Multi-focus image fusion using a morphology-based focus measure in a quad-tree structure

Finite depth-of-field poses a problem in light optical imaging systems since the objects present outside the range of depth-of-field appear blurry in the recorded image. Effective depth-of-field of a sensor can be enhanced considerably without compromising the quality of the image by combining multi-focus images of a scene. This paper presents a block-based algorithm for multi-focus image fusion. In general, finding a suitable block-size is a problem in block-based methods. A large block is more likely to contain portions from both focused and defocused regions. This may lead to selection of considerable amount of defocused regions. On the other hand, small blocks do not vary much in relative contrast and hence difficult to choose from. Moreover, small blocks are more affected by mis-registration problems. In this work, we present a block-based algorithm which do not use a fixed block-size and rather makes use of a quad-tree structure to obtain an optimal subdivision of blocks. Though the algorithm starts with blocks, it ultimately identifies sharply focused regions in input images. The algorithm is simple, computationally efficient and gives good results. A new focus-measure called energy of morphologic gradients is introduced and is used in the algorithm. It is comparable with other focus measures viz.energy of gradients, variance, Tenengrad, energy of Laplacian and sum modified Laplacian. The algorithm is robust since it works with any of the above focus measures. It is also robust against pixel mis-registration. Performance of the algorithm has been evaluated by using two different quantitative measures.     

Enhancing effective depth-of-field by image fusion using mathematical morphology

Reduced depth-of-field (DOF) poses a problem in the light optical imaging system, since the objects present outside this zone appear blurry in the recorded image. The effective DOF of the sensor may be enhanced considerably without compromising the quality of the image by fusing images captured with different focused regions. This paper presents an image fusion technique suitable for combining multifocus images of a scene. The method employs morphological filters to select sharply focused regions from various images and then combines them together to reconstruct the image in which all the regions are properly focused. A performance measure based on image gradients is used to compare the results obtained by the proposed method with those obtained by other image fusion techniques.     

LGVTON: a landmark guided approach for model to person virtual try-on

Multimedia Tools and Applications - In this paper, we propose a Landmark Guided Virtual Try-On (LGVTON) method for clothes, which aims to solve the problem of clothing trials on e-commerce...     

Local jet pattern: a robust descriptor for texture classification

Multimedia Tools and Applications - Methods based on locally encoded image features have recently become popular for texture classification tasks, particularly in the existence of large intra-class...     

Robust vectorization method for electrical circuit drawings using component morphology

Pattern Analysis and Applications - Decomposition and representation of electrical circuit drawings to a suitable vector form has widespread applications related to data compression, storage,...     

3D cell growth and proliferation on a RGD functionalized nanofibrillar hydrogel based on a conformationally restricted residue containing dipeptide

Three-dimensional (3D) hydrogels incorporating a compendium of bioactive molecules can allow efficient proliferation and differentiation of cells and can thus act as successful tissue engineering scaffolds. Self-assembled peptide-based hydrogels can be worthy candidates for such applications as peptides are biocompatible, biodegradable and can be easily functionalized with desired moieties. Here, we report 3D growth and proliferation of mammalian cells (HeLa and L929) on a dipeptide hydrogel chemically functionalized with a pentapeptide containing Arg-Gly-Asp (RGD) motif. The method of functionalization is simple, direct and can be adapted to other functional moieties as well. The functionalized gel was noncytotoxic, exhibited enhanced cell growth promoting properties, and promoted 3D growth and proliferation of cells for almost 2 weeks, with simultaneous preservation of their metabolic activities. The presence of effective cell growth supporting properties in a simple and easy to functionalize dipeptide hydrogel is unique and makes it a promising candidate for tissue engineering and cell biological applications.     

Super resolution image reconstruction through bregman iteration using morphologic regularization

Multiscale morphological operators are studied extensively in the literature for image processing and feature extraction purposes. In this paper, we model a nonlinear regularization method based on multiscale morphology for edge-preserving super resolution (SR) image reconstruction. We formulate SR image reconstruction as a deblurring problem and then solve the inverse problem using Bregman iterations. The proposed algorithm can suppress inherent noise generated during low-resolution image formation as well as during SR image estimation efficiently. Experimental results show the effectiveness of the proposed regularization and reconstruction method for SR image.     

Color image compression based on block truncation coding using pattern fitting principle

This paper describes a color image compression technique based on block truncation coding using pattern fitting (BTC-PF). High degree of correlation between the RGB planes of a color image is reduced by transforming them to O₁O₂O₃ planes. Each O_i plane (1?i?3) is then encoded using BTC-PF method. Size of the pattern book and the block size are selected based on the information content of the corresponding plane. The result of the proposed method is compared with that of several BTC based methods and the former is found superior. Though this method is a spatial domain technique, it is also compared with JPEG compression method, which is one of most popular frequency domain techniques. It is found that the performance of the proposed method is a little inferior to that of the JPEG in terms of quality of the reconstructed image. Decoding time is another important criterion where the compressed image is decoded frequently for various purposes. As the proposed method requires negligible decoding time compared to JPEG, the former is preferred over the latter in those cases.     

Extraction and recognition of geographical features from paper maps

With the emergence of Geographical Information Systems (GIS), map acquisition and recognition have become hotly pursued topics, both by the industry and the academia. The paper presents a novel methodology for the extraction and recognition of symbol features from topographic maps. The method proceeds by separating the map into its constituent layers and then attempting to recognize the features in different layers on the basis of symbol-specific geometrical and morphological attributes. Text strings have also been separated. The output is obtained in the form of an ‘e-map’ that is vectorized and hence is suitable for GIS. To demonstrate the usefulness of the proposed system a simple database along with a query processing facility is constructed integrating the information obtained from the e-map and ‘some’ user inputs. The methodology has been observed to perform quite satisfactorily.