Efficient Maximally Stable Extremal Region (MSER) Tracking

Extremal Regions of Extremum Levels (EREL) are regions detected from a set of all extremal regions of an image. Maximally Stable Extremal Regions (MSER) which is a novel affine covariant region detector, detects regions from a same set of extremal regions as well. Although MSER results in regions with almost high repeatability, it is heavily dependent on the union-find approach which is a fairly complicated algorithm, and should be completed sequentially. Furthermore, it detects regions with low repeatability under the blur transformations. The reason for the latter shortcoming is the absence of boundaries information in stability criterion. To tackle these problems we propose to employ prior information about boundaries of regions, which results in a novel region detector algorithm that not only outperforms MSER, but avoids the MSER's rather complicated steps of union-finding. To achieve that, we introduce Maxima of Gradient Magnitudes (MGMs) and use them to find handful of Extremum Levels (ELs). The chosen ELs are then scanned to detect their Extremal Regions (ER). The proposed algorithm which is called Extremal Regions of Extremum Levels (EREL) has been tested on the public benchmark dataset of Mikolajczyk [1]. Our experimental evaluations illustrate that, in many cases EREL achieves higher repeatability scores than MSER even for very low overlap errors.

문서 이미지의 이진화는 문서 인식의 이전 단계에서 주로 사용되며, 이진화의 성공 여부에 따라 문서 인식의 결과에 영향을 미치는 중요한 단계로 볼 수 있다. 지금까지 문서 이미지를 이진화 하기 위한 다양한 기법들이 연구되었지만, 문서 이미지의 상태에 따라 그 결과는 다양하다. 본 논문에서는 객체 추출에 많이 이용되는 MSER(Maximally Stable Extremal Region)을 이용하여 문서 이미지를 이진화하는 기법을 제안한다. 먼저 문서 이미지에서 MSER 객체를 추출한다. 추출된 MSER 객체는 그 자체로 문서 이미지 이진화에 사용되기는 어렵기 때문에 사용하기 적합한 형태로 변경되는 과정을 거친다. 그리고 최종 MSER 객체와 문서 이미지로부터 추출한 대비 이진 이미지를 이용하여 최종 이진 이미지를 계산한다. 실험결과는 본 논문에서 제안한 방법이 문서 이미지의 이진화에 유용함을 보여준다. Document image binarization is largely used as previous stage of document recognition. And the result of document recognition is much affected from the result of document image binarization. There were many studies to binarize document images. The results of previous studies for document image binarization is varied according to the state of document images. In this paper, we propose a technique for document image binarization using MSER that is applied to extract objects from an image. At first, raw MSER objects are extracted from a document image. Because the raw MSER objects cannot be used for document image binarization, the extracted raw MSER objects are modified. Then the final MSER objects are used for document image binarization with the contrast image that is extracted from the document image. Experimental results show that the proposed technique is useful for document image binarization.

The Max-Tree is an efficient data structure that represents all connected components resulting from all possible image upper threshold values. Usually, most of its nodes represent irrelevant extrem a, i.e. noise, or small variations of a connected component. This paper proposes the Maximal Max-Tree Simplification (MMS) filter with a normalized threshold criterion (MMS-T) and a Maximally Stable Extremal Regions (MSER) criterion (MMS-MSER) and a methodology to apply them using the Extinction filter We show that after applying our simplification methodology which sets the number of maxima in the image, the number of Max-Tree nodes is at most twice this number. Two applications of the proposed methodology are illustrated.

This paper presents the method to classify the speed limit traffic sign using multiple features, namely histogram of oriented gradient (HOG) and maximally stable extremal regions (MSER) features. The classification process is divided into the outer circular ring matching and the inner part matching. The HOG feature is employed to match the outer circular ring of the sign, while MSER feature is employed to extract the digit number in the inner part of the sign. Both features are extracted from the grayscale image. The algorithm detects the rotation angle of the sign by analyzing the blobs which is extracted using MSER. In the matching process, tested images are matched with the standard reference images by calculating the Euclidean distance. The experimental results show that the proposed method for matching the outer circular ring works properly to recognize the circular sign. Further, the digit number matching achieves the high classification rate of 93.67% for classifying the normal and rotated speed limit signs. The total execution time for classifying six types of speed limit sign is 10.75 ms.

In order to solve the problem of automation of cable number identification in the process of cable assembly of large equipment, we proposed a cable number detection and recognition method based on image Maximally Stable Extremal Regions (MSER) and convolutional neural network (CNN). We used image processing algorithms combined with deep learning to achieve end-to-end identification of cable number images. In view of the uncertain arrangement order and scattered distribution of numbers on the cable, the MSER algorithm was improved by image processing in order to extract the area containing numbers more accurately. The number identification problem in the case of lack of sample database is solved by the migration-trained CNN network. In addition, the recognition effect of the method under different environmental conditions has been studied. Through experimental research, the optimized MSER and CNN algorithms can better complete the cable number detection and identification tasks.

In order to reduce the impact of shooting angle, distance and motion blur on vehicle number localization, and improve the accuracy of the localization in complex environments, a new vehicle number localization method based on edge-enhanced maximally stable extremal region (MSER) is proposed. This method first performs edge enhancement on the vehicle number image and extracts the character candidate region in the edge-enhanced image by using MSER. Then, the candidate region is filtered according to the vehicle number character features using the heuristic rule and the stroke width rule, and the remaining candidate regions are merged. The final step is completed by combining the geometric features of the vehicle number. The experimental results show that the comprehensive localization accuracy of this method is as high as 98.50%. High accuracy and robustness can be achieved by using this method in vehicle number localization with images acquired in complicated scenes with various shooting distances and angles.

In this paper, an affine invariance feature detection method based on Scale Invariant Feature Transform (SIFT) and Maximally Stable Extremal Regions (MSER) is proposed. Classical SIFT algorithm is not robust to affine deformations, because it is based on DOG detector which extracts circle regions for keypoint location. In order to overcome this disadvantage, DOG detector in conventional SIFT algorithm is replaced by MSER detector which is robust to affine deformation. Then these regions are normalized and extracted using SIFT. Simulation studies are carried out to show the effectiveness of the proposed method to affine transform in comparison to traditional SIFT algorithm.

In this paper, we introduce WignerMSER, a robust local feature detector for thermal infrared images, a new variant of renowned maximally stable extremal regions (MSER) feature detector. Thermal infrared images pose challenges of low contrast and lack of sharp edges or boundaries typically required for identifying keypoints using traditional feature detectors. This paper addresses this challenge by designing a local feature detector suitable for infrared images. The proposed WignerMSER features are obtained by transforming the image from the spatial domain to joint space-spatial-frequency domain using pseudo Wigner-Ville Distribution, and thereafter detecting MSERs in the Wigner transformed space. Different variants of WignerMSER are proposed and their performance is compared over traditional MSER feature detector in a bag of words framework for the purpose of object recognition in thermal infrared images. The performance of WignerMSER feature detector is evaluated on two infrared datasets. The first dataset is CSIR-CSIO thermal infrared (TIR) vehicle dataset comprising of four vehicle categories commonly seen on urban roads in India and the second dataset is a subset of FLIR Thermal dataset comprising of two object categories, vehicles, and pedestrians. All the variants of the proposed WignerMSER feature detector have demonstrated superior classification performance against traditional MSER features for object categorization on both the TIR datasets. Fused WignerMSER feature detector has shown the best performance with more than 5% increase in overall classification accuracy over traditional MSER for both the datasets.

The visual analysis of foods on social media by using food recognition algorithm provides valuable insight from the health, cultural and marketing. Food recognition offers a means to automatically recognise foods as well the useful information such as calories and nutritional estimation by using image processing and machine learning technique. The interest points in food image can be detected effectively by using Maximally Stable Extremal Region (MSER). As MSER used global segmentation and many food images have a complex background, there are numerous irrelevant interest points are detected. These interest points are considered as noises that lead to computation burden in the overall recognition process. Therefore, this research proposes an Extremal Region Selection (ERS) algorithm to improve MSER detection by reducing the number of irrelevant extremal regions by using unsupervised learning based on the k-means algorithm. The performance of ERS algorithm is evaluated based on the classification performance metrics by using classification rate (CR), error rate (ERT), precision (Prec.) and recall (rec.) as well as the number of extremal regions produced by ERS. UECFOOD-100 and UNICT-FD1200 are the two food datasets used to benchmark the proposed algorithm. The results of this research have found that the ERS algorithm by using optimum parameters and thresholds, be able to reduce the number of extremal regions with sustained classification performance.

Short-axis Cine Cardiac Magnetic Resonance Images (MRI) provide a good contrast between left ventricle (LV) blood pool and myocardium. Thus, they are suitable for segmenting left ventricle cavity and computing left ventricle areas, volumes and some important clinical parameters like ejection fraction and myocardial mass. Many recent endocardium segmentation methods utilize optimal thresholding as simple and fast technique to segment left ventricle cavity. Unfortunately, intensity inhomogeneity decreases the efficiency of optimal thresholding. In this paper, we propose an automatic technique to overcome the aforementioned problems. The proposed technique is based mainly on Maximally Stable Extremal Regions (MSER) detector. The proposed technique consists of two main steps: First, generating of MSERs and Second, these regions were analyzed to select the region representing the LV cavity. The proposed technique was validated using 15 test sets from MICCAI 2009 LV segmentation challenge. Results were compared with thresholding-based method applied to the same dataset. Results show better performance in terms of segmentation accuracy and computation time.

In this paper a novel robust algorithm that extracts text from signage images is proposed. Existing methods are constructed to work on certain limitations, as detecting text under perfect lightning conditions. Maximally Stable Extremal Regions (MSER) detector is used to extract text areas because it shows better performance to lightning conditions. To enhance the MSER regions, novel enhancement technique is used by combining edge detection algorithm. Enhance edge image is then put to the stroke width detection algorithm. Based on stroke width information, connected components are obtained. Afterwards connected components are filtered to remove non text pixels. Text recognition is not touched by this algorithm and resulted binary image can be used on any text recognition algorithm. This algorithm shows best performance on light effected text images and noisy images.

Food object recognition has gained popularity in recent years. This can perhaps be attributed to its potential applications in fields such as nutrition and fitness. Recognizing food images however is a challenging task since various foods come in many shapes and sizes. Besides having unexpected deformities and texture, food images are also captured in differing lighting conditions and camera viewpoints. From a computer vision perspective, using global image features to train a supervised classifier might be unsuitable due to the complex nature of the food images. Local features on the other hand seem the better alternative since they are able to capture minute intricacies such as interest points and other intricate information. In this paper, two local features namely SURF (Speeded- Up Robust Feature) and MSER (Maximally Stable Extremal Regions) are investigated for food object recognition. Both features are computationally inexpensive and have shown to be effective local descriptors for complex images. Specifically, each feature is firstly evaluated separately. This is followed by feature fusion to observe whether a combined representation could better represent food images. Experimental evaluations using a Support Vector Machine classifier shows that feature fusion generates better recognition accuracy at 86.6%.

Emotion recognition has been one of the stimulating issues over the years due to the irregularities in the complexity of models and unpredictability between expression categories. So many Emotion detection algorithms have developed in the last two decades and still facing problems in accuracy, complexity and real-world implementation. In this paper, we propose two feature extraction techniques: Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method. In Mouth based feature extraction method mouth area is calculated and based on that value the emotions are classified. In the MSER method, the features are extracted by using connecting components and then the extracted features are given to a simple ANN for classification. Experimental results shows that the Mouth area based feature extraction method gives 86% accuracy and MSER based feature extraction method outperforms it by achieving 89% accuracy on DEAP. Thus, it can be concluded that the proposed methods can be effectively used for emotion detection.

Logo/brand name detection and recognition in unstructured and highly unpredictable natural images has always been a challenging problem. We notice that in most natural images logos are accompanied with associated text. Therefore, we address the problem of logo recognition by first detecting and isolating text of varying color, font size and orientation in the input image using affine invariant maximally stable extremal regions (MSERs). Using an off-the-shelf OCR, we identify the text associated with the logo image. Then an effective grouping technique is employed to combine the remaining stable regions based on spatial proximity of MSERs. Deep learning has the advantage that optimal features can be learned automatically from image pixel data. This motivates us to feed the clustered logo candidate image regions to a pre-trained deep convolutional neural network (DCNN) to generate a set of complex features which are further input to a multiclass support vector machine (SVM) for classification. We tested our proposed logo recognition system on 32 logo classes, and a non-logo class obtained by combining FlickrLogos-32 and MICC logo databases, amounting to a total of 23582 training and testing images. Our method yields robust recognition performance, outperforming state-of-the-art techniques achieving 97.8% precision, 95.7% recall and 95.7% average accuracy on the combined MICC and FlickrLogos-32 datasets and a precision of 98.6%, recall of 97.9% and average accuracy of 99.6% on only the FlickrLogos-32 dataset.

We propose a novel system for the automatic detection and recognition of text in traffic signs. Scene structure is used to define search regions within the image, in which traffic sign candidates are then found. Maximally stable extremal regions (MSERs) and hue, saturation, and value color thresholding are used to locate a large number of candidates, which are then reduced by applying constraints based on temporal and structural information. A recognition stage interprets the text contained within detected candidate regions. Individual text characters are detected as MSERs and are grouped into lines, before being interpreted using optical character recognition (OCR). Recognition accuracy is vastly improved through the temporal fusion of text results across consecutive frames. The method is comparatively evaluated and achieves an overall $F_{\rm measure}$ of 0.87.