Degraded image analysis: an invariant approach

Analysis and interpretation of an image which was acquired by a nonideal imaging system is the key problem in many application areas. The observed image is usually corrupted by blurring, spatial degradations, and random noise. Classical methods like blind deconvolution try to estimate the blur parameters and to restore the image. We propose an alternative approach. We derive the features for image representation which are invariant with respect to blur regardless of the degradation PSF provided that it is centrally symmetric. As we prove in the paper, there exist two classes of such features: the first one in the spatial domain and the second one in the frequency domain. We also derive so-called combined invariants, which are invariant to composite geometric and blur degradations. Knowing these features, we can recognize objects in the degraded scene without any restoration     

Variation-based approach to image segmentation Variation-based approach to image segmentation

A new approach to image segmentation is presented using a variation framework. Regarding the edge points as interpolating points and minimizing an energy functional to interpolate a smooth threshold surface it carries out the image segmentation. In order to preserve the edge information of the original image in the threshold surface, without unduly sharping the edge of the image, a non-convex energy functional is adopted. A relaxation algorithm with the property of global convergence, for solving the optimization problem, is proposed by introducing a binary energy. As a result the non-convex optimization problem is transformed into a series of convex optimization problems, and the problem of slow convergence or nonconvergence is solved. The presented method is also tested experimentally. Finally the method of determining the parameters in optimizing is also explored.     

Fuzzy reasoning approach to similarity evaluation in image analysis

In image analysis, the concept of similarity has been widely explored and various measures of similarity, or of distance, have been proposed that yield a quantitative evaluation. There are cases, however, in which the evaluation of similarity should reproduce the judgment of a human observer based mainly on qualitative and, possibly, subjective appraisal of perceptual features. This process is best modeled as a cognitive process based on knowledge structures and inference strategies, able to incorporate the human reasoning mechanisms and to handle their inherent uncertainties. This articlea proposes a general strategy for similarity evaluation in image analysis considered as a cognitive process. A salient aspect is the use of fuzzy logic propositions to represent knowledge structures, and fuzzy reasoning to model inference mechanisms. Specific similarity evaluation procedures are presented that demonstrate how the same general strategy can be applied to different image analysis problems. © 1993 John Wily & Sons, Inc.     

Projective active shape models for pose-variant image analysis of quasi-planar objects: Application to facial analysis

One of the important obstacles in the image-based analysis of the human face is the 3D nature of the problem and the 2D nature of most imaging systems used for biometric applications. Due to this, accuracy is strongly influenced by the viewpoint of the images, being frontal views the most thoroughly studied. However, when fully automatic face analysis systems are designed, capturing frontal-view images cannot be guaranteed. Examples of this situation can be found in surveillance systems, car driver images or whenever there are architectural constraints that prevent from placing a camera frontal to the subject. Taking advantage of the fact that most facial features lie approximately on the same plane, we propose the use of projective geometry across different views. An active shape model constructed with frontal-view images can then be directly applied to the segmentation of pictures taken from other viewpoints. The proposed extension demonstrates being significantly more invariant than the standard approach. Validation of the method is presented in 360 images from the AV@CAR database, systematically divided into three different rotations (to both sides), as well as upper and lower views due to nodding. The presented tests are among the largest quantitative results reported to date in face segmentation under varying poses.     

利用SIFT算子与图像插值实现图像匹配

尺度不变特征算子SIFT具有良好的尺度、旋转、光照不变特性,广泛应用于图像匹配领域。该算子利用128维的特征描述向量来表征每个特征点,由于维数过高,影响图像匹配的速度。为此,提出了利用图像插值技术对特征描述向量进行降维处理,提高匹配速度,并通过实验验证了该方法的有效性。     

Projection-based approach to image analysis: pattern recognitionand representation in the position-orientation space

A method for image analysis, based on the formalism of functional analysis is proposed. This approach, which is employed for pattern recognition in the combined position-orientation space, is motivated by a variety of neurophysiological findings that emphasize the importance of the orientation feature in visual image processing and analysis. The approach is applied to the so-called Glass patterns, which are special cases of images characterized uniquely by their orientational feature of local correlations between pairs of corresponding dots. The approach can be of interest in the analysis of optical flow     

A statistics-based approach to binary image registration with uncertainty analysis

A new technique is described for the registration of edge-detected images. While an extensive literature exists on the problem of image registration, few of the current approaches include a well-defined measure of the statistical confidence associated with the solution. Such a measure is essential for many autonomous applications, where registration solutions that are dubious (involving poorly focused images or terrain that is obscured by clouds) must be distinguished from those that are reliable (based on clear images of highly structured scenes). The technique developed herein utilizes straightforward edge pixel matching to determine the "best" among a class of candidate translations. A well-established statistical procedure, the McNemar test, is then applied to identify which other candidate solutions are not significantly worse than the best solution. This allows for the construction of confidence regions in the space of the registration parameters. The approach is validated through a simulation study and examples are provided of its application in numerous challenging scenarios. While the algorithm is limited to solving for two-dimensional translations, its use in validating solutions to higher-order (rigid body, affine) transformation problems is demonstrated     

Image analysis by wavelet-type transforms: Group theoretic approach

A group theoretic approach to image representation and analysis is presented. The concept of a wavelet transform is extended to incorporate different types of groups. The wavelet approach is generalized to Lie groups that satisfy conditions of compactness and commutability and to groups that are determined in a particular way by subgroups that satisfy these conditions. These conditions are fundamental to finding the invariance measure for the admissibility condition of a mother wavelet-type transform. The following special cases of interest in image representation and in biological and computer vision are discussed: 2- and 3-D rigid motion, similarity and Lorentzian groups, and 2-D projective groups obtained from 3-D camera rotation.This research was supported by U.S.—Israel Binational Science Foundation grant 8800320, by the Franz Ollendorff Center of the Department of Electrical Engineering, and by the Fund for Promotion of Research at the Technion. J. Segman is a VATAT (Israel National Committee for Planning and Budgeting Universities) Fellow at the Technion.     

Variation-based approach to image segmentation

A new approach to image segmentation is presented using a variation framework. Re-garding the edge points as interpolating points and minimizing an energy functional to interpolate a smooth threshold surface it carries out the image segmentation. In order to preserve the edge informa-tion of the original image in the threshold surface, without unduly sharping the edge of the image, a non-convex energy functional is adopted. A relaxation algorithm with the property of global conver-gence, for solving the optimization problem, is proposed by introducing a binary energy. As a result the non-convex optimization problem is transformed into a series of convex optimization problems, and the problem of slow convergence or nonconvergence is solved. The presented method is also tested experimentally. Finally the method of determining the parameters in optimizing is also explored.     

A type-2 fuzzy system-based approach for image data fusion to create building information models

Building Information Modelling (BIM) is a standard digital process that fuses buildings information from different sources into a 3D model during their lifecycle. For new construction sites using BIM, it is possible to monitor the cost, schedule, and changes throughout the lifecycle; however, existing buildings do not have a BIM model. Manually creating the BIM models for existing buildings is a high-cost task, both in time and money, hence there is a need for extracting information from available paper-based documentation and fuse it into a BIM model. The struggle of facility management and utility companies to fully adopt a BIM process (due to their high volumes of paper-based documentation of existing buildings) has led to the research on creating these 3D BIM models from 2D floor plan images.This paper presents a novel processing pipeline to extract 2D digital information from floorplans, fusing it into a 3D BIM model. The work focuses on fusing the available information to create the structure of the building in BIM format, which is considered the essential step before looking on working with other sources of data. In this process, we introduce a type-2 fuzzy logic based Explainable Artificial Intelligence (XAI) approach for the semantic segmentation step. The approach consists of using the outputs of type-2 fuzzy logic systems to classify a pixel as wall or background, by using information around and from the pixel of interest as the inputs to the system. After the semantic segmentation step, the output of the type-2 fuzzy logic goes through a noise removal process and finally a transformation from 2D to 3D by assigning the corresponding BIM tag to each identified element. The proposed type-2 fuzzy logic semantic segmentation approach produced comparable results (97.3% mean Intersection over Union (IoU) performance metric value) to the opaque box model approach based on Convolutional Neural Network (CNN) (99.3% mean IoU performance metric value). However, the type-2 fuzzy XAI system benefits from being an augmentable and interpretable model, which means that human users can understand the decision process and modify the model using their expert knowledge.     

HIRBIR: A hierarchical approach to region-based image retrieval

This paper proposes a hierarchical approach to region-based image retrieval (HIRBIR) based on wavelet transform whose decomposition property is similar to human visual processing. First, automated image segmentation is performed fast in the low-low (LL) frequency subband of the wavelet domain that shows the desirable low image resolution. In the proposed system, boundaries between segmented regions are deleted to improve the robustness of region-based image retrieval against segmentation-related uncertainty. Second, a region feature vector is hierarchically represented by information in all wavelet subbands, and each feature component of a feature vector is a unified color–texture feature. Such a feature vector captures well the distinctive features (e.g., semantic texture) inside one region. Finally, employing a hierarchical feature vector, the weighted distance function for region matching is tuned meaningfully and easily, and a progressive stepwise indexing mechanism with relevance feedback is performed naturally and effectively in our system. Through experimental results and comparison with other methods, the proposed HIRBIR shows a good tradeoff between retrieval effectiveness and efficiency as well as easy implementation for region-based image retrieval.     

A mathematical morphology approach to image based 3D particle shape analysis

Angularity is a critically important property in terms of the performance of natural particulate materials. It is also one of the most difficult to measure objectively using traditional methods. Here we present an innovative and efficient approach to the determination of particle angularity using image analysis. The direct use of three-dimensional data offers a more robust solution than the two-dimensional methods proposed previously. The algorithm is based on the application of mathematical morphological techniques to range imagery, and effectively simulates the natural wear processes by which rock particles become rounded. The analysis of simulated volume loss is used to provide a valuable measure of angularity that is geometrically commensurate with the traditional definitions. Experimental data obtained using real particle samples are presented and results correlated with existing methods in order to demonstrate the validity of the new approach. The implementation of technologies such as these has the potential to offer significant process optimisation and environmental benefits to the producers of aggregates and their composites. The technique is theoretically extendable to the quantification of surface texture.     

Medical image analysis based on deep learning approach

Multimedia Tools and Applications - Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and...     

New neutrosophic approach to image segmentation

Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. NS is a formal framework that has been recently proposed. However, NS needs to be specified from a technical point of view for a given application or field. We apply NS, after defining some concepts and operations, for image segmentation.The image is transformed into the NS domain, which is described using three membership sets: T, I and F. The entropy in NS is defined and employed to evaluate the indeterminacy. Two operations, α-mean and β-enhancement operations are proposed to reduce the set indeterminacy. Finally, the proposed method is employed to perform image segmentation using a γ-means clustering. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment the images automatically and effectively. Especially, it can segment the “clean” images and the images having noise with different noise levels.