Computed ultrasonic bone tomography (USCT) is a non-invasive and non-ionizing technique, which ensures the protection of child being against x-rays. The main objective of this article is to use an image processing algorithm to improve the signal-to-noise ratio of ultrasonic computed tomography (USCT) of children bones  for automatic detection of osteopathologies. For this fact, we construct an application of image processing with Microsoft Foundation Class Library (FMC) integrated in visual Studio using Haar wavelet algorithm to detect edges. Different methods of image processing for automatic detection are used. Hence, we make accessible the detection of distance between bones due to the application of wavelet transform. As a result, the quality of USCT image was improved and the detection of child osteopathologies became accessible.

Optimization is one of the techniques used in the estimation of projects to obtain the optimal parameter sequence at different levels for the best project conditions, such as size, duration and function points. In this paper, to select the significant process parameter sequence at different levels, a combination of Grey Relational Analysis (GRA) and Taguchi method applied during the estimation. This parameter sequence is essential for the industries in producing quality product at a lower cost. Taguchi method is used to improve the product quality and reduce the cost. Among the various methods of Taguchi as a standard Orthogonal Array (OA) produces better parameters to be considered at different levels. This paper uses L16 Orthogonal Array (OA) whose efficiency is proven in the experimental results. Here, a variant of GRA, GRG has been used to assign grades for projects in the dataset. Finally, the optimized process parameter sequence at different levels is obtained through the application of GRG over L16 Orthogonal Array (OA). In this paper, Grey-Taguchi method is implemented to find out the levels of software process parameters such as Duration, KSLOC, Adjustment Function Points and Raw Function Points necessary for minimizing software effort. Experimental results show that parameter levels suggested by Grey-Taguchi method result in improved GRG, which results in better software effort estimation.

Human-Computer Interaction (HCI) is one of the most interesting and challenging research topics in computer vision community. Among different HCI methods, hand gesture is the natural way of human-computer interaction and is focused on by many researchers. It allows the human to use their hand movements to interact with machine easily and conveniently. With the birth of depth sensors, many new techniques have been developed and gained a lot of achievements. In this work, we propose a set of features extracted from depth maps for dynamic hand gesture recognition. We extract HOG2 for shape and appearance of hand in gesture representation. Moreover, to capture the movement of the hands, we propose a new feature named HOF2, which is extracted based on optical flow algorithm. These spatial-temporal descriptors are easy to comprehend and implement but perform very well in multi-class classification. They also have a low computational cost, so it is suitable for real-time recognition systems. Furthermore, we applied Robust PCA to reduce feature’s dimension to build robust and compact gesture descriptors. The robust results are evaluated by cross-validation scheme using a SVM classifier, which shows good outcome on challenging MSR Hand Gestures Dataset and VIVA Challenge Dataset with 95.51% and 55.95% in accuracy, respectively. 

Facial expression is one of the nonverbal communication methods of identifying an emotional state of a human being.  Due to its crucial importance in Human-Robot interaction, facial expression recognition (FER) is in the limelight of recent research activities.  Most of the studies consider the whole expression images in their analysis, and it has several has several drawbacks concerning illumination, orientation, texture, zoom level, time and space complexity. In this paper, a novel feature extraction technique called the pattern averaging is studied on whole image data using reduction in the dimension of the image by averaging the neighboring pixels. The study is found to give better results on standard datasets using support vector machine classifier. 

Traffic Analysis has been a problem that city planners have dealt with for years. Smarter ways are being developed to analyze traffic and streamline the process. Analysis of traffic may account for the number of vehicles in an area per some arbitrary time period and the class of vehicles. People have designed such mechanism for decades now but most of them involve use of sensors to detect the vehicles i.e. a couple of proximity sensors to calculate the direction of the moving vehicle and to keep the vehicle count. Even though over the time these systems have matured and are highly effective, they are not very budget friendly. The problem is such systems require maintenance and periodic calibration. Therefore, this study has purposed a vision based vehicle counting and classification system. The system involves capturing of frames from the video to perform background subtraction in order detect and count the vehicles using Gaussian Mixture Model (GMM) background subtraction then it classifies the vehicles by comparing the contour areas to the assumed values. The substantial contribution of the work is the comparison of two classification methods. Classification has been implemented using Contour Comparison (CC) as well as Bag of Features (BoF) and Support Vector Machine (SVM) method. 

Recognition of handwritten digits is most challenging sub task of character recognition due to various shapes, sizes, large variation in writing styles from person to person and also similarity in shapes of different digits. This paper presents a robust Telugu language handwritten digit recognition system. The Telugu language is most popular and one of classical languages of India. This language is spoken by more than 80 million people. The proposed method initially performs preprocessing on input digit pattern for removing noise, slat correction, size normalization and thinning. This paper divides the preprocessed Telugu handwritten digits into four differential zones of 2x2, 3x3, 4x4 and 6x6 pixels and extracts 65 features using Fractal dimension (FD) from each zone. The proposed zonal fractal dimension (ZFD) method uses, Feed forward backward propagation neural network (FFBPNN) for classifying the digits with learning rate of 0.01 and sigmoid function as an activation function on extracted 65 features. This paper evaluated the efficiency of the proposed method based on 5000 Telugu handwritten digit samples, each consists of ten digits from different groups of people and totally 50,000 samples. The performance of classification of the proposed method also evaluated using statistical parameters like recall, precision, F-measure and accuracy.

In this paper, a novel Galois Field-based approach is proposed for rotation and scale invariant texture classification. The commutative and associative properties of Galois Field addition operator are useful for accomplishing the rotation and scale invariance of texture representation. Firstly, the Galois field operator is constructed, which is applied to the input textural image. The normalized cumulative histogram is constructed for Galois Field operated image. The bin values of the histogram are considered as rotation and scale invariant texture features. The classification is performed using the K-Nearest Neighbour classifier. The experimental results of the proposed method are compared with that of Rotation Invariant Local Binary Pattern (RILBP) and Log-Polar transform methods. These results obtained using the proposed method are encouraging and show the possibility of classifying texture successfully irrespective of its rotation and scale.