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To solve the problem that maximally stable extremal regions (MSER) will become unstable when the image is blurred due to the change of scale, a novel affine invariant feature called Multi-Scale Maximally Stable Extremal Region (MMSER) which is maximally stable both in the image space and the scale space is proposed by defining a criterion to evaluate the stability of extremal regions in scale space...
In this paper, we propose a novel face representation in which a face is represented in terms of dense Scale Invariant Feature Transform (d-SIFT) and shape contexts of the face image. The application of the representation in gender recognition has been investigated. There are four problems when applying the SIFT to facial gender recognition. (1) There may be only a few keypoints that can be found...
We address the problem of object detection and segmentation using holistic properties of object shape. Global shape representations are highly susceptible to clutter inevitably present in realistic images, and can be robustly recognized only using a precise segmentation of the object. To this end, we propose a figure/ground segmentation method for extraction of image regions that resemble the global...
Local image features are used for a wide range of applications in computer vision and range imaging. While there is a great variety of detector-descriptor combinations for image data and 3D point clouds, there is no general method readily available for 2D range data. For this reason, the paper first proposes a set of benchmark experiments on detector repeatability and descriptor matching performance...
Programs expressed using logic representations can be more easily analysed and transformed. Transformations will depend on the target language semantics. A field encapsulation refactorization will be different for a Java program and an Eiffel program. Logic based representations of programs and its metamodel allows writing generic rules capable of performing some language independent transformations...
This paper presents a new behavior classification system that can analyze human behaviors from arbitrary views. Technically, if different viewing angle are used for observing a person, his appearances will change significantly. To freely recognize his behaviors, traditional methods tend to adopt 3-D data for behavior analysis. However, its inherent correspondence process will make it inappropriate...
Global context descriptors are vectors of additional information appended to an existing descriptor, and are computed as a log-polar histogram of nearby curvature values. These have been proposed in the past to make Scale Invariant Feature Transform (SIFT) matching more robust. This additional information improved matching results especially for images with repetitive features. We propose a similar...
This paper proposes a relative shape context and relaxation labeling (RSC-RL) based approach for point pattern matching (PPM). First of all, a new point set based invariant feature, Relative Shape Context (RSC), is proposed. Using the test statistic of relative shape context descriptor's matching scores as the foundation of support function, the point pattern matching probability matrix can be iteratively...
In this paper, we develop methodology to locate cephalometric landmarks on X-ray images based on probabilistic relaxation, which combines local contextual information from the general shape of the bones of the head (used as measurements specific to each landmark in the form of its shape context) and relational information, expressing the relative position of the landmarks with respect to each other.
Text is a vital feature in applications of computer vision. Traditional Chinese character recognition techniques are mainly based on optical character recognition (OCR), however, they can't obtain satisfactory results from images affected by complex circumstance, such as different viewpoint, scale changes, addition of noise and complex background. To solve these problems, inspired by SIFT descriptor,...
In this paper, we consider the feature correspondence task as a graph matching problem. Our approach tends to maximize a similarity objective function, which consists of not only the feature vectors but also their corresponding constrained global spatial structures, by a new polynomial-time approximate optimization algorithm. This algorithm allows every node in a smaller graph to potentially be linked...
This paper presents a novel framework for object-based video inpainting. To complete an occluded object, our method first samples a 3-D volume of the video into directional spatio-temporal slices, and then performs patch-based image inpainting to repair the partially damaged object trajectories in the 2-D slices. The completed slices are subsequently combined to obtain a sequence of virtual contours...
Asking questions is an inevitable part of collaborative interactions between humans and robots. However, robotics novices may have difficulty answering the robots' questions if they do not understand what the robot is asking. We are particularly interested in whether robots can supplement their questions with information about their state in a manner that increases the accuracy of human responses...
In this paper we propose a trainable system that learns grounded language models from examples with a minimum of user intervention and without feedback. We have focused on the acquisition of grounded meanings of spatial and adjective/noun terms. The system has been used to understand and subsequently to generate appropriate natural language descriptions of real objects and to engage in verbal interactions...
This paper presents a novel viewer counter for an environment in which a stationary camera can count the number of people watching an electronic billboard without counting the repetitions in real time video streams. The potential buyers actually watching an advertisement or merchandise are captured via frontal face detection techniques. To count the number of viewer precisely, the problem of occlusions...
A novel ear recognition approach is proposed in this paper, which use the SIFT descriptor with global context and the projective invariants to obtain ear features. At first, as the ear images have multiple similar local regions, the SIFT descriptor with global context is used for computing the matching points. This kind of descriptor can discriminate the keypoints with similar local appearances effectively...
Graphics detection and recognition are fundamental research problems in document image analysis and retrieval. As one of the most pervasive graphical elements in business and government documents, logos may enable immediate identification of organizational entities and serve extensively as a declaration of a document's source and ownership. In this work, we developed an automatic logo-based document...
In this paper, we present a multi-level recognizer for online Arabic handwriting. In Arabic script (handwritten and printed), cursive writing - is not a style - it is an inherent part of the script. In addition, the connection between letters is done with almost no ligatures, which complicates segmenting a word into individual letters. In this work, we have adopted the holistic approach and avoided...
This paper proposes a highly robust method for face authentication. Techniques introduced in this work are composed of two stages. Firstly, the feature of face is to be detected by the principle of Trace Transform. Then, in the second stage, the Hausdorff-Shape Context is employed to measure and determine of similarity between models and test images. From the experimental result of 2,520 images form...
We present work on vision based robotic grasping. The proposed method relies on extracting and representing the global contour of an object in a monocular image. A suitable grasp is then generated using a learning framework where prototypical grasping points are learned from several examples and then used on novel objects. For representation purposes, we apply the concept of shape context and for...
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