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A novel approach for recognizing human actions using sequences of 3D point clouds of agents over time is presented. It is claimed that some regions that have a long distance to the body center (boundary regions of human body) are very discriminative for understanding human actions. Based on this idea, a novel descriptor based on weighted boundary of 3D point cloud is introduced to recognize the actions...
In computer vision, tracking human pose has received a growing attention in recent years. The existing methods used multi-view videos and camera calibrations to enhance the shape of the object in 3D view. In this paper, tracking and partial reconstruction of the shape of the object from a single view video is identified. The goal of the proposed integrated method is to detect the movement of a person...
As an increasing number of digital images are generated, a demand for an efficient and effective image retrieval mechanisms grows. In this work, we present a new skeleton-based shape retrieval algorithm, which starts by drawing circles of increasing radius around skeleton points. Since each skeleton corresponds to the center of a maximally inscribed circle, this process results in circles that are...
The research is focused on depth evaluation using a single camera. 2D images can be used for finding the depth of a particular object in the image. Algorithm uses marker(s) to update its orientation and to compute the distance. Initially the shape of the marker is fixed and is pre-defined but as the research expands improvements will make the algorithm to work autonomously using everyday objects as...
Human detection in computer vision field is an active field of research. Extending this to human-like drawings such as the main characters in comic book stories is not trivial. Comics analysis is a very recent field of research at the intersection of graphics, texts, objects and people recognition. The detection of the main comic characters is an essential steptowards a fully automatic comic book...
In this paper, we propose a noise removal approach that significantly improves the performance of sophisticated shape matching techniques. Existing shape matching techniques focus on complex algorithms without giving much consideration to the noise removing preprocessing techniques. These preprocessing techniques can reasonably improve the accuracy where the shapes are affected by cracks. We present...
In computer vision extracting an object from an image automatically is too hard. Towards addressing this issue a comprehensive analysis of most of the Object detection through different Segmentations is performed taken from the major recent publications covering various aspects of the research in this area. We identify the following methods of the state-of-the-art techniques in which an object can...
Automatic facial point detection plays arguably the most important role in face analysis. Several methods have been proposed which reported their results on databases of both constrained and unconstrained conditions. Most of these databases provide annotations with different mark-ups and in some cases the are problems related to the accuracy of the fiducial points. The aforementioned issues as well...
We address the problem of joint detection and segmentation of multiple object instances in an image, a key step towards scene understanding. Inspired by data-driven methods, we propose an exemplar-based approach to the task of multi-instance segmentation using a small set of annotated reference images. We design a novel CRF model that jointly models object appearance, shape deformation, and object...
Shape matching is a very important issue and challenging task in computer vision. In this paper, the problem of finding a matching between two shapes is addressed by establishing correspondences between two their skeleton graphs based on random walk framework. We first propose a novel skeleton graph model in which nodes represent end-nodes of skeleton while edges describe relations between two end-nodes...
In this paper, we present a novel and original framework for computing Local Binary Pattern (LBP)-like patterns on a triangular mesh manifold. This framework, dubbed mesh-LBP can be adapted to all the LBP variants employed in 2D image analysis. As such, it allows extending the related techniques to mesh surfaces. First, we describe the foundations, the construction and the features of the mesh-LBP...
A conventional approach to image analysis is to perform separately feature extraction at a low level (such as edge detection) and follow this with high level feature extraction to determine structure (e.g. by collecting edge points using the Hough transform. The original image Ray Transform (IRT) demonstrated capability to extract structures at a low level. Here we extend the IRT to add shape specificity...
In this paper, we propose a new approach to detect hand-waving motion in crowds. Different from previous approaches which are often based on segmentation and motion detection, our method can be seen as a complexity reduction process from the problem of 3D motion detection to 2D object detection. Through arranging the same row of per video frame along the time sequence, we obtain 2D images composed...
Gender classification of depth images is a challenging problem, most research work attempted to use shape information to solve this problem in the past literature. In this work, we propose a new fusion scheme for gender classification using both texture and shape features. A new ensemble scheme is advocated to combine texture and shape feature at the feature level. To evaluate the performance of our...
In this paper, an improved method is proposed to detect falls using an uncalibrated camera. The proposed fall detection technique combines human shape analysis and human head detection together to detect falls from normal daily activities. The human shape is represented with an ellipse shape and features extracted from the ellipse are used to detect fall events. The head detection helps to distinguish...
This paper proposes a novel technique for 3-D recovery of a non-rigid object, such as a human in motion, from a single camera view. To achieve the 3-D recovery, the proposed technique performs segmentation of an object under deformation into respective parts which are all regarded as rigid objects. For high accuracy segmentation, multi-stage learning and local subspace affinity are employed for the...
This paper extends the bag-of-visual-words representations to a bag-of-visual-phrases model. The introduced bag-of-visual-phrases representation is constructed upon a proposed method for probabilistic description of co-occurring visual words, which is adapted for each reference word. This bag-of-visual-phrases representation implicitly encodes spatial relationships among visual words, thus being a...
In this paper we discuss the design and development of an improved CLUSPI method for augmented computer vision and positioning of autonomous agents in indoor settings. The method employs environmental patterns posted on walls, ceilings, floors, and other surrounding surfaces that are accessible for digital imaging. Such patterns are blended into the environment as decorative elements where the encoding...
Interaction with large surfaces such as walls and floors has become an interesting topic among scholars and researchers. Several approaches to implement such surfaces have been explored, as well as ways of evaluating its user interaction. The objective of this paper is three-folded: first, it sought to shed light on how to implement a low-cost computer vision interactive floor, based on a shape-based...
Human action recognition is the process of labeling videos contain human motion with action classes. The run time complexity is one of the most important challenges in action recognition. In this paper, we address this problem using video abstraction techniques including key-frame extraction and video skimming. At first we extract key-frames and then skim the video clip by concatenating excerpts around...
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