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Occlusion is one important problem in single object tracking. However, conventional methods are not capable of making full use of the spatial information because of occlusion, which may lead to the drift. In this paper, we propose a robust patches-based tracking method via sparse representation, namely RPSR, which selects the unoccluded patches, and adaptively assigns larger contribution factors to...
Complexity of understanding a visual scene is the single biggest challenge in creating intelligent devices for visually impaired people. The requirement of real time operation makes it inevitable to design algorithms that obey the computing and memory limits of available hardware. We present a hierarchical scene understanding system implemented on a vision system chip. It is restricted to extract...
We study the problem of how to build a deep learning representation for 3D shape. Deep learning has shown to be very effective in variety of visual applications, such as image classification and object detection. However, it has not been successfully applied to 3D shape recognition. This is because 3D shape has complex structure in 3D space and there are limited number of 3D shapes for feature learning...
Caricature is a popular artistic media widely used for effective communications. The fascination of caricature lies in its expressive depiction of a person's prominent features, which is usually realized through the so called exaggeration technique. This paper proposes a new example based automatic caricature generation system supporting the exaggeration of visual appearance features. The system comprises...
Coordinating vision with movements of the body is a fundamental prerequisite for the development of complex motor and cognitive skills. Visuo-motor coordination seems to rely on processes that map spatial vision onto patterns of muscular contraction.
With shorter calibration times and higher information transfer rates, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been studied most activity in recent years. Target identification is the ongoing core task in BCI researches, and plays a significant role in practical applications. In order to improve the performance of SSVEP-based BCI system, we proposed...
Micro-blog has been increasingly used for the public to express their opinions, and for organisations to detect public sentiment about social events. In contrast to the effort and progress made in English-based micro-blog analysis, research on Chinese micro-blog received relatively little attention. In this paper we examine and identify the key problems of this field, focusing particularly on the...
Vision plays an important role in improving comprehension and perception of electromagnetic fields. One reason electromagnetic fields is considered by students to be a difficult course is because electromagnetic fields, being vector functions of space and time, unlike mechanics, which deals with concrete objects, are conceptually abstract and hard to visualize. Invisibility is a considerable obstacle...
It is essential to detect the state of EMU's component while running, since any small and subtle failure may cause major accidents in high-speed running. The traditional detection approach adopts the image matching technology, which suffers from the problem when the two images dislocation. The drawback stems from the image matching approach based on the visual features only easily affected by the...
In order to evaluate the heart function, it is valuable to detect and visualize the blood flow inside the left ventricle (LV) of the heart. In this paper, a method for flow detection in LV is proposed based on the echocardiography by using the speckle image velocimetry (SIV). It first segments the LV from the echocardiography and then conducts the SIV in the LV and estimates displacement vectors of...
Keyframe extraction for shot representation is the most common video summarization approach. Any reliable keyframe extraction algorithm should automatically detect the number of keyframes, while extracting non-repetitive keyframes that can efficiently summarize the video content. Moreover, it is important that key-frame extraction is performed in reasonable time. The proposed method is based on a...
This paper presents a multiple feature fusion method using topic model for social image visualization. Images in social media are represented from several aspects such as their visual information and tags. The proposed method extracts low-level features from social images and their tags and calculates their integrated high-level features. Specifically, the proposed method applies multilayer multimodal...
The regularity of everyday tasks enables us to reuse existing solutions for task variations. For instance, most door-handles require the same basic skill (reach, grasp, turn, pull), but small adaptations of the basic skill are required to adapt to the variations that exist (e.g. levers vs. knobs). We introduce the algorithm “Simultaneous On-line Discovery and Improvement of Robotic Skills” (SODIRS)...
Loop-closure detection, which is the ability to recognize a previously visited place, is of primary importance for robotic localization and navigation problems. We here introduce SAIL-MAP, a method for loop-closure detection based on vision only, applied to topological simultaneous localization and mapping (SLAM). Our method allows the matching of camera images using a novel saliency-based feature...
In many robotic applications, especially long-term outdoor deployments, the success or failure of feature-based image registration is largely determined by changes in lighting. This paper reports on a method to learn visual feature point descriptors that are more robust to changes in scene lighting than standard hand-designed features. We demonstrate that, by tracking feature points in time-lapse...
Visual markers are useful tools assisting visual recognition of object pose in robotic applications. But they have two fundamental problems in orientation estimation. One is degradation of orientation accuracy in frontal observation. The other is “pose ambiguity” that the orientation cannot be determined uniquely. We previously developed a novel visual marker “LentiMark” which solves the former problem...
We present an approach for object class learning using a part-based shape categorization in RGB-augmented 3D point clouds captured from cluttered indoor scenes with a Kinect-like sensor. We propose an unsupervised hierarchical learning procedure which allows to symbolically classify shape parts by different specificity levels of detailedness of their surface-structural appearance. Further, a hierarchical...
In the field of intelligent robotics, object handling by robots can be achieved by capturing not only the object concept through object categorization, but also other concepts (e.g., the movement while using the object), as well as the relationship between concepts. Moreover, capturing the concepts of places and people is also necessary to enable the robot to gain real-world understanding. In this...
We propose a discriminative and compact scene descriptor for single-view place recognition that facilitates long-term visual SLAM in familiar, semi-dynamic and partially changing environments. In contrast to popular bag-of-words scene descriptors, which rely on a library of vector quantized visual features, our proposed scene descriptor is based on a library of raw image data (such as an available...
In this paper, we present a new approach to spatially self-organize a modular artificial skin in 3D space. We were motivated by the demand to efficiently and automatically acquire the position and orientation of a steadily growing number of artificial skin sensor elements. Here, we combine our 3D surface reconstruction algorithm for individual patches of artificial skin, with a common active visual...
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