International Journal of Image and Graphics

This paper proposes a compression algorithm using octonary repetition tree (ORT) based on run length encoding (RLE). Generally, RLE is one type of lossless data compression method which has duplication problem as a major issue due to the usage of code word or flag. Hence, ORT is offered instead of using a flag or code word to overcome this issue. This method gives better performance by means of compression ratio, i.e. 99.75%. But, the functioning of ORT is not good in terms of compression speed. For that reason, physical- next generation secure computing (PHY-NGSC) is hybridized with ORT to raise the compression speed. It uses an MPI-open MP programming paradigm on ORT to improve the compression speed of encoder. The planned work achieves multiple levels of parallelism within an image such as MPI and open MP for parallelism across a group of pictures level and slice level, respectively. At the same time, wide range of data compression like multimedia, executive files and documents are possible in the proposed method. The performance of the proposed work is compared with other methods like accordian RLE, context adaptive variable length coding (CAVLC) and context-based arithmetic coding (CBAC) through the implementation in Matlab working platform.

This paper presents a complete analysis of wavelet-based image compression encoding techniques. The techniques involved in this paper are embedded zerotree wavelet (EZW), set partitioning in hierarchical trees (SPIHT), wavelet difference reduction (WDR), adaptively scanned wavelet difference reduction (ASWDR), set partitioned embedded block coder (SPECK), compression with reversible embedded wavelet (CREW) and spatial orientation tree wavelet (STW). Experiments are done by varying level of the decomposition, bits per pixel and compression ratio. The evaluation is done by taking parameters like peak signal to noise ratio (PSNR), mean square error (MSE), image quality index (IQI) and structural similarity index (SSIM), average difference (AD), normalized cross-correlation (NK), structural content (SC), maximum difference (MD), Laplacian mean squared error (LMSE) and normalized absolute error (NAE).

The natural scene images contain text as an integral part of that image that supplies paramount knowledge about it. This information and knowledge can be used in the variety of purposes like image-based searching, automatic number plate recognition, robot navigation, etc. but text region extraction and detection in scenery images could be quite a challenging job due to image blur, distortion, noise, etc. In this paper, we discuss a method for extraction of text regions by generating prospective components by applying maximally stable extremal regions (MSER) and boundary smoothing by Alternating guided image filter, which is one of the newest filters to deal with noise and halo effect elimination. The separation of non-text & text components is achieved by AdaBoost classifier that separates text and non-text on the basis of the three text specific features namely maximum stroke width ratio, compactness, color divergence. The proposed method assist in extracting text regions from the blurred and low contrast natural scene images effectively. The ICDAR 2013 training and testing dataset is applied for the experiments and evaluation of the method. The evaluation is carried out using deteval software for calculating precision, f-measure, recall for the detected, and extracted text regions.

This paper describes an algorithm utilizing a modified multi-scale fractional order-based operator combined with a probabilistic tonal operator, adaptive color enhancement and bilateral filtering to process hazy and underwater images. The multi-scale algorithm complements the tonal operator by enhancing edges, preventing overexposure of bright image regions, while enhancing details in the dark areas. The addition of a previously developed global enhancement operator removes color cast and improves global contrast in underwater images. The color enhancement function augments the color results of the dehazing algorithm without distorting image intensity. Furthermore, the bilateral filter suppresses noise while preserving enhanced details/edges due to the multi-scale algorithm. Experimental results indicate that the proposed system yields comparable or better results than other algorithms from the literature.

In this paper, we propose an arc recognition method based on the number theoretic properties of isothetic covers. A definition of digital circles is given based on the dilation of Euclidean circles with unit squares. We show that a variant of the digital circle is equivalent to the grid centers between the isothetic covers of disks. Number theoretic properties of the isothetic covers of disks are explored and show that the distribution of square numbers can be used to find the run lengths of the isothetic covers. Arc recognition algorithms are developed based on the number theoretic properties.

We present a novel technique for transcribing crowds in video scenes that allows extracting the positions of moving objects in video frames. The technique can be used as a more precise alternative to image processing methods, such as background-removal or automated pedestrian detection based on feature extraction and classification. By manually projecting pedestrian actors on a two-dimensional plane and translating screen coordinates to absolute real-world positions using the cross ratio, we provide highly accurate and complete results at the cost of increased processing time. We are able to completely avoid most errors found in other automated annotation techniques, resulting from sources such as noise, occlusion, shadows, view angle or the density of pedestrians. It is further possible to process scenes that are difficult or impossible to transcribe by automated image processing methods, such as low-contrast or low-light environments. We validate our model by comparing it to the results of both background-removal and feature extraction and classification in a variety of scenes.

The ancient Arabic manuscripts are considered to be more complex regarding enhancement compared to others written in other languages. Complexity comes from character degradation, stains, low-quality images, curves of the text, character overlapping, etc. To facilitate the restoration, a set of well-known binarization techniques designed for historical document images is presented in this paper. Existing binarization techniques focus on either finding an appropriate global threshold for the whole image or adapting a local threshold for each area to achieve better enhancement quality. This improvement aims to remove noises, strains, uneven illumination, etc. The goal of our work is to assess these methods when applied to Arabic manuscripts in terms of readability, elimination of original spots and production of unwanted noise. Results show that no techniques work well for all types of manuscripts, but some techniques work better than others for particular types. Experimental results also indicate that Nick and Wolf techniques performed the best in terms of readability in most of the processed manuscripts.

Generally, the database is a gathering of data that is arranged for simple storage, retrieval and modernize. This data comprises of numerous structures like text, table, and image, outline and chart and so on. Content-based image retrieval (CBIR) is valuable for calculating the huge amount of image databases and records and for distinguishes retrieving similar images. Rather than text-based searching, CBIR effectively recovers images that are similar like query image. CBIR assumes a significant role in various areas including restorative finding, industry estimation, geographical information satellite frameworks (GIS frameworks), and biometrics; online searching and authentic research, etc. Here different medical database images are considered to the CBIR procedure is done by the proposed strategy. The proposed method considers the input features are shape, texture feature, wavelet feature, and SIFT feature. To retrieve the input image based on the features, the suggested method utilizes artificial neural network (ANN) structure. Back-propagation technique, which is an organizational structure for learning is utilized for training the neural network framework. Trial demonstrates that the proposed work improves the results of the retrieval system. From the outcomes minimizes the image retrieval time and maximum Precision 87.3% in distance based ANN process.

The liver cancer is one of the most common fatal diseases worldwide, and its early detection through medical imaging is a major contributor to the reduction in mortality from certain cancer. This paves the way to work on diagnosing liver diseases effectively. An accurate diagnosis of liver disease in CT image requires an efficient description of textures and classification methods. This paper performs comparative analysis on proposed texture feature descriptor with the different existing texture features with various classifiers to classify six types of diffused and focal liver diseases. The classification of liver diseases is done in two stages. In first stage, features like segmentation based fractal texture analysis, counting label occurrence matrix, local configuration pattern, eXtended center-symmetric local binary pattern and the proposed local symmetric tetra pattern are used for extracting information from the CT liver structure and classifiers like support vector machine, $k$-nearest neighbor, and naive Bayes are used for classifying the pathologic liver. When pathologic conditions are detected, the best feature descriptors and classifiers are used to classify the results into any of six exclusive pathologic liver diseases, in second stage. The experiments are carried out in medically validated liver datasets containing normal and six-disease category of liver. The first experiment is analyzed using sensitivity, specificity, and accuracy. The second experiment is evaluated using precision, recall, BCR, and F-measure. The results demonstrate that the local symmetric tetra pattern with $k$-nearest neighbor classifier culminates in a state-of-the-art performance for diagnosing liver diseases.

Image segmentation and classification are the major challenges to satellite imagery. Also, the identification of unique objects in the satellite image is a significant aspect in the application of remote sensing. Many satellite image classification techniques have been presented earlier. However, the accuracy of the image classification has to be further improved. So that, optimal artificial neural network with kernel-based fuzzy c-means ($\mathrm{\text{KFCM}}+\mathrm{\text{OANN}}$) clustering based satellite image classification is proposed in this paper. Initially, the images are segmented with the help of KFCM algorithm. Then, color features and gray level co-occurrence matrix (GLCM) features to be extracted from the segmented regions. Then, these extracted features are given to the OANN classifier. Based on these features, segmented regions are classified as building, road, shadow, and tree. To enhance the performance of the classifier, the weight values are optimally selected with the help of fruit fly algorithm. Simulation results show that the performance of proposed classifier outperforms that of the existing filters in terms of accuracy.