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Human Computer Interaction takes the attention of many researches all over the world, researches try to invent more natural and intuitive interaction ways between humans and computers. However, people depend mainly on their hands to interact with surrounding objects in everyday life. In this paper we present a novel appearance based method to recognize and track human hand in an unknown environment...
This paper presents an approach to detect moving and static objects occurring in a video by a novel model-based tracking. The method exploits the spatial and motion coherence of objects across image frames that results from the known bounded shape distortion and object's velocity between two consecutive frames. The interframe transformation space is thus reduced to a reasonable small space of only...
Object recognition in real scenes is a central problem in computer vision. In this paper we propose a new approach for shape based recognition of objects in real scenes. This approach uses moment invariants for identification of shape features. Moment Invariants are functions of central moments. They are invariant against linear transformations such as rotation, translation and scaling. Therefore,...
In Bangla alphabet, most of the characters share same features and such similarity misleads a recognizer as it makes decision based on measures of absolute difference. A preprocessing step called ‘scrambling’ and modification of existing grouping scheme are proposed to decrease the inter-character similarity among Bangla characters. The theory behind this proposal is originally inspired by experiments...
While motion tracking is of use in many computer vision applications, optical flow estimation has become a challenging task that attracts vast interest in the past decade. In this paper, different from conventional pixel level energy formulation, we propose incorporating image structure analysis for content-aware optical flow estimation. In our content-aware method, image content analysis is carried...
An automated system that can provide feedback about aesthetic value or quality of headshot photos based on learned rules could be a very useful support in photo searching, sorting and editing. This is a challenging problem as it requires semantic understanding of photos, which is beyond the state-of-the-art in computer vision. In this paper, we present a method built on most important rules or guidelines...
Scene-context plays an important role in scene analysis and object recognition. Among various sources of scene-context, we focus on scene-context scale, which means the effective region size of local context to classify an image pixel in a scene. This paper presents texton clustering for local classification using scene-context scale. The scene-context scale can be estimated by the entropy of the...
Many computer vision tasks such as large-scale image retrieval and nearest-neighbor classification perform similarity searches using Approximate Nearest Neighbor (ANN) indexes. These applications rely on the quality of ANN retrieval for success. Popular indexing methods for ANN queries include forests of kd-trees (KDT) and hierarchical k-means (HKM). The dominance of these two methods has led to implementations...
Barcode detection is required in a wide range of real-life applications. Imaging conditions and techniques vary considerably and each application has its own requirements for detection speed and accuracy. In our earlier works we built barcode detectors using morphological operations and uniform partitioning with several approaches and showed their behaviour on a set of test images. In this work, we...
Barcode technology is the pillar of automatic identification, that is used in a wide range of real-time applications with various types of codes. The different types of codes and applications impose special problems, so there is a continuous need for solutions with improved effectiveness. There are several methods for barcode localization, that are well characterized by accuracy and speed. Particularly,...
In order to have a rich representation for human action, we propose to combine two complementary features so that a human posture can be characterized in more details. In particular, the distance signal feature and the width feature are combined in an effective way to enhance each other's discriminating capability. The resulting feature vector is quantized into mid-level features using k-means clustering...
There are two main approaches for scene classification: holistic and object-based. Holistic approach is good at representing scenes with simple content. However, since it does not take into account the internal object relationship, holistic approach does not well characterize complex scenes with multiple objects. by contrast, object-based approach estimates the scene class by analyzing the object...
Date fruits are small fruits that are abundant and popular in the Middle East, and have growing international presence. There are many different types of dates, each with different features. Sorting of dates is a key process in the date industry, and can be a tedious job. In this paper, we present a method for automatic classification of date fruits based on computer vision and pattern recognition...
This paper deals with a scene pre-processing task — depth image segmentation. Efficiency and accuracy of several methods for depth map segmentation are explored. To meet real-time capable constraints, state-of-the-art techniques needed to be modified. Along with these modifications, new segmentation approaches are presented which aim at optimizing performance characteristics. They benefit from an...
In this paper, we design, evaluate and compare two phase-based passive stereovision architectures. We present two approaches to implement phase-based correspondence search algorithms in real-time for sparse stereovision applications. The first approach enhances the accuracy of the 1D phase correlation method. The second approach optimizes the 2D phase correlation method at the cost of degradation...
This paper proposes a novel system for automatically detecting children from a color monocular back-up camera, as part of a back-up warning device in passenger vehicles. We presented the use of an attentional mechansim that focuses compute-intensive bounding-box classifiers on a subset of all possible bounding-box solutions to enable real-time performance of 248ms per frame with negligible reduction...
Motion segmentation is an important research field as it forms the stepping stone for traffic monitoring, video surveillance, activity analysis, gait recognition and many other automatic imaging applications. In this work, a novel generic multiresolution (MR) based framework has been proposed in conjunction with Sigma-delta based motion segmentation algorithm. The framework provides a general platform...
The selection of regions or sets of points to track is a key task in motion-based video analysis, which has significant performance effects in terms of accuracy and computational efficiency. Computational efficiency is an unavoidable requirement in video surveillance applications. Well established methods, e.g. Good Features to Track, select points to be tracked based on appearance features such as...
The availability of dense motion information in computer vision domain allows for the effective application of Lagrangian techniques that have their origin in fluid flow analysis and dynamical systems theory. A well established technique that has been proven to be useful in image-based crowd analysis are Finite Time Lyapunov Exponents (FTLE). Based on this, we present a method to detect people carrying...
Motion segmentation has been a well explored research topic due to its vast application area. This work proposes a real-time motion segmentation method based on 3D histogram and temporal mode selection. The temporal distribution of a video sequence consists of the motion in the foreground and the relatively immobile background. A 3D histogram provides a short-term memory of the aforementioned distribution...
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