Computer Vision and Image Processing: A Paper Review

Abstract

Computer vision has been studied from many persective. It expands from raw data recording into techniques and ideas combining digital image processing, pattern recognition, machine learning and computer graphics. The wide usage has attracted many scholars to integrate with many disciplines and fields. This paper provide a survey of the recent technologies and theoretical concept explaining the development of computer vision especially related to image processing using different areas of their field application. Computer vision helps scholars to analyze images and video to obtain necessary information, understand information on events or descriptions, and scenic pattern. It used method of multi-range application domain with massive data analysis. This paper provides contribution of recent development on reviews related to computer vision, image processing, and their related studies. We categorized the computer vision mainstream into four group e.g., image processing, object recognition, and machine learning. We also provide brief explanation on the up-to-date information about the techniques and their performance.

Artificial Intelligence in Information Science , Approaches and Effects

“…A couple of approaches can be used to train datasets. The most prominent strategy is MNIST, which is an approach that has 70,000 handwritten digits (Wiley and Lucas, 2018). Of these 70,000, 60,000 are used for training, while 10,000 are for testing.…”

“…Therefore, it is apparent that computer vision has unique applications. For instance, Amazon has introduced 18 AmazonGo outlets to enable customers to bypass the queuing system and pay for products immediately (Wiley and Lucas, 2018). In essence, it uses computer vision cameras that notify its employees when customers take products off the shelves.…”

Towards an Automatic Annotation of French Sign Language Videos: Detection of Lexical Signs

“…These works were mainly focusing on the MC as features as it was believed that hands had the main information in an SL speech. Nowadays, many image processing and object segmentation techniques were developed [20] and with the revolution in machine learning, the systems are capable of estimating and tracking face [18] and body [19] in real time with high success rate using only 2D image features. Most works on SL recognition focus on specific datasets, made in controlled environments (uniform background, signer with dark clothes) and dealing with a specific topic, such as weather [11].…”

The Implementation of Multilevel Colour Thresholding on a Prototype Coffee Machine

“…In visual machines, the recording data cannot be directly interpreted by the machine. Hence, the image processing step is the key point of interpreting the given data before a task is executed [3].…”