Knee osteoarthritis (KOA) is a degenerative joint condition affecting many people across the globe. The KOA offers a serious healthcare concern owing to the steady degeneration of knee joints. This condition causes pain and inhibits the movement of a person. Early detection of KOA is crucial for implementing successful treatment strategies and preventing disease progression. Advancements in Computer-Aided Detection (CAD) and Computer Vision (CV) technologies have paved the way for more effective and accurate diagnosis of KOA. This study explores the effectiveness of the Local Directional Octa Pattern (LDOP), a computationally minimal yet effective approach, in reliably identifying early-stage KOA. LDOP gathers essential information from images, such as joint space narrowing and osteophyte development, to differentiate among healthy and KOA-affected knees. This LDOP based KOA detection system produced the best accuracy (97.8

Computer vision (CV) is a part of artificial intelligence (AI) that helps computers to interpret, understand and develop intuition about the real-world objects and scenes to annotate, classify and identify them with accurate precision. CV techniques have been gaining popularity since its inception and becoming a fundamental part of technological development and digital transformation. To enhance the domain knowledge of CV in the area of human identification and the use of the intrinsic properties of image interpretation and understanding biometrics systems have been proposed with several behavioral and physiological body evidences. Biometrics recognition employs various aspects of AI to enable a computer system to recognize a biometric pattern for identification purposes. It is an inevitable part of multiple applications, such as border control cyber security, 3D faces modeling and recognition, intelligent video surveillance, finger vein recognition, and forensic biometrics where vision techniques have been integrated with and instilled into the biometrics systems in order to perform the desired tasks. The main objective of this chapter is to introduce biometric-based CV and discuss the essential components of biometrics technologies for CV. The discussion also includes different processes, state-of-the-art techniques, challenges of biometric-based CV, application areas, the selection criteria of suitable biometrics, and the future of biometric-based CV applications.

Agriculture is one of the potential parameters in the economic sector. Traditional modes of farming are not able to meet the growing need of food as the population is increasing rapidly. Agricultural automation is very much essential to meet the supply-demand requirement of food and to minimize the employment issue and problems of food security. The introduction of artificial intelligence (AI) in agriculture has brought revolution by improving the overall accuracy and harvest quality, detecting pests and diseases in plants using applications like drones, smart monitoring systems and robots. Agricultural AI bots can harvest crops in a fast manner and in higher volume, which reduces the need of workers in higher numbers. Machine learning (ML), a subdomain under the umbrella of AI, is also used to capture the quality of seeds, pruning, parameters of soil, application of fertilizer and environmental conditions. In addition, using AI and ML, farmers can solve other challenges like forecasting crop prices, market demand analysis, finding optimal time and conditions for harvesting and sowing, nutrient deficiencies in soil, weight-diet balance using weight prediction systems. Using predictive analysis, ML techniques help to predict right genes for different weather conditions and to reduce chances of crop failures. The aim of this chapter is to provide an insight into the effectiveness of introducing ML in the field of agricultural applications. This chapter includes present scenarios of agricultural need, challenges, application of ML techniques and future development of agricultural applications using ML.

Exchange rate forecasting has proven challenging for players like traders and professionals in this current financial industry. Econometric and statistical models are often utilized in the analysis and forecasting of foreign exchange rate. Governments, financial organizations, and investors prioritize analyzing the future behaviour of currency pairs because this analyzing technique is being utilized to understand a country's economic status and to make a decision on whether to do any transactions of goods from that country. Several models are used to predict this kind of time-series with adequate accuracy. However, because of the random nature of these time series, strong predicting performance is difficult to achieve. During the Covid-19 situation, there is a drastic change in the exchange rate worldwide. This paper examines the behaviour of Australia's (AUD) daily foreign exchange rates against the US Dollar from January 2016 to December 2020 and forecasts the 2021 exchange rate using the ARIMA model. For better accuracy, technical indicators such as Interest Rate Differential, GDP Growth Rate and Unemployment Rate are also taken into account. In exchange rate forecasting, there are various types of performance measures based on which the accuracy of the forecasted result is computed. This paper examines seven performance measures and found that the accuracy of the forecasted results is adequate with the actual data.

This paper presents an efficient pattern matching method which makes use of the local-region-based light-weight feature descriptor, called Symmetric Neighbour Local Pattern (SNLP). It relies on the spatial relationship between reference pixel and its neighbours located symmetrically in horizontal, vertical and diagonal directions. SNLP is proved to be invariant to scale, illumination, image distortion and partial occlusion. Efficacy of the proposed method has been evaluated on two publically available databases and the results are found to be convincing under several challenges.

Automatic human identification using biometric information like Iris, leads on to the significant progress in the field of computer vision as it returns better authenticity and accuracy compared to other biometric recognition. This is because of its non-contact acquisition and user-friendly interface. But for unconstrained environment, it suffers when facial expression changes and light intensity differs. In this work, a novel approach for iris recognition called Multi Variant Symmetric Ternary Local Pattern (MVSTLP) is presented using the fundamental idea of pattern matching with the aim to find similarity between scene iris image and query iris pattern image by extracting distinctive features from them, where scene iris image is logically divided into number of query pattern size candidate windows. MVSTLP focuses on neighbour pixels selection in symmetric way within small image area and has unique ability to prioritize distinct feature extraction by establishing strong association between pixels. In effect of these, it can track very minute variations in image property and able to localize iris pattern within the scene iris image very accurately.

In today’s global economy, precision in projecting macroeconomic characteristics such as the foreign exchange rate, or at the very least properly gauging the trend, is critical for any prospective investment. In recent time, application of artificial intelligence-based forecasting models for macroeconomic variables has been extremely fruitful. The global currency rate changed dramatically during the Covid-19 incident. This study examines the behaviour of the Australian dollar’s (AUD) daily exchange rates against the US dollar’s (USD) daily exchange rates from January 2016 to December 2020 and makes LSTM RNN-based predictions for the 2021 exchange rate. There are different sorts of performance metrics used in exchange rate forecasting to compute the accuracy of the projected result. This research investigates six performance metrics and discovers that the accuracy of the anticipated outcomes is satisfactory when compared to the actual data.

Hand gesture recognition provides a significant impact in the field of human–computer interaction. It introduces the information, tools, and systematic design techniques, by which accuracy and easy implementation of daily tasks can be achieved. Gesture recognition is the approach by which computers can detect hand gestures. Human–computer interaction provides appropriateness in feedback, effortless implementation, and timely completion of the goal. Computer vision plays an important role in extracting high levels of comprehension from electronic images and videos. It is applied to a hand gesture recognition system to provide input to the computer to manipulate virtual objects by simply moving hand parts which act as a command. Providing a low-cost infrastructure device that alters the need for keyboards and mouse in laptops and computers.

Fingerprint matching, one of the sophisticated biometric authentication techniques, is popular for its easy implementation, persistent nature of the fingerprint and non-similarity nature of two fingerprints. Uniqueness of fingerprint is characterized by distinctive features present in fingerprint image. This paper presents a novel relational descriptor based fingerprint matching process using pattern matching concept called Multi-Variant Symmetric Ternary Pattern (MVSTP). Orientation and illumination invariant local descriptor MVSTP extract distinct features from fingerprint image by referring non-overlapping neighbor pixels in symmetric way with respect to source pixel positioned at the center of 5×5 pixel area. After feature extraction from query fingerprint image and stored fingerprint images in the database, features are compared to find similarity match. MVSTP aims to increase fingerprint matching accuracy in contrast with other processes by addressing challenges related to fingerprint pattern’s appearance variation with slight orientation and the variations present in image properties. The computational proficiency of the proposed fingerprint matching process is tested on FVC 2004 database and local database of fingerprint images with higher note of matching accuracy, manifesting its intensity in the process.

This paper discusses an efficient pattern matching approach on the use of K-nearest neighbour (K-NN) based rank order reduction and Haar transform in order to detect a pattern in a large scene image. To accomplish the task, scene image is divided into a number of candidate windows and both input pattern and candidate windows are characterised by Haar transform. This characterisation seeks to determine distinctive coefficients known as Haar projection values (HPVs). To obtain more relevant and useful representation of HPVs, rectangle sum is computed and further, sum of absolute (SAD) correlation measure is applied as successive measures between the input pattern and candidate windows. This leads to increase the possibility of finding the object in the scene image before being detected and localised. The proposed pattern matching approach is tested on COIL-100 database and the matching accuracy proves the efficacy of the proposed algorithm.

Pattern matching aims to search for the pattern components in the image to look for precise similarity. However, the pattern matching is affected due to changes in orientation, resolution, illumination and occlusion in the image. This article presents a novel local descriptor called all-direction symmetric local graph structure (AdSLGS) for pattern matching. AdSLGS is invariant to scale and illumination, which extracts features in a more symmetric way with respect to local binary pattern, local graph structure and symmetric local graph structure. It increases the accuracy of pattern matching in contrast to other processes while image variations are observed, and simultaneously, it reduces time and memory requirements by the computing system. The proposed pattern matching algorithm is tested on two benchmark databases, namely, COIL-100 and Caltech-101 with a high note of matching accuracy, exhibiting its robustness in the process in the presence of challenging image constraints.

This paper reports a fast pattern matching algorithm which makes use of K-NN (K-nearest neighbor) based rank order reduction approach to detect a pattern or object in a given image efficiently. Initially, the given image is divided into several candidate windows, each of size the input pattern. In the next step, both the input pattern and the candidate windows are characterized by Haar transform. From the characterization, Haar Projection Values (HPV) is determined. Further, rectangle sum on both input pattern and candidate windows is computed using integral image technique. Subsequently, by using sum of absolute difference (SAD) correlation distance between the input pattern and candidate windows is determined. In order to detect the pattern, rank order approach using K-NN is applied to determine the first k number of most similar candidate windows containing the input pattern. To reduce the computational complexity of selecting a perfectly matched window, again sum of absolute differences (SAD) is applied and this leads to select the best match pattern having the total object. Decoupling correlation measures also increase the accuracy of matching pattern. Finally, the input pattern is detected and localized in the given image. The pattern matching accuracy proves the efficacy of the proposed algorithm.

One of the fundamental tasks of pattern recognition is pattern matching. It is the act of checking for presence of a pattern's constituents within token image to have exact match. For that, most distinctive fiducial features of pattern have to be assessed and searched in the sliding windows of same pattern size formed by logically dividing the token scene image. As huge numbers of sliding windows are to be checked with pattern, pattern matching process should be time efficient and to increase pattern matching accuracy impacts due to illumination, resolution, occlusion and pose variation must be reduced. For pattern matching, this paper presents a novel local feature descriptor, multi variant symmetric local graph structure (MVSLGS) taking into account symmetric local graph structure (SLGS) as precedent approach. The computational adequacy of the proposed approach is tested on two publicly available databases with high matching accuracy, showing its proficiency over the process of pattern matching.

The objective of pattern matching problem is to find the most similar image pattern in a scene image by matching to an instance of the given pattern. For pattern matching, most distinctive features are computed from a pattern that is to be searched in the scene image. Scene image is logically divided into sliding windows of pattern size, and all the sliding windows are to be checked with the pattern for matching. Due to constant matching between the pattern and the sliding window, the matching process should be very efficient in terms of space, time and impacts due to orientation, illumination and occlusion must be minimized to obtain better matching accuracy. This paper presents a novel local feature descriptor called Multi-variant Local Binary Pattern (MVLBP) for pattern matching process while LBP is considered as base-line technique. The efficacy of the proposed pattern matching algorithm is tested on two databases and proved to be a computationally efficient one.

: Pattern matching problem aims to search the most similar pattern or object by matching to an instance of that pattern in a scene image. In order to address the issue of finding an object in the target image efficiently, the most distinctive features are computed from the query pattern and need to be searched in the scene image. The scene image is logically divided into a number of candidate windows which are then to be matched with the query pattern. Due to repeated matching of the query pattern with local candidate windows, the pattern matching process requires a large amount of space in memory as well as it needs to be executed fast. Thus, pattern matching algorithms need to be memory efficient and as fast as possible. This paper makes an attempt to deal with these issues by presenting two effective pattern matching algorithms, namely, strip subtraction and strip division. The efficacy of the proposed pattern matching algorithms is tested on two databases, viz. a local database and MIT-CSAIL database containing random objects. The experimental results are proved to be computationally efficient ones while the proposed algorithms are compared with some existing algorithms possessing a uniform experimental setup.

Pattern matching is a fundamental machine vision problem that deals with searching an object in a comparatively large scene. It can use to solve many vision problems ranging from typical human detection to searching defective parts in industrial automation. This paper reports a fast pattern matching technique which makes use of cumulative subtraction and cumulative division operations based on Image Integral model. The idea is to use both the cumulative subtraction and division operations to evaluate the image values on a very small rectangular region of the image scene as well as on the input pattern to be searched for. Image values are transformed to Haar Projection Values (HPVs) using Haar transform in order to achieve pattern matching on sliding window of the image scene. Computation of HPV needs seven arithmetic operations, including two addition and five subtraction operations, which are found to be same as that of Image Integral technique. Besides, the proposed pattern matching technique is identified as computationally effective in terms of both time and memory.