Метод обработки цифрового изображения для локализации информативных областей

В работе предложен многокритериальный подход к обработке изображения для локализации информативных областей. Основное отличие подхода – обработка изображения параллельно тремя процессами, а именно локализация информативных областей на цветном и бинарном представлениях изображения по цвету, и локализация областей на бинарном представлении по геометрическим признакам объекта.There is offered multicriterion from is in-process of image’s working for localization informing areas in this work. A basic difference of approach is treatment of image parallell by three processes, namely localization of informing areas on the coloured and binary presentations of image in color, and localization of areas on binary presentation on the geometrical features of object

An Intelligent Reconnaissance Framework for Homeland Security

The cross border terrorism and internal terrorist attacks are critical issues for any country to deal with. In India, such types of incidents that breach homeland security are increasing now a day. Tracking and combating such incidents depends only on the radio communications and manual operations of security agencies. These security agencies face various challenges to get the real-time location of the targeted vehicles, their direction of fleeing, etc. This paper proposes a novel application for automatic tracking of suspicious vehicles in real-time. The proposed application tracks the vehicle based on their registration number, type, colour and RFID tag. The proposed approach for vehicle recognition based on image processing achieves 92.45 per cent accuracy. The RFID-based vehicle identification technique achieves 100 per cent accuracy. This paper also proposes an approach for vehicle classification. The average classification accuracy obtained by the proposed approach is 93.3 per cent. An integrated framework for tracking of any vehicle at the request of security agencies is also proposed. Security agencies can track any vehicles in a specific time period by using the user interface of the application

COMPRESSED DOMAIN IMAGE INDEXING AND RETRIEVAL BASED ON THE MINIMAL SPANNING TREE

ABSTRACT In this paper, a method for content-based retrieval of JPEG images is presented, utilizing features directly from the discrete cosine transform (DCT) domain. Image indexing is achieved by extracting color and texture feature vectors, using an efficient technique applied on the DCT coefficients. Similarity between the query-and database-images is provided based on a statistical graph matching approach. The proposed measure makes use of the Wald-Wolfowitz test, a nonparametric test that assesses the commonality between two different sets of multivariate observations. Experimental results demonstrate the enhanced performance of our approach, compared to previously reported methods

Pattern recognition for high throughput zebrafish imaging using genetic algorithm optimization

Computer Systems, Imagery and Medi

Pedestrian detection and counting in surveillance videos

"December 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Zhihai He.Pedestrian detection and counting have important application in video surveillance for entrance monitoring, customer behavior analysis, and public service management. In this thesis, we propose an accurate, reliable and fast method for pedestrian detection and counting in video surveillance. To this end, we first develop an effective method for background modeling, subtraction, update, and shadow removal. To effectively differentiate person image patches from other background patches, we develop a head-shoulder classification and detection method. A foreground mask curve analysis method is to determine the possible position of persons, and then use a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented) feature and bag of words to detect the head-shoulder of people. Based on the foreground detection and head-shoulder classification at each frame, we develop a person counting algorithm in the temporal domain to analyze the frame-level classification results. Our experiments with real-world surveillance videos demonstrate the proposed method has achieved accurate and reliable pedestrian detection and counting.Includes bibliographical references (pages 46-54)

Efficient and robust shape retrieval from deformable templates

Computer Systems, Imagery and Medi

An Innovative SIFT-Based Method for Rigid Video Object Recognition

This paper presents an innovative SIFT-based method for rigid video object recognition (hereafter called RVO-SIFT). Just like what happens in the vision system of human being, this method makes the object recognition and feature updating process organically unify together, using both trajectory and feature matching, and thereby it can learn new features not only in the training stage but also in the recognition stage, which can improve greatly the completeness of the video object’s features automatically and, in turn, increases the ratio of correct recognition drastically. The experimental results on real video sequences demonstrate its surprising robustness and efficiency

Text-detection and -recognition from natural images

Text detection and recognition from images could have numerous functional applications for document analysis, such as assistance for visually impaired people; recognition of vehicle license plates; evaluation of articles containing tables, street signs, maps, and diagrams; keyword-based image exploration; document retrieval; recognition of parts within industrial automation; content-based extraction; object recognition; address block location; and text-based video indexing. This research exploited the advantages of artificial intelligence (AI) to detect and recognise text from natural images. Machine learning and deep learning were used to accomplish this task.In this research, we conducted an in-depth literature review on the current detection and recognition methods used by researchers to identify the existing challenges, wherein the differences in text resulting from disparity in alignment, style, size, and orientation combined with low image contrast and a complex background make automatic text extraction a considerably challenging and problematic task. Therefore, the state-of-the-art suggested approaches obtain low detection rates (often less than 80%) and recognition rates (often less than 60%). This has led to the development of new approaches. The aim of the study was to develop a robust text detection and recognition method from natural images with high accuracy and recall, which would be used as the target of the experiments. This method could detect all the text in the scene images, despite certain specific features associated with the text pattern. Furthermore, we aimed to find a solution to the two main problems concerning arbitrarily shaped text (horizontal, multi-oriented, and curved text) detection and recognition in a low-resolution scene and with various scales and of different sizes.In this research, we propose a methodology to handle the problem of text detection by using novel combination and selection features to deal with the classification algorithms of the text/non-text regions. The text-region candidates were extracted from the grey-scale images by using the MSER technique. A machine learning-based method was then applied to refine and validate the initial detection. The effectiveness of the features based on the aspect ratio, GLCM, LBP, and HOG descriptors was investigated. The text-region classifiers of MLP, SVM, and RF were trained using selections of these features and their combinations. The publicly available datasets ICDAR 2003 and ICDAR 2011 were used to evaluate the proposed method. This method achieved the state-of-the-art performance by using machine learning methodologies on both databases, and the improvements were significant in terms of Precision, Recall, and F-measure. The F-measure for ICDAR 2003 and ICDAR 2011 was 81% and 84%, respectively. The results showed that the use of a suitable feature combination and selection approach could significantly increase the accuracy of the algorithms.A new dataset has been proposed to fill the gap of character-level annotation and the availability of text in different orientations and of curved text. The proposed dataset was created particularly for deep learning methods which require a massive completed and varying range of training data. The proposed dataset includes 2,100 images annotated at the character and word levels to obtain 38,500 samples of English characters and 12,500 words. Furthermore, an augmentation tool has been proposed to support the proposed dataset. The missing of object detection augmentation tool encroach to proposed tool which has the ability to update the position of bounding boxes after applying transformations on images. This technique helps to increase the number of samples in the dataset and reduce the time of annotations where no annotation is required. The final part of the thesis presents a novel approach for text spotting, which is a new framework for an end-to-end character detection and recognition system designed using an improved SSD convolutional neural network, wherein layers are added to the SSD networks and the aspect ratio of the characters is considered because it is different from that of the other objects. Compared with the other methods considered, the proposed method could detect and recognise characters by training the end-to-end model completely. The performance of the proposed method was better on the proposed dataset; it was 90.34. Furthermore, the F-measure of the method’s accuracy on ICDAR 2015, ICDAR 2013, and SVT was 84.5, 91.9, and 54.8, respectively. On ICDAR13, the method achieved the second-best accuracy. The proposed method could spot text in arbitrarily shaped (horizontal, oriented, and curved) scene text.</div

Scene Segmentation and Object Classification for Place Recognition

This dissertation tries to solve the place recognition and loop closing problem in a way similar to human visual system. First, a novel image segmentation algorithm is developed. The image segmentation algorithm is based on a Perceptual Organization model, which allows the image segmentation algorithm to ‘perceive’ the special structural relations among the constituent parts of an unknown object and hence to group them together without object-specific knowledge. Then a new object recognition method is developed. Based on the fairly accurate segmentations generated by the image segmentation algorithm, an informative object description that includes not only the appearance (colors and textures), but also the parts layout and shape information is built. Then a novel feature selection algorithm is developed. The feature selection method can select a subset of features that best describes the characteristics of an object class. Classifiers trained with the selected features can classify objects with high accuracy. In next step, a subset of the salient objects in a scene is selected as landmark objects to label the place. The landmark objects are highly distinctive and widely visible. Each landmark object is represented by a list of SIFT descriptors extracted from the object surface. This object representation allows us to reliably recognize an object under certain viewpoint changes. To achieve efficient scene-matching, an indexing structure is developed. Both texture feature and color feature of objects are used as indexing features. The texture feature and the color feature are viewpoint-invariant and hence can be used to effectively find the candidate objects with similar surface characteristics to a query object. Experimental results show that the object-based place recognition and loop detection method can efficiently recognize a place in a large complex outdoor environment