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Proc. of Int. Conf. on

Computing, Communication & Manufacturing 2014

A Comprehensive Survey on Different Techniques

and Applications of Digital Image Processing Satadal Saha1, Subhadip Basu2, and Mita Nasipuri2

1ECE Dept., MCKV Institute of Engg., Liluah, Howhah, India Email: satadalsaha@ieee.org

2CSE Dept., Jadavpur University, Kolkata, India Email: {subhadip, mnasipuri}@cse.jdvu.ac.in

Abstract—Digital image found its first application almost hundred years back. But the evolution of digital image processing is intimately tied to the development of the digital computer. Because of its inherent nature, digital image requires so much storage space and their processing requires so much computational power that progress in the field of digital image processing had been highly dependent on the development of modern digital computer which came only in 1940s. Till then digital image processing has become a key area of research to a group of people. Today it has enormous applications in the fields of medical science, biotechnology, transportation management, document analysis and retrieval, outdoor surveillance, remote sensing etc. This paper presents a brief introduction to digital image processing, followed by a comprehensive survey of its various techniques and applications. Index Terms—digital image processing, computer vision, object detection, text localization, human motion detection, image segmentation

Introduction Digital Image Processing refers to the field of processing digital images with the help of a digital computer. A digital image is composed of a finite number of elements commonly referred to as picture elements, image elements, pels or pixels. Pixel is the term most widely used to denote the basic element of a digital image. Like human imaging system, digital imaging devices can acquire digital images and store them for processing. But unlike human who can capture images only for visible band of electromagnetic spectrum, imaging devices can capture images covering whole range of electromagnetic spectrum. Thus, digital image processing encompasses a wide variety of application areas in the domains of medical science, remote sensing, traffic monitoring, document analysis and retrieval etc.

On the other hand, Computer Vision is another domain of research area, where the ultimate objective is to use computer system to emulate human vision by learning from the environment and being able to make inferences on specific situations and take necessary actions on the basis of the inferences. This area includes another branch of expertise known as Artificial Intelligence (AI) whose objective is to emulate human intelligence. The area of Image Analysis or Image Understanding is in between image processing and computer vision. There is no general agreement among research community regarding the borderline between image processing, image analysis and computer vision. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of the process are images. It is a process that involves primitive operations such as reduction of noise, contrast enhancement, and image sharpening etc. Image analysis is a process that is characterized by the fact that its inputs are generally images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). And finally, computer vision may be defined as a process that involves “making sense” of an ensemble of

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recognized objects, as in image analysis and performing the cognitive functions normally associated with vision.

Based on the preceding discussions, it is seen that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects within an image. Thus, in a broader sense, digital image processing encompasses processes whose inputs and outputs are images and also the processes that extract attributes from images, up to and including the recognition of individual objects. To clarify the concept further, let us consider the problem of automated recognition of text within a general scene image. The processes of acquiring an image containing the text, pre-processing that image, segmenting the individual characters, describing the characters in the form of feature values suitable for computer processing and recognizing those individual characters can well be considered within the scope of digital image processing. Making sense of the content of the image may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity of the problem corresponding to the level of expected solution implied by the statement “making sense”.

Applications of Digital Image Processing There are numerous applications of digital image processing staring from early 20th century till now. One of the oldest applications of digital images, as reported in literatures, was sending pictures by submarine cable between London and New York. In 1920, introduction of the Bartlane cable picture transmission system improved the time required to transport a picture across the Atlantic ocean [1]. Specialized printing equipment coded the pictures for cable transmission and then the images were reconstructed at the receiving end. Visual quality of these early digital pictures was improved towards the end of 1921 by introducing a technique based on photographic reproduction. The early Bartlane systems were capable of coding images in five distinct grey levels, which was increased to 15 grey levels in 1929. Although the examples just cited involve digital images, the evolution of digital image processing is intimately tied to the development of the digital computer. Because of its inherent nature, digital images require so much storage space and their processing requires so much computational power that progress in the field of digital image processing had been highly dependent on the development of digital computers. Though the idea of a computer was conceived long back, what is called a modern digital computer came only in 1940s with the introduction of two key concepts by John von Neumann: (1) a memory to hold a stored program and data, and (2) conditional branching. These two ideas are the foundation of a central processing unit (CPU), which is the heart of computers today. Starting with the von Neumann architecture, there were a series of key advances from the invention of the transistor by Bell Laboratories in 1948 to the present use of ultra large scale integration (ULSI) in 1980s that led to computers powerful enough to be used for digital image processing. The first computers powerful enough to carry out meaningful image processing tasks appeared in the early 1960s and the first potential digital image processing using those computers for improving images from a space probe began at the Jet Propulsion Laboratory, California in 1964 when the pictures of the moon, transmitted by Ranger 7, were processed by a computer to correct various types of image distortions [2]. The invention of computed tomography (CT) in the early 1970s is one of the most important events in the application of image processing in medical science. Another emerging area of application is image registration, which is the process of aligning two or more images (the reference and sensed images) of the same scene taken at different time, from different viewpoints, and/or by different sensors. This has enormous applications in medical science field and others [3–5]. From the 1960s until the present days, the field of image processing has grown rapidly [6–8]. In addition to applications in medicine and in the space programs, digital image processing techniques are now used in a broad range of applications. Study of the aerial and satellite imagery is another area of the application of image processing. In physics and allied fields, image processing techniques are used to enhance resulting images of the experiments in areas like high-energy plasmas and electron microscopy. Archaeological use of image processing includes restoration of blurred images that were the only available records of rare artefacts lost or damaged after being photographed. Other successful applications of image processing techniques can be found in the field of bioinformatics, astronomy, chemistry, nuclear medicine, law enforcement, defence, industrial applications etc. The aforesaid examples illustrate the cases where the results of processing are intended for human interpretation.

Another major area of application of digital image processing techniques is in solving problems dealing with machine perception which may have little resemblance to visual features that humans use in interpreting the content of an image. Biometric recognition is an important and wide application area where digital image processing plays an important role. Automatic face recognition [9], fingerprint recognition, palm

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recognition, retina recognition have been the well researched areas in this domain. Content based image retrieval [10] is another domain of research in which images are classified and indexed according their content such that the desired class of images can be retrieved as and when needed in future. Sign language [11] and attitude analysis [12] are the areas where lexical meaning of arm/body movement and facial expression are analyzed and high level semantics are inferred. Other typical areas of machine perception that utilize image processing techniques are optical character recognition (OCR), robotics, military signal processing, meteorology and environmental assessment.

Human motion tracking With more advancement in image processing techniques, evolution in electronic devices and development of highly efficient sensors and imaging devices, automatic surveillance has become a growing demand from different administrative authorities over the globe. Human motion tracking is one of the needs in this domain. Many survey reports [13–15] have been published on computer vision based human motion tracking. Real time tracking of human motion has been reported in [16–19]. A 3D-model based human motion tracking has been published in [20]. An analysis and synthesis of human movement is reported in [21], [22]. Surveillance in virtual reality is also reported in different literatures [23–25]. Tracking human motion in a scene and counting number of interactive people has been reported in [26], [27]. Other related applications like gesture recognition, prediction of motion trajectories and robotics are also important research areas of digital image processing [28–30].

Pedestrian behaviour analysis Another application area related to human motion tracking is pedestrian behaviour analysis and collision avoidance. A report on Pedestrian Collision Avoidance Systems is reported in [31]. A generalized way of object motion detection has been reported in [32], [33]. Technical report on pedestrian behaviour analysis has been published in a number of literatures [34–42]. Along with pedestrian behaviour and motion tracking, prediction of trajectory of pedestrian leading to collision with on-road vehicles has also been an area of research in vehicular technology [43], [44]. Tracking the pedestrian [45], [46] is not only important in day light but also at night at very low light condition, in which case normal surveillance cameras may not capture quality images. Night vision cameras are used for this purpose and digital image processing techniques are used on the images captured from night vision devices [47–50].

Document image processing Document image processing has become a growing area of research because of the requirement of automatic processing of scanned document images in the field of content-based information indexing and retrieval, automatic annotation and structuring of images. Text extraction involves detection, localization, tracking, enhancement and recognition of the text from a given image. However, variations of text due to differences in size, style, orientation, and alignment, as well as low image contrast and complex background make the problem of automatic text extraction extremely difficult and challenging job [51]. Lot of research work has already been done in this field during last couple of decades. Poignant et al. [52] presented a video OCR system that detects and recognizes overlaid texts in video as well as its application to person identification in video documents. An end-to-end real-time scene text localization and recognition method is proposed in [53].

Texts contain textural features, which are normally classified using Support Vector Machine (SVM) or Multilayer Perceptron (MLP) for the detection of regions as text or non-text. Random forest is another classifier which has the ability of maintaining accuracy in small number of labelled dataset and providing good performance in unbalanced dataset as in the case of unbalanced text and non-text distribution. In [54], a method of merging different random forests trained with different kinds of features is proposed to improve the accuracy of classification.

Gupta et al. [55] applied discrete wavelet transform (DWT) for extracting text region from complex background. In another work [56], Haar Discrete Wavelet Transform is employed. Liang et al. [57] also used

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DWT to detect edges of candidate text regions. The resulted detail component sub-bands contain both text and non-text edges. Then thresholding is used on the intensity of the edges to distinguish text and non-text edges. Text regions are composed of vertical, horizontal and diagonal edges. Morphological dilation operators are applied to connect isolated text edges of each detail component sub-band in a transformed binary image. According to the experimental results, real text regions are the overlapped portion of three kinds of dilated edges. Hence, logical AND operator is applied to three kinds of dilated edges and final text regions are obtained.

Stroke width and local contrast are used by Subramanian et al. [58] to develop a fast, simple, and effective algorithm to detect character strokes. Strokes of a pen or brush are the marks created when writing or painting. Stroke filter is a spatial filter which can detect the strokes in images. By nature, at the stroke points, (1) the image has intensities different from its lateral regions, (2) its lateral regions have similar intensities to each other, and (3) it is nearly homogenous. Jung et al. [59], [60] used stroke filter to localize text within scene and video images. The candidature of the text regions thus identified is verified using SVM classifier. The text regions are then refined by boundary shrinking, character recombination and character extension.

Video image processing Anthimopoulos et al. [61] proposed a two-stage system for text detection in video images. In the first stage, text lines are detected based on the edge map of the image leading to a high recall rate with low computational time expenses. In the second stage, the result is refined using a sliding window and an SVM classifier trained on features obtained by a new Local Binary Pattern-based operator that describes the local edge distribution. The whole algorithm is used in a multi-resolution fashion enabling detection of texts with larger variation in size. A survey on text information extraction in images and videos is reported in [62].

Based on multilingual analysis, Lyu et al. [63] proposed a comprehensive, efficient video text detection, localization, and extraction method, which emphasizes the multilingual capability over the whole processing. The text detection is carried out by edge detection, local thresholding and hysteresis edge recovery. The coarse-to-fine localization scheme is then performed to identify text regions accurately. The text extraction consists of adaptive thresholding, dam point labelling, and inward filling. The proposed method is also robust to various background complexities and text appearances.

Very recently, due to the ease availability of camera mounted hand held devices, document images are also generated by camera captured devices rather than traditional scanning devices. Moreover, in commercial surveillance applications, processing the text within the scene has become a growing need. Yang et al. proposed an approach [64] for automated text detection in video data combining two stages: firstly, an edge-based multi-scale text detector to identify potential text candidates with high recall rate and small computational time expenses and secondly, candidate text lines are refined by an image entropy based improvement algorithm and a Stroke Width Transform (SWT) based verification procedure.

Vehicle license plate localization Most of the existing automatic traffic monitoring systems [65], [66] concentrate on localization of license plate regions and subsequent interpretation of the characters therein. In general, most of the works on license plate recognition system apply edge based features for localizing standardized license plate regions. Some of these works [65], [66] capture the image of a vehicle carefully placed in front of a camera occupying the full view of it and taking a clear image of the license plate. Spanish license plate is localized and recognized in [67] using connected component labeling (CCL) algorithm of different binarized images generated from different thresholds. It also uses the aspect ratio and distance of the plate from the center of the image as a characteristic for localization of the plate. Kwasnicka et al. proposed a hybrid method [66] to localize the license plate. During the localization phase, the position of the characters is used in [65]. It assumes that no significant edge lies near the license plate and characters are disjoint. An FPGA based license plate recognition system is reported in [68] using real time video processing. It uses low memory and is relatively faster than software based solutions. A work on localization of Iranian license plate is done in [69]. The system designed by Jia et al. [70] uses mean shift algorithm for localization of license plate giving

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satisfactory result for license plates having color different from the body color of the vehicle. An application of generalized text localization and segmentation scheme is reported in [71], [72], where text color and spatial information like edge, texture and spatial distribution are used to localize the text within the image. A morphology based method is proposed in [73] giving high accuracy of localization with slightly lower recognition accuracy.

Most of the Indian commercial vehicles have license plates with yellow background and black foreground (for texts). A technique based on the method of region growing with color based segmentation has been developed in [74]. RGB and HSI color models are used for color segmentation. An artificial neural network based technique has been reported in [75], in which the network is trained with color features of the characters of the license plate. The trained network is then used to localize the license plate in the video snapshots captured from the surveillance cameras. It is seen that when the characters of the license number are written horizontally, the vertical edges of each character appear at regular interval and they have a specific height. The pattern and concentration of the vertical edges also remain in conformity with the pattern of the license number. This phenomenon have been explored to develop technique [76], [77] to find the license plate region within the image. The edges created by the characters within the license plate are extracted using Sobel’s vertical edge operator [78]. A license plate localization technique based on Hough transform has been reported in [79]. A hybrid method is developed and reported in [80], [81] that uses Hough transform inside the regions found by vertical edge based multi-stage technique to identify the desired regions where the vertical edges are very close to each other and select the license plate accordingly. An effective edge based technique [82] has been developed for localization of license plate region from the captured video snapshots taken by the surveillance cameras in unconstrained outdoor environment. The overall technique is applied on pre-processed grey scale images. Different edge map images are subsequently generated using different heuristically chosen thresholds. A rule based segment analysis engine then identifies relevant connected regions from the binarized image for true localization of the license plate regions.

Image Processing Techniques

A. Image binarization Binarization has been the area of research for the last forty years or so [83], mainly to find a single threshold value or a set of threshold values for converting a grey scale image into a binary image. Most of the algorithms till developed are of generic type with or without using local information or special content within the image. The major contribution of research for binarization is to recover or extract information from a degraded document images. A nonparametric and unsupervised method of automatic threshold selection is presented by Nobuyuki Otsu [84] which uses a method based on grey level histogram and it maximizes the intra-class variance to total variance. Bernsen’s binarization method [85] computes the local maximum and minimum intensities for each pixel around the neighborhood of that pixel. Niblack proposed a method [86] which determines the threshold for each pixel by shifting a rectangular window across the image. Sauvola et al. developed an algorithm [87] for text and picture segmentation within an image and binarized the image using local threshold. In this method, neighbors of each pixel are considered to determine whether the central pixel will become black or white. In [88], adaptive Wiener filter [89] is used as a pre-processing step based on the statistics of the neighbouring pixels. It then performs an initial estimation of foreground pixels and the result is then refined using Sauvola’s adaptive thresholding method [87] with 𝑘=0.2, to get the binarized image 𝑆(𝑥, 𝑦). It then performs approximate estimation of background surface 𝐵(𝑥, 𝑦) of the pre-processed image 𝐼(𝑥, 𝑦) using the method proposed in [90]. Valverde et al. proposed a method [91] that binarizes the image using Niblack’s technique, with 𝑘=-0.2, within a neighbourhood of 15×15 pixels. It then performs morphological opening with a structuring element favouring 4-connectivity. A slight modification of Niblack’s method is applied in [92] by Zhang et al. to binarize the document image of bound volumes. It first detects the shadow boundary. It then binarizes the image using slight modification of Niblack’s method with 𝑘=0.1 and 𝑅=100. Milewski et al. [93] uses a binarization based method for separating handwritten text from carbon copy medical form images. A comparative study of different document image binarization methods have been discussed in different literatures [94–96]. Saha et al. proposed three methods for giving the membership value to each pixel at each level of histogram equalization. The net membership value of each

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pixel is then calculated by merging the membership values obtained at each level of hierarchy linearly [97], [98] or non-linearly [99]. The image is then binarized depending on the net membership value of each pixel.

B. Image segmentation Segmentation is a procedure that is used to partition an image into its constituent parts or objects. Autonomous segmentation is one of the most difficult tasks in digital image processing. Lot of works in the area of image segmentation has already been reported in the form of research papers during last couple of decades or so. Three types of algorithms are discussed in a research report [100]. In one earlier work [101], a region based representation of image and video useful to multimedia services has been represented. It then proposes a partition tree representation of image. Bouman et al. [102] uses multi-scale Bayesian image segmentation on multispectral remotely sensed images. A work is reported in [103] in which text is localized and segmented from complex images and videos. The method applies multi-resolution approach and uses multimedia text detector and segmentor. It also estimates text color using vector quantization method. A new discrimination function is proposed in [104] for segmenting touching characters in printed document image based on both pixel and profile projections. A dynamic recursive segmentation algorithm is developed for effectively segmenting touching characters. In [105], a grey scale based character segmentation and recognition methodology is proposed in which the candidate segmentation points could be found efficiently by using topographic features and projection profiles of grey scale images. Basu et al. [106] has reported text line extraction from multi-skewed handwritten document images using water flow technique. In another work [107], text lines are segmented from unconstrained handwritten document images with four types of scripts by comparing neighborhood of connected components. A character segmentation algorithm for Arabic text is proposed in [108]. An iterative multi-model sub-image binarization of handwritten character segmentation is proposed in [109]. A lexicon-driven handwritten Japanese character string segmentation is proposed in [110].

In [111], a license plate localization and character segmentation method is proposed. The proposed method employed hybrid binarization technique to reduce the segmentation problem caused by dirt. Hegt et al. [112] proposed a projection based method for license plate character segmentation. A vertical projection based character segmentation of license plate characters is proposed in [113]. Haris et al. [114] proposed a hybrid multidimensional image segmentation algorithm which combines edge and region-based techniques through the morphological algorithm of watersheds. Saha et al. proposed a method [115], which uses hierarchical segmentation of touching characters from camera captured images is based on the method of binarization at the different levels of object hierarchy.

C. Optical character recognition Recognition is the process that assigns a label to an object based on its descriptors. Different objects or patterns can be classified by means of finding discriminating feature set exhibiting different properties of the objects or patterns. More specifically, features are the quantitative measures of a pattern, that as a whole impose a membership value of the pattern to a particular class. The purpose of feature extraction is to reduce the amount of data required for representation of the properties of different patterns. An exhaustive survey on different feature extraction techniques for recognition of isolated/segmented characters has been published in [116], [117]. Surveys on feature descriptors from the perspective of shape analysis and object recognition has been published in different literatures [118–120]. Several features descriptors, viz. Fourier descriptors, graph descriptions, projection profile, contour profiles, geometric moment, Zernike moments, spline curve approximations have been investigated and subsequently published in different literatures [121–123]. With the progress of research in feature extraction techniques, different effective feature descriptors, viz. gradient-based features [124], [125], contour based features [126], concavity and curvature features [127], [128], pixel distance features [129] , directional distance features [130], wavelet based features [131], chain code features [132], histogram of gradient (HOG) based features [133–136] are being reported in the domain of character recognition. A topological feature combination has been proposed in [137–139] for recognition of handwritten characters/numerals. Different types of textural features are extracted in [140]. In [141], a work is reported for feature extraction for different type of scripts. An efficient fuzzy based system is reported in a work [142] for localization of license plate and Kohonen’s self organizing neural model is used for

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recognition of characters. A two stage hybrid recognition system combining statistical and structural features is proposed in [143]. Llorens et al. [67] used HMM to recognize the characters. Quad-tree based longest run (QTLR) features are extracted from each binarized character segment using a technique described in [141], [144].

Conclusions Digital image processing has found its utility not only in the field of computer science but also in other domains of engineering and technology. This paper presents a comprehensive survey of different applications of it. It has other applications as well, which has not been reported here. Similarly, regarding image processing techniques, only image binarization, image segmentation and character recognition have been discussed here. Binarization plays very crucial role in pre-processing stage for most of the image processing applications. Though lot of research work has already been done in this field, still there is huge scope of research in this area. There is still scope of research in the field of image segmentation because a good segmentation result always leads to a good recognition result. There may not be enough scope of research for optical character recognition for English script. But there is growing scope of research for recognition of regional scripts across different parts of India. Development of new feature set and optimization of it may be a new direction of research in this regard.

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