Neuropsychological investigations of visual imagery and representations have led to a deeper understanding of the spatial perception, representation and memory. But how each individual perceives object's geometrical properties and how they differ from person to person, both under event-related memory and normal recollecting memory in the presence or in the absence of direct sensory stimulation is still unclear. Spatial knowledge is diverse, complex, and multimodal, as are the situations in which it is used. All seem to agree that a cognitive map is a mental representation of an external environment. The image scaling is important in understanding the psychological dysfunctions of patients suffering from spatial cognition problems. The scaling becomes self-evident in art forms, when people are asked to draw image of objects they see actively or from their short or long term memory. In this paper we develop a comprehensive model of this scaling factor and its implications in spatial image representation and memory. We also extend its notion in understanding the perception of objects whose representations are normally not possible (like the perception of universal scales, infinities and parallel lines) but are well comprehended by the human brains. Here we give a scaling factor which is variable depending on the situations for a person based on his visual memory and drawing capabilities. And then extend it to analyse his cognitive strengths, disorders and any imperfections. This model also helps in formalizing the architectural cognitive maps needed to change the scaling factor, depending on the types of visual works one performs.
Image processing play a major role in day to day life. The implementation of image processing applications using MATLAB takes place in practice. The block based implementation of image processing applications can be done with LabVIEW (Laboratory Virtual Instrument Engineering Workbench). This paper discusses the basic operations on images like extraction of the RGB (Red, Green, and Blue) components in a color image, converting the gray scale image to binary image and the edge detection process in both MATLAB and LabVIEW. Based on the experimental results, the merits and demerits of programming languages are discussed.
Image inpainting is a technique to repair damaged images or to remove/replace selected regions. It was used to repair old artwork and also a part of movie special effects. This paper presents review of many successful algorithms for image inpainting which are Texture based algorithms, Diffusion(PDE) based algorithms, Exemplar and search based algorithms and Sparsity based algorithms. Here an evaluation of two classes of algorithms: Partial Differential Equations (PDEs) based algorithms and Exemplar-Based algorithms are presented. The results show the advantages and disadvantages.Index Terms-Image inpainting, PDEs-based algorithm, Exemplar-based algorithm.
Nowadays Cheque Truncating System (CTS) compliant cheques are used in banks for saving time and reducing effort in depositing cheques. With the development of image processing it is possible to make machines read the documents instead of humans. Applying the image processing technique in the cheque deposition in banks can reduce the human effort, time and also will be cost effective. In this paper, an automatic cheque clearance system is discussed using Optical Character Recognition (OCR) system by feature extraction and pattern recognition concepts in image processing. Using this one, can reduce time, human effort and money in processing of cheques. The algorithms are executed using MATLAB.
This paper aims to develop a novel deep learning concept to deal with video inpainting. Initially, motion tracking is performed, which is the process of determining motion vectors that describe the transformation from adjacent frames in a video sequence. Further, the regions or patches of each frame are categorized using the optimized recurrent neural network (RNN), in which the region is split into a smooth and structure region. It is performed using the texture feature called grey-level co-occurrence matrix. The filling of the smooth region is accomplished by replacing with the mean pixels of unmasked region, and the structure region is done by optimized patch matching approach based on scale-invariant feature transform (SIFT). The main objective optimized patch matching is based on the minimized Euclidean distance between the extracted SIFT features of the original patch and reference patch, and the certain patch is inpainted by the optimized patch. Here, the hybridization of two meta-heuristic algorithms like cuckoo search algorithm and multi-verse optimization (MVO) called Cuckoo Search-based MVO is used to optimize the RNN and patch matching. Finally, the experimental results verify the reliability of the proposed algorithm over existing algorithms.
In this paper we investigate the blocktype pilot channel estimation for orthogonal frequency division multiplexing (OFDM) systems. The estimation is based on the minimum mean square error (MMSE) estimator and the least square (LS) estimator. We derive the MMSE and LS estimators' architecture and investigate their performances. We prove that the MMSE estimator performance is better but computational complexity is high, contrary the LS estimator has low complexity but poor performance. For reducing complexity we proposed two different solutions which are the Simplified Least Square (SLS) estimator and the modified MMSE estimator. In the SLS estimator, we apply an auto-correlation function with the LS estimator to remove the noise. In the modified MMSE estimator, we consider only the significant energy samples and ignore the remaining noisy samples. Based on this idea we introduce the modified MMSE estimator. We evaluate estimator's performance on basis of mean square error and symbol error rate for 16 QAM systems using MATLAB.
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