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Generation of random fields (GRF) for image segmentation represents partitioning an image into different regions that are homogeneous or have similar facets of the image. It is one of the most challenging tasks in image processing and a very important pre-processing step in the fields of computer vision, image analysis, medical image processing, pattern recognition, remote sensing, and geographical information system. Many researchers have presented numerous image segmentation approaches, but still, there are challenges like segmentation of low contrast images, removal of shadow in the images, reduction of high dimensional images, and computational complexity of segmentation techniques. In this review paper, the authors address these issues. The experiments are conducted and tested on the Berkely dataset (BSD500), Semantic dataset, and our own dataset, and the results are shown in the form of tables and graphs. [ABSTRACT FROM AUTHOR]

Lip segmentation is one of critical steps in a lip-reading system, because it closely relates to the accuracy of system recognition. In this paper, we aim to improve the accuracy of lip segmentation. A novel color space is proposed which consists of the component in the CIE-LUV space and the sum of 2 and 3 components of the image after discrete Hartley transform (DHT). We select a rhombus as the initial contour as its shape is approximate to a closed lip shape relatively. These notions are achieved based on the method of the Active contour model. The active contour model (ACM) is performed by the Chan-Vese model, and the result of each component is gained separately. Finally, the ultimate results are obtained by merging the result of each component together. Through experiments we can get a conclusion that this method can get more accurate and smoother lip contour. Meanwhile, the proposed method is more efficient compared with the classic ACM because it avoids some problems in the classic active contour model, like the radius of the initial contour needs to be set manually according to the size of images. [ABSTRACT FROM AUTHOR]

Traditional methods for automatic recognition of container code in visual images are based on segmentation and recognition of isolated characters. However, when the segment fails to separate each character from the others, those methods will not function properly. Sometimes the container code characters are printed or arranged very closely, which makes it a challenge to isolate each character. To address this issue, a new framework for automatic container code recognition (ACCR) in visual images is proposed in this paper. In this framework, code-character regions are first located by applying a horizontal high-pass filter and scan line analysis. Then, character blocks are extracted from the code-character regions and further classified into two categories, i.e. single-character block and multi-character block. Finally, a segmentation-based approach is implemented for recognition of the characters in single-character blocks, and a hidden Markov model (HMM)-based method is proposed for the multi-character blocks. The experimental results demonstrate the effectiveness of the proposed method, which can successfully recognize the container code with closely arranged characters. [ABSTRACT FROM AUTHOR]

Edge detection is an active and critical topic in the field of image processing, and plays a vital role for some important applications such as image segmentation, pattern classification, object tracking, etc. In this paper, an edge detection approach is proposed using local edge pattern descriptor which possesses multiscale and multiresolution property, and is named varied local edge pattern (VLEP) descriptor. This method contains the following steps: firstly, Gaussian filter is used to smooth the original image. Secondly, the edge strength values, which are used to calculate the edge gradient values and can be obtained by one or more groups of VLEPs. Then, weighted fusion idea is considered when multiple groups of VLEP descriptors are used. Finally, the appropriate threshold is set to perform binarization processing on the gradient version of the image. Experimental results show that the proposed edge detection method achieved better performance than other state-of-the-art edge detection methods. [ABSTRACT FROM AUTHOR]

The given text discusses a road segmentation method called MSAN that uses multi-modal scale-aware attentions to segment roads in real-time for autonomous driving. The method combines RGB and depth information to improve prediction results and outperforms single modality models. The MSAN architecture consists of two attention modules, one for feature fusion across modalities and scales and another for scale-aware fusion. The experiments show that MSAN achieves high performance and efficiency on multiple datasets. However, the method relies heavily on labeled data, which limits its adaptability to different scenarios. Future research aims to reduce data dependency through few-shot learning strategies. [Extracted from the article]

In document image analysis (DIA) especially in handwritten document recognition, standard databases play significant roles for evaluating performances of algorithms and comparing results obtained by different groups of researchers. The field of DIA regard to Indo-Persian documents is still at its infancy compared to Latin script-based documents; as such standard datasets are not still available in literature. This paper is an effort towards alleviating this gap. In this paper, an unconstrained handwritten dataset containing documents of Persian, Bangla, Oriya and Kannada (PBOK) is introduced. The PBOK contains 707 text-pages written in four different languages (Persian, Bangla, Oriya and Kannada) by 436 individuals. Total number of text-lines, words/subwords and characters are 12,565, 104,541 and 423,980, respectively. In most documents of PBOK dataset contain either an overlapping or a touching text-lines. The average number of text-lines in text-pages of the PBOK dataset is 18. Two types of ground truths, based on pixels information and content information, are generated for the dataset. Because of such ground truths, the PBOK dataset can be utilized in many areas of document image processing e.g. text-line segmentation, word segmentation and word recognition. To provide an insight for other researches, recent text-line segmentation results on this dataset are also reported. [ABSTRACT FROM AUTHOR]

Deriving from the artificial life theory, this paper proposes an artificial co-evolving tribes model and applies it to solve the image segmentation problem. During the evolution process, the individuals in this model making up the tribes effect communication cooperatively from one agent to the other in order to increase the homogeneity of the ensemble of the image regions they represent. Two remarkable properties, that is, the monotone contraction and the conservation of the system are proved. Stability and scale control of the proposed method are carefully analyzed. Experimental results are presented and compared with two latest segmentation methods, both quantitatively and visually. We also discuss the results matching with human visual perception. [ABSTRACT FROM AUTHOR]

As relics of history, ancient copper inscriptions are found in many countries. Information in the image or letter forms contained on copper ancient inscription has a very high value. The age and environmental factors caused damage to the surface of the inscription and also reduced the appearances of the image and letter. In this paper, we describe a novel segmentation methodology based on multi-texture features for ancient copper inscriptions which were severely damaged. The segmentation results of letters on ancient copper inscriptions by using the proposed method have an average accuracy of 90%. Based on these results, the proposed method is suitable for letter segmentation of the ancient copper inscriptions. [ABSTRACT FROM AUTHOR]

In document image processing, arc segmentation plays an important role in vectorization and graphic recognition. Moreover, the unsatisfactory results of several recent arc segmentation contests indicate that conventional methods are inadequate. This paper proposes a new arc segmentation algorithm called SymCAve (an acronym for Symmetry axis, Circle fitting and Average distribution points). First, we locate several seed points and adopt three strategies to ensure that the seed points are proper; then we calculate the center and radius utilizing the seed points. Second, the coordinates of the center and radius are adjusted by employing symmetry axes. Third, the average distribution points method is used to verify whether the points on the circumference are all black pixels. It is a complete circle if all of the points are black pixels. Otherwise, it is a partial circle if some of the points are black pixels and are continuous. Based on this information, the start and end angles of the partial circle can be determined. Finally, these arcs are verified to ensure that the results are accurate. Images and the evaluation tool were obtained from the GREC Workshop's Arc Segmentation contests, to test the systematic performance of the SymCAve algorithm. The experiments demonstrate that the proposed method can provide promising results. However, the algorithm has some drawbacks: it cannot detect a line with width of one pixel, small angles, and any large radius arcs. It is suited for segmenting images with appropriate symmetry axes. [ABSTRACT FROM AUTHOR]

With the accumulation of people's wealth and the improvement of purchasing power, more and more people are buying cars as a means of travel. Walking and cycling of the past have now become a car trip. License plate recognition technology is especially important in intelligent transportation systems. It has been widely used in large shopping malls or supermarket parking lots, highway toll stations, speeding violation supervision and other fields. However, the accuracy and efficiency of license plate image recognition are insufficient. To solve the above problems, we propose a license plate character recognition method based on local HOG and layered LBP feature fusion from the perspectives of image pre-processing, license location, characters' segmentation and recognition. First, pre-processing the license image area by highlighting the license plate image; then, the license plate is positioned based on wavelet decomposition and brightness moment; next the tilted license plate image is corrected, the license plate frame is adjusted, and characterization is performed by using the improved projection method based on the fact that the projection of the character is a single peak or a double peak. Finally, the local HOG and hierarchical LBP feature fusion methods are used to identify the license characters. The results show that the license plate's character recognition rate of the proposed method reaches 99.71%, and the time taken is small. This not only improves the character recognition rate, but also saves recognition time. The results show that the method has important practical significance in license plates' recognition. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of automatically segmenting non-rigid pedestrians in still images. Since this task is well known difficult for any type of model or cue alone, a novel approach utilizing shape, puzzle and appearance cues is presented. The major contribution of this approach lies in the combination of multiple cues to refine pedestrian segmentation successively, which has two characterizations: (1) a shape guided puzzle integration scheme, which extracts pedestrians via assembling puzzles with constraint of a shape template; (2) a pedestrian refinement scheme, which is fulfilled by optimizing an automatically generated trimap that encodes both human silhouette and skeleton. Qualitative and quantitative evaluations on several public datasets verify the approach's effectiveness to various articulated bodies, human appearance and partial occlusion, and that this approach is able to segment pedestrians more accurately than methods based only on appearance or shape cue. [ABSTRACT FROM AUTHOR]

In this paper, we present a passive-blind scheme for detection of frame duplication forgery in videos. The scheme is a coarse-to-fine approach that is implemented in four stages: candidate segment selection, spatial similarity measurement, frame duplication classification, and post-processing. To screen and select duplicated candidates in the temporal domain, the histogram difference of two adjacent frames in the RGB color space is adopted as a feature. Then, to evaluate the similarity of two images, we use a block-based algorithm to measure the spatial correlation between the candidate segment and the corresponding frame in the query template. Based on the results of spatial and temporal analysis, we construct a classifier to detect duplicated clips. In addition, to deal with the partial detection problem, we develop a post-processing technique that examines and merges two adjacent detected candidates into a com-plete duplicated video clip. Our experiment results demonstrate that the proposed scheme can not only achieve detection of frame duplication forgery but also accurately detect and localize duplicated clips in different kinds of videos. The results also show that the scheme outperforms an existing method in terms of precision, recall, accuracy, and computation time. [ABSTRACT FROM AUTHOR]

Image analysis represents an invaluable tool in processing and analyzing biological data in an expeditious and reliable way. This paper describes the design and implementation of an image analysis system for the automatic assessment of the growth of Heliothis-zea insects from color images. Specifically, the Heliothis zea is a corn earworm eating corn crops. Biotech researchers are interested in developing insecticidal bio-toxins with the best performance to kill or stunt the growth of the pest. Currently, assessing the effectiveness of different bio-toxin solutions is done mostly manually by biotech experts. The goal of this study is to investigate the use of image analysis for automating and improving the efficiency of this process. In this context, we have developed a prototype system for assessing insect growth from color images which contains three main stages: (1) insect segmentation from background, (2) region processing and feature extraction, and (3) categorization of insect growth. A probabilistic model based on mixtures of Gaussians has been adopted to segment the insect images. Also, back-propagation neural networks have been trained to classify the instar stage and life stage. The proposed system has been evaluated on a set of real images. [ABSTRACT FROM AUTHOR]

Many algorithms for applications such as pattern recognition, computer vision, and computer graphics seek to compute actual optimal paths in weighted directed graphs. The standard approach for reporting an actual optimal path is based on building a single-source optimal path tree. A technique by Chen et al.2 was given for a class of problems such that a single actual optimal path can be reported without maintaining any single-source optimal path tree, thus significantly reducing the space bound of those problems with no or little increase in their running time. In this paper, we extend the technique by Chen et al.2 to the generalized problem of reporting many actual optimal paths with different starting and ending vertices in certain directed graphs, and show how this new technique yields improved results on several application problems, such as reconstructing a 3-D surface band bounded by two simple closed curves, finding various constrained segmentation of 2-D medical images, and circular string-to-string correction. We also correct an error in the time/space complexity for the well-cited circular string-to-string correction algorithm12 and give an improved result for this problem. Although the generalized many-path problem seems more difficult than the single-path cases, our algorithms have nearly the same space and time bounds as those of the single-path cases. Our technique is likely to help improve many other optimal paths or dynamic programming algorithms. [ABSTRACT FROM AUTHOR]

A novel hybrid-fitting energy-based active contours model in the level set framework is proposed. The method fuses the local image fitting term and the global image fitting term to drive the contour evolution, and a special extra term that penalizes the deviation of the level set function from a signed distance function is also included in our method, so the complex and costly reinitialization procedure is completely eliminated. Our model can efficiently segment the images with intensity inhomogeneity no matter where the initial curve is located in the image. In its numerical implementation, two efficient numerical schemes are used to ensure the sufficient efficiency of the evolution process, one is the Lattice Boltzmann Model (LBM), which is used for breaking the restrictions on time step, the other is the Sparse Field Method (SFM), which is introduced for fast local computation. Compared with the traditional schemes, these two strategies can further shorten the time consumption of the evolution process, this allows the level set to quickly reach the true target location. The extensive and promising experimental results on numerous synthetic and real images have shown that our method can efficiently improve the image segmentation performance, in terms of accuracy, efficiency, and robustness. [ABSTRACT FROM AUTHOR]

This paper proposes an alternative approach to emotion recognition from the outer lip contour of the subjects. Subjects exhibit their emotions through their facial expressions, and the lip region is segmented from their facial images. A lip contour model has been developed, and the parameters of the model are adapted using differential evolution algorithm to match with the actual outer contour of the lip. An SVM classifier is then employed to classify the emotion of the subject from the parameter set of the subjects' lip contour. The experiment was performed on 50 subjects in the age group 20-30 years, and the worst accuracy in emotion classification is found to be 86%. [ABSTRACT FROM AUTHOR]

Problem statement: The optical Braille character recognition (OBR) system is in substantial need in order to preserve the Braille documents to make them available in future for the large section of visually impaired people. The recognition and transcribing of the double sided Braille document into its corresponding natural text is indeed a challenging task. This difficulty is due to the overlapping of the front side dots (recto) with that of the back side dots (verso) in the inter-point Braille document. In such settings, the habitual method of template matching to distinguish recto and verso dots is unproductive. Approach: A fresh system for double sided Braille dot recognition is proposed, which employs a two-stage highly efficient and an adaptive technique to differentiate the recto and verso dots from an inter-point Braille expending the horizontal and vertical projection profiles along with distance thresholding for Braille character segmentation. Materials: The efficacy of this segmentation technique is demonstrated on a large dataset consisting of Hindi Devanagari Braille documents with varying image resolution and with diverse word patterns. The primary reason for choosing the Hindi Devanagari Braille is that, Hindi is the national language of India and OBR for the Hindi Devanagari Braille is not available. Results: Braille line segmentation accuracy of 100%, word segmentation accuracy of 99.8% and character segmentation accuracy of 99.4% has been accomplished. Conclusion: This effort of OBR development for Hindi Devanagari Braille has been done for the first time. The proposed method is tolerant to merging of Braille dots and presence of half characters. [ABSTRACT FROM AUTHOR]

Late enhancement cardiac magnetic resonance images (MRI) has the ability to precisely delineate myocardial scars. We present a semi-automated method for detecting scars in cardiac MRI. This model has the potential to improve routine clinical practice since quantification is not currently offered due to time constraints. A first segmentation step was developed for extracting the target regions for potential scar and determining pre-candidate objects. Pattern recognition methods are then applied to the segmented images in order to detect the position of the myocardial scar. The database of late gadolinium enhancement (LE) cardiac MR images consists of 111 blocks of images acquired from 63 patients at the University Hospital Southampton NHS Foundation Trust (UK). At least one scar was present for each patient, and all the scars were manually annotated by an expert. A group of images (around one third of the entire set) was used for training the system which was subsequently tested on all the remaining images. Four different classifiers were trained (Support Vector Machine (SVM), k-nearest neighbor (KNN), Bayesian and feed-forward neural network) and their performance was evaluated by using Free response Receiver Operating Characteristic (FROC) analysis. Feature selection was implemented for analyzing the importance of the various features. The segmentation method proposed allowed the region affected by the scar to be extracted correctly in 96% of the blocks of images. The SVM was shown to be the best classifier for our task, and our system reached an overall sensitivity of 80% with less than 7 false positives per patient. The method we present provides an effective tool for detection of scars on cardiac MRI. This may be of value in clinical practice by permitting routine reporting of scar quantification. [ABSTRACT FROM AUTHOR]

Perception of content displayed on the screen of a computer display using computer vision is a challenging topic if the treated target is changed from physical world to digital world. Screen area from the given computer display image should be segmented and corrected primarily before perceiving the content displayed on the screen. An automatic approach is proposed to the segmentation and deformation correction of screen area for a computer display image. Due to some inherent characteristics existing on ordinary computer displays, the segmentation can be performed by contour tracing. After contouring the screen area, its four corner locations can be readily identified. By approximating the obtained corners to the closest normal screen region, the deformed screen image can be further restored with affine transformation. As a computer vision application on the "look at" screen image, the effectively segmented screen region can be fixed after a little time. The experiments demonstrate that about 70% cases can be fixed under 33 processed frames, others under 51 processed frames, and thus confirm the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]