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In this paper, we propose a novel region grouping approach to shape matching. It is proposed as an alternative region based approach to the traditional edge based shape matching using distance transforms. It has the advantage of obtaining a higher detection rate and obtaining meaningful object segmentation simultaneously. Each image is first segmented into image regions, and possible matches are found...
We propose a cascade of two complementary features classifiers to detect pedestrians from static images quickly and accurately. Co-occurrence Histograms of Oriented Gradients (CoHOG) descriptors have a strong classification capability but are extremely high dimensional. On the other hand, Haar-like features are computationally efficient but not highly discriminative for extremely varying texture and...
We propose a Vector quantization (VQ) based index cube model for content based image retrieval. VQ captures the pixel intensity and the spatial information of the image blocks. An indexing and retrieval algorithm is implemented and different similarity measures are evaluated with the precision and recall curves. It can be used for content based image retrieval in image databases using the incremental...
Due to the prevalence of digital cameras, it is easy to retrieve digital images from the Internet. With the rapid development of digital image processing, databases, and Internet technologies, how to efficiently manage a large amount of digital images is very important. In this paper, we proposed a novel approach for automatic image annotation. We extract color, texture, and shape features from a...
In this work we present a new approach for learning a layered stacked graphical model for the problem of visual object detection and segmentation. It is obvious that visual objects can be represented by multiple feature cues, such as color, texture, shape. The idea is to treat different feature types in different processes for learning classifiers and then integrate them into a unified model. We employ...
Using well-established techniques of Genetic Programming (GP), we automatically optimize image feature filters over several inputs and within transformation images, improving the Automatic Construction of Tree-Structural Image Transformation (ACTIT) system. Our objective is to also produce optimal solutions in substantially less computation time than require for generating features of ACTIT. We improved...
Crowd density analysis is crucial for crowd monitoring and management. This paper proposes a novel method for crowd density analysis. According to the framework, input images are firstly divided into patches, and each patch is associated with a density label based on its texture features. Finally, local information is synthesized for global density estimation. Local image content is described by features...
Boosting has been widely used for discriminative modeling of objects in images. Conventionally, pixel- and patch-based features have been used, but recently, features defined on multilevel aggregate regions were incorporated into the boosting framework, and demonstrated significant improvement in object labeling tasks. In this paper, we further extend the boosting on multilevel aggregates method to...
Remotely sensed hyperspectral imagery plays an important role in land cover classification by supplying the user with additional spectral data as compared to high-resolution color imagery. The web application described in this paper enables users to test their classification algorithms without the risk of bias by withholding the majority of the true classification data and only providing a small section...
The problem of object detection in image and video has been treated by a large number of researchers. Many design factors degrade the reliability of the problem solutions, such as manual modeling of the object, manual features selection, handcrafting architecture, and learning algorithm selection. Here, a generalized object detection and localization system is presented. It has the ability to learn...
This paper presents an efficient pedestrian detection by Bin-interleaved Histogram of Oriented Gradients (Bi-HOG) for automotive applications. The state-of-art feature named HOG [5] is adopted as the basic feature. We arrange alternately even-bin cells and odd-bin cells in one block and then extract the only even-bin feature elements for even-bin cells and the only odd-bin feature elements for odd-bin...
Face detection is one of the challenging problems in image processing. A novel face detection system is presented in this paper and we propose a new approach using Takagi-Sugeno (T-S) fuzzy model and Hue Saturation and Value (HSV) color model. The algorithm uses fuzzy classifier in conjunction with HSV color model to quickly locate faces in the image. The fuzzy classifier basically examines small...
In this paper a method of real-time target recognition is proposed. When the target is moving, the features of the target are changed. So the features for BP neural network training were obtained according to the moving direction and the target's position in the video Scene. And the recognition results of the previous frames were also considered to get the result of the current frame. The experiments...
This paper describes the Arabic handwriting recognition competition held at International Conference on Frontiers in Handwriting Recognition (ICFHR 2010) in Kolkata, India. This fourth competition (the first was at ICDAR 2005 in Seoul, South Korea, the second at ICDAR 2007 in Curitiba, Brazil and the third at ICDAR 2009 in Barcelona, Spain) again used the IfN/ENIT-database with Arabic handwritten...
The objective of this competition (4NSigComp2010) is to ascertain the performance of automatic off-line signature verifiers to evaluate recent technology developments in the areas of document analysis and machine learning. The current paper focuses on the second scenario, which aims at performance evaluation of off-line signature verification systems on a newly-created large dataset that comprises...
The general objective of the ICFHR 2010 Handwriting Segmentation Contest organized in the context of ICFHR 2010 conference was to use well established evaluation practices and procedures in order to record recent advances in off-line handwriting segmentation. Two new benchmarking datasets, one for text line and one for word segmentation, were created in order to test and compare recent algorithms...
Handwriting recognition has always been a challenging task in image processing and pattern recognition. India is a multi-lingual, multi-script country, where eighteen official scripts are accepted and there are over a hundred regional languages. The feature extraction method is probably the most effective method in achieving high recognition performance. In this study we proposed a zone-based feature...
Paper often includes pre-printed ruling lines to help people write more neatly. This particular example of real- world noise can have a serious impact on applications such as handwriting recognition and writer identification, however. In this work, we investigate the effects of ruling lines on writer ID. We study a method for detecting and removing ruling lines and test its utility for Arabic writer...
In this paper we address the problem of hand-drawn symbol spotting in document images. We use stochastic graphical models (SGMs) to represent the structure and variations of hand-drawn symbols. We use a framework which first carries out segmentation and graph formation of the input image, followed by sub-graph matching for spotting of hand-drawn symbols. We used SGMs in place of sub-graphs in a semi-definite...
Automatic transcription of historical documents is vital for the creation of digital libraries. In this paper we propose graph similarity features as a novel descriptor for handwriting recognition in historical documents based on Hidden Markov Models. Using a structural graph-based representation of text images, a sequence of graph similarity features is extracted by means of dissimilarity embedding...
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