texture patterns can be a problem to search an adequate pattern for the 3D shapes in the scene physically and psychologically. So it is required to supply intuitive and simple method...
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