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I. Introduction
Computer vision has been expanded into the vast area of field ranging from recording raw data into the extraction of image pattern and information interpretation [1]. It has a combination of concepts, techniques, and ideas from digital image processing, pattern recognition, artificial intelligence and computer graphics [2]. Most of the tasks in computer vision are related to the process of obtaining information on events or descriptions, from input scenes (digital images) and feature extraction. The methods used to solve problems in computer vision depend on the application domain and the nature of the data being analyzed.
Computer vision is a combination of image processing and pattern recognition [2],[3]. The output of the Computer Vision process is image understanding. Development of this field is done by adapting the ability of human vision in taking information. Computer Vision is the discipline of extracting information from images, as opposed to Computer Graphics [4]. The development of computer vision depends on the computer technology system, whether about image quality improvement or image recognition. There is an overlap with Image Processing on basic techniques, and some authors use both terms interchangeably [4],[5].
The primary purpose of Computer Vision is to create models and data extracts and information from images, while Image Processing is about implementing computational transformations for images, such as sharpening, contrast, among others[4]. It also has similar meaning and sometimes overlapping with In Human and Computer Interaction (HCI)[6]. HCI coverage focus on full design, interface and all aspects of technologies related to the interaction between human and computer. HCI is then developed as a separate discipline (which is the field of interdisciplinary science) which discusses the interrelationships between human-computer mediated by technology development including human aspects. Functionally, computer vision and human vision are the same [7], with the aim of interpreting spatial data, i.e., data indexed by more than one dimension. However, computer vision cannot be expected to replicate just like the human eye [8].
This is due to computer vision system has limited performance and function compared to the human eye. Even though many scholars have proposed broad area of computer vision techniques to replicate human eye, however, in many cases, there is any limitations of the performance of computer vision system [9]. One of the significant challenges in their technique is the sensitivity of the parameters, the strength of the algorithm, and the accuracy of the results. It impacts on the complexity of performance evaluation of computer vision systems. Generally, the performance evaluation involves measuring some of the basic behaviors of an algorithm to achieve accuracy, strength, or extensibility to control and monitor system performance.
As the performance of computer vision system depends on the application system design, there is comprehensive effort proposed by many scholars to expand and categorized computer vision into many areas and specific applications such as automation on the assembly line, remote sensing[11], robotics[10], computer and human communications[12], tools for the visually impaired, and others.
II. Literature Review
Computer vision works by using an algorithm and optical sensors to stimulate human visualization to automatically extract valuable information from an object[13]. Compared to conventional methods that take a long time and require sophisticated laboratory analysis, computer vision has been expanded into a branch of artificial intelligence (artificial intelligence) and simulated human visualization. It also combined with lighting systems to facilitate image acquisition continued with image analysis. In more detail, the stages of image analysis are: 1) image formation, in which image of object is captured and stored in computer; 2) image preprocessing, whereby quality of image is improved to enhance the image detail; 3) image segmentation, in which the object image is identified and separated from the background, 4) image measurement, where several significant features are quantized, and 5) image interpretation, where the extracted images are then interpreted [14]. The recent development of image processing technology has provided the possibility to create a system to recognize a digital image. The construction is summarized in table 1.
Digital image processing or other image processing tend to evolve into the most extensive mainstream with support from other theoretical fields supported by the rapid development of specific disciplines such as mathematics, Linear algebra, statistics, Soft Computing, and Computational neurosciences the supported regulations supporting the development of digital image processing are given in Table 2.
Pattern Recognition as a branch of computer vision focused on the process of object identification through image transformation to get a better image quality and image interpretation. This process aims to extract information to make decisions based on images obtained from sensors [5]. In other words, computer vision seeks to build an intelligent machine to "see." Common frameworks used in computer vision are image acquisition, pre-processing, feature extraction, detection/segmentation, high-level processing, and decision-making [5] , [6]. The computer vision frameworks consisted two main groups, e.g., 3D morphological analysis and pixel optimization. The 3D morphological review has been a standard theory for computer image processing and pattern recognition, whereas pixel optimization is related to characterization of pixel morphology, including structural analysis and internal components for a better understanding of vector function [32]. Also, the approach should be performed on relatively large data sets covering many layers of geometrical composition. Therefore, efficient and accurate computing algorithms to extract the relevant quantitative information are important to understand the complex color clusters as a whole. The integration of morphological analysis with some artificial intelligence methods can result in better performance through computing algorithms. The computing algorithm is fuzzy logic[33], artificial neural networks[34], and genetic algorithms[35]. They can be combined to completely complete complex tasks.
There are two approaches to the segmentation and retrieval of image data. Segmentation is basically to divide an image into areas that are not overlapping (overlapping) [36] through specific algorithms to estimate an area of the image. The city is a collection of pixels that have the same unique characteristics as color, gray level, texture, and others.
The area is retrieval region of images, e.g., search engine parts, human search, and similar image search. In general, the image segmentation approach that is often used is the method of intensity, color approach and shape approach [37]. In most computer vision applications, edge/border detection and image segmentation are a very important in the object recognition and interpretation.
In image analysis literature, the performance of segmentation is shown in a minimal sample image. However, in large-scale image database annotations require parameter settings.
Segmentation is obtained by gradient texture and feature space [38], [39], or by unsupervised clustering [40],[41],[42] or by texture classification [43]. Segmentation of labeling is important in localization performance and boundary localization [44]. It uses grouping and segmentation as an initial estimate of objects in the image by setting the threshold on the feature grouping algorithm especially in estimating the number of areas [45].
Fundamentally, segmentation has four main stages as below (Fig.1).
Input image
Segmented map before integration
Edge map before integration
Segmented map and edge map after combination
Pixel clustering
Segmentation has a primary goal to create resemblance map which derived from a prominent object detection model or hierarchical segmentation of the input image. The plan is an aggregation model tries to form a more accurate salience map.
It needs components of pixel salience value x toward i-salience map cell location. In Borji et al.[46], it proposed a model of the standard saliency method of aggregation. The image is segmented into saliency score for n-total pixels and n-segments index which labeled as a prominent cluster. As the groups are an aggregation model [46], it adopted pixel-wise aggregation asset of model parameters. It has a weakness that such direct incorporation is ignorance of interaction between neighboring pixels.
Therefore, CRF is proposed by Khan [47] to combine caliber maps of several methods and capture values of neighboring pixels. CRF aggregation model parameters are considered better to optimize training data since the reliability of each pixel has a higher probability of prominent when it is trained with CRF. Whereas data extraction requires photographed objects from camera, sensor, or satellite devices in the form of single images or image sequences. This extraction aims to separate background objects with foreground objects. It has three combinations, e.g., (a) the objectives are still the same color as the original, (b) the object changes color to black and white, or (c) the purpose becomes transparent.
Fig.2(a) object instance is changed to black and white; (b) objects have a different size than their original size after scale factor; (c) purposes being transparent or color combinations; (d) objects with BW color after scale arrangements and changing positions differently from their real opinions. Pixel is determinant of object sharpness in the image. Pixel optimization is useful for object detection, segmentation, and recognition. In boundary-based techniques, an edge detector is used to locate the boundary of an object. This method is based on the fact that the intensity of pixels will change rapidly on the perimeter of two regions. For color segmentation, the edge detection is performed on each RGB color channel. It results in edges which can be combined to get final edge image. In local based techniques, pixels are grouped according to uniformity criteria. Examples of these methods are regional growth and separation techniques and split and merge. In regional growth techniques, pixels are arranged into larger areas based on predetermined criteria [48]. This method started from a set of core points and grew into more extensive regions by adding each neighboring pixel from the above starting points that have similar features, such as gray or color values. The technique of separation and merging the pixels begin by dividing an image into several regions and then combining the areas to meet predetermined criteria [48]. This region-based technique has two significant weaknesses [5]. Firstly, methods of separation and incorporation of regional growth depend primarily on predetermined global criteria at the outset. Secondly, the process of area growth depends on the initial segment and original pixels which impact the object detection performance. Object detection is useful for searching an object or display by querying data from recorded and real-time datasets.
Object detection often has error rate because the object or object does not include the pattern specified by the algorithm and usually has to be supplemented with an additional algorithm. The algorithms are usually used for detecting smaller parts to obtain more detailed images. For example, in face processing, the algorithm is used to identify the element of head and face which has a lower resolution [49],[50]. The algorithm also improves the machine performance to determine the eyes, eyebrows, and mouth [51],[52],[53]. Whereas, the final parts such as ears and neck are rarely studied in face processing.