The importance of software estimation is utmost, as it is one of the most crucial activities for software project management. Although numerous software estimation techniques exist, the accuracy achieved by these techniques is questionable. This work studies the existing software estimation techniques for Agile software development (ASD), identifies the gap, and proposes a decentralized framework for estimation of ASD using machine‐learning (ML) algorithms, which utilize the blockchain technology. The estimation model uses nearest neighbors with four ML techniques for ASD. Using an available ASD dataset, after the augmentation on the dataset, the proposed model emits results for the completion time prediction of software. Use of another popular dataset for ASD predicts the software effort using the same proposed model. The crux of the proposed model is that it simulates blockchain technology to predict the completion time and the effort of a software using ML algorithms. This type of estimation model, using ML, making use of blockchain technology, does not exist in the literature, and this is the core novelty of this proposed model. The final prediction of the software effort integrates another technique for improving the calculated estimation, the standard deviation technique proposed by the authors previously. This model helped lessening the overall mean magnitude of relative error (MMRE) of the original model from 6.82% to 1.73% for the augmented dataset of 126 projects. All four ML techniques used for the proposed model give a better p ‐value than the original model using statistical testing through the Wilcoxon test. The average of the MMRE for effort estimation of all four techniques is below 25% on a dataset of 136 projects. The application of the standard deviation technique further helps in lessening the MMRE of the proposed model at 70%, 80%, and 90% confidence levels. The work will give insight to researchers and experts and open the doors for new research in this area.

In shape from focus (SFF) methods, the focus measure (FM) operator plays a key role in determining the ultimate shape of the object. Usually, vector-valued (color) images are converted into grayscale images before applying the FM operator. This conversion saves the computations; however, it affects the accuracy of focus values which deteriorates the depth map. This paper proposes an effective FM operator to find the relative degree of focus for the vector-valued pixels in the image sequence. In the first step, vector-valued pixels are transformed into scalar-valued pixels by computing the scaled norm of the resultant vector differences. The scaling factor is computed through various features based on vector operations including dot product, cross product, projections, and vectors distances. Then differential kernels with gap are applied on the scalar image to compute the focus values. Experiments conducted using synthetic and real image sequences reveal that the proposed method is effective in providing better quality 3D shapes of the objects.

Shape from focus (SFF) reconstructs 3D shape of the scene from a sequence of multi-focus images, and the quality of reconstructed shape mainly depends on the accuracy of image focus volume (FV). Traditional SFF techniques exhibit poor performance in preserving structural edges and fine details while removing noisy artifacts, and mostly they do not incorporate any additional shape prior. Therefore, in this paper, we propose to refine FV by formulating an energy minimization framework that employs a nonconvex regularizer and incorporates two types of shape priors. The proposed regularizer is robust against noisy focus values. The first proposed shape prior is input image sequence and it is a single and static shape prior. While, the second shape prior corresponds to a series of shape priors. These shape priors are FVs which are iteratively obtained on-the-fly. Both of these shape priors constrain the solution space for output FV. We optimize nonconvex energy function through majorize-minimization algorithm which iteratively guarantees a local minimum and converges quickly. Experiments have been conducted to evaluate accuracy and convergence properties of the proposed method. Experimental results of synthetic and real image sequences demonstrate that our method achieves superior results in terms of ability to reconstruct accurate 3D shapes as compared to existing approaches.

Sports broadcasters generate an enormous amount of video content on the cyberspace due to massive viewership all over the world. Analysis and consumption of this huge repository urges the broadcasters to apply video summarisation to extract the exciting segments from the entire video to capture user's interest and reap the storage and transmission benefits. Therefore, in this study an automatic method for key‐events detection and summarisation based on audio‐visual features is presented for cricket videos. Acoustic local binary pattern features are used to capture excitement level in the audio stream, which is used to train a binary support vector machine (SVM) classifier. Trained SVM classifier is used to label audio frame as an excited or non‐excited frame. Excited audio frames are used to select candidate key‐video frames. A decision tree‐based classifier is trained to detect key‐events in the input cricket videos that are then used for video summarisation. Performance of the proposed framework has been evaluated on a diverse dataset of cricket videos belonging to different tournaments and broadcasters. Experimental results indicate that the proposed method achieves an average accuracy of 95.5%, which signifies its effectiveness.

The elderly people living alone or life of a patient face distress situations particularly in case of falling and becoming unable to ask for help. Fall in elderly people may result in head injury, broken hips, and bones that need immediate hospitalization to lower the mortality risk. During the last decade, several technological solutions were presented for early fall detection but most of them have critical limitations and are impeded by several environmental constraints. In this paper, we have analyzed the environmental sounds for early fall detection utilizing the fact that reflection of pain directly occurs through sound. The proposed framework first analyzes the environmental sounds by suppressing the silence zones in signals and distinguishing overlapping sound signals through hidden Markov model based component analysis (HMM-CA). The source separated components are then represented by acoustic local ternary patterns (acoustic-LTPs) by extending the existing ideas of acoustic local binary patterns (acoustic-LBPs). In the proposed work, we have also introduced the concept of rotation invariance through uniform patterns for audio signals that, arguably, is a fundamental requirement for an acoustic descriptor. Once the signal representation is completed, we classify the signals through SVM classifier. The performance of the proposed acoustic-LTP is evaluated against state-of-the-art methods and acoustic-LBP. Results clearly evince that proposed method is more powerful and reliable in terms of fall detection when compared against other methods.

In this letter, we propose an effective registration method for cloudy satellite images based on global and local thresholds. First, cloud candidates are determined by using optimal threshold and κ-means clustering. Then, using the local threshold, the cloud candidates are further classified into three categories: thick clouds, thin clouds, and ground. Finally, accurate registration is performed by eliminating features relating to cloudy areas. The experiments show that the proposed method provides segmentation accuracy of 93.29%. In addition, registration accuracy is improved by 24.83%, as compared with conventional methods.

In this paper, we propose a multiple support vector machine-based architecture for content-based image retrieval (CBIR) in a grid computing environment. In order to maximize the performance of the proposed technique, an efficient feature extraction method is introduced, which is based on the concept of in-depth texture analysis. For this, we are using wavelet packets, Gabor filters and curvelet transformed features for the repository image representation. To ensure semantically identical image retrieval, an association scheme is presented which utilizes OurGrid computational grid, and guarantees the retrieval of images in an efficient way. To demonstrate the effectiveness of the present work, the proposed method is compared with several existing CBIR systems, which shows that the proposed method performs better than all of the comparative systems.

Composite filters based on Mathematical Morphological (MM) operators are getting considerable attraction in denoising Magnetic Resonance (MR) images. However, most of the approaches depend on pre-fixed combination of MM operators. In this paper, we propose a genetic programming (GP) based approach for denoising MR images. An Optimal Composite Morphological Supervised Filter FOCMSF is developed through a certain number of generations by combining the gray-scale MM operators under a fitness criterion. The proposed method does not need any prior information about the noise variance. The improved performance of the developed filter is investigated using the standard MRI data sets and its performance is compared with previously proposed state-of-the art methods. Comparative analysis demonstrates the superiority of the proposed GP based scheme over the existing approaches.

Mostly, shape from focus (SFF) methods utilize initial depth estimate to obtain 3D shape of an object. However, accuracy of these methods is limited due to erroneous initial focus and depth measurements. In this paper, we introduce a Gaussian process regression based approach, which estimates 3D shape of the object from the noisy initial depth values and focus measurements. Initial depth is estimated by applying a conventional focus measure. Eigenvalues from 3D neighborhood around the initial depth are computed to form the input feature vectors. A latent function is developed through Gaussian process regression to estimate accurate depth through these features. The proposed approach takes advantages of the multivariate statistical features and covariance function. The proposed method is tested by using image sequences of various objects. Experimental results demonstrate the efficacy of the proposed scheme.

Mostly, shape-from-focus algorithms use local averaging using a fixed rectangle window to enhance the initial focus volume. In this linear filtering, the window size affects the accuracy of the depth map. A small window is unable to suppress the noise properly, whereas a large window oversmoothes the object shape. Moreover, the use of any window size smoothes focus values uniformly. Consequently, an erroneous depth map is obtained. In this paper, we suggest the use of iterative 3-D anisotropic nonlinear diffusion filtering (ANDF) to enhance the image focus volume. In contrast to linear filtering, ANDF utilizes the local structure of the focus values to suppress the noise while preserving edges. The proposed scheme is tested using image sequences of synthetic and real objects, and results have demonstrated its effectiveness.

Generally, impulse noise filtering schemes consider all pixels within a large neighborhood. However, the estimate from all pixels within the neighborhood may not be accurate. Moreover, large window may remove edges and fine details. In contrast to this approach, we propose iterative impulse noise removal scheme that emphasizes on few noise-free pixels within a small neighborhood. This iterative process continues until all noisy pixels are replaced with the estimated values. To estimate the optimal value of noisy pixel, we developed genetic programming (GP) based estimator using noise-free pixels. The estimator is constituent of useful local pixels information. Experimental results show that the proposed scheme is capable of removing impulse noise effectively while preserving the fine details. Especially, our approach has shown effectiveness against high impulse noise density.

The use of intelligent and sophisticated approaches in the domain of computer vision and pattern recognition is consistently increasing. This paper introduces a novel machine learning based approach for Shape From Focus (SFF), where the in-focus pixels are selected from a sequence of images. In contrast to computing focus values directly in spatial or transform domain, the proposed method first nonlinearly transforms the input space into feature space and then computes the focus value by transforming the data into eigenspace. First, the nonlinear transformation is performed by using kernel function and then Principal Component Analysis (PCA) is applied. This idea is also supported by the fact that out-of-focus is analogous to blurring and is a nonlinear phenomenon. An initial depth map is computed by maximizing the focus measure. To further refine the 3D shape, bilateral filter is applied. The proposed method is experimented using synthetic and real image sequences. The results demonstrate the effectiveness and the robustness of the new method.

Estimating 3D shape of the object is an important research topic in the area of computer vision, with a wide range of applications. This paper introduces a new method for focused-based passive methods (like SFF) to approximate the 3D shape of the object using Bezier surface. The discrete nature of image sampling results in the loss of information between two consecutive images. Conventional approximation methods optimize or interpolate focus values. We have suggested interpolating depth values instead of focus value over a small patch on the object surface. The method approximates the surface more accurately and also reduces any noise caused by focus measure. The proposed method is tested and analyzed to demonstrate the effectiveness against traditional methods.

In conventional focus measures, focus values are locally aggregated to suppress the noise and to obtain better depth maps. However, this enlarges the difference between focus values of two consecutive frames which results in inaccurate shape. In this paper, we propose a nonparametric approach for 3D shape from image focus by applying an unsupervised formulation of kernel regression estimate. The focus volume is obtained through a focus measure and then Nadaraya and Watson Estimate (NWE) is applied to each frame. The depth is then computed by finding the frame number which maximizes the focus value. The kernel regression is again applied on depth values to obtain an accurate 3D shape. The proposed approach is experimented using synthetic and real image sequences. The results demonstrate the effectiveness of the proposed approach.

This paper introduces a new robust algorithm for shape from focus (SFF). Principal component analysis (PCA) is applied to transform the data into eigenspace and the first feature is employed to calculate the depth value. Contrary to computing the focus value locally by a focus measure in first step and then, in second step, approximating the depth map, the proposed method finds the location of the best focused value over a sequence of pixels. The proposed method is experimented using synthetic and real image sequences. The evaluation is gauged on the basis of unimodality and monotonicity of the focus curve. Two other global statistical metrics root mean square error (RMSE) and correlation have also been applied for synthetic image sequence. Experimental results have demonstrated the effectiveness and the robustness of the new method.